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Topic Editors

Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China

Inflammatory Tumor Immune Microenvironment

Abstract submission deadline
closed (15 January 2025)
Manuscript submission deadline
15 March 2025
Viewed by
40227

Topic Information

Dear Colleagues,

Cancer cells, as well as surrounding stromal and inflammatory cells, engage in well-orchestrated reciprocal interactions to form an inflammatory tumor microenvironment. The inflammatory tumor microenvironment plays multiple roles in different stages of tumor development, including tumorigenesis, primary tumor growth and distant metastasis. Cells within the tumor microenvironment are highly plastic, continuously changing their phenotypic and functional characteristics. High-throughput single-cell methods, including single-cell sequencing and mass spectrometry, have greatly improved the understanding of inflammatory tumor microenvironment in the past few years. Metabolic adaptation, epigenetic heterogeneity and post-translational modification continue to propel the conceptual advances of inflammatory tumor microenvironment. Given the critical involvement of the inflammatory tumor microenvironment in tumor development, investigations into the inflammatory tumor microenvironment may favor further development of anti-cancer therapies.

This Research Topic aims to shed light on inflammatory tumor microenvironment in tumorigenesis, primary tumor growth and distant metastasis. We welcome contributions in form of Original Research articles, Reviews and Mini-Reviews that cover but are not limited to the following topics related to inflammatory tumor microenvironment in cancer:

  1. Inflammation-manipulated immune cell plasticity within the tumor microenvironment.
  2. Inflammation-involved immune exhaustion in cancer.
  3. Mechanisms of inflammatory leukocytes accumulation in tumor microenvironment. 
  4. Metabolic adaptation of inflammatory cells in tumor microenvironment.
  5. Targeting pro-tumoral inflammation in tumor therapy.
  6. Signaling pathways of inflammation-induced tumorigenesis.
  7. Plasticity of the pre-metastatic microenvironment.
  8. Functions and mechanisms of action of inflammatory cytokines in the tumor microenvironment.
  9. Epigenetic modifications in the tumor microenvironment.
  10. Immune checkpoints in tumor microenvironment.
  11. Development of tumor-related markers (diagnostic, surveillance, prognostic and immune checkpoints).

Dr. William Cho
Dr. Anquan Shang
Topic Editors

Keywords

  • tumor microenvironment
  • immune microenvironment
  • inflammation
  • neutrophil
  • macrophage

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
BioMedInformatics
biomedinformatics
- 1.7 2021 22 Days CHF 1000 Submit
Cancers
cancers
4.5 8.0 2009 17.4 Days CHF 2900 Submit
Cells
cells
5.1 9.9 2012 17 Days CHF 2700 Submit
Diagnostics
diagnostics
3.0 4.7 2011 20.3 Days CHF 2600 Submit
Immuno
immuno
2.1 2.6 2021 26.8 Days CHF 1000 Submit
International Journal of Molecular Sciences
ijms
4.9 8.1 2000 16.8 Days CHF 2900 Submit

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Published Papers (15 papers)

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36 pages, 11803 KiB  
Article
Interplay of Transcriptomic Regulation, Microbiota, and Signaling Pathways in Lung and Gut Inflammation-Induced Tumorigenesis
by Beatriz Andrea Otálora-Otálora, César Payán-Gómez, Juan Javier López-Rivera, Natalia Belén Pedroza-Aconcha, Sally Lorena Arboleda-Mojica, Claudia Aristizábal-Guzmán, Mario Arturo Isaza-Ruget and Carlos Arturo Álvarez-Moreno
Cells 2025, 14(1), 1; https://doi.org/10.3390/cells14010001 - 24 Dec 2024
Viewed by 1113
Abstract
Inflammation can positively and negatively affect tumorigenesis based on the duration, scope, and sequence of related events through the regulation of signaling pathways. A transcriptomic analysis of five pulmonary arterial hypertension, twelve Crohn’s disease, and twelve ulcerative colitis high throughput sequencing datasets using [...] Read more.
Inflammation can positively and negatively affect tumorigenesis based on the duration, scope, and sequence of related events through the regulation of signaling pathways. A transcriptomic analysis of five pulmonary arterial hypertension, twelve Crohn’s disease, and twelve ulcerative colitis high throughput sequencing datasets using R language specialized libraries and gene enrichment analyses identified a regulatory network in each inflammatory disease. IRF9 and LINC01089 in pulmonary arterial hypertension are related to the regulation of signaling pathways like MAPK, NOTCH, human papillomavirus, and hepatitis c infection. ZNF91 and TP53TG1 in Crohn’s disease are related to the regulation of PPAR, MAPK, and metabolic signaling pathways. ZNF91, VDR, DLEU1, SATB2-AS1, and TP53TG1 in ulcerative colitis are related to the regulation of PPAR, AMPK, and metabolic signaling pathways. The activation of the transcriptomic network and signaling pathways might be related to the interaction of the characteristic microbiota of the inflammatory disease, with the lung and gut cell receptors present in membrane rafts and complexes. The transcriptomic analysis highlights the impact of several coding and non-coding RNAs, suggesting their relationship with the unlocking of cell phenotypic plasticity for the acquisition of the hallmarks of cancer during lung and gut cell adaptation to inflammatory phenotypes. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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Figure 1

Figure 1
<p>Venn diagram with the transcriptomic metafirm in common and unique to each type of inflammatory disease. Created with BioRender.com.</p>
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<p>Coding (lilium and grey) and non-coding (purple) transcriptional regulatory network (cncTRN) of key upregulated transcription factors (TFs) and lncRNA in pulmonary arterial hypertension (PAH). Created with Cytoscape.</p>
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<p>Coding (lilium and grey) and non-coding (purple) transcriptional regulatory network (cncTRN) of key upregulated transcription factors in CD. Created with Cytoscape.</p>
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<p>Coding (lilium and grey) and non-coding (purple) transcriptional regulatory network (cncTRN) of key upregulated transcription factors in ulcerative colitis. Created with Cytoscape.</p>
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<p>Microbiome interaction with membrane receptor of PAH-related cells activating signaling pathways involved in transcriptional regulation during lung inflammation. In red are the upregulated genes and TFs; in black are the key upregulated TFs. Created with BioRender.com.</p>
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<p>Microbiome interaction with membrane receptor of CD-related cells, activating signaling pathways involved in transcriptional regulation during gut inflammation. In red are the upregulated genes and TFs; in black are the key upregulated TFs. Created with BioRender.com.</p>
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<p>Microbiome interaction with membrane receptor of UC-related cells, activating signaling pathways involved in transcriptional regulation during gut inflammation. In red are the upregulated genes and TFs; in black are the key upregulated TFs. Created with BioRender.com.</p>
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17 pages, 44291 KiB  
Article
Inflammation-Triggering Engineered Macrophages (MacTriggers) Enhance Reactivity of Immune Checkpoint Inhibitor Only in Tumor Tissues
by Kenta Tanito, Teruki Nii, Kanae Wakuya, Yusuke Hamabe, Toma Yoshimi, Takanatsu Hosokawa, Akihiro Kishimura, Takeshi Mori and Yoshiki Katayama
Cancers 2024, 16(22), 3787; https://doi.org/10.3390/cancers16223787 - 10 Nov 2024
Viewed by 1584
Abstract
Background: We have previously reported engineered macrophages (MacTriggers) that can accelerate the release of tumor necrosis factor-α in response to M2 polarization. MacTriggers are characterized by two original characteristics of macrophages: (1) migration to tumors; and (2) polarization to the M2 phenotype in [...] Read more.
Background: We have previously reported engineered macrophages (MacTriggers) that can accelerate the release of tumor necrosis factor-α in response to M2 polarization. MacTriggers are characterized by two original characteristics of macrophages: (1) migration to tumors; and (2) polarization to the M2 phenotype in tumors. Intravenously administered MacTriggers efficiently accumulated in the tumors and induced tumor-specific inflammation. This study reports a novel methodology for enhancing the anti-tumor effects of immune checkpoint inhibitors (ICIs). Results: In this study, we newly found that the intravenously administered MacTriggers in BALB/c mouse models upregulated the expression levels of immune checkpoint proteins, such as programmed cell death (PD)-1 in CD8+ T cells and PD-ligand 1 (PD-L1) in cancer cells and macrophages. Consequently, in two ICI-resistant tumor-inoculated mouse models, the combined administration of MacTrigger and anti-PD-1 antibody (aPD-1) synergistically inhibited tumor growth, whereas monotherapy with aPD-1 did not exhibit anti-tumor effects. This synergistic effect was mainly from aPD-1 enhancing the tumor-attacking ability of CD8+ T cells, which could infiltrate into the tumors following MacTrigger treatment. Importantly, no side effects were observed in normal tissues, particularly in the liver and spleen, indicating that the MacTriggers did not enhance the aPD-1 reactivity in normal tissues. This specificity was from the MacTriggers not polarizing to the M2 phenotype in normal tissues, thereby avoiding inflammation and increased PD-1/PD-L1 expression. MacTriggers could enhance aPD-1 reactivity only in tumors following tumor-specific inflammation induction. Conclusions: Our findings suggest that the MacTrigger and aPD-1 combination therapy is a novel approach for potentially overcoming the current low ICI response rates while avoiding side effects. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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Graphical abstract

Graphical abstract
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<p>MacTriggers are engineered macrophages that express tumor necrosis factor (TNF)-α in response to <span class="html-italic">Arg1</span> promoter activity. MacTriggers can migrate to tumor tissues and then polarize from the M0 state to the M2 state. TNF-α secreted from MacTriggers can induce CD8<sup>+</sup> T cell and natural killer (NK) cell infiltration, resulting in anti-tumor effects. In normal tissues, especially the liver and spleen, MacTriggers do not polarize from the M0 state to the M2 phenotype, nor do they express TNF-α. This figure was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>(<b>A</b>) Ex vivo imaging of administered RAW264.7 macrophage accumulation in several tissues (tumor, liver, spleen, kidney, and lung) 1 and 4 days after administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice. (<b>B</b>) Mean fluorescent intensity of CD206 of administered macrophages in tumor, liver, and spleen tissues 1 and 4 days after macrophage administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice (<span class="html-italic">n</span> = 4, median (IQR)). * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>(<b>A</b>) Percent of programmed cell death (PD)-1<sup>+</sup> cells in CD8<sup>+</sup> T cells 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). (<b>B</b>) Percent of PD-ligand 1 (PD-L1)<sup>+</sup> cells in cancer cells 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). (<b>C</b>) Percent of PD-L1<sup>+</sup> cells in macrophages 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. ns: not significant.</p>
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<p>(<b>A</b>) Percent of tumor necrosis factor (TNF)-α<sup>+</sup> cells in neutrophils 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). (<b>B</b>) Percent of TNF-α<sup>+</sup> cells in macrophages 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). (<b>C</b>) Percent of TNF-α<sup>+</sup> cells in T cells 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). (<b>D</b>) Percent of interferon (IFN)-γ<sup>+</sup> cells in macrophages 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). (<b>E</b>) Percent of IFN-γ<sup>+</sup> cells in T cells 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. ns: not significant.</p>
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<p>(<b>A</b>) Schematic showing the mouse experimental protocols we performed. (<b>B</b>) Time course of tumor volume measurements over 14 days in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice (<span class="html-italic">n</span> = 5, median (IQR)) and images of tumor tissues 14 days after treatment. (<b>C</b>) Survival rates of mice in each treatment group (<span class="html-italic">n</span> = 5). (<b>D</b>) Immunohistochemistry (IHC) staining of tumor sections excised from 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice for Ki67 14 days after administration. Images were obtained under a microscope using a 20× objective lens. Scale bar, 50 μm. (<b>E</b>) Percent of regulatory T cells in CD4<sup>+</sup> T cells 4 and 8 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice (<span class="html-italic">n</span> = 5, median (IQR)). (<b>F</b>) Time course of body weight measurements in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice (<span class="html-italic">n</span> = 5, median (IQR)). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. ns: not significant.</p>
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<p>(<b>A</b>) Percent of liver weight/body weight in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice (<span class="html-italic">n</span> = 5, median (IQR)). (<b>B</b>) Percent of spleen weight/body weight in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice (<span class="html-italic">n</span> = 5, median (IQR)). (<b>C</b>) Hematoxylin and eosin (H&amp;E)-stained tissue sections (liver, spleen, kidney, and lung) excised from 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumor-bearing mice 14 days after treatment. Images were obtained under a microscope using a 20× objective lens. Scale bar, 50 μm. (<b>D</b>) Percent of programmed cell death (PD)-1<sup>+</sup> cells in CD8<sup>+</sup> T cells in liver and spleen tissues 4 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). (<b>E</b>) Percent of PD-ligand 1 (PD-L1)<sup>+</sup> cells in macrophages in liver and spleen tissues 4 days after MacTrigger administration in 4T1 (<b>i</b>) and Colon-26 (<b>ii</b>) tumors (<span class="html-italic">n</span> = 5, median (IQR)). ns: not significant.</p>
Full article ">
7 pages, 1009 KiB  
Communication
Galectin-1 Induces the Production of Immune-Suppressive Cytokines in Human and Mouse T Cells
by Kimberly D. Herman, Ian Holyer, Duncan C. Humphries, Anna Adamska, James A. Roper, Kristoffer Peterson, Fredrik R. Zetterberg, Anders Pedersen, Alison C. MacKinnon and Robert J. Slack
Int. J. Mol. Sci. 2024, 25(22), 11948; https://doi.org/10.3390/ijms252211948 - 7 Nov 2024
Viewed by 1055
Abstract
Galectin-1 is implicated in several pro-tumourigenic mechanisms and is considered immune-suppressive. The pharmacological inhibition of galectin-1 may be beneficial in cancers in which galectin-1 is overexpressed and driving cancer progression. This study aimed to further characterise the immunosuppressive cytokines influenced by galectin-1 in [...] Read more.
Galectin-1 is implicated in several pro-tumourigenic mechanisms and is considered immune-suppressive. The pharmacological inhibition of galectin-1 may be beneficial in cancers in which galectin-1 is overexpressed and driving cancer progression. This study aimed to further characterise the immunosuppressive cytokines influenced by galectin-1 in in vitro immune cell cultures and an in vivo inflammatory model using a recently discovered selective inhibitor of galectin-1, GB1908. To enable a translational approach and link mouse and human pharmacology, anti-CD3/anti-CD28 stimulated T cells cultured from human whole blood and mouse spleens were compared. For in vivo studies of T cell-mediated inflammation, the concanavalin-A (Con-A) mouse model was used to induce a T lymphocyte-driven acute liver injury phenotype. The inhibition of galectin-1 with GB1908 reduced IL-17A, IFNγ and TNFα in a concentration-dependent manner in both mouse and human T cells in vitro. The immunosuppressive cytokines measured in Con-A-treated mice were all upregulated compared to naïve mice. Subsequently, mice treated with GB1908 demonstrated a significant reduction in IL-17A, IFNγ, IL-6 and TNFα compared to vehicle-treated mice. In conclusion, galectin-1 induced the production of several important immune-suppressive cytokines from T cells in vitro and in vivo. This result suggests that, in the context of cancer therapy, a selective galectin-1 could be a viable approach as a monotherapy, or in combination with chemotherapeutic agents and/or checkpoint inhibitors, to enhance the numbers and activity of cytotoxic T cells in the tumour microenvironment of high galectin-1 expressing cancers. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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Figure 1

