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11 pages, 1098 KiB  
Review
Mechanism of DAPK1 for Regulating Cancer Stem Cells in Thyroid Cancer
by Mi-Hyeon You
Curr. Issues Mol. Biol. 2024, 46(7), 7086-7096; https://doi.org/10.3390/cimb46070422 - 5 Jul 2024
Viewed by 453
Abstract
Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin (Ca2+/CaM)-dependent serine/threonine (Ser/Thr) protein kinase and is characteristically downregulated in metastatic cancer. Several studies showed that DAPK1 is involved in both the early and late stages of cancer. DAPK1 downregulation is elaborately controlled [...] Read more.
Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin (Ca2+/CaM)-dependent serine/threonine (Ser/Thr) protein kinase and is characteristically downregulated in metastatic cancer. Several studies showed that DAPK1 is involved in both the early and late stages of cancer. DAPK1 downregulation is elaborately controlled by epigenetic, transcriptional, posttranscriptional, and posttranslational processes. DAPK1 is known to regulate not only cancer cells but also stromal cells. Recent studies showed that DAPK1 was involved not only in tumor suppression but also in epithelial-mesenchymal transition (EMT) and cancer stem cell (CSC) formation in colon and thyroid cancers. CSCs are major factors in determining cancer aggressiveness in cancer metastasis and treatment prognosis by influencing EMT. However, the molecular mechanism involved in the regulation of cancer cells by DAPK1 remains unclear. In particular, little is known about the existence of CSCs and how they are regulated in papillary thyroid carcinoma (PTC) among thyroid cancers. In this review, we describe the molecular mechanism of CSC regulation by DAPK1 in PTC progression. Full article
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Figure 1
<p>Schematic depiction of the multi-domain organization of the (DAPK1) protein. (<b>a</b>) The size of the amino acid regions of DAPK1 and the interacting proteins in each region are shown in the diagram. The blue protein is an interactor that activates the function of DAPK1, and the red protein is an interactor that inhibits it. The blue protein is indicated above the DAPK1 amino acid sequence, and the red protein is indicated below. (<b>b</b>) Schematic diagram of the DAPK family.</p>
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<p>Role of DAPK1 in cancer stem cell progression.</p>
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15 pages, 4308 KiB  
Article
Therapeutic Effect of Donepezil on Neuroinflammation and Cognitive Impairment after Moderate Traumatic Brain Injury
by Dong Hyuk Youn, Younghyurk Lee, Sung Woo Han, Jong-Tae Kim, Harry Jung, Gui Seung Han, Jung In Yoon, Jae Jun Lee and Jin Pyeong Jeon
Life 2024, 14(7), 839; https://doi.org/10.3390/life14070839 - 1 Jul 2024
Cited by 1 | Viewed by 738
Abstract
Background: Despite the important clinical issue of cognitive impairment after moderate traumatic brain injury (TBI), there is currently no suitable treatment. Here, we used in vitro and in vivo models to investigate the effect of Donepezil—an acetylcholinesterase (AChE) inhibitor—on cognitive impairment in the [...] Read more.
Background: Despite the important clinical issue of cognitive impairment after moderate traumatic brain injury (TBI), there is currently no suitable treatment. Here, we used in vitro and in vivo models to investigate the effect of Donepezil—an acetylcholinesterase (AChE) inhibitor—on cognitive impairment in the acute period following injury, while focusing on neuroinflammation and autophagy- and mitophagy-related markers. Methods: The purpose of the in vitro study was to investigate potential neuroprotective effects in TBI-induced cells after donepezil treatment, and the in vivo study, the purpose was to investigate therapeutic effects on cognitive impairment in the acute period after injury by analyzing neuroinflammation and autophagy- and mitophagy-related markers. The in vitro TBI model involved injuring SH-SY5Y cells using a cell-injury controller and then investigating the effect of donepezil at a concentration of 80 μM. The in vivo TBI model was made using a stereotaxic impactor for male C57BL/6J mice. Immuno-histochemical markers and cognitive functions were compared after 7 days of donepezil treatment (1 mg/kg/day). Mice were divided into four groups: sham operation with saline treatment, sham operation with donepezil treatment, TBI with saline treatment, and TBI with donepezil treatment (18 mice in each group). Donepezil treatment was administered within 4 h post-TBI. Results: In vitro, donepezil was found to lead to increased cell viability and 5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethylbenzimi-dazolylcarbocyanine iodide (JC-1), along with decreased reactive oxygen species (ROS), lactate-dehydrogenase (LDH), 2′-7′-dichlorodihydrofluorescein diacetate (DCFH-DA)-positive cells, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-positive cells. The mRNA and protein expressions of neuroinflammation (Cyclooxygenase-2, COX-2; NOD-like receptor protein 3, NLRP3; Caspase-1; and Interleukin-1 beta, IL-1β), as