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Ovarian Cancer: Advances on Pathophysiology and Therapies
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Ovarian Cancer: Advances on Pathophysiology and Therapies

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 18333

Special Issue Editors

Dr. Giovanni Tossetta

E-Mail Website
Guest Editor
Department of Experimental and Clinical Medicine, Polytechnic University of Marche, Via Tronto, 10/a, 60126 Ancona, Italy
Interests: pregnancy complications; preeclampsia; preterm birth; ovarian cancer; early marker of pregnancy complications; oxidative stress; chemoresistance
Special Issues, Collections and Topics in MDPI journals
Dr. Annalisa Inversetti

E-Mail Website
Guest Editor
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
Interests: prenatal diagnosis; fetal echocardiography; fetal surgery; preeclampsia biomarkers; complications of endometriosis in pregnant women
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit a manuscript to the Special Issue “Ovarian Cancer: Advances on Pathophysiology and Therapies”. The incidence of ovarian cancer has increased significantly over the past 50 years. Despite advances in medical tumor therapy, the occurrence of chemoresistance and metastatic disease is a common cause of death in patients with ovarian cancer. Thus, it is necessary to develop new therapeutic approaches that can improve diagnosis and treatment outcomes. To this aim, we need a better understanding of the molecular changes occurring in ovarian cancer and the development of molecular biomarkers able to predict tumor behavior and risk of disease recurrence and chemoresistance.

Topics will include (but are not limited to):

  • Pathogenesis of ovarian cancer
  • Diagnostic and prognostic molecular markers
  • Molecular mechanism of cancer onset and progression
  • Novel treatments of ovarian cancer
  • Ovarian cancer prevention.

This Special Issue of IJMS, therefore, welcomes original research articles and reviews related to Ovarian Cancer. Communications, mini-reviews, systematic reviews and meta-analyses are also welcome.

We look forward to receiving your contributions.

Dr. Giovanni Tossetta
Dr. Annalisa Inversetti
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ovarian cancer
  • chemoresistance
  • therapy
  • pathogenesis
  • marker
  • diagnosis

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Editorial

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3 pages, 177 KiB  
Editorial
Special Issue “Ovarian Cancer: Advances on Pathophysiology and Therapies”
by Giovanni Tossetta and Annalisa Inversetti
Int. J. Mol. Sci. 2024, 25(10), 5282; https://doi.org/10.3390/ijms25105282 - 13 May 2024
Viewed by 746
Abstract
Ovarian cancer is a gynecologic cancer with a high mortality rate, and its incidence has increased significantly over the past 50 years [...] Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
3 pages, 194 KiB  
Editorial
Ovarian Cancer: Advances in Pathophysiology and Therapies
by Giovanni Tossetta and Annalisa Inversetti
Int. J. Mol. Sci. 2023, 24(10), 8930; https://doi.org/10.3390/ijms24108930 - 18 May 2023
Cited by 8 | Viewed by 1992
Abstract
We are pleased to present this Special Issue of the International Journal of Molecular Sciences, entitled “Ovarian Cancer: Advances in Pathophysiology and Therapies” [...] Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)

Research

Jump to: Editorial, Review

18 pages, 15523 KiB  
Article
Effects of Resveratrol on In Vivo Ovarian Cancer Cells Implanted on the Chorioallantoic Membrane (CAM) of a Chicken Embryo Model
by Kenny Chitcholtan, Melanie Singh, Alex Tino, Ashley Garrill and Peter Sykes
Int. J. Mol. Sci. 2024, 25(8), 4374; https://doi.org/10.3390/ijms25084374 - 16 Apr 2024
Viewed by 1020
Abstract
Ovarian cancer poses a significant threat to patients in its advanced stages, often with limited treatment options available. In such cases, palliative management becomes the primary approach to maintaining a reasonable quality of life. Therefore, the administration of any medication that can benefit [...] Read more.
Ovarian cancer poses a significant threat to patients in its advanced stages, often with limited treatment options available. In such cases, palliative management becomes the primary approach to maintaining a reasonable quality of life. Therefore, the administration of any medication that can benefit patients without a curative option holds potential. Resveratrol, a natural compound known for its in vitro anticancer activities, has generated contrasting results in vivo and human studies. In this study, we aimed to assess the anticancer effects of resveratrol on ovarian cancer cells grown on the chorioallantoic membrane (CAM) of chicken embryos. Two ovarian cancer cell lines, OVCAR-8 and SKOV-3, were cultured in collagen scaffolds for four days before being implanted on the CAM of chicken embryos on day 7. Different doses of resveratrol were applied to the CAM every two days for six days. Subsequently, CAM tissues were excised, fixed, and subjected to histological analysis. Some CAM tumours were extracted to analyse proteins through Western blotting. Our findings indicate that specific doses of resveratrol significantly reduce angiogenic activities, pNF-κB levels, and SLUG protein levels by using immunohistochemistry. These results suggest that resveratrol may have the potential to impact the behaviour of ovarian cancer CAM tumours, thereby warranting further consideration as a complementary treatment option for women with incurable ovarian cancer. Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
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Figure 1