Figure 1
<p>Galectin-1 inhibition reduces IL-17A, IFNγ and TNFα production in stimulated mouse and human T cells. T lymphocytes isolated from human whole blood (<b>A</b>,<b>C</b>,<b>E</b>) or mouse spleens (<b>B</b>,<b>D</b>,<b>F</b>) were stimulated with anti-CD3 and anti-CD28, along with a dose range of GB1908 (left column). IL-17A (<b>A</b>,<b>B</b>), IFNγ (<b>C</b>,<b>D</b>) and TNFα (<b>E</b>,<b>F</b>) measured in supernatants after 48 h. Bars show mean ± SEM, each dot representing one donor (n = 3). Data shown as % change from control (DMSO)-treated cells for each donor. Statistical analysis completed using repeated measures one-way ANOVA and Dunnett’s post-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Galectin-1 inhibition reduces cytokine production in the mouse concanavalin-A model of acute inflammation. Summary of the Con-A study (<b>A</b>). IL-17A, IFNγ, IL-6 and TNFα were measured in terminal blood samples by BioLegend<sup>®</sup> LEGENDPlex™ assay (<b>B</b>–<b>E</b>). Bars show mean ± SEM, and each dot represents one mouse. Statistical analysis completed using unpaired Student’s <span class="html-italic">t</span>-test comparing vehicle vs. GB1908 (** <span class="html-italic">p</span> &lt; 0.01).</p>
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17 pages, 932 KiB  
Review
Mechanisms of Immune Evasion in PTEN Loss Prostate Cancer
by Jorge Esteban-Villarrubia, Pablo Alvarez Ballesteros, Miguel Martín-Serrano, María Ruiz Vico, Juan M Funes, Guillermo de Velasco, Elena Castro, David Olmos, Daniel Castellano and Enrique González-Billalabeitia
Immuno 2024, 4(4), 444-460; https://doi.org/10.3390/immuno4040028 - 1 Nov 2024
Viewed by 1365
Abstract
PTEN (phosphatase and tensin homolog) is a frequently lost tumor suppressor gene in prostate cancer, leading to aggressive tumor behavior and poor clinical outcomes. PTEN loss results in aberrant activation of the PI3K/AKT/mTOR pathway, promoting oncogenesis. These alterations also lead to an immunosuppressive [...] Read more.
PTEN (phosphatase and tensin homolog) is a frequently lost tumor suppressor gene in prostate cancer, leading to aggressive tumor behavior and poor clinical outcomes. PTEN loss results in aberrant activation of the PI3K/AKT/mTOR pathway, promoting oncogenesis. These alterations also lead to an immunosuppressive tumor microenvironment with altered immune cell infiltration, cytokine profiles, and immune checkpoint regulation. This review aims to provide a comprehensive overview of the mechanisms underlying PTEN loss in prostate cancer and the consequent immune alterations observed in this subtype, thus underscoring the importance of understanding PTEN-mediated immune modulation for the development of effective therapeutic interventions in prostate cancer. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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Figure 1
<p>Main alterations in immune system caused by <span class="html-italic">PTEN</span> loss in PCa. <span class="html-italic">PTEN</span>-null tumors are characterized by an immunosuppressive TME caused by a paracrine loop between cancer cells and MDSCs due to increased secretion of chemokines regulated by <span class="html-italic">NF-ϰB</span>, Hippo/Yap or JAK/STAT signalling. MDSCs are also able to maintain tumor growth and collaborate to promote tumor growth and castration resistance. Hyperactivation of PI3K pathway also increases checkpoint inhibitor expression on membrane collaborating to create an immunosuppressive environment. Increased glycolysis leads to histone lactylation and reduced macrophagic activity.</p>
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26 pages, 10009 KiB  
Article
Contemporaneous Inflammatory, Angiogenic, Fibrogenic, and Angiostatic Cytokine Profiles of the Time-to-Tumor Development by Cancer Cells to Orchestrate Tumor Neovascularization, Progression, and Metastasis
by Elizabeth Skapinker, Emilyn B. Aucoin, Haley L. Kombargi, Abdulrahman M. Yaish, Yunfan Li, Leili Baghaie and Myron R. Szewczuk
Cells 2024, 13(20), 1739; https://doi.org/10.3390/cells13201739 - 20 Oct 2024
Cited by 1 | Viewed by 1497
Abstract
Cytokines can promote various cancer processes, such as angiogenesis, epithelial to mesenchymal transition (EMT), invasion, and tumor progression, and maintain cancer stem-cell-like (CSCs) cells. The mechanism(s) that continuously promote(s) tumors to progress in the TME still need(s) to be investigated. The data in [...] Read more.
Cytokines can promote various cancer processes, such as angiogenesis, epithelial to mesenchymal transition (EMT), invasion, and tumor progression, and maintain cancer stem-cell-like (CSCs) cells. The mechanism(s) that continuously promote(s) tumors to progress in the TME still need(s) to be investigated. The data in the present study analyzed the inflammatory, angiogenic, fibrogenic, and angiostatic cytokine profiles in the host serum during tumor development in a mouse model of human pancreatic cancer. Pancreatic MiaPaCa-2-eGFP cancer cells were subcutaneously implanted in RAG2xCγ double mutant mice. Blood samples were collected before cancer cell implantation and every week until the end point of the study. The extracted serum from the blood of each mouse at different time points during tumor development was analyzed using a Bio-Plex microarray analysis and a Bio-Plex 200 system for proinflammatory (IL-1β, IL-10, IFN-γ, and TNF-α) and angiogenic and fibrogenic (IL-15, IL-18, basic FGF, LIF, M-CSF, MIG, MIP-2, PDGF-BB, and VEGF) cytokines. Here, we find that during cancer cell colonization for tumor development, host angiogenic, fibrogenic, and proinflammatory cytokine profiling in the tumor-bearing mice has been shown to significantly reduce host angiostatic and proinflammatory cytokines that restrain tumor development and increase those for tumor growth. The proinflammatory cytokines IL-15, IL-18, and IL-1β profiles reveal a significant host serum increase after day 35 when the tumor began to progress in growth. In contrast, the angiostatic cytokine profiles of TNFα, MIG, M-CSF, IL-10, and IFNγ in the host serum revealed a dramatic and significant decrease after day 5 post-implantation of cancer cells. OP treatment of tumor-bearing mice on day 35 maintained high levels of angiostatic and fibrogenic cytokines. The data suggest an entirely new regulation by cancer cells for tumor development. The findings identify for the first time how pancreatic cancer cells use host cytokine profiling to orchestrate the initiation of tumor development. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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Figure 1