Figure 1
<p>Stereomicroscopic images of OVCAR-8 tumour implants after 18 days of growth on CAMs. Tumour implants were treated with various doses of resveratrol for six days, starting on day 12 of embryonic development, and the images were captured. Control (<b>A</b>), 0.228 µg (5 µM) (<b>B</b>), 0.456 µg (10 µM) (<b>C</b>), 45.6 µg (1 mM) (<b>D</b>), 91.24 µg (2 mM) (<b>E</b>). Scale bar, 1 mm.</p> Full article ">Figure 2
<p>Stereomicroscopic images of excised and inverted OVCAR-8 tumour implants after 18 days of growth on CAMs. Tumour implants were excised from the surrounding CAM and inverted so the vascular networks could be viewed from underneath. Control (<b>A</b>), 0.228 µg (5 µM) (<b>B</b>), 0.456 µg (10 µM) (<b>C</b>), 45.6 µg (1 mM) (<b>D</b>), 91.24 µg (2 mM) (<b>E</b>), 182.4 µg (4 mM) resveratrol (<b>F</b>). Scale bar, 1 mm.</p> Full article ">Figure 3
<p>Stereomicroscopic images of SKOV-3 tumour implants after 18 days of growth on CAMs. Tumour implants were with various doses of resveratrol for six days, starting at day 12 of embryonic development, and then images were captured. Control (<b>A</b>), 0.228 µg (5 µM) (<b>B</b>), 0.456 µg (10 µM) (<b>C</b>), 45.6 µg (1 mM) (<b>D</b>), and 91.24 µg (2 mM) resveratrol (<b>E</b>). Scale bar, 1 mm.</p> Full article ">Figure 4
<p>Stereomicroscopic images of excised and inverted SKOV-3 tumour implants after 18 days of growth on CAMS. Tumour implants were excised from the surrounding CAM and inverted so the vascular networks could be viewed from underneath. Control (<b>A</b>), 0.228 µg (5 µM) (<b>B</b>), 45.6 µg (1 mM) (<b>C</b>), 91.24 µg (2 mM) resveratrol (<b>D</b>). Scale bar, 1 mm.</p> Full article ">Figure 5
<p><b>Left</b>: Microscopic images of OVCAR-8 tumour implants after six days of treatment with various doses of resveratrol ((<b>A</b>) control, (<b>B</b>) 0.228 µg (5 µM), (<b>C</b>) 0.456 µg (10 µM), (<b>D</b>) 45.6 µg (1 mM), (<b>E</b>) 91.24 µg (2 mM), (<b>F</b>) 182.4 µg (4 mM)). Red blood cells are in red (arrows), and CAM areas are indicated by stars. <b>Right</b>: The number of red blood cells was counted and plotted using various doses of resveratrol. Data are expressed as means ± SEM, <span class="html-italic">n</span> = number of sectioned tumour implants, which were from at least five embryos. Data considered statistically significant compared to controls are indicated as <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">t</span>-student test. The scale bar is 200 µm.</p> Full article ">Figure 6
<p><b>Left</b>: Microscopic images of SKOV-3 tumour implants after six days of treatment with various doses of resveratrol ((<b>A</b>) control, (<b>B</b>) 0.228 µg (5 µM), (<b>C</b>) 0.456 µg (10 µM), (<b>D</b>) 45.6 µg (1 mM), (<b>E</b>) 91.24 µg (2 mM), (<b>F</b>) 182.4 µg (4 mM)). Red blood cells are in red (arrows), and CAM areas are indicated by stars. <b>Right</b>: The number of red blood cells that were counted and plotted with various doses of resveratrol. Data are expressed as means ± SEM, <span class="html-italic">n</span> = number of sectioned tumour implants, which were from at least five embryos. Data considered statistically significant compared to controls are indicated as <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">t</span>-student test. The scale bar is 200 µm.</p> Full article ">Figure 7
<p>Immunohistological staining of vascular endothelial growth factor (VEGF) in tumour sections. Left-microscopic images of OVCAR-8 (<b>Top:</b> (<b>A</b>) control, (<b>B</b>) 0.3 µg (5 µM), (<b>C</b>) 91.3 µg (2 mM), (<b>D</b>) 182.6 µg (4 mM)) and SKOV-3 (<b>Bottom:</b> (<b>A</b>) control, (<b>B</b>) 0.3 µg (5 µM), (<b>C</b>) 91.3 µg (2 mM), (<b>D</b>) 182.6 µg (4 mM)) tumour implants were immune-stained with an anti-VEGF antibody. The red-brown colour represents levels of VEGF protein (arrows). The intensity of immunostaining of anti-VEGF antibody was subjected to analysis using Fiji ImageJ software 1.53m, and the densitometry of grey value was calculated and plotted. Data are expressed as means ± SEM. The scale bar is 200 µm.</p> Full article ">Figure 8
<p>Immunohistological staining of NF-κB (<b>A</b>,<b>C</b>) and pNF-κB (<b>B</b>,<b>D</b>) in OVCAR-8 (<b>A</b>,<b>B</b>) and SKOV-3 (<b>C</b>,<b>D</b>) tumour implants treated with three doses of resveratrol (45.6 (1 mM), 91.24 (2 mM) and 182.4 µg (4 mM)) for six days. Tissue implants were sectioned and stained with anti-NF-κB and phospho-NF-κB antibodies. The histological immunostaining of the antigens was quantitative using Fiji ImageJ software 1.53m. Data are expressed as means ± SEM. Data considered statistically significant compared to controls are indicated as <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">t</span>-student test.</p> Full article ">Figure 9
<p>The number of cancer cells in the area of mesoderm layers of CAM implanted with OVCAR-8 cell line (<b>A</b>) and SKOV-3 (<b>B</b>) treated with various doses of resveratrol. Tumour implants were treated with different doses of resveratrol, 0.228 µg (5 µM), 0.456 µg (10 µM), 45.6 µg (1 mM), 91.24 µg (2 mM), and 182.4 µg (4 mM). Data are expressed as means ± SEM, <span class="html-italic">n</span> = number of sectioned tumour implants, which were from at least five embryos. Data considered statistically significant compared to controls are indicated as <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">t</span>-student test.</p> Full article ">Figure 10
<p>Western blot analysis of specific proteins extracted from whole tumour CAM tissue of the OVCAR-8 cell line. The ratios of protein band intensities for PCNA/GAPDH (<b>A</b>), NFκB/GAPDH (<b>B</b>), pNFκB/GAPDH (<b>C</b>), pNFκB/NFκB (<b>D</b>), and SLUG/GAPDH (<b>E</b>) are presented. Bands of each protein were analysed by Western Blotting (<b>F</b>). A resveratrol dose of 91.24 µg (2 mM) was selected for the inhibition study with tumour implants.</p> Full article ">Figure 11
<p>Western blot analysis of specific proteins extracted from whole tumour CAM tissue of the SKOV-3 cell line. The ratios of protein band intensities for PCNA/GAPDH (<b>A</b>), NFκB/GAPDH (<b>B</b>), pNFκB/GAPDH (<b>C</b>), pNFκB/NFκB (<b>D</b>), and SLUG/GAPDH (<b>E</b>) are presented. Bands of each proteins were analysed by Western Blotting (<b>F</b>). A resveratrol dose of 91.24 µg (2 mM) was selected for the inhibition study with tumour implants.</p> Full article ">Figure 12
<p>Immunohistochemical staining of SLUG antigens in OVCAR-8 (<b>A</b>) and SKOV-3 (<b>B</b>) tumour implants treated with three doses of resveratrol (45.6 (1 mM), 91.24 (2 mM), and 182.4 (4 mM) µg) for six days. The immunostaining of the antigens was quantitated using Fiji ImageJ software 1.53m. Data considered statistically significant compared to controls are indicated as <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">t</span>-student test.</p> Full article ">
16 pages, 2871 KiB  
Article
Transcriptomic Analysis of the Aged Nulliparous Mouse Ovary Suggests a Stress State That Promotes Pro-Inflammatory Lipid Signaling and Epithelial Cell Enrichment
by Carlos Chacón, Constanza Mounieres, Sandra Ampuero and Ulises Urzúa
Int. J. Mol. Sci. 2024, 25(1), 513; https://doi.org/10.3390/ijms25010513 - 30 Dec 2023
Viewed by 1169
Abstract
Ovarian cancer (OC) incidence and mortality peaks at post-menopause while OC risk is either reduced by parity or increased by nulliparity during fertile life. The long-term effect of nulliparity on ovarian gene expression is largely unknown. In this study, we describe a bioinformatic/data-mining [...] Read more.
Ovarian cancer (OC) incidence and mortality peaks at post-menopause while OC risk is either reduced by parity or increased by nulliparity during fertile life. The long-term effect of nulliparity on ovarian gene expression is largely unknown. In this study, we describe a bioinformatic/data-mining analysis of 112 coding genes upregulated in the aged nulliparous (NP) mouse ovary compared to the aged multiparous one as reference. Canonical gene ontology and pathway analyses indicated a pro-oxidant, xenobiotic-like state accompanied by increased metabolism of inflammatory lipid mediators. Up-regulation of typical epithelial cell markers in the aged NP ovary was consistent with synchronized overexpression of Cldn3, Ezr, Krt7, Krt8 and Krt18 during the pre-neoplastic phase of mOSE cell cultures in a former transcriptome study. In addition, 61/112 genes were upregulated in knockout mice for Fshr and for three other tumor suppressor genes (Pten, Cdh1 and Smad3) known to regulate follicular homeostasis in the mammalian ovary. We conclude that the aged NP ovary displays a multifaceted stress state resulting from oxidative imbalance and pro-inflammatory lipid signaling. The enriched epithelial cell content might be linked to follicle depletion and is consistent with abundant clefts and cysts observed in aged human and mouse ovaries. It also suggests a mesenchymal-to-epithelial transition in the mOSE of the aged NP ovary. Our analysis suggests that in the long term, nulliparity worsens a variety of deleterious effects of aging and senescence thereby increasing susceptibility to cancer initiation in the ovary. Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
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Figure 1