Figure 1
<p>(<b>A</b>) Necropsy tumor images at day 47 from RAG2xCγ double mutant mice implanted with MiaPaCa-2-eGFP pancreatic cancer cells following intraperitoneal daily treatment with soluble 100 mg/kg OP. (<b>B</b>) Time-to-tumor progression of daily OP treatment started at day 35 (arrow) at indicated dosages, (<b>C</b>) tumor wet weight, (<b>D</b>) body weight of cohorts following OP treatment at indicated dosages. The animals were monitored daily for health by a certified veterinarian technician. (<b>E</b>) The probability of survival for days post-implantation of MiaPaCa-2-eGFP of untreated mice and mice treated with OP concentrations at 2 mg/kg, 10 mg/kg, 50 mg/kg, and 100 mg/kg. The log-rank (Mantel–Cox) test was used to test the probability that the survival curves were significantly different using the Chi-square. The probability of survival of the OP-treated mice compared to the untreated was significant, <span class="html-italic">p</span> &lt; 0.0050. The one-way ANOVA Fisher test comparisons with 95% confidence use asterisks to indicate statistical significance. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>A</b>–<b>C</b>) Tumor growth inhibition rate following one, four, and five intraperitoneal injections of oseltamivir phosphate (OP) dosages of 50, 100, and 200 mg/kg at day 42 post-implantation of MiaPaCa-2-eGFP cells. The arrows indicate the number of intraperitoneal injections of OP and the percent inhibition of tumor growth. (<b>D</b>–<b>F</b>) Flow cytometry results of the presence of the characteristic mouse CD31+ endothelial cells in the blood during MiaPaCa-2-eGFP tumor-bearing RAG2xCγ double mutant mice which had received various treatments of oseltamivir phosphate (OP) of 50, 100, and 200 mg/kg at day 42 (arrow). Blood was collected retro-orbitally at indicated time points, and the percentage of CD31+ endothelial cells was measured using flow cytometry. Results were graphically depicted as line graphs with points depicting the mean± SEM, n = 4 mice for tumor volume and the percentage of CD31+ cells of each group. (<b>G</b>–<b>I</b>) Necropsy tumors after eight IP injections of indicated dosages of OP expressed subcutaneously, exposed under the skin, showing the extent of neovasculature and tumor size.</p>
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<p>(<b>A</b>) Biophotonic images of live, non-processed necropsy of a—tumor, b—liver, from the untreated cohort, 2 mg/kg OP cohort, 10 mg/kg OP cohort, 50 mg/kg OP cohort, and 100 mg/kg OP cohort. The dark tissue images represent the invasion of MiaPaCa-2-eGFP cells. (<b>B</b>) Representative images of tumor neovascularization from untreated cohort and OP-treated cohorts. (<b>C</b>) H&amp;E staining of tumor and liver from untreated cohort and OP-treated cohorts. The scalebar represents 100 µm.</p>
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<p>Time-to-tumor progression of host serum cytokine profiles during pancreatic MiaPaCa-2-eGFP tumor development in RAG2xCγ double mutant mice. (<b>A</b>) Interleukin 15 (IL-15), (<b>B</b>) monokine induced by interferon-gamma (MIG), (<b>C</b>) interleukin-18, (<b>D</b>) macrophage colony-stimulating factor (M-CSF), (<b>E</b>) interleukin- 1β (IL-1β), (<b>F</b>) interleukin-10 (IL-10), (<b>G</b>) tumor necrosis factor-alpha (TNF-α), and (<b>H</b>) interferon-gamma (IFN-γ) cytokine levels were measured (pg/mL) in host serum before and every five (5) days after MiaPaCa-2-eGFP implanted xenografts in RAG2xCγ double mutant mice for 45 days. After MiaPaCa-2-eGFP implantation, tumors grew for 50 days, and tumor volumes were measured in mm<sup>3</sup>. Blood was collected retro-orbitally, and cytokine levels were measured using a magnetic bead-based Luminex Bio-Plex microarray mouse cytokine assay. Results are depicted as mean ± standard error of the mean ± SEMwith indicated mouse numbers. A one-way ANOVA was used to test for linear trends and measure statistical significance.</p>
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<p>Dose-dependent effect of oseltamivir phosphate (OP) on (<b>A</b>) interleukin-15 (IL-15), (<b>B</b>) IL-18, and (<b>C</b>) macrophage colony-stimulating factor (M-CSF) angiogenic cytokine profiles affecting pancreatic MiaPaCa-2-eGFP tumor growth in heterotopic xenograft mice. Host serum cytokine profiles (pg/mL) were measured in a time-to-tumor progression of pancreatic MiaPaCa-2-eGFP tumor development. Cytokine levels in the serum were analyzed using a magnetic bead-based Luminex Bio-Plex microarray mouse cytokine assay. Results are depicted as mean ± standard error of the mean (SEM) with indicated mouse numbers. Tumor volumes (mm<sup>3</sup>) of untreated and OP-treated mice were plotted concomitantly with cytokine profiles. OP treatment at indicated different dosages was injected after day 35 post-implantation (arrow). One-way ANOVA to test for linear trend was used to measure statistical significance, <span class="html-italic">p</span> &lt; 0.0001 at indicated n values of untreated mice for groups tested.</p>
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<p>Dose-dependent effect of oseltamivir phosphate (OP) on (<b>A</b>) leukemia inhibitory factor (LIF), (<b>B</b>) monokine induced by IFNγ (MIG), and (<b>C</b>) macrophage inflammatory protein-2 (MIP-2) angiogenic and proinflammatory cytokine profiles affecting pancreatic MiaPaCa-2-eGFP tumor growth in heterotopic xenograft mice. Host serum cytokine profiles (pg/mL) were measured in a time-to-tumor progression of pancreatic MiaPaCa-2-eGFP tumor development. Cytokine levels in the serum were analyzed using a magnetic bead-based Luminex Bio-Plex microarray mouse cytokine assay. Results are depicted as mean ± standard error of the mean (SEM) with indicated mouse numbers. Tumor volumes (mm<sup>3</sup>) of untreated and OP-treated mice were plotted in concomitance with cytokine profiles. OP treatment at indicated different dosages was injected after day 35 post-implantation (arrow). One-way ANOVA to test for linear trend was used to measure statistical significance, <span class="html-italic">p</span> &lt; 0.0001 at indicated n values of untreated mice for groups tested.</p>
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<p>Dose-dependent effect of oseltamivir phosphate (OP) on (<b>A</b>) vascular endothelial growth factor (VEGF), (<b>B</b>) fibroblast growth factor β (FGFβ), and (<b>C</b>) platelet-derived growth factor-BB (PDGF-BB) angiogenic cytokine profiles affecting pancreatic MiaPaCa-2-eGFP tumor growth in heterotopic xenograft mice. Host serum cytokine profiles (pg/mL) were measured in a time-to-tumor progression of pancreatic MiaPaCa-2-eGFP tumor development. Cytokine levels in the serum were analyzed using a magnetic bead-based Luminex Bio-Plex microarray mouse cytokine assay. Results are depicted as mean ± standard error of the mean (SEM) with indicated mouse numbers. Tumor volumes (mm<sup>3</sup>) of untreated and OP-treated mice were plotted in concomitance with cytokine profiles. OP treatment at indicated different dosages was injected after day 35 post-implantation (arrow). One-way ANOVA to test for linear trend was used to measure statistical significance, <span class="html-italic">p</span> &lt; 0.0001 at indicated n values of untreated mice for groups tested.</p>
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23 pages, 7113 KiB  
Article
Tumor-Infiltrating T Cells in Skin Basal Cell Carcinomas and Squamous Cell Carcinomas: Global Th1 Preponderance with Th17 Enrichment—A Cross-Sectional Study
by Daniela Cunha, Marco Neves, Daniela Silva, Ana Rita Silvestre, Paula Borralho Nunes, Fernando Arrobas, Julie C. Ribot, Fernando Ferreira, Luís F. Moita, Luís Soares-de-Almeida, João Maia Silva, Paulo Filipe and João Ferreira
Cells 2024, 13(11), 964; https://doi.org/10.3390/cells13110964 - 3 Jun 2024
Cited by 1 | Viewed by 2163
Abstract
Basal cell carcinomas (BCCs) and squamous cell carcinomas (SCCs) are high-incidence, non-melanoma skin cancers (NMSCs). The success of immune-targeted therapies in advanced NMSCs led us to anticipate that NMSCs harbored significant populations of tumor-infiltrating lymphocytes with potential anti-tumor activity. The main aim of [...] Read more.
Basal cell carcinomas (BCCs) and squamous cell carcinomas (SCCs) are high-incidence, non-melanoma skin cancers (NMSCs). The success of immune-targeted therapies in advanced NMSCs led us to anticipate that NMSCs harbored significant populations of tumor-infiltrating lymphocytes with potential anti-tumor activity. The main aim of this study was to characterize T cells infiltrating NMSCs. Flow cytometry and immunohistochemistry were used to assess, respectively, the proportions and densities of T cell subpopulations in BCCs (n = 118), SCCs (n = 33), and normal skin (NS, n = 30). CD8+ T cells, CD4+ T cell subsets, namely, Th1, Th2, Th17, Th9, and regulatory T cells (Tregs), CD8+ and CD4+ memory T cells, and γδ T cells were compared between NMSCs and NS samples. Remarkably, both BCCs and SCCs featured a significantly higher Th1/Th2 ratio (~four-fold) and an enrichment for Th17 cells. NMSCs also showed a significant enrichment for IFN-γ-producing CD8+T cells, and a depletion of γδ T cells. Using immunohistochemistry, NMSCs featured denser T cell infiltrates (CD4+, CD8+, and Tregs) than NS. Overall, these data favor a Th1-predominant response in BCCs and SCCs, providing support for immune-based treatments in NMSCs. Th17-mediated inflammation may play a role in the progression of NMSCs and thus become a potential therapeutic target in NMSCs. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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Figure 1
<p>Flow cytometry charts from one representative BCC, one SCC, and one NS sample. In the dot plots, percentages denote the frequency of events/cells within selected regions relative to the total depicted events. Shown are percentages of (<b>A</b>) CD4+ and CD8+ cells within CD3+ cells, (<b>B</b>) CD45RO+ cells within the CD3+ population, (<b>C</b>) IFN-γ+ cells within the CD4+ population (Th1 cells), (<b>D</b>) IL-4+ cells within the CD4+ population (Th2 cells), and (<b>E</b>) IL-17+ cells within the CD4+ population (Th17 cells). In (<b>F</b>), the graphs depict the combined forward scatter plot (FSC-A) of lymphocytes, featuring the expected dispersion of cell sizes (y axis) and concurrent staining for the TCR γδ (GD). The lymphocyte population was gated from a prior compound scatter plot (forward plus side scattering). Also shown are the percentages of IFN-γ+ cells within the CD8+ population (<b>G</b>). In the graphs, y and x axes depict the logarithm of relative fluorescence intensities.</p>
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<p>Flow cytometry charts from one representative BCC, one SCC, and one NS sample. In the dot plots, percentages denote the frequency of events/cells within selected regions relative to the total depicted events. Shown are percentages of (<b>A</b>) CD4+ and CD8+ cells within CD3+ cells, (<b>B</b>) CD45RO+ cells within the CD3+ population, (<b>C</b>) IFN-γ+ cells within the CD4+ population (Th1 cells), (<b>D</b>) IL-4+ cells within the CD4+ population (Th2 cells), and (<b>E</b>) IL-17+ cells within the CD4+ population (Th17 cells). In (<b>F</b>), the graphs depict the combined forward scatter plot (FSC-A) of lymphocytes, featuring the expected dispersion of cell sizes (y axis) and concurrent staining for the TCR γδ (GD). The lymphocyte population was gated from a prior compound scatter plot (forward plus side scattering). Also shown are the percentages of IFN-γ+ cells within the CD8+ population (<b>G</b>). In the graphs, y and x axes depict the logarithm of relative fluorescence intensities.</p>
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<p>NMSCs feature altered percentages of CD4 and CD8 T cell subpopulations relative to NS controls. (<b>A</b>–<b>J</b>) Graphs displaying frequencies and ratios of T cell subsets in BCCs, SCCs, and NS. (<b>A</b>) Percentages of CD4+ and CD8+ cells after gating for CD3+ T cells. (<b>B</b>) Ratios between CD4+ and CD8+ T cells. (<b>C</b>) Percentage of CD45RO+ cells after gating for CD3+ T cells. (<b>D</b>) Percentages of Th1 and Th2 cells after gating for CD4+ T cells. (<b>E</b>) Ratios between Th1 and Th2 T cells. (<b>F</b>) Percentages of IFNγ+ T cells after gating for CD8+ T cells. (<b>G</b>) Percentages of Th17 and FoxP3+ Treg cells after gating for CD4+ T cells. (<b>H</b>) Ratios between CD8+ T cells and Treg cells. (<b>I</b>) Percentages of T cells expressing the γδ receptor after gating for CD3+ T cells. (<b>J</b>) Ratios between Th17 and Treg cells. Horizontal bars represent medians, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Infiltrates of CD3+, CD4+, CD8+, and FoxP3+ T cells are denser in BCCs and SCCs than in NS and are mainly localized at the tumor periphery. (<b>A</b>–<b>D</b>) Graphs showing IHC scores in BCCs and SCCs compared to NS for (<b>A</b>) CD3+, (<b>B</b>) CD4+, (<b>C</b>) CD8+, and (<b>D</b>) FoxP3+ T cells. (<b>E</b>–<b>H</b>) Graphs representing the spatial distribution of the infiltrates of (<b>E</b>) CD3+, (<b>F</b>) CD4+, (<b>G</b>) CD8+, and (<b>H</b>) FoxP3+ T cells at the inner areas of the tumor (<span class="html-italic">In</span>) and at the tumor periphery (<span class="html-italic">P</span>). Horizontal bars represent medians, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>BCCs and SCCs feature a dense stromal T cell infiltrate. Shown are contiguous sections of exemplary samples of BCC stained for CD3 ((<b>A</b>), 50×; (<b>B</b>), 200×), CD4 ((<b>C</b>), 50×; (<b>D</b>), 200×), CD8 ((<b>E</b>), 50×; (<b>F</b>), 200×), and FoxP3 ((<b>G</b>), 50×; (<b>H</b>), 200×), and of SCC, also stained for CD3 ((<b>I</b>), 50×; (<b>J</b>), 200×), CD4 ((<b>K</b>), 50×; (<b>L</b>), 200×), CD8 ((<b>M</b>), 50×; (<b>N</b>), 200×), and FoxP3 ((<b>O</b>), 50×; (<b>P</b>), 200×). Note the overall paucity of T cells within the tumoral epithelia. Magnifications provided correspond to original magnifications.</p>
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<p>T cell populations in normal skin. Shown are contiguous sections of normal skin samples stained for CD3 ((<b>A</b>), 50×; (<b>B</b>), 200×), CD4 ((<b>C</b>), 50×; (<b>D</b>), 200×), CD8 ((<b>E</b>), 50×; (<b>F</b>), 200×), and FoxP3 ((<b>G</b>), 50×; (<b>H</b>), 200×). Lymphoid infiltrates are scarce and mainly circumscribed to the perivascular and perifollicular areas. Magnifications provided correspond to original magnifications.</p>
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11 pages, 2281 KiB  
Review
Immune System Disorder and Cancer-Associated Cachexia
by Lingbing Zhang and Philip D. Bonomi
Cancers 2024, 16(9), 1709; https://doi.org/10.3390/cancers16091709 - 27 Apr 2024
Cited by 4 | Viewed by 3538
Abstract
Cancer-associated cachexia (CAC) is a debilitating condition marked by muscle and fat loss, that is unresponsive to nutritional support and contributes significantly to morbidity and mortality in patients with cancer. Immune dysfunction, driven by cytokine imbalance, contributes to CAC progression. This review explores [...] Read more.
Cancer-associated cachexia (CAC) is a debilitating condition marked by muscle and fat loss, that is unresponsive to nutritional support and contributes significantly to morbidity and mortality in patients with cancer. Immune dysfunction, driven by cytokine imbalance, contributes to CAC progression. This review explores the potential relationship between CAC and anti-cancer immune response in pre-clinical and clinical studies. Pre-clinical studies showcase the involvement of cytokines like IL-1β, IL-6, IL-8, IFN-γ, TNF-α, and TGF-β, in CAC. IL-6 and TNF-α, interacting with muscle and adipose tissues, induce wasting through JAK/STAT and NF-κB pathways. Myeloid-derived suppressor cells (MDSCs) exacerbate CAC by promoting inflammation. Clinical studies confirm elevated pro-inflammatory cytokines (IL-6, IL-8, TNFα) and immune markers like the neutrophil-to-lymphocyte ratio (NLR) in patients with CAC. Thus, immunomodulatory mechanisms involved in CAC may impact the anti-neoplastic immune response. Inhibiting CAC mechanisms could enhance anti-cancer therapies, notably immunotherapy. R-ketorolac, a new immunomodulator, reversed the weight loss and increased survival in mice. Combining these agents with immunotherapy may benefit patients with cancer experiencing CAC. Further research is vital to understand the complex interplay between tumor-induced immune dysregulation and CAC during immunotherapy. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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Graphical abstract