Figure 1
<p>Previous study, data processing and canonical analysis of DEGs in the aged NP ovary. (<b>A</b>) Two C57BL6 female mice cohorts were maintained in nulliparous (NP) and multiparous (MP) breeding regimens from 4 through 16 months old. Short overlapped white arrows depict gestation plus lactation periods in MP mice. (<b>B</b>) Total ovarian RNA from the two conditions was analyzed with Illumina™ beadchip expression microarrays resulting in 177 differentially expressed genes (DEGs) between the NP and MP conditions [<a href="#B13-ijms-25-00513" class="html-bibr">13</a>]. (<b>C</b>) Summary of a gene set enrichment analysis (GSEA) of the 112 DEGs of higher expression in NP ovaries, using the MSigDBv7.3 database (details in Methods and <a href="#ijms-25-00513-t001" class="html-table">Table 1</a>). The gene number associated to each functional theme is indicated in the respective bar, and their identities described in <a href="#ijms-25-00513-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 2
<p>Overlap and gene network among xenobiotic metabolism, ion/small molecule transport and electrolyte homeostasis. (<b>A</b>) Venn diagram to determine the gene coincidence between 3 of the functional themes of <a href="#ijms-25-00513-f001" class="html-fig">Figure 1</a>C. The gene lists of each theme are described in <a href="#ijms-25-00513-t001" class="html-table">Table 1</a>. (<b>B</b>) Known and predicted relationships among the 20 coincident genes shown as a gene network obtained with STRING v12.0 (0.35 confidence score; unconnected nodes removed). The meaning of color lines depicting gene interactions is shown at the bottom-right and was adapted from the software’s output.</p> Full article ">Figure 3
<p>Overlap and gene networks among oxidative metabolism and epithelium related themes. Venn diagrams showing the gene coincidence between themes related to oxidative metabolism (<b>A</b>) and to epithelium (<b>C</b>). Detailed gene groups are shown in <a href="#ijms-25-00513-t001" class="html-table">Table 1</a>. Gene networks among the 20 coincident genes of themes related to oxidative metabolism (<b>B</b>) and among the 11 coincident genes of themes related to epithelium (<b>D</b>). Parameters of STRING v12.0 analysis and meaning of color-coded line interactions as in <a href="#ijms-25-00513-f002" class="html-fig">Figure 2</a>B.</p> Full article ">Figure 4
<p>GEO datamining of KO mouse models. The 112 DEGs were mined in the Enrichr database <span class="html-italic">crowd</span> tab, <span class="html-italic">gene perturbations from GEO up</span> output. (<b>A</b>) The top 10 mouse KO datasets ranked by <span class="html-italic">adj-p</span> values. KO gene symbol and GEO accession is indicated within each bar. The number at the right indicates the gene count from the 112 DEGs which are contained in each dataset. (<b>B</b>) Overlap of the top 20 genes in the 10 KO datasets shown in (<b>A</b>). (<b>C</b>) Gene coincidence between the top 4 KOs; (<b>D</b>) Selected heatmaps of 46/112 genes that were found in the transcriptome dataset of pre-malignant, cultured mOSE cells. Labels p2 to p28 indicate passage 2 through passage 28 log<sub>2</sub> fold change averages of 4 replicate two-channel arrays [<a href="#B53-ijms-25-00513" class="html-bibr">53</a>].</p> Full article ">Figure 5
<p>Selected gene expression in human ovarian carcinomas. A subset of 12 DEGs were queried in TNMplot as their respective human orthologs (see Methods). Gene expression density plots are shown for the indicated genes (left) in 133 normal and 374 human ovarian cystadenocarcinoma samples (“Tumor” label). The fold change (FC) corresponds to the direct ratio between mean expression values for each group; <span class="html-italic">p</span> values were obtained from a Mann–Whitney U test. Density plots are supported by boxplots of <a href="#app1-ijms-25-00513" class="html-app">Figure S2</a>.</p> Full article ">
14 pages, 1873 KiB  
Article
Evaluating the Utility of ctDNA in Detecting Residual Cancer and Predicting Recurrence in Patients with Serous Ovarian Cancer
by Jie Wei Zhu, Fabian Wong, Agata Szymiczek, Gabrielle E. V. Ene, Shiyu Zhang, Taymaa May, Steven A. Narod, Joanne Kotsopoulos and Mohammad R. Akbari
Int. J. Mol. Sci. 2023, 24(18), 14388; https://doi.org/10.3390/ijms241814388 - 21 Sep 2023
Cited by 7 | Viewed by 1492
Abstract
Ovarian cancer has a high case fatality rate, but patients who have no visible residual disease after surgery have a relatively good prognosis. The presence of any cancer cells left in the peritoneal cavity after treatment may precipitate a cancer recurrence. In many [...] Read more.
Ovarian cancer has a high case fatality rate, but patients who have no visible residual disease after surgery have a relatively good prognosis. The presence of any cancer cells left in the peritoneal cavity after treatment may precipitate a cancer recurrence. In many cases, these cells are occult and are not visible to the surgeon. Analysis of circulating tumour DNA in the blood (ctDNA) may offer a sensitive method to predict the presence of occult (non-visible) residual disease after surgery and may help predict disease recurrence. We assessed 48 women diagnosed with serous ovarian cancer (47 high-grade and 1 low-grade) for visible residual disease and for ctDNA. Plasma, formalin-fixed paraffin-embedded (FFPE) tumour tissue and white blood cells were used to extract circulating free DNA (cfDNA), tumour DNA and germline DNA, respectively. We sequenced DNA samples for 59 breast and ovarian cancer driver genes. The plasma sample was collected after surgery and before initiating chemotherapy. We compared survival in women with no residual disease, with and without a positive plasma ctDNA test. We found tumour-specific variants (TSVs) in cancer cells from 47 patients, and these variants were sought in ctDNA in their post-surgery plasma. Fifteen (31.9%) of the 47 patients had visible residual disease; of these, all 15 had detectable ctDNA. Thirty-one patients (65.9%) had no visible residual disease; of these, 24 (77.4%) patients had detectable ctDNA. Of the patients with no visible residual disease, those patients with detectable ctDNA had higher mortality (20 of 27 died) than those without detectable ctDNA (3 of 7 died) (HR 2.32; 95% CI: 0.67–8.05), although this difference was not statistically significant (p = 0.18). ctDNA in post-surgical serum samples may predict the presence of microscopic residual disease and may be a predictor of recurrence among women with ovarian cancer. Larger studies are necessary to validate these findings. Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
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Figure 1