Graphical abstract
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<p>Flowchart of literature search and article screening.</p>
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<p>Tumor-induced immune disorder drives CAC development in organs and tissues.</p>
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<p>Major altered cytokines identified in CAC by clinical studies.</p>
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22 pages, 11501 KiB  
Article
Radiation-Induced Innate Neutrophil Response in Tumor Is Mediated by the CXCLs/CXCR2 Axis
by Faya Zhang, Oscar Mulvaney, Erica Salcedo, Subrata Manna, James Z. Zhu, Tao Wang, Chul Ahn, Laurentiu M. Pop and Raquibul Hannan
Cancers 2023, 15(23), 5686; https://doi.org/10.3390/cancers15235686 - 1 Dec 2023
Cited by 3 | Viewed by 2546
Abstract
The early events that lead to the inflammatory and immune-modulatory effects of radiation therapy (RT) in the tumor microenvironment (TME) after its DNA damage response activating the innate DNA-sensing pathways are largely unknown. Neutrophilic infiltration into the TME in response to RT is [...] Read more.
The early events that lead to the inflammatory and immune-modulatory effects of radiation therapy (RT) in the tumor microenvironment (TME) after its DNA damage response activating the innate DNA-sensing pathways are largely unknown. Neutrophilic infiltration into the TME in response to RT is an early innate inflammatory response that occurs within 24–48 h. Using two different syngeneic murine tumor models (RM-9 and MC-38), we demonstrated that CXCR2 blockade significantly reduced RT-induced neutrophilic infiltration. CXCR2 blockade showed the same effects on RT-induced tumor inhibition and host survival as direct neutrophil depletion. Neutrophils highly and preferentially expressed CXCR2 compared to other immune cells. Importantly, RT induced both gene and protein expression of CXCLs in the TME within 24 h, attracting neutrophils into the tumor. Expectedly, RT also upregulated the gene expression of both cGAS and AIM2 DNA-sensing pathways in cGAS-positive MC-38 tumors but not in cGAS-negative RM-9 tumors. Activation of these pathways resulted in increased IL-1β, which is known to activate the CXCLs/CXCR2 axis. Gene ontology analysis of mRNA-Seq supported these findings. Taken together, the findings suggest that the CXCLs/CXCR2 axis mediates the RT-induced innate inflammatory response in the TME, likely translating the effects of innate DNA-sensing pathways that are activated in response to RT-induced DNA damage. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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Figure 1
<p>CXCR2 blockade impedes RT-induced neutrophilic infiltration into the RM-9 and MC-38 tumors. (<b>A</b>) A schematic illustration of the experimental design. C57BL/6J mice were injected subcutaneously (s.c.) with RM9 tumor cells in the right hind leg. When tumors reached about 7 mm in diameter, mice were randomly assigned to one of four treatment groups and administered AZD-5069 by oral gavage twice per day for two days. One day after AZD-5069 treatment initiation, tumors were irradiated with a single dose of 15 Gy. Mice were euthanized at 24 h after irradiation, and tumors were collected for flow cytometry analysis. (<b>B</b>) Gating strategy for tumor-infiltrating myeloid cell populations in the RM-9 tumor model for flow cytometry analysis. Percentages and cell numbers of tumor-infiltrating neutrophils and other myeloid cells in RM-9 tumors (<b>C</b>) and MC-38 tumors (<b>D</b>). Veh: drug vehicle; Con: sham-treated with RT. Bar graphs showing RT-induced changes on percentages and cell counts of tumor-infiltrating myeloid populations with or without AZD-5069 treatment. n = 5 per group, values = mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; ns = nonsignificant; one-way ANOVA and Bonferroni’s test were used for statistical analysis.</p>
Full article ">Figure 1 Cont.
<p>CXCR2 blockade impedes RT-induced neutrophilic infiltration into the RM-9 and MC-38 tumors. (<b>A</b>) A schematic illustration of the experimental design. C57BL/6J mice were injected subcutaneously (s.c.) with RM9 tumor cells in the right hind leg. When tumors reached about 7 mm in diameter, mice were randomly assigned to one of four treatment groups and administered AZD-5069 by oral gavage twice per day for two days. One day after AZD-5069 treatment initiation, tumors were irradiated with a single dose of 15 Gy. Mice were euthanized at 24 h after irradiation, and tumors were collected for flow cytometry analysis. (<b>B</b>) Gating strategy for tumor-infiltrating myeloid cell populations in the RM-9 tumor model for flow cytometry analysis. Percentages and cell numbers of tumor-infiltrating neutrophils and other myeloid cells in RM-9 tumors (<b>C</b>) and MC-38 tumors (<b>D</b>). Veh: drug vehicle; Con: sham-treated with RT. Bar graphs showing RT-induced changes on percentages and cell counts of tumor-infiltrating myeloid populations with or without AZD-5069 treatment. n = 5 per group, values = mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; ns = nonsignificant; one-way ANOVA and Bonferroni’s test were used for statistical analysis.</p>
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<p>Similar to neutrophil depletion, CXCR2 blockade reduces RT-induced tumor inhibition and decreases survival in the RM-9 model. (<b>A</b>) A schematic illustration of the experimental design for the CXCR2 blockade experiment. (<b>B</b>) Tumor growth curves with or without CXCR2 blockade. n = 9–10 per group, values = mean ± SEM, *** <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) Mouse survival curves with or without CXCR2 blockade. CXCR2 blockade significantly (<span class="html-italic">p</span> &lt; 0.05) reduced the survival of mice with irradiated tumors. n = 9 per group. (<b>D</b>) A schematic illustration of the experimental design for the neutrophil depletion experiment. (<b>E</b>) Tumor growth curves with or without neutrophil depletion. n = 9–10 per group, values = mean ± SEM, *** <span class="html-italic">p</span> &lt; 0.001. (<b>F</b>) Mouse survival curves with or without neutrophil depletion. Neutrophil depletion significantly (<span class="html-italic">p</span> &lt; 0.05) reduced the survival of mice with irradiated tumors. n = 8–9 per group. For tumor growth curves comparison, a generalized estimating equation (GEE) was used for statistical analysis. For survival curves comparison (<b>C</b>,<b>F</b>), Log-rank (Mantel–Cox) test was used for statistical analysis. Veh: drug vehicle; Con: sham-treated with RT.</p>
Full article ">Figure 2 Cont.
<p>Similar to neutrophil depletion, CXCR2 blockade reduces RT-induced tumor inhibition and decreases survival in the RM-9 model. (<b>A</b>) A schematic illustration of the experimental design for the CXCR2 blockade experiment. (<b>B</b>) Tumor growth curves with or without CXCR2 blockade. n = 9–10 per group, values = mean ± SEM, *** <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) Mouse survival curves with or without CXCR2 blockade. CXCR2 blockade significantly (<span class="html-italic">p</span> &lt; 0.05) reduced the survival of mice with irradiated tumors. n = 9 per group. (<b>D</b>) A schematic illustration of the experimental design for the neutrophil depletion experiment. (<b>E</b>) Tumor growth curves with or without neutrophil depletion. n = 9–10 per group, values = mean ± SEM, *** <span class="html-italic">p</span> &lt; 0.001. (<b>F</b>) Mouse survival curves with or without neutrophil depletion. Neutrophil depletion significantly (<span class="html-italic">p</span> &lt; 0.05) reduced the survival of mice with irradiated tumors. n = 8–9 per group. For tumor growth curves comparison, a generalized estimating equation (GEE) was used for statistical analysis. For survival curves comparison (<b>C</b>,<b>F</b>), Log-rank (Mantel–Cox) test was used for statistical analysis. Veh: drug vehicle; Con: sham-treated with RT.</p>
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<p>CXCR2 is highly and preferentially expressed on neutrophils. For graphs (<b>A</b>–<b>D</b>), spleens from C57BL/6J control and RM-9 tumor-bearing mice were collected and processed for flow cytometry analysis. Gating strategies for flow cytometry analysis are shown in <a href="#app1-cancers-15-05686" class="html-app">Supplementary Figure S3</a>. n = 4 for both C57BL/6J control and RM-9 tumor-bearing groups, values = mean ± SEM. Student’s <span class="html-italic">t</span>-test was used for comparing the data from control mice with those from tumor-bearing mice. ns = nonsignificant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. (<b>A</b>) Bar graphs showing percentages of different myeloid populations accounting for total leukocytes in the spleen. (<b>B</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different myeloid populations. (<b>C</b>) Bar graphs showing percentages of different lymphoid populations accounting for total splenocytes in the spleen. (<b>D</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different lymphoid populations. For graphs (<b>E</b>,<b>F</b>), RM-9 tumor tissue was collected and processed for flow cytometry analysis, with gating strategies shown in <a href="#app1-cancers-15-05686" class="html-app">Supplementary Figure S4</a>. n = 5, values = mean ± SEM. (<b>E</b>) Bar graphs showing percentages of different myeloid populations accounting for total tumor-infiltrating leukocytes. (<b>F</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different tumor-infiltrating myeloid populations.</p>
Full article ">Figure 3 Cont.
<p>CXCR2 is highly and preferentially expressed on neutrophils. For graphs (<b>A</b>–<b>D</b>), spleens from C57BL/6J control and RM-9 tumor-bearing mice were collected and processed for flow cytometry analysis. Gating strategies for flow cytometry analysis are shown in <a href="#app1-cancers-15-05686" class="html-app">Supplementary Figure S3</a>. n = 4 for both C57BL/6J control and RM-9 tumor-bearing groups, values = mean ± SEM. Student’s <span class="html-italic">t</span>-test was used for comparing the data from control mice with those from tumor-bearing mice. ns = nonsignificant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. (<b>A</b>) Bar graphs showing percentages of different myeloid populations accounting for total leukocytes in the spleen. (<b>B</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different myeloid populations. (<b>C</b>) Bar graphs showing percentages of different lymphoid populations accounting for total splenocytes in the spleen. (<b>D</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different lymphoid populations. For graphs (<b>E</b>,<b>F</b>), RM-9 tumor tissue was collected and processed for flow cytometry analysis, with gating strategies shown in <a href="#app1-cancers-15-05686" class="html-app">Supplementary Figure S4</a>. n = 5, values = mean ± SEM. (<b>E</b>) Bar graphs showing percentages of different myeloid populations accounting for total tumor-infiltrating leukocytes. (<b>F</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different tumor-infiltrating myeloid populations.</p>
Full article ">Figure 3 Cont.
<p>CXCR2 is highly and preferentially expressed on neutrophils. For graphs (<b>A</b>–<b>D</b>), spleens from C57BL/6J control and RM-9 tumor-bearing mice were collected and processed for flow cytometry analysis. Gating strategies for flow cytometry analysis are shown in <a href="#app1-cancers-15-05686" class="html-app">Supplementary Figure S3</a>. n = 4 for both C57BL/6J control and RM-9 tumor-bearing groups, values = mean ± SEM. Student’s <span class="html-italic">t</span>-test was used for comparing the data from control mice with those from tumor-bearing mice. ns = nonsignificant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. (<b>A</b>) Bar graphs showing percentages of different myeloid populations accounting for total leukocytes in the spleen. (<b>B</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different myeloid populations. (<b>C</b>) Bar graphs showing percentages of different lymphoid populations accounting for total splenocytes in the spleen. (<b>D</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different lymphoid populations. For graphs (<b>E</b>,<b>F</b>), RM-9 tumor tissue was collected and processed for flow cytometry analysis, with gating strategies shown in <a href="#app1-cancers-15-05686" class="html-app">Supplementary Figure S4</a>. n = 5, values = mean ± SEM. (<b>E</b>) Bar graphs showing percentages of different myeloid populations accounting for total tumor-infiltrating leukocytes. (<b>F</b>) Bar graphs showing percentages of CXCR2<sup>+</sup> cells in different tumor-infiltrating myeloid populations.</p>
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<p>Radiation increases the expression of CXCR2 ligands within 24 h. (<b>A</b>) A schematic illustration of the experimental design. Five groups of mice were injected s.c. with RM9 tumor cells on the right hind leg. When tumor sizes reached about 8 mm in diameter, tumors were irradiated with a single dose of 15 Gy. At 3 h (or 6 h), 12 h, 24 h, and 48 h post-RT, different groups of mice were euthanized, and tumors were collected and stored at −80 °C for mRNA sequencing and chemokine multiplex assay. (<b>B</b>) In the RM-9 model, mRNA expression of CXCL1, CXCL2, CXCL3, CXCL5, and PPBP (CXCL7) determined by TPM values from mRNA-Seq analysis of the tumor tissue at 3 h, 12 h, 24 h post-RT. n = 3–4 per group, values = mean ± SEM. (<b>C</b>) In the RM-9 model, protein expression of CXCL1, CXCL2, and CXCL5 determined by chemokine multiplex assay of the tumor tissue at 6 h, 12 h, 24 h, 48 h post-RT. n = 4–5 per group, values = mean. (<b>D</b>) In the MC-38 model, mRNA expression of chemokines CXCL1, CXCL2, CXCL3, CXCL5, and PPBP (CXCL7) in the tumor tissue at 3h, 12h, 24h post-RT. n = 4 per group, values = mean ± SEM. (<b>E</b>) In the MC-38 model, protein expression of CXCL1, CXCL2, and CXCL5 in the tumor tissue at 3 h, 6 h, 12 h, 24 h, 48 h post-RT. n = 5 per group, values = mean. (<b>B</b>–<b>E</b>) * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; one-way ANOVA and Dunnett’s multiple comparisons test were used for statistical analyses.</p>