Figure 1
<p>Flow of enrolled participants from collected samples to final clinical outcome.</p> Full article ">Figure 2
<p>Results of ctDNA analysis of post-surgery plasma samples of patients with no surgical residual disease and their clinical outcome.</p> Full article ">Figure 3
<p>Overview of study design.</p> Full article ">
11 pages, 1225 KiB  
Article
SYNE1 Mutation Is Associated with Increased Tumor Mutation Burden and Immune Cell Infiltration in Ovarian Cancer
by Laura M. Harbin, Nan Lin, Frederick R. Ueland and Jill M. Kolesar
Int. J. Mol. Sci. 2023, 24(18), 14212; https://doi.org/10.3390/ijms241814212 - 18 Sep 2023
Cited by 5 | Viewed by 1341
Abstract
SYNE1, a nuclear envelope protein critical for cellular structure and signaling, is downregulated in numerous malignancies. SYNE1 alterations are found in 10% of gynecologic malignancies and 5% of epithelial ovarian cancers. Previous studies demonstrated an association between SYNE1 mutation, increased tumor mutation [...] Read more.
SYNE1, a nuclear envelope protein critical for cellular structure and signaling, is downregulated in numerous malignancies. SYNE1 alterations are found in 10% of gynecologic malignancies and 5% of epithelial ovarian cancers. Previous studies demonstrated an association between SYNE1 mutation, increased tumor mutation burden (TMB), and immunotherapy response. This study evaluates the SYNE1 mutation frequency, association with TMB, and downstream effects of SYNE1 mutation in ovarian cancer. Genetic information, including whole-exome sequencing, RNA analysis, and somatic tumor testing, was obtained for consenting ovarian cancer patients at an academic medical center. Mutation frequencies were compared between the institutional cohort and The Cancer Genome Atlas (TCGA). Bioinformatics analyses were performed. In our cohort of 50 patients, 16 had a SYNE1 mutation, and 15 had recurrent disease. Median TMB for SYNE1 mutated patients was 25 compared to 7 for SYNE1 wild-type patients (p < 0.0001). Compared to the TCGA cohort, our cohort had higher SYNE1 mutation rates (32% vs. 6%, p < 0.001). Gene expression related to immune cell trafficking, inflammatory response, and immune response (z > 2.0) was significantly increased in SYNE1 mutated patients. SYNE1 mutation is associated with increased TMB and immune cell infiltration in ovarian cancer and may serve as an additional biomarker for immunotherapy response. Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
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Figure 1

Figure 1
<p><span class="html-italic">SYNE1</span> lollipop plot for mutations in 50 ovarian cancer patients at The Markey Cancer Center. Missense mutations are represented in green, truncating mutations in black, and all other mutation types in red (excluding fusions and in-frame deletions or insertions). (aa: amino acid).</p> Full article ">Figure 2
<p>Comparison of tumor mutation burden (TMB) between <span class="html-italic">SYNE1</span> mutated and <span class="html-italic">SYNE1</span> wild-type patients. When treated as a continuous variable <span class="html-italic">SYNE1</span> mutated patients had a median TMB of 25 compared to median TMB of 7 for <span class="html-italic">SYNE1</span> WT patients (<span class="html-italic">p</span> &lt; 0.0001). (TMB: tumor mutation burden (mutations per Megabase), Mut: mutated, WT: wild-type).</p> Full article ">Figure 3
<p>Differential gene expression between <span class="html-italic">SYNE1</span> mutated and <span class="html-italic">SYNE1</span> wild-type patients.</p> Full article ">Figure 4
<p>Differential gene expression and pathway analysis between <span class="html-italic">SYNE1</span> mutant and <span class="html-italic">SYNE1</span> wild-type patients. The size of the box denotes the -log (<span class="html-italic">p</span>-value). The color of the boxes correlates with the z-score with the intensity of blue representing z &lt; 0 and orange z &gt; 0. Significantly different gene expression is noted for leukocyte migration, recruitment of leukocytes, myeloid cells, and granulocytes, and localization of myeloid cells. There are also significant increases in immune cell trafficking, inflammatory response, and humoral immune response.</p> Full article ">
18 pages, 21326 KiB  
Article
Selective Killing of BRCA2-Deficient Ovarian Cancer Cells via MRE11 Blockade
by Adel Alblihy, Reem Ali, Mashael Algethami, Alison A. Ritchie, Ahmed Shoqafi, Shatha Alqahtani, Katia A. Mesquita, Michael S. Toss, Paloma Ordóñez-Morán, Jennie N. Jeyapalan, Lodewijk Dekker, Martina Salerno, Edgar Hartsuiker, Anna M. Grabowska, Emad A. Rakha, Nigel P. Mongan and Srinivasan Madhusudan
Int. J. Mol. Sci. 2023, 24(13), 10966; https://doi.org/10.3390/ijms241310966 - 30 Jun 2023
Cited by 1 | Viewed by 1853
Abstract
The MRE11 nuclease is essential during DNA damage recognition, homologous recombination, and replication. BRCA2 plays important roles during homologous recombination and replication. Here, we show that effecting an MRE11 blockade using a prototypical inhibitor (Mirin) induces synthetic lethality (SL) in BRCA2-deficient ovarian cancer [...] Read more.
The MRE11 nuclease is essential during DNA damage recognition, homologous recombination, and replication. BRCA2 plays important roles during homologous recombination and replication. Here, we show that effecting an MRE11 blockade using a prototypical inhibitor (Mirin) induces synthetic lethality (SL) in BRCA2-deficient ovarian cancer cells, HeLa cells, and 3D spheroids compared to BRCA2-proficient controls. Increased cytotoxicity was associated with double-strand break accumulation, S-phase cell cycle arrest, and increased apoptosis. An in silico analysis revealed Mirin docking onto the active site of MRE11. While Mirin sensitises DT40 MRE11+/− cells to the Top1 poison SN-38, it does not sensitise nuclease-dead MRE11 cells to this compound confirming that Mirin specifically inhibits Mre11 nuclease activity. MRE11 knockdown reduced cell viability in BRCA2-deficient PEO1 cells but not in BRCA2-proficient PEO4 cells. In a Mirin-resistant model, we show the downregulation of 53BP1 and DNA repair upregulation, leading to resistance, including in in vivo xenograft models. In a clinical cohort of human ovarian tumours, low levels of BRCA2 expression with high levels of MRE11 co-expression were linked with worse progression-free survival (PFS) (p = 0.005) and overall survival (OS) (p = 0.001). We conclude that MRE11 is an attractive SL target, and the pharmaceutical development of MRE11 inhibitors for precision oncology therapeutics may be of clinical benefit. Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>MRE11 blockade induces synthetic lethality in BRCA2-deficient ovarian cancer cells. (<b>A</b>) Western blot for BRCA2 and β-actin (control) protein levels in PEO1 and PE04 cells. (<b>B</b>) Mirin sensitivity, determined via clonogenic survival assay, in PEO1 and PEO4 cells. (<b>C</b>) Representative photomicrographic images for immunofluorescence staining of γH2AX, 53BP1, and DAPI (control) in PEO1 and PEO4 cells treated with Mirin (18 µM) for 24 h (magnification, ×20). (<b>D</b>) Quantification of 53BP1 nuclear fluorescence using ImageJ software (version 1.8.0_112) (UT = untreated; fluor = fluorescence). (<b>E</b>) Quantification of γH2AX nuclear fluorescence by ImageJ software (UT = untreated, flour = fluorescence). (<b>F</b>) γH2AX analysis via FACS. (<b>G</b>) Cell cycle analysis via flow cytometry in PEO4 cells treated with Mirin (18 µM) for 48 h (UT = untreated). (<b>H</b>) AnnexinV analysis to determine apoptotic cells in PEO4 cells treated with Mirin (18 µM) for 48 h (UT = untreated). (<b>I</b>) Representative photomicrographic images of PEO1 and PEO4 3D spheroids treated with Mirin (18 µM) (UT = untreated) (magnification, ×20). (<b>J</b>) Quantification of spheroid sizes using ImageJ software (UT = untreated). (<b>K</b>) Quantification of spheroids cell viability by flow cytometry (UT = untreated). ‘*’—<span class="html-italic">p</span> ≤ 0.05; ‘**’—<span class="html-italic">p</span> ≤ 0.01; ‘***’—<span class="html-italic">p</span> ≤ 0.001. All figures are representative of 3 or more experiments. Error bars represent standard errors of mean between experiments.</p> Full article ">Figure 2