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<p>Radiation increases the gene expression of DNA-sensing pathways in the tumor microenvironment within 24 h in the MC-38 model but not in the RM-9 model. The experiment design was the same as described in <a href="#cancers-15-05686-f004" class="html-fig">Figure 4</a>A. In brief, four groups of mice were injected s.c. with RM-9 (or MC-38) tumor cells on the right hind leg. When tumor sizes reached about 8 mm in diameter, tumors were irradiated with a single dose of 15 Gy. At 3 h, 12 h, and 24 h post-RT, different groups of mice were euthanized, and tumor tissues were collected and stored at −80 °C for mRNA-Seq and western blot analysis. (<b>A</b>,<b>B</b>) In the RM-9 model, mRNA expression of cGAS and AIM2 DNA-sensing pathways, respectively, determined by TPM values from mRNA-Seq analysis of the tumor tissue at 3 h, 12 h, and 24 h post-RT. (<b>C</b>,<b>D</b>) In the MC-38 model, mRNA expression of cGAS and AIM2 DNA-sensing pathways, respectively, determined by TPM values from mRNA-Seq analysis of tumor tissue at 3 h, 12 h, and 24 h post-RT. (<b>E</b>,<b>F</b>) In the RM-9 and MC-38 models, respectively, western blot band pictures and intensity analysis of IL-1β and vinculin from tumor tissue samples of control, 3 h, and 24 h post-RT groups. Vinculin was used as the loading control. (<b>A</b>–<b>F</b>) n = 3–5 per group, value = mean ± SEM, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; one-way ANOVA and Dunnett’s multiple comparisons test were used for statistical analyses.</p>
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<p>Gene expression heatmaps of activated biological processes from gene ontology analysis. The experiment design was the same as described in <a href="#cancers-15-05686-f004" class="html-fig">Figure 4</a>A. In brief, four groups of mice were injected s.c. with RM-9 (or MC-38) tumor cells on the right hind leg. When tumor sizes reached about 8 mm in diameter, tumors were irradiated with a single dose of 15 Gy. At 3 h, 12 h, and 24 h post-RT, different groups of mice were euthanized, and tumor tissues were collected for RNA extraction and mRNA-Seq analysis. TPM values were used for gene ontology analysis. (<b>A</b>,<b>B</b>) Heatmaps of normalized individual gene expression involved in Cellular Response to DNA Damage Stimulus (GO: 0006974). (<b>C</b>,<b>D</b>) Heatmaps of normalized gene expression involved in Cellular Response to Interleukin-1 (GO: 0071347). (<b>E</b>,<b>F</b>) Heatmaps of normalized gene expression involved in Neutrophil Chemotaxis (GO: 0030593). (<b>A</b>,<b>C</b>,<b>E</b>) are results from the RM-9 model, and (<b>B</b>,<b>D</b>,<b>F</b>) are results from the MC-38 model.</p>
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<p>Gene expression heatmaps of activated biological processes from gene ontology analysis. The experiment design was the same as described in <a href="#cancers-15-05686-f004" class="html-fig">Figure 4</a>A. In brief, four groups of mice were injected s.c. with RM-9 (or MC-38) tumor cells on the right hind leg. When tumor sizes reached about 8 mm in diameter, tumors were irradiated with a single dose of 15 Gy. At 3 h, 12 h, and 24 h post-RT, different groups of mice were euthanized, and tumor tissues were collected for RNA extraction and mRNA-Seq analysis. TPM values were used for gene ontology analysis. (<b>A</b>,<b>B</b>) Heatmaps of normalized individual gene expression involved in Cellular Response to DNA Damage Stimulus (GO: 0006974). (<b>C</b>,<b>D</b>) Heatmaps of normalized gene expression involved in Cellular Response to Interleukin-1 (GO: 0071347). (<b>E</b>,<b>F</b>) Heatmaps of normalized gene expression involved in Neutrophil Chemotaxis (GO: 0030593). (<b>A</b>,<b>C</b>,<b>E</b>) are results from the RM-9 model, and (<b>B</b>,<b>D</b>,<b>F</b>) are results from the MC-38 model.</p>
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<p>(<b>A</b>) A schematic illustration of CXCLs/CXCR2 axis-mediated neutrophilic infiltration into the tumor within 24 h post-RT. RT induces IL-1β in the tumor microenvironment, which can further increase the levels of chemokines CXCL1, CXCL2, and CXCL5. These elevated levels of chemokines can be sequestered by endothelial cells of the blood vessel and form a chemoattractant gradient for neutrophils. The interaction between these chemokines and CXCR2 expressed on neutrophils can promote chemotaxis of neutrophils into the tumor tissue and facilitates the extravasation of neutrophils, which can eventually lead to the increased number of neutrophils in the tumor. (<b>B</b>) The hypothetical upstream mechanism underlying the activation of CXCLs/CXCR2 axis upon RT. In MC-38 tumors, the DNA damage induced by RT likely activates (1) the cGAS/STING DNA-sensing pathway which can lead to the upregulation of pro-IL-1β, and (2) the AIM2 DNA-sensing pathway, which can activate IL-1β. In RM-9 tumors, RT induces DNA damage, which likely leads to the increased expression of IL-1β by an as-yet unidentified mechanism. Increased expression and/or activation of IL-1β can upregulate the production of CXCLs, which attract neutrophils into the tumor.</p>
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16 pages, 2384 KiB  
Article
Inflammatory Bone Marrow Mesenchymal Stem Cells in Multiple Myeloma: Transcriptional Signature and In Vitro Modeling
by Lei Wang, Weijun Yi, Li Ma, Emily Lecea, Lori A. Hazlehurst, Donald A. Adjeroh and Gangqing Hu
Cancers 2023, 15(21), 5148; https://doi.org/10.3390/cancers15215148 - 26 Oct 2023
Cited by 4 | Viewed by 2961
Abstract
Bone marrow mesenchymal stem cells (BM MSCs) play a tumor-supportive role in promoting drug resistance and disease relapse in multiple myeloma (MM). Recent studies have discovered a sub-population of MSCs, known as inflammatory MSCs (iMSCs), exclusive to the MM BM microenvironment and implicated [...] Read more.
Bone marrow mesenchymal stem cells (BM MSCs) play a tumor-supportive role in promoting drug resistance and disease relapse in multiple myeloma (MM). Recent studies have discovered a sub-population of MSCs, known as inflammatory MSCs (iMSCs), exclusive to the MM BM microenvironment and implicated in drug resistance. Through a sophisticated analysis of public expression data from unexpanded BM MSCs, we uncovered a positive association between iMSC signature expression and minimal residual disease. While in vitro expansion generally results in the loss of the iMSC signature, our meta-analysis of additional public expression data demonstrated that cytokine stimulation, including IL1-β and TNF-α, as well as immune cells such as neutrophils, macrophages, and MM cells, can reactivate the signature expression of iMSCs to varying extents. These findings underscore the importance and potential utility of cytokine stimulation in mimicking the gene expression signature of early passage of iMSCs for functional characterizations of their tumor-supportive roles in MM. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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<p>Expression changes in iMSC signature genes associated with MRD status. (<b>A</b>) Volcano plot displaying fold changes of expression and <span class="html-italic">p</span>-values from bulk-cell RNA-Seq analysis of BM MSCs to differentiate MM samples during MRD positivity from those matched at diagnosis. Red: upregulated genes during MRD positivity. Blue: downregulated genes. Gray: other expressed genes. Indicated by orange dots and arrow heads are examples of immune-related genes. (<b>B</b>) Expression level of <span class="html-italic">IL6</span> (<b>upper</b> panel) and <span class="html-italic">CXCL3</span> (<b>lower</b> panel) in BM MSCs comparing MM patients during MRD positivity to matched samples at diagnosis, indicated by dot lines. <span class="html-italic">p</span>-value by paired <span class="html-italic">t</span>-test. (<b>C</b>) GSEA of expressed genes sorted by expression changes from high (red) to low (blue) in MSCs collected at MRD+ compared to at diagnosis (calculated from bulk-cell RNA-Seq data) against MSigDB hallmark gene set “TNF-α signaling via NF-κB” (vertical bars). Highlighted are top 10 leading genes. NES: normalized enrichment score. (<b>D</b>) GSEA like panel C but against iMSC signature genes defined for MM patients from single-cell RNA-Seq analysis. (<b>E</b>) Volcano plot like panel A but comparing BM MSC samples collected during MRD negativity to matched samples at diagnosis. (<b>F</b>) Expression level of <span class="html-italic">IL6</span> (upper panel) and <span class="html-italic">CXCL3</span> (lower panel) comparing MM patients during MRD negativity to those matched at diagnosis. (<b>G</b>) GSEA like panel C but comparing BM MSC samples collected during MRD negativity to those matched at diagnosis. (<b>H</b>) GSEA like panel D but comparing BM MSC samples collected during MRD negativity to those matched at diagnosis.</p>
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<p>Loss of expression upregulation in inflammatory genes in expanded MM MSCs. (<b>A</b>) Fold change of expression (MM/HD) for inflammatory genes highlighted in de Jong, et al. [<a href="#B11-cancers-15-05148" class="html-bibr">11</a>] from studies based on expanded MSCs (GSE#s) or sorted primary MSCs (E-MTAB-9285). (<b>B</b>) GSEA of expressed genes sorted by expression changes in in vitro expanded MSCs (MM/HD) from high (<b>left</b>) to low (<b>right</b>) against iMSC signature genes. NES: normalized enrichment score. GEO accession numbers indicated for each study. (<b>C</b>) Heatmap visualization of expression fold change (MM/HD) for iMSC signature genes (rows) across studies (columns).</p>
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<p>Expression activation of iMSC signature genes by cytokines. (<b>A</b>) Hierarchy clustering analysis for cytokine(s) based on Pearson correlation of genome-wide expression changes induced by the stimulations. Indicated are cytokine, doses, and durations from different studies with accession numbers. (<b>B</b>) Bubble plots for NES and FDR q values of GSEA applied to genes sorted by expression changes induced by cytokines against signature genes of iMSCs in MM patients (<b>leftmost</b> column) and iCAFs in colorectal cancer patients (<b>middle</b> column) or pancreatic cancer patients (<b>rightmost</b> column). Each row represents a cytokine stimulation with annotation aligned with panel A. (<b>C</b>) Representative results for GSEA from panel B for stimulations by IL1-β (<b>top</b> panel), TNF-α (<b>middle</b> panel), and IFN-γ (<b>bottom</b> panel). (<b>D</b>) Heatmap visualization of expression changes induced by mono-treatment of cytokines such as IFN-γ, IL1-β, TNA-α, and TGF-β for iMSC signature genes defined for MM patients. (<b>E</b>) Bar plots for expression changes of <span class="html-italic">TNFA</span>, <span class="html-italic">IFNG</span>, <span class="html-italic">TGFB1</span>, and <span class="html-italic">IL1B</span> in BM MSCs stimulated by IL1-β, IFN-γ, TNF-α, or TGF-β1. (<b>F</b>) Heatmap visualization of CytoSig score, which predicts cellular response to cytokines (columns), based on expression changes induced by external stimulations (rows). Black arrow heads: cellular response to IL-1β was upregulated when stimulated by TNF-α or IL-1β; White arrow heads: cellular response to IL-1β was further upregulated when co-stimulated by TNF-α and IL-1β; Blue arrow heads: cellular response to TNF-α was not activated when stimulated by IL-1β.</p>
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<p>Expression activation of iMSC signature genes with neutrophils or macrophages. (<b>A</b>) GSEA of expressed genes sorted by expression changes in BM MSCs induced by neutrophils (NP) from high (red) to low (blue) against hallmark gene set “TNF-α signaling via NF-κB” (vertical bars). Highlighted are top 10 leading genes. NES: normalized enrichment score. (<b>B</b>) Like panel A, but against iMSC signature genes for MM patients. (<b>C</b>) Venn diagram for genes upregulated by neutrophils and iMSC signature genes. (<b>D</b>) Bar plot for CytoSig score, which predicts cytokines contributing to the expression changes in BM MSCs as induced by neutrophils. (<b>E</b>) GSEA of expressed genes sorted by expression changes of BM MSCs induced by inflammatory macrophages (MΦ) from high (red) to low (blue) against hallmark gene set “TNF-α signaling via NF-κB” (vertical bars). Highlighted are top 10 leading genes. (<b>F</b>) Like panel E, but against iMSC signature genes. (<b>G</b>) Venn diagram for genes upregulated by inflammatory macrophages and iMSC signature genes. (<b>H</b>) Bar plot for CytoSig score, which predicts cytokines contributing to the expression changes in BM MSCs as induced by inflammatory macrophages.</p>
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<p>Expression activation of iMSC signature genes by MM.1S. (<b>A</b>) GSEA of expressed genes sorted by expression changes induced by MM.1S (as compared to monoculture; Mono) in BM MSCs of MM patients (MM; red line) or healthy donors (HD; black line) from high (<b>left</b> side) to low (<b>right</b> side) against MSigDB hallmark gene set “TNF-α signaling via NF-κB”. NES: normalized enrichment score. (<b>B</b>) Scatter plot comparing the expression changes in genes from “TNF-α signaling via NF-κB” induced by MM.1S in BM MSCs from MM (<span class="html-italic">x</span>-axis) to those from HDs (<span class="html-italic">y</span>-axis). (<b>C</b>) Like panel A, but against iMSC signature genes. (<b>D</b>) Like panel B, but for iMSC signature genes. (<b>E</b>) Dot plots for the expression of immune-related genes in BM MSCs from MM and HDs in coculture with MM.1S or in monoculture. (<b>F</b>) Pearson correlation measuring the similarity of expression changes induced by MM.1S (in HD MSCs and MM MSCs) and those induced by cytokines, with an example comparing a stimulation by MM.1S to by IL1-β. (<b>G</b>) Normalized ChIP-seq read density for histone post-translational modifications H3K4me3 and H3K27me3 across gene locus IL1B (<b>upper</b> panel) and TNF (<b>lower</b> panel) in BM MSCs.</p>
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16 pages, 2442 KiB  