<p>MRE11 blockade induces synthetic lethality in BRCA2-deficient HeLa cancer cells. (<b>A</b>) Western blot for BRCA2 levels in HeLa control and HeLa BRCA2_KD cells. (<b>B</b>) Mirin sensitivity in Hela control and HeLa BRCA2_KD cells (clonogenic assay). (<b>C</b>) Quantification of γH2AX-positive cells via flow cytometry in untreated and Mirin-treated HeLa control and HeLa BRCA2_KD cells (UT = untreated). (<b>D</b>) Cell cycle analysis via flow cytometry in untreated and Mirin-treated HeLa control and HeLa BRCA2_KD cells (UT = untreated). (<b>E</b>) AnnexinV analysis by flow cytometry in untreated (UT) and Mirin-treated HeLa control and HeLa BRCA2_KD cells. (<b>F</b>) Representative photomicrographic images of HeLa control and HeLa BRCA2 3D spheroids treated with Mirin (18 µM) for 48 h (UT = untreated) (magnification, ×20). (<b>G</b>) Quantification of spheroid sizes using ImageJ software (UT = untreated). (<b>H</b>) Quantification of spheroid cell viability via flow cytomery (UT = untreated). ‘*’—<span class="html-italic">p</span> ≤ 0.05; ‘**’—<span class="html-italic">p</span> ≤ 0.01; ‘***’—<span class="html-italic">p</span> ≤ 0.001. Figures are representative of 3 or more experiments. Error bars represent standard errors of mean between experiments.</p> Full article ">Figure 3
<p>Investigating the specificity of Mirin in silico and in vitro. (<b>A</b>) In silico docking of Mirin onto MRE11. Location of His129 in the Apo structure of <span class="html-italic">H. sapiens</span> Mre11 (PDB: 3T1i), close to the Mn<sup>2+</sup> ions (medium purple) in the active site. (<b>B</b>) Remodelling loop 127-134 of <span class="html-italic">H. sapiens</span> Mre11 [<a href="#B17-ijms-24-10966" class="html-bibr">17</a>] creates a conformation capable of accommodating Mirin (carbon atoms coloured salmon) that mirrors the known Mirin binding conformation of <span class="html-italic">Thermotoga maritima</span> MRE11 [<a href="#B22-ijms-24-10966" class="html-bibr">22</a>]. Mirin binding is associated with His129 projecting away from the active site (<b>C</b>) Overlay of panels (<b>A</b>,<b>B</b>). (<b>D</b>) Mirin sensitivity in DT40 <span class="html-italic">MRE11<sup>+/</sup></span><sup>−</sup> cell line tested in the absence and presence of SN-38, as determined using an XTT assay. (<b>E</b>) Mirin sensitivity in nuclease-dead <span class="html-italic">MRE11<sup>H129N/−</sup></span> cell lines tested in the absence and presence of SN-38, as determined via an XTT assay. (<b>F</b>) Cell viability in control PEO1 cells and MRE11_KD PEO1 cells, determined via an MTS assay. (<b>G</b>) Cell viability in control PEO4 cells and MRE11_KD PEO4 cells, determined via an MTS assay. ‘***’—<span class="html-italic">p</span> ≤ 0.001. Error bars represent standard deviations, <span class="html-italic">n</span> = 3.</p> Full article ">Figure 4
<p>Development and evaluation of Mirin-resistant ovarian cancer cell line. (<b>A</b>) Mirin sensitivity, determined via clonogenic survival assay, in PEO1 and PEO1R cells. (<b>B</b>) γH2AX analysis via flow cytometry in Mirin-treated PEO1 and PEO1R cells (UT = untreated). (<b>C</b>) Cell cycle analysis via flow cytometry in Mirin-treated PEO1 and PEO1R cells (UT = untreated). (<b>D</b>) AnnexinV analysis via flow cytometry in Mirin-treated PEO1 and PEO1R cells (UT = untreated). (<b>E</b>) Representative photomicrographic images for PEO1 and PEO1R 3D spheroids treated with Mirin (18 µM) for 48 h (magnification, ×20). (<b>F</b>) Quantification of spheroid size using ImageJ software for PEO1 spheroids treated with Mirin or Cisplatin (UT = untreated). (<b>G</b>) Quantification of spheroid size using ImageJ software for PEO1R spheroids treated with Mirin (UT = untreated). (<b>H</b>) Quantification of spheroid cell viability via flow cytometry for PEO1/PEO1R spheroids treated with Mirin (UT = untreated). (<b>I</b>) Olaparib sensitivity, determined via clonogenic survival assay, in PEO1 and PEO1R cells. (<b>J</b>) Western blot for 53BP1 in PEO1 and PEO1R cells. (<b>K</b>) Western blot for BRCA2 in PEO1 and PEO1R cells. (<b>L</b>) Western blot for MRE11, RAD50, and NBS1 protein levels in PEO1 and PEO1R cells. (<b>M</b>) <span class="html-italic">MRE11</span> mRNA expression in PEO1 and PEO1R cells. (<b>N</b>) MRE11 knockdown using siRNA in PEO1R cells. (<b>O</b>) Mirin sensitivity, determined via clonogenic survival assay, in PEO1, PEO1R, and PEO1R_MRE11_KD. (<b>P</b>) Pie chart showing the upregulation of DNA repair genes that are involved in several DNA repair pathways. (<b>Q</b>) Western blot for RPA1, ATM, PARP1, LIG3, and ERCC1 protein levels in PEO1 and PEO1R cells. ‘*’—<span class="html-italic">p</span> ≤ 0.05; ‘**’—<span class="html-italic">p</span> ≤ 0.01; ‘***’—<span class="html-italic">p</span> ≤ 0.001. All figures are representative of 3 or more experiments. Error bars represent standard errors of mean between experiments.</p> Full article ">Figure 5
<p>(<b>A</b>) The tumour volume in PEO1-bearing CD-1 NuNu mice compared to PEO1R-bearing CD-1 NuNu mice (see methods for details; 3 mice were used per group). (<b>B</b>) Tumour volumes in PEO1R were substantially larger in PEO1R-bearing CD-1 NuNu mice compared to PEO1-bearing CD-1 NuNu mice (‘*’—<span class="html-italic">p</span> ≤ 0.05). (<b>C</b>) Immunohistochemical expressions of MRE11 in PEO1 and PEO1R xenografts (magnification, ×20). (<b>D</b>) GSEA comparing PEO1R vs. PEO1 signatures to the DNA repair gene set. The graphical representation shows significant enrichments in the DNA repair genes (shown as black lines) in PEO1R cells (red bar) compared to PEO1 cells (represented as the blue bars). Normalized enrichment scores (NES), nominal <span class="html-italic">p</span>-values, and FDR corrected <span class="html-italic">p</span>-values shown. GSEA comparing PEO1R vs. PEO1 signatures to the DNA repair gene set rare shown here. (see <a href="#sec2-ijms-24-10966" class="html-sec">Section 2</a> for more details). (<b>E</b>) Representative photomicrographic images of negative, MRE11 overexpression, and BRCA2 overexpression in human ovarian tumours (magnification, ×20). (<b>F</b>) BRCA2-MRE11 co-expression and Kaplan–Meier curves for progression-free survival (PFS) in ovarian cancer. The <span class="html-italic">p</span>-values indicate univariate overall comparisons between BRCA2+/MRE11+, BRCA2+/MRE11−, BRCA2−/MRE11+, and BRCA2−/MRE11+ tumours. (<b>G</b>) MRE11and BRCA2 co-expression and Kaplan–Meier curves for overall survival in ovarian cancer.</p> Full article ">Figure 6
<p>A model for MRE11-blockade-induced synthetic lethality. (<b>A</b>). BRCA2-deficiency results in replication stress. MRE11 blockade prevents resolution of replication stress, leading to DSB accumulation and cell death (<b>B</b>). Replication-independent DSBs can be generated endogenously (e.g., free-radical-induced DNA damage) in BRCA2-deficient cells. MRE11 blockade can lead to DSB accumulation, cell cycle arrest, and cell death. DSB = double-strand breaks.</p> Full article ">

Review

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15 pages, 1140 KiB  
Review
PROTACs in Ovarian Cancer: Current Advancements and Future Perspectives
by Makenzie Vorderbruggen, Carlos A. Velázquez-Martínez, Amarnath Natarajan and Adam R. Karpf
Int. J. Mol. Sci. 2024, 25(10), 5067; https://doi.org/10.3390/ijms25105067 - 7 May 2024
Viewed by 1717
Abstract
Ovarian cancer is the deadliest gynecologic malignancy. The majority of patients diagnosed with advanced ovarian cancer will relapse, at which point additional therapies can be administered but, for the most part, these are not curative. As such, a need exists for the development [...] Read more.
Ovarian cancer is the deadliest gynecologic malignancy. The majority of patients diagnosed with advanced ovarian cancer will relapse, at which point additional therapies can be administered but, for the most part, these are not curative. As such, a need exists for the development of novel therapeutic options for ovarian cancer patients. Research in the field of targeted protein degradation (TPD) through the use of proteolysis-targeting chimeras (PROTACs) has significantly increased in recent years. The ability of PROTACs to target proteins of interest (POI) for degradation, overcoming limitations such as the incomplete inhibition of POI function and the development of resistance seen with other inhibitors, is of particular interest in cancer research, including ovarian cancer research. This review provides a synopsis of PROTACs tested in ovarian cancer models and highlights PROTACs characterized in other types of cancers with potential high utility in ovarian cancer. Finally, we discuss methods that will help to enable the selective delivery of PROTACs to ovarian cancer and improve the pharmacodynamic properties of these agents. Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
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Figure 1