Article
Interleukin-8 and Interleukin-6 Are Biomarkers of Poor Prognosis in Esophageal Squamous Cell Carcinoma
by Paula Roberta Aguiar Pastrez, Ana Margarida Barbosa, Vânia Sammartino Mariano, Rhafaela Lima Causin, Antonio Gil Castro, Egídio Torrado and Adhemar Longatto-Filho
Cancers 2023, 15(7), 1997; https://doi.org/10.3390/cancers15071997 - 27 Mar 2023
Cited by 5 | Viewed by 2274
Abstract
Esophageal squamous cell carcinoma (ESCC) is a common type of cancer characterized by fast progression and high mortality rates, which generally implies a poor prognosis at time of diagnosis. Intricate interaction networks of cytokines produced by resident and inflammatory cells in the tumor [...] Read more.
Esophageal squamous cell carcinoma (ESCC) is a common type of cancer characterized by fast progression and high mortality rates, which generally implies a poor prognosis at time of diagnosis. Intricate interaction networks of cytokines produced by resident and inflammatory cells in the tumor microenvironment play crucial roles in ESCC development and metastasis, thus influencing therapy efficiency. As such, cytokines are the most prominent targets for specific therapies and prognostic parameters to predict tumor progression and aggressiveness. In this work, we examined the association between ESCC progression and the systemic levels of inflammatory cytokines to determine their usefulness as diagnostic biomarkers. We analyzed the levels of IL-1β, IL-6, IL-8, IL-10, TNF-α e IL-12p70 in a group of 70 ESCC patients and 70 healthy individuals using Cytometric Bead Array (CBA) technology. We detected increased levels of IL-1β, IL-6, IL-8, and IL-10 in ESCC patients compared to controls. However, multivariate analysis revealed that only IL8 was an independent prognostic factor for ESCC, as were the well-known risk factors: alcohol consumption, tobacco usage, and exposure to pesticides/insecticides. Importantly, patients with low IL-6, IL-8, TNM I/II, or those who underwent surgery had a significantly higher overall survival rate. We also studied cultured Kyse-30 and Kyse-410 cells in mice. We determined that the ESCC cell line Kyse-30 grew more aggressively than the Kyse-410 cell line. This enhanced growth was associated with the recruitment/accumulation of intratumoral polymorphonuclear leukocytes. In conclusion, our data suggest IL-8 as a valuable prognostic factor with potential as a biomarker for ESCC. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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<p>Graphs of relative expression of genes of interest performed by real-time PCR. Graphs representing the relative expression of the genes (<b>a</b>) CXCL-2; (<b>b</b>) CXCL-1; (<b>c</b>) CCL2; and (<b>d</b>) VEGF in the tumors induced by the Kyse-30 and Kyse-410 cell lines. “BDL: Below Detection Levels”. Data represent 10 animals (5 per group) from the same experiment. Each dot represents one animal; the blue dots indicate the relative expression of the different genes of each animal inoculated with Kyse-30 and the red dots of each animal inoculated with Kyse-410. All experiments were performed only once. Statistical significance was calculated by using unpaired <span class="html-italic">t</span>-test and <span class="html-italic">p</span> values are shown in the figure.</p>
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<p>Evaluation of the intra-tumor immune cell infiltrate in the tumors induced by the Kyse-30 and Kyse-410 cell lines. (<b>A</b>) Representative haematoxylin and eosin (H&amp;E) of histological section tumors induced by Kyse-30 (Original magnification ×20); (<b>B</b>) Representative H&amp;E of histological sections tumors induced by Kyse-30 (Original magnification ×40); (<b>C</b>) Representative H&amp;E of histological sections tumors induced by Kyse-410 (Original magnification ×20); (<b>D</b>) Representative H&amp;E of histological sections tumors induced by Kyse-410 (Original magnification ×40). (<b>E</b>) Tumor growth curve in NSG mice inoculated with the Kyse-30 and Kyse-410 cell lines. Data represent 10 animals (5 per group) from the same experiment. Each dot represents one animal. The square dots represent each animal inoculated with Kyse-30 cell line and the spherical dots represent each animal inoculated with Kyse-410 cell line. The y-axis represents the tumor volume and the x-axis the days after inoculation of the cells. This experiment was performed only once. Statistical significance was calculated by using Anova test (* <span class="html-italic">p</span> = 0.024; ** <span class="html-italic">p</span> = 0.007; **** <span class="html-italic">p</span> &lt; 0.0001). (<b>F</b>) Representative flow cytometry data of intratumoral polymorphonuclear leukocytes (CD11b+ Ly6G+) in Kyse-30 or Kyse-410 tumor-bearing mice. (<b>G</b>) Evaluation of the intra-tumor immune cell infiltrate in the tumors induced by the Kyse-30 and Kyse-410 cell lines by flow cytometry. Data represent 10 animals (5 per group) from the same experiment. Each dot represents one animal; the blue dots indicate CD11b+Ly6G+ expression of each animal inoculated with Kyse-30 and the red dots indicate CD11b+Ly6G+ expression of each animal inoculated with Kyse-410. All experiments were performed only once. Statistical significance was calculated by using unpaired <span class="html-italic">t</span>-test (<span class="html-italic">p</span> = 0.0367).</p>
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17 pages, 4038 KiB  
Article
The CXCL10/CXCR3 Pathway Contributes to the Synergy of Thermal Ablation and PD-1 Blockade Therapy against Tumors
by Wenlu Xiao, Hao Huang, Panpan Zheng, Yingting Liu, Yaping Chen, Junjun Chen, Xiao Zheng, Lujun Chen and Jingting Jiang
Cancers 2023, 15(5), 1427; https://doi.org/10.3390/cancers15051427 - 23 Feb 2023
Cited by 12 | Viewed by 2545
Abstract
As a practical local therapeutic approach to destroy tumor tissue, thermal ablation can activate tumor-specific T cells via enhancing tumor antigen presentation to the immune system. In the present study, we investigated changes in infiltrating immune cells in tumor tissues from the non-radiofrequency [...] Read more.
As a practical local therapeutic approach to destroy tumor tissue, thermal ablation can activate tumor-specific T cells via enhancing tumor antigen presentation to the immune system. In the present study, we investigated changes in infiltrating immune cells in tumor tissues from the non-radiofrequency ablation (RFA) side by analyzing single-cell RNA sequencing (scRNA-seq) data of tumor-bearing mice compared with control tumors. We showed that ablation treatment could increase the proportion of CD8+T cells and the interaction between macrophages and T cells was altered. Another thermal ablation treatment, microwave ablation (MWA), increased the enrichment of signaling pathways for chemotaxis and chemokine response and was associated with the chemokine CXCL10. In addition, the immune checkpoint PD-1 was especially up-regulated in the infiltrating T cells of tumors on the non-ablation side after thermal ablation treatment. Combination therapy of ablation and PD-1 blockade had a synergistic anti-tumor effect. Furthermore, we found that the CXCL10/CXCR3 axis contributed to the therapeutic efficacy of ablation combined with anti-PD-1 therapy, and activation of the CXCL10/CXCR3 signaling pathway might improve the synergistic effect of this combination treatment against solid tumors. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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<p>scRNA-seq identifies the changes in tumor-infiltrating immune cells. (<b>A</b>). UMAP plot showing CD45<sup>+</sup> immune cells colored by computationally determined clusters based on scRNA-seq data in the Panc02 tumor-bearing mouse model (<b>above</b>). The frequencies of cell composition in the tumor-infiltrating CD45<sup>+</sup> immune cells with the control group (not subjected to ablation, <span class="html-italic">n</span> = 14,837) and ablation group (<span class="html-italic">n</span> = 11,247) (<b>below</b>). (<b>B</b>). Dotplot showing the top 10 marker genes across five different CD45<sup>+</sup> immune cell subgroups. (<b>C</b>). Percentages of cells in the control group and ablation group among the different cell subgroups of CD45<sup>+</sup> immune cells.</p>
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<p>Enrichment analysis of DEGs in CD8<sup>+</sup>TILs after MWA treatment. (<b>A</b>). GSEA analysis showing the top enriched chemotaxis and chemokine-mediated signal transduction regulatory pathways in CD8<sup>+</sup>TILs of the MWA treatment group. (<b>B</b>). GO enrichment analysis of up-regulated genes in CD8<sup>+</sup>TILs of the MWA treatment group.</p>
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<p>CXCL10-CXCR3 plays a vital role in ablation-induced cell–cell interactions. (<b>A</b>). Heatmap showing the cell–cell communication among immune cell populations between subgroups of CD45<sup>+</sup> immune cells predicted by CellphoneDB2. (<b>B</b>). Dotplot showing the expression intensity of selected ligand–receptor pairs among the different cell subgroups of CD45<sup>+</sup> immune cells. Sizes of dots represent the <span class="html-italic">p</span>-value, and colors of dots represent the strength interaction between two subpopulations. (<b>C</b>). Dotplot showing the expression levels of chemokines and its receptors across cell types in the control and ablation treatment groups. (<b>D</b>). UMAP plot showing expressions of chemokine <span class="html-italic">Cxcl10</span> and its receptor <span class="html-italic">Cxcr3</span> in two groups.</p>
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<p>Ablation leads to the remodeling of tumor-infiltrating CD45<sup>+</sup> immune cell subsets. (<b>A</b>). UMAP plot showing sub-clusters of T cells based on scRNA-seq data in the Panc02 tumor-bearing mouse model (<b>left</b>). The frequency of cell composition in the T cells of the control group (<span class="html-italic">n</span> = 2042) and ablation group (<span class="html-italic">n</span> = 1480) (<b>right</b>). (<b>B</b>). Heatmap displaying marker genes expressed in different subpopulations of TILs. (<b>C</b>). UMAP plot showing sub-clusters of macrophages colored by computationally determined clusters (<b>left</b>). The frequency of cell composition in the macrophages of the control group (<span class="html-italic">n</span> = 5860) and RFA group (<span class="html-italic">n</span> = 4153) (<b>right</b>). (<b>D</b>). Heatmaps showing potential ligands from macrophages interacting with receptors expressed on CD8<sup>+</sup>T cells on the non-ablation side in the ablation group. (<b>E</b>). Heatmaps showing that potential ligands from macrophages might influence the gene expression in CD8<sup>+</sup>T cells of the RFA group.</p>
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<p>CXCL10 contributes essentially to thermal-ablation-induced anti-tumor effects. (<b>A</b>). Normalized expression of the chemokine receptor <span class="html-italic">Cxcr3</span> gene in T cell sub-clusters shown by the violin plot. (<b>B</b>). Normalized expression of the chemokine <span class="html-italic">Cxcl10</span> gene in macrophage sub-clusters shown by the violin plot. (<b>C</b>). GSEA analysis showing top-down enriched IFN-α/γ response and oxidative phosphorylation in the MWA group (<b>left</b>). GSEA analysis showing top-down enriched TNFA signaling via NF-κB, P53 pathway, and glycolysis in the RFA group (<b>right</b>). NES denotes normalized enrichment score. (<b>D</b>). Schematic diagram of the protocol for MWA treatment of tumor-bearing mice. C57BL/6 and <span class="html-italic">Cxcl10</span><sup>−/−</sup> mice were bilaterally inoculated with MC38 cells on the back to construct tumor-bearing mouse models, and one side of the tumor was treated with MWA. (<b>E</b>). Tumor burden in C57BL/6 and <span class="html-italic">Cxcl10</span><sup>−/−</sup> mice treated with MWA (<span class="html-italic">n</span> = 6). (<b>F</b>). Mouse survival after treatment with MWA in C57BL/6 and <span class="html-italic">Cxcl10</span><sup>−/−</sup> mice (<span class="html-italic">n</span> = 8). Data are presented as the mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 according to the one-way ANOVA test and the log-rank test.</p>
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<p>CXCL10 deletion dampens PD-1 blockade efficacy. (<b>A</b>). Schematic diagram of the protocol for the anti-PD-1 treatment of tumor-bearing mice. C57BL/6 and <span class="html-italic">Cxcl10</span><sup>−/−</sup> mice were subcutaneously inoculated with 2 × 10<sup>6</sup> MC38 tumor cells, then injected i.p. with 200 μg of anti-PD-1 antibody or isotype control antibody on days 4, 7, 10, and 13 after tumor cell inoculation. Tumor growth was monitored until the experimental endpoints. (<b>B</b>). Tumor growth in C57BL/6 and <span class="html-italic">Cxcl10<sup>−/−</sup></span> mice treated with control or anti-PD-1 antibody. Six mice were in each group. (<b>C</b>). Flow cytometry analysis followed by quantification of CD45<sup>+</sup> cells, CD4<sup>+</sup>TILs, CD8<sup>+</sup>TILs, and Foxp3<sup>+</sup>Treg within tumors of C57BL/6 and <span class="html-italic">Cxcl10</span><sup>−/−</sup> mice treated with control or anti-PD-1 antibodies (<span class="html-italic">n</span> = 6). (<b>D</b>)<b>.</b> Representative flow cytometry plots and quantitation of the percentages of IFN-γ expression in CD8<sup>+</sup>TILs (<span class="html-italic">n</span> = 6). Data are presented as the mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001 according to the one-way ANOVA test and the log-rank test.</p>
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<p>CXCL10 contributes to the synergy of MWA and PD-1 blockade. (<b>A</b>). Normalized expression of <span class="html-italic">Pdcd1</span> gene in the lymphocyte subsets shown by the violin plot. (<b>B</b>). The percentage of PD-1 in CD4<sup>+</sup>TILs and CD8<sup>+</sup>TILs on day 12 of MWA in the MC38 tumor-bearing mouse model dosed with control or MWA treatment. (<b>C</b>). Schematic drawing of the study. C57BL/6 and <span class="html-italic">Cxcl10</span><sup>−/−</sup> mice were bilaterally inoculated with MC38 cells on the back to construct tumor-bearing mouse models, and one side of the tumor was treated with MWA. Mice were injected i.p. with isotype control or anti-PD-1 antibody on day 1 after MWA and then every 3 days four times. (<b>D</b>). The size of the tumors on the non-MWA area tumor was recorded every 2 days after MWA. Five mice were in each group. (<b>E</b>). Flow cytometric analysis of the percentages of CD45<sup>+</sup> tumor-infiltrating cells, CD4<sup>+</sup>TILs, and CD8<sup>+</sup>TILs of C57BL/6 and <span class="html-italic">Cxcl10</span><sup>−/−</sup> mice treated with control or MWA treatment (<span class="html-italic">n</span> = 4). (<b>F</b>). Representative flow cytometry plots and quantitation of the percentage of IFN-γ expression in CD8<sup>+</sup>TILs (<span class="html-italic">n</span> = 4). Data are represented as the mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 according to the one-way ANOVA test and the log-rank test.</p>
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19 pages, 7918 KiB  
Article
A Combined Risk Score Model to Assess Prognostic Value in Patients with Soft Tissue Sarcomas
by Zihua Li, Zhengwei Duan, Keyao Jia, Yiwen Yao, Kaiyuan Liu, Yue Qiao, Qiuming Gao, Yunfeng Yang, Guodong Li and Anquan Shang
Cells 2022, 11(24), 4077; https://doi.org/10.3390/cells11244077 - 16 Dec 2022
Cited by 4 | Viewed by 2233
Abstract
A study by Tsvetkov et al. recently published a proposed novel form of copper-induced cell death in Science; however, few studies have looked into the possible mechanism in soft tissue sarcoma (STS). Herein, this study sought to investigate the function of cuproptosis-related [...] Read more.
A study by Tsvetkov et al. recently published a proposed novel form of copper-induced cell death in Science; however, few studies have looked into the possible mechanism in soft tissue sarcoma (STS). Herein, this study sought to investigate the function of cuproptosis-related genes (CRGs) in the development of tumor-associated immune cells and the prognosis of sarcoma. Herein, this study aimed to explore the role of cuproptosis-related genes (CRGs) in the development, tumor-associated immune cells, and the prognosis of sarcoma. Methods: The prognostic model was established via the least absolute shrinkage and selection operator (LASSO) algorithm as well as multivariate Cox regression analysis. The stromal scores, immune scores, ESTIMA scores, and tumor purity of sarcoma patients were evaluated by the ESTIMATE algorithm. Functional analyses were performed to investigate the underlying mechanisms of immune cell infiltration and the prognosis of CRGs in sarcoma. Results: Two molecular subgroups with different CRG expression patterns were recognized, which showed that patients with a higher immune score and more active immune status were prone to have better prognostic survival. Moreover, GO and KEGG analyses showed that these differentially expressed CRGs were mainly enriched in metabolic/ions-related signaling pathways, indicating that CRGs may have impacts on the immune cell infiltration and prognosis of sarcoma via regulating the bioprocess of mitochondria and consequently affecting the immune microenvironment. The expression levels of CRGs were closely correlated to the immunity condition and prognostic survival of sarcoma patients. Conclusions: The interaction between cuproptosis and immunity in sarcoma may provide a novel insight into the study of molecular mechanisms and candidate biomarkers for the prognosis, resulting in effective treatments for sarcoma patients. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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<p>WGCNA revealed potential mechanisms and pathway enrichment analysis of CRGs in STS patients. (<b>A</b>,<b>B</b>) The distribution and tendency of scale-free topology model fit and mean connectivity accompanied by a soft threshold. (<b>C</b>) A dynamic tree cut was merged with a dynamic method to decipher the clustering of genes among different modules. (<b>D</b>) The mean correlations between module and cuproptosis. The colors of the cells represent associated intensity, and the numbers in parentheses represent the <span class="html-italic">p</span> value of the correlation test. (<b>E</b>) the enriched item in the gene ontology analysis; (<b>F</b>) the enriched item in the Kyoto Encyclopedia of Genes and Genomes analysis. The size of the circles represents the number of enriched genes. BP: biological process; CC: cellular component; MF: molecular function; and CRG: cuproptosis-related gene.</p>
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<p>Construction and validation of prognosis-associated genes model of STS using LASSO regression. (<b>A</b>) The result of cox regression analysis of prognostic genes for patients with sarcoma. (<b>B</b>,<b>C</b>) the results of LASSO regression model.</p>
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<p>The association survival difference between high- and low-risk groups. (<b>A</b>,<b>B</b>) The risk curves and the distribution of patients are ordered according to the risk scores, from low to high. The dots in the lower part indicate the distribution of cases. (<b>C</b>) The correlation between overall survival and risk score. (<b>D</b>) Survival differences between the high- and low-risk groups in patients with sarcoma from the database. The table below the survival curves shows the number of patients alive in each year.</p>
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<p>Exploring of DEGs of high- and low-risk groups in patients with sarcoma. (<b>A</b>) The result of a volcano plot. The dots on the right site indicated the high-risk genes, the dot on the left site is the low-risk genes, and the in-between dots indicated the other genes without significant difference. (<b>B</b>) The result of PLS-DA indicated the two groups of people could be clearly distinguished in the model. (<b>C</b>,<b>D</b>) The GO functional enrichment analysis of differential genes includes three domains: molecular function (MF), biological process (BP), and cell composition (CC). KEGG pathway analysis of differentially expressed genes.</p>
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<p>Immune cell populations and TME landscape. (<b>A</b>,<b>B</b>) The result of ssGSEA and CIBESORT to demonstrate the difference of immune cell populations between the high- and low-risk groups. (<b>C</b>,<b>D</b>) The association and proportions of risk and immune-infiltrations. * <span class="html-italic">p</span> &lt; 0.05. ** <span class="html-italic">p</span> &lt; 0.01. *** <span class="html-italic">p</span> &lt; 0.001. ns: not significant.</p>
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<p>The correlation between different immune cells.</p>
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<p>Relationships between expression of five hub genes and tumor immune infiltrations. (<b>A</b>) LIPT1, (<b>B</b>) GCSH, (<b>C</b>) ATP7B, (<b>D</b>) NCOA6, (<b>E</b>) PRPF4B.</p>
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<p>The association of CRGs with immune scores. (<b>A</b>–<b>F</b>) Immune scores, stromal scores, and ESTIMATE scores of five CRGs signatures between the high- and low-risk groups. Immune and stromal scores were calculated by analyzing specific gene expression signatures of immune and stromal cells to predict non-tumor cell infiltration. ** <span class="html-italic">p</span> &lt; 0.01. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The association of CRGs with immune checkpoints predicting the response to chemotherapy. (<b>A</b>) The differences in expression levels of immune checkpoints between the in patients with sarcoma. (<b>B</b>–<b>D</b>) The results of CYT, TIDE1, and TIDE2. CYT: cytolytic activity, TIDE: tumor immune dysfunction and exclusion. * <span class="html-italic">p</span> &lt; 0.05. ** <span class="html-italic">p</span> &lt; 0.01. *** <span class="html-italic">p</span> &lt; 0.001. ns: not significant.</p>
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<p>Mutation landscape. (<b>A</b>–<b>H</b>) Variant classification. X axis indicated variant numbers. Y axis showed different variant classifications. (<b>B</b>–<b>I</b>) Variant type. X axis indicated variant numbers. Y axis represented different variant types. (<b>C</b>–<b>J</b>) SNVs type. X axis indicated the ratio. Y axis represented the type of nucleotide substitution. (<b>D</b>–<b>E</b>, <b>K</b>–<b>L</b>) The variants per sample and the summary of variant classification. Each sample contains a statistical plot of the number of mutations and a box plot of the classification of the various mutations in the sample (<b>F</b>–<b>M</b>) Top10 mutated genes. X axis indicated variant numbers. Y axis represented different genes. The genes were ordered by their mutation frequency.) (<b>G</b>–<b>N</b>) The upper bar shows the total gene mutation amount and corresponding mutation types. The right bar shows the mutation frequency of the top 20 mutated genes.</p>
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<p>Relative expression level of selected CRGs in sarcoma tissues. The expression level of CRGs in high- and low-risk groups of patients with sarcoma by informatic analysis. (<b>A</b>) Validation of the expression of LIPT1, GSCH, ATP7B, NCOA6, and PRPF4B in sarcoma tissue between two groups (<b>B</b>) The expression level of LIPT1, DLD, DBT, DLAT, PDHA1, PDHB, SCL31A1, ATP7A, and ATP7B between the high- and low-risk groups (<b>C</b>). GAPDH was used as the internal reference gene for qRT-PCR relative expression. Error bars indicate the standard deviation or the standard error of the data. * <span class="html-italic">p</span> &lt; 0.05. ** <span class="html-italic">p</span> &lt; 0.01. *** <span class="html-italic">p</span> &lt; 0.001. **** <span class="html-italic">p</span> &lt; 0.0001. ns: not significant.</p>
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15 pages, 2284 KiB  
Article
Clinical Value of Ultrasonography and Serum Markers in Preoperative N Staging of Thyroid Cancer
by Hui Wang, Shanshan Zhao, Chunyang Xu, Jincao Yao, Xiuhua Yu and Dong Xu
Cells 2022, 11(22), 3621; https://doi.org/10.3390/cells11223621 - 15 Nov 2022
Cited by 3 | Viewed by 2185
Abstract
We aimed to determine factors influencing lymph node metastasis (LNM) and develop a more effective method to assess preoperative N staging. Overall, data of 2130 patients who underwent thyroidectomy for thyroid cancer between 2018 and 2021 were retrospectively analysed. Patients were divided into [...] Read more.
We aimed to determine factors influencing lymph node metastasis (LNM) and develop a more effective method to assess preoperative N staging. Overall, data of 2130 patients who underwent thyroidectomy for thyroid cancer between 2018 and 2021 were retrospectively analysed. Patients were divided into groups according to pN0, pN1a, and pN1b stages. Pathology was used to analyse the correlation between preoperative serum marker indicators and LNM. Receiver operating characteristic curves were used to compare the diagnostic value of ultrasound (US) examination alone, serum thyroglobulin, age, and combined method for LNM. A significant moderate agreement was observed between preoperative US and postoperative pathology for N staging. Between the pN0 and pN1 (pN1a + pN1b) groups, the differences in free triiodothyronine, anti-thyroid peroxidase antibody, and serum thyroglobulin levels were statistically significant. Among the indicators, serum thyroglobulin was an independent predictor of LNM. The area under the receiver operating characteristic curve was 0.610 for serum thyroglobulin level for predicting LNM, 0.689 for US alone, and 0.742 for the combined method. Both preoperative US and serum thyroglobulin level provide a specific value when evaluating the N staging of thyroid cancer, and the combined method is more valuable in the diagnosis of LNM than US alone. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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<p>Risk factors and the combined method for predicting LNM in the model establishing group. (<b>a</b>) Tg. (<b>b</b>) Age. (<b>c</b>) US alone. (<b>d</b>) Combined diagnosis. LNM, lymph node metastasis; ROC, receiver operating characteristic; Tg, serum thyroglobulin; US, ultrasound.</p>
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<p>Risk factors and the combined method for predicting LNM in the external validation group. (<b>a</b>) Tg. (<b>b</b>) Age. (<b>c</b>) US alone. (<b>d</b>) Combined diagnosis. LNM, lymph node metastasis; ROC, receiver operating characteristic; Tg, serum thyroglobulin; US, ultrasound.</p>
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<p>A 57-year-old female patient with a preoperative serum Tg level of 2.95 ng/mL. Ultrasound findings: thyroid left lower lobe nodule, TI-RADS 5 categories (<b>a</b>) Left neck IV lymph node enlargement, suspicious metastatic lymph nodes (<b>b</b>) combined with puncture if necessary. Postoperative pathology shows papillary thyroid microcarcinoma ((<b>c</b>) shows HE staining, magnification ×100). Left cervical lymph node chronic inflammation (<b>d</b>) shows HE staining, magnification ×100). Tg, serum thyroglobulin; HE, haematoxylin and eosin; TI-RADS, Thyroid Imaging Reporting and Data System.</p>
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<p>A 35-year-old female patient with a preoperative serum Tg of 2.31 ng/mL. Ultrasound findings: thyroid left lower lobe nodule, TI-RADS 5 categories (<b>a</b>) Left cervical lymph node enlargement, suspicious metastatic lymph nodes (<b>b</b>) Postoperative pathology shows papillary thyroid microcarcinoma (<b>c</b>) shows haematoxylin and eosin [HE] staining, magnification ×100). Left cervical lymph node chronic inflammation (<b>d</b>) shows HE staining, magnification ×100). Tg, serum thyroglobulin; HE, haematoxylin and eosin; TI-RADS, Thyroid Imaging Reporting and Data System.</p>
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20 pages, 2577 KiB  
Systematic Review
Focus on the Dynamics of Neutrophil-to-Lymphocyte Ratio in Cancer Patients Treated with Immune Checkpoint Inhibitors: A Meta-Analysis and Systematic Review