Figure 1
<p>PROTAC structure and mechanism of action (MOA): (<b>A</b>) the PROTAC structure includes a warhead that binds the POI, a linker, and an E3 ligase ligand; and (<b>B</b>) the PROTAC MOA includes the formation of a ternary complex comprised of the POI, the PROTAC, and the E3 ligase. The transfer of Ubiquitin to the POI leads to its proteolytic degradation by the proteosome, while the PROTAC is recycled and can engage another molecule of POI. Figure created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p> Full article ">Figure 2
<p>Biological effects of PROTACs tested in EOC models. Figure created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p> Full article ">Figure 3
<p>Methods to improve selective delivery of PROTACs in EOC: (<b>A</b>) conjugation of folate to PROTACs results in selectivity for cells expressing FRα; (<b>B</b>) inorganic, lipid-based, and polymeric nanoparticle-based PROTAC delivery; (<b>C</b>) conjugation of PROTACs to antibodies facilitates selective delivery; and (<b>D</b>) light irradiation removes the caging group on opto-PROTACs, activating the PROTAC. Created with BioRender.com.</p> Full article ">
17 pages, 1984 KiB  
Review
Role of SLC7A11/xCT in Ovarian Cancer
by Sonia Fantone, Federica Piani, Fabiola Olivieri, Maria Rita Rippo, Angelo Sirico, Nicoletta Di Simone, Daniela Marzioni and Giovanni Tossetta
Int. J. Mol. Sci. 2024, 25(1), 587; https://doi.org/10.3390/ijms25010587 - 2 Jan 2024
Cited by 14 | Viewed by 2215
Abstract
Ovarian cancer is one of the most dangerous gynecologic cancers worldwide and has a high fatality rate due to diagnosis at an advanced stage of the disease as well as a high recurrence rate due to the occurrence of chemotherapy resistance. In fact, [...] Read more.
Ovarian cancer is one of the most dangerous gynecologic cancers worldwide and has a high fatality rate due to diagnosis at an advanced stage of the disease as well as a high recurrence rate due to the occurrence of chemotherapy resistance. In fact, chemoresistance weakens the therapeutic effects, worsening the outcome of this pathology. Solute Carrier Family 7 Member 11 (SLC7A11, also known as xCT) is the functional subunit of the Xc− system, an anionic L-cystine/L-glutamate antiporter expressed on the cell surface. SLC7A11 expression is significantly upregulated in several types of cancers in which it can inhibit ferroptosis and favor cancer cell proliferation, invasion and chemoresistance. SLC7A11 expression is also increased in ovarian cancer tissues, suggesting a possible role of this protein as a therapeutic target. In this review, we provide an overview of the current literature regarding the role of SLC7A11 in ovarian cancer to provide new insights on SLC7A11 modulation and evaluate the potential role of SLC7A11 as a therapeutic target. Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
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Figure 1

Figure 1
<p>Representation of the function of SLC7A11. SLC7A11 imports extracellular cystine, which is reduced to cysteine and is used in conjugation with glutamate to form γglutamylcysteine (reaction catalyzed by GLC). γGlutamylcysteine is conjugated with glycine (via GSS) to produce GSH, which is used as a cofactor for enzymes, such as GPX4, that protect cells from lipid peroxidation. In particular, GPX4 uses GSH to reduce lipid hydroperoxides (a ferroptosis inducer) to lipid alcohols, suppressing ferroptosis. In this reaction GSH is oxidized to GSSG. GSH also protects cells from ROS exposure by decreasing ROS levels. Thus, high SLC7A11 levels in cancer cells cause cell proliferation, drug resistance, cancer progression and metastasis.</p> Full article ">Figure 2
<p>Representation of SLC7A11 modulation in ovarian cancer cells. SLC7A11 expression can be regulated by natural (in black outside the cell) and synthetic (in red outside the cell) compounds. In addition, SLC7A11 expression can be regulated by cellular modulators, such as miR-382-5p, miR-587, miR-194-5p, circSnx12, lncRNA ADAMTS9-AS1, lncRNA SLC7A11, sterol CoA desaturase (SCD1), SNAI2, erythroblastosis virus E26 oncogene homolog 1 (Ets-1), CCAAT enhancer binding protein gamma (CEBPG) and AT-rich interacting domain containing protein 1A (ARID1A). Decreased expression of SLC7A11 causes a decrease in intracellular glutathione (GSH) levels, which favors ferroptosis, apoptosis and drug sensitivity.</p> Full article ">
14 pages, 1070 KiB  
Review
Recent Insight about HE4 Role in Ovarian Cancer Oncogenesis
by Emanuela Anastasi, Antonella Farina, Teresa Granato, Flavia Colaiacovo, Beatrice Pucci, Sara Tartaglione and Antonio Angeloni
Int. J. Mol. Sci. 2023, 24(13), 10479; https://doi.org/10.3390/ijms241310479 - 22 Jun 2023
Cited by 7 | Viewed by 3822
Abstract
Currently, ovarian cancer (OC) is a target of intense biomarkers research because of its frequent late diagnosis and poor prognosis. Serum determination of Human epididymis protein 4 (HE4) is a very important early detection test. Most interestingly, HE4 plays a unique role in [...] Read more.
Currently, ovarian cancer (OC) is a target of intense biomarkers research because of its frequent late diagnosis and poor prognosis. Serum determination of Human epididymis protein 4 (HE4) is a very important early detection test. Most interestingly, HE4 plays a unique role in OC as it has been implicated not only in OC diagnosis but also in the prognosis and recurrence of this lethal neoplasm, actually acting as a clinical biomarker. There are several evidence about the predictive power of HE4 clinically, conversely less has been described concerning its role in OC oncogenesis. Based on these considerations, the main goal of this review is to clarify the role of HE4 in OC proliferation, angiogenesis, metastatization, immune response and also in the development of targeted therapy. Through a deeper understanding of its functions as a key molecule in the oncogenetic processes underlying OC, HE4 could be possibly considered as an essential resource not only for diagnosis but also for prognosis and therapy choice. Full article
(This article belongs to the Special Issue Ovarian Cancer: Advances on Pathophysiology and Therapies)
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<p>HE4 as a disease checkpoint. After the appearance of symptoms, HE4 is a biomarker of crucial importance since it can lead to a correct and tempestive diagnosis.</p> Full article ">Figure 2
<p>Schematic representation of HE4 interaction network in OC oncogenesis. HE4 is involved in cellular pathways of neoplastic proliferation and immunological evasion. Intracellular signaling and interaction with microenvironment can increase aggressive phenotypes that underlie angiogenesis and metastatic dissemination. Drugs that can interfere with each pathway shown in figure. STAT3, HIF 1α, HDAC3, OPN, DUSP6, LEWIS Y and CD147 may represent possible target of OC therapy (represented by dotted line), still under investigation.</p> Full article ">
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