by Yusheng Guo, Dongqiao Xiang, Jiayu Wan, Lian Yang and Chuansheng Zheng
Cancers 2022, 14(21), 5297; https://doi.org/10.3390/cancers14215297 - 27 Oct 2022
Cited by 32 | Viewed by 3378
Abstract
Background: A number of studies have reported an association between the dynamics of neutrophil-to-lymphocyte ratio (NLR) and clinical efficacy in patients treated with immune checkpoint inhibitors (ICIs), but there is still a lack of a meta-analysis or systematic review. Methods: PubMed, Embase, Web [...] Read more.
Background: A number of studies have reported an association between the dynamics of neutrophil-to-lymphocyte ratio (NLR) and clinical efficacy in patients treated with immune checkpoint inhibitors (ICIs), but there is still a lack of a meta-analysis or systematic review. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched until September 2022 for studies reporting on the association between the change in NLR after ICI treatment and clinical outcomes. Outcome measures of interest included: change in NLR before and after treatment, overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Results: A total of 4154 patients in 38 studies were included. The pooled percentage of patients with increased NLR was 49.7% (95CI%: 43.7–55.8%). Six studies discussing the change in NLR in patients with different tumor responses all showed that the NLR level in patients without response to immunotherapy may increase after ICI treatment. The upward trend in NLR was associated with shorter OS (pooled HR: 2.05, 95%CI: 1.79–2.35, p < 0.001) and PFS (pooled HR: 1.89, 95%CI: 1.66–2.14, p < 0.001) and higher ORR (pooled OR: 0.27, 95%CI: 0.19–0.39, p < 0.001), and downward trend in NLR was associated with longer OS (pooled HR: 0.49, 95%CI: 0.42–0.58, p < 0.001) and PFS (pooled HR: 0.55, 95%CI: 0.48–0.63, p < 0.001) and lower ORR (pooled OR: 3.26, 95%CI: 1.92–5.53, p < 0.001). In addition, post-treatment high NLR was associated with more impaired survival than baseline high NLR (pooled HR of baseline high NLR: 1.82, 95%CI: 1.52–2.18; pooled HR of post-treatment high NLR: 2.93, 95%CI: 2.26–3.81), but the NLR at different time points may have a similar predictive effect on PFS (pooled HR of baseline high NLR: 1.68, 95%CI: 1.44–1.97; pooled HR of post-treatment high NLR: 2.00, 95%CI: 1.54–2.59). Conclusions: The NLR level of tumor patients after ICI treatment is stable overall, but the NLR level in patients without response to immunotherapy may increase after ICI treatment. Patients with an upward trend in NLR after ICI treatment were associated with worse clinical outcomes; meanwhile, the downward trend in NLR was associated with better clinical outcomes. Post-treatment high NLR was associated with more impaired survival than baseline high NLR. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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<p>Flow diagram of study selection for inclusion in this meta-analysis and systematic review.</p>
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<p>(<b>A</b>) Forest plot for the association between upward trend in NLR after ICI treatment and OS. (<b>B</b>) Forest plot for the association between downward trend in NLR after ICI treatment and OS. (<b>C</b>) Forest plot for the association between upward trend in NLR after ICI treatment and PFS. (<b>D</b>) Forest plot for the association between downward trend in NLR after ICI treatment and PFS. (<b>E</b>) Forest plot for the association between upward trend in NLR after ICI treatment and ORR. (<b>F</b>) Forest plot for the association between downward trend in NLR after ICI treatment and ORR.</p>
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<p>(<b>A</b>) Forest plot for the association between high level of baseline NLR and OS (blue); the association between high level of post-treatment NLR and OS (red). (<b>B</b>) Forest plot for the association between high level of baseline NLR and PFS (blue); the association between high level of post-treatment NLR and PFS (red).</p>
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<p>(<b>A</b>) Funnel plot for the association between upward trend in NLR after ICI treatment and OS. (<b>B</b>) Funnel plot for the association between downward trend in NLR after ICI treatment and OS. (<b>C</b>) Funnel plot for the association between upward trend in NLR after ICI treatment and PFS. (<b>D</b>) Funnel plot for the association between downward trend in NLR after ICI treatment and PFS. (<b>E</b>) Funnel plot for the association between upward trend in NLR after ICI treatment and ORR. (<b>F</b>) Funnel plot for the association between downward trend in NLR after ICI treatment and ORR. (<b>G</b>) Funnel plot for the association between high level of baseline NLR and OS. (<b>H</b>) Funnel plot for the association between high level of post-treatment NLR and OS. (<b>I</b>) Funnel plot for the association between high level of baseline NLR and PFS. (<b>J</b>) Funnel plot for the association between high level of post-treatment NLR and PFS.</p>
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17 pages, 19297 KiB  
Article
Inhibition of Autophagy Promotes the Anti-Tumor Effect of Metformin in Oral Squamous Cell Carcinoma
by Wei Zhao, Chen Chen, Jianjun Zhou, Xiaoqing Chen, Kuan Cai, Miaomiao Shen, Xuan Chen, Lei Jiang and Guodong Wang
Cancers 2022, 14(17), 4185; https://doi.org/10.3390/cancers14174185 - 29 Aug 2022
Cited by 11 | Viewed by 2967
Abstract
Oral Squamous Cell Carcinoma (OSCC) is the most common malignant tumor in the head and neck. Due to its high malignancy and easy recurrence, the five-year survival rate is only 50–60%. Currently, commonly used chemotherapy drugs for OSCC include cisplatin, paclitaxel, and fluorouracil, [...] Read more.
Oral Squamous Cell Carcinoma (OSCC) is the most common malignant tumor in the head and neck. Due to its high malignancy and easy recurrence, the five-year survival rate is only 50–60%. Currently, commonly used chemotherapy drugs for OSCC include cisplatin, paclitaxel, and fluorouracil, which are highly cytotoxic and cause drug resistance in patients. Therefore, a safe and effective treatment strategy for OSCC is urgent. To address this issue, our study investigated the anti-tumor activity of metformin (the first-line diabetes drug) in OSCC. We found that metformin could inhibit OSCC cell proliferation by promoting apoptosis and blocking the cell cycle in G1 phase. Additionally, we also found that metformin could induce protective autophagy of OSCC cells. After inhibiting autophagy with hydroxychloroquine (HCQ), the metformin-induced apoptosis was enhanced. In vitro, metformin inhibited the growth of subcutaneous xenograft tumor in nude mice and HCQ enhanced this effect of metformin. Therefore, metformin combined with HCQ may become a safe and effective treatment strategy for OSCC. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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<p>Metformin inhibited the ability of proliferation and colony-forming in OSCC cells. (<b>A</b>). The OSCC cell lines (CAL27, SCC9, SCC25) were incubated with different concentrations of metformin for 24 h, and then the cell morphology was observed by the inverted microscopy. The magnification = 100×, scale bars = 100 μm. (<b>B</b>–<b>D</b>) The OSCC cell lines were treated with the gradient concentrations of metformin for 24 h and 48 h, and then the CCK-8 assay was used to detect the cell viability. (<b>E</b>, <b>F</b>) The OSCC cell lines were treated with different concentrations of metformin for 48 h and incubated with fresh culture medium for 12 days. The cells were fixed with paraformaldehyde and stained with crystal violet. Finally, the colony-forming rate was calculated as follows: The colony-forming rate = (number of cell clones/number of seeded cells) × 100%. The data are shown in the bar graph as mean ± SD. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01 and *** <span class="html-italic">p</span> ≤ 0.001 versus the control group.</p>
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<p>Metformin inhibited migration and invasion in OSCC cells. (<b>A</b>–<b>D</b>) The wound-scratch assay was carried out to detect the migrative ability of CAL27 cells treated with metformin for 24 h and 48 h. The wound healing rate = (scratch area at 24 h and 48 h-scratch area at 0 h/scratch area at 0 h) × 100%. The magnification = 100×, scale bars = 100 μm. (<b>E,F</b>) A transwell assay for the invasion was carried out to detect the invasive ability of CAL27 cells treated with metformin for 48 h. The magnification = 400×, scale bars = 25 μm. The data are shown in the bar graph as mean ± SD, *** <span class="html-italic">p</span> ≤ 0.001 versus the control group.</p>
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<p>Metformin induced G1 phase cell cycle block in OSCC cells. (<b>A</b>–<b>D</b>) After treatment with various concentrations of metformin for 48 h, flow cytometry assays were performed to assess the impact of metformin on the cell cycle distribution of OSCC cell lines (CAL27, SCC9, SCC25). (<b>E</b>,<b>F</b>) CAL27 cells were treated with different concentrations of metformin for 48 h, and, then, the cell cycle related proteins were analyzed by western blot. Original blots see <a href="#app1-cancers-14-04185" class="html-app">Supplementary Figure S4</a>. GAPDH was used as the internal reference protein. The data are shown in the bar graph as mean ± SD, *** <span class="html-italic">p</span> ≤ 0.001 versus control group.</p>
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<p>Metformin induced apoptosis in OSCC cells via a non-ROS dependent pathway. (<b>A</b>,<b>B</b>) The apoptosis assays were performed to assess the impact of metformin on apoptosis of OSCC cell lines (CAL27, SCC9, SCC25). The early apoptotic cells are located in the lower right quadrant, the late apoptotic cells are located in the upper right quadrant, and the necrotic cells are located in the upper left quadrant. (<b>C</b>,<b>D</b>) CAL27 cells were treated with different concentrations of metformin for 48 h, and then the apoptosis related proteins were analyzed by western blot. Original blots see <a href="#app1-cancers-14-04185" class="html-app">Supplementary Figure S4</a>. GAPDH was used as the internal reference protein. (<b>E</b>,<b>F</b>) Flow cytometry was used to measure intracellular ROS levels in CAL27 cells treated with various concentrations of metformin. The data are shown in the bar graph as mean ± SD, * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01 and *** <span class="html-italic">p</span> ≤ 0.001 versus control group.</p>
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<p>Metformin promoted autophagy in OSCC cells. (<b>A</b>). CAL27 cells were treated with various concentrations of metformin for 48 h. Meanwhile, the mRFP-GFP-LC3 adenovirus was used to detect the autophagic flux in CAL27 cells. The yellow dots (RFP+GFP+) were autophagosomes, whereas the red dots were (RFP+GFP-) were autolysosomes. The magnification = 1000×, scale bars = 10 μm (<b>B</b>). The autophagosomes and nucleus were observed by the TEM. CAL27 cells were treated with different concentrations of metformin for 48 h. Scale bars = 2 μm and 1 μm (<b>C</b>). CAL27 cells were treated with different concentrations of metformin for 48 h, and the autophagy-related proteins were analyzed by western blot. Original blots see <a href="#app1-cancers-14-04185" class="html-app">Supplementary Figure S4</a>. GAPDH was used as the internal reference protein.</p>
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<p>Inhibiting autophagy with HCQ increased the apoptosis of OSCC cells caused by metformin. (<b>A</b>) The OSCC cell lines (CAL27, SCC9, SCC25) were treated with metformin (12 mM), HCQ (20 μM), and combined treatment (metformin and HCQ) for 48 h. Flow cytometry assays were performed to assess the apoptosis level in OSCC cells. (<b>B</b>–<b>D</b>) The OSCC cell lines were treated with various concentrations of metformin (0, 12, 24 mM), with or without HCQ (20 μM), for 24 h. The cell viability was detected by CCK-8 assay. (<b>E</b>) CAL27 cells were treated as <a href="#cancers-14-04185-f006" class="html-fig">Figure 6</a>A, and then the apoptosis related proteins were analyzed by western blot. Original blots see <a href="#app1-cancers-14-04185" class="html-app">Supplementary Figure S4</a>. GAPDH was used as the internal reference protein. The data are shown in the bar graph as mean ± SD. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001 and <sup>ns</sup> <span class="html-italic">p</span> &gt; 0.05. Metformin group and HCQ group versus control group, metformin + HCQ group versus metformin group.</p>
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<p>Metformin and HCQ synergistically suppressed OSCC growth in vivo. (<b>A</b>) CAL27 cells (1 × 10<sup>7</sup> cells) were injected into the subcutaneous area of female nude mice. After treatment with PBS, metformin (250 mg/kg/d), HCQ (50 mg/kg/d), and combined treatment (metformin and HCQ) for 18 days, all animals were sacrificed. (<b>B</b>) The tumor volume was measured every three days after treatment. (<b>C</b>) The tumor weight was measured after all mice were sacrificed. (<b>D</b>) The expression of Ki-67 and Cleaved-caspase3 in tumor tissue was analyzed by IHC. The magnification = 100×, scale bars = 100 μm (<b>E</b>) H&amp;E staining was conducted to assess the toxicity to the major organ after treatment. The magnification = 200×, scale bars = 50 μm. The data are shown in the bar graph as mean ± SD. ** <span class="html-italic">p</span> ≤ 0.01. Metformin group versus control group, the combined group versus metformin group.</p>
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<p>Biochemical pathways involved in the effects of metformin treatment on Oral Squamous Cell Carcinoma (OSCC) cells. Metformin suppresses the proliferation of OSCC cells via the induction of cell cycle arrest and apoptosis. Inhibition of metformin-induced autophagy with HCQ increases the apoptosis of OSCC cells. The figure was created by the Figdraw.</p>
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