well as autophagy- and mitophagy-related markers (death-associated protein kinase 1, DAPK1; PTEN-induced kinase 1, PINK1; BCL2/adenovirus E1B 19 kDa protein-interacting protein 3-like, BNIP3L; Beclin-1, BECN1; BCL2-associated X protein, BAX; microtubule-associated protein 1A/1B-light chain 3B (LC3B); Sequestosome-1; and p62) were all found to decrease after donepezil treatment. The in vivo study also showed that donepezil treatment resulted in decreased levels of cortical tissue losses and brain swelling in TBI compared to the TBI group without donepezil treatment. Donepezil treatment was also shown to decrease the mRNA and Western blotting expressions of all markers, and especially COX-2 and BNIP3L, which showed the most significant decreases. Moreover, TBI mice showed an decreased escape latency, increased alteration rate, and improved preference index, altogether pointing to better cognitive performance after donepezil treatment. Conclusions: Donepezil treatment may be beneficial in improving cognitive impairment in the early phase of moderate traumatic brain injury by ameliorating neuroinflammation, as well as autophagy and mitophagy. Full article
(This article belongs to the Topic Oxidative Stress and Inflammation, 2nd Volume)
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<p>Results of the in vitro study. (<b>A</b>,<b>B</b>) Cell counting kit-8 (CCK-8) viability assay in SH-SY5Y cells with different dosage of donepezil (DPZ) and in in vitro traumatic brain injury model (n = 9, each group). (<b>C</b>–<b>F</b>) Comparison of lactate dehydrogenase (LDH) test (n = 9, each group), 5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethylbenzimi-dazolylcarbocyanine iodide (JC-1) (n = 9, each group), reactive oxygen species (ROS) scavenging (n = 6, each group), and 2′-7′-dichlorodihydrofluorescein diacetate (DCFH-DA) staining (n = 6 in each group). (<b>G</b>–<b>P</b>) Differences in mRNA expression levels, Western blotting (n = 4, each group), and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-positive cells (green, n = 6, each group) according to donepezil treatment. All in vitro experiments were repeated three times. Scale bar = 200 μm. Error bars indicate 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; 0.005. Superoxide dismutase 2, SOD2; tumor necrosis factor, TNF-α; Interleukin-6, IL-6; Interleukin-10, IL-10; Cyclooxygenase-2, COX-2; NOD-like receptor protein 3, NLRP3; Caspase-1; Interleukin-1 beta, IL-1β.</p>
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<p>mRNA expression (n = 4, each group) (<b>A</b>–<b>G</b>) and Western blotting (n = 3, each group) (<b>H</b>,<b>I</b>) in autophagy- and mitophagy-related markers based on 80 μM donepezil (DZP) treatment in in vitro model of traumatic brain injury using SH-SY5Y. Error bars indicate 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; 0.005. Death-associated protein kinase 1, DAPK1; PTEN-induced kinase 1, PINK1; BCL2/adenovirus E1B 19 kDa protein-interacting protein 3-like, BNIP3L; Beclin 1, BECN1; Bcl-2-associated X-protein, BAX; microtubule-associated proteins 1A/1B light-chain 3B (LC3B); Sequestosome 1; p62/SQSTM1; Actin, β-actin.</p>
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<p>Results of the in vivo study using traumatic brain injury model. (<b>A</b>,<b>B</b>) Comparison of optical visible image of mouse brains (n = 5, each group) and brain water content (n = 6, each group). (<b>C</b>,<b>D</b>) Representative images of FJB-positive cells and their differences (n = 6, each group). Scale bar = 200 μm. (<b>E</b>–<b>I</b>) mRNA expression (n = 4, each group) and Western blotting of COX-2, NLRP3, Caspase-1, and IL-1β according to donepezil (DZP) treatment (n = 3, each group). Scale bar = 200 μm. Error bars indicate 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; 0.005. Fluoro-jade, FJB; Cyclooxygenase-2, COX-2; NOD-like receptor protein 3, NLRP3; Caspase-1; Interleukin-1 beta, IL-1β.</p>
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<p>Differences in mRNA expression in (n = 8, each group) (<b>A</b>–<b>G</b>) and Western blotting (n = 3, each group) (<b>H</b>,<b>I</b>) in autophagy- and mitophagy-related markers based on donepezil (DZP) treatment in in vivo model of traumatic brain injury (TBI). Error bars indicate 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; 0.005. Death-associated protein kinase 1, DAPK1; PTEN-induced kinase 1, PINK1; BCL2/adenovirus E1B 19 kDa protein-interacting protein 3-like, BNIP3L; Beclin 1, BECN1; Bcl-2-associated X-protein, BAX; microtubule-associated proteins 1A/1B light-chain 3B, LC3B; Sequestosome 1; p62/SQSTM1; Actin, β-actin.</p>
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<p>Cognitive function tests after donepezil (DZP) treatment in in vivo mouse model of traumatic brain injury (TBI) (n = 9, each group). The images are representative of the group. Error bars indicate 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; 0.005. Novel object recognition, NOR. Red and blue indicate the highest or lowest colors in heatmaps.</p>
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20 pages, 5721 KiB  
Article
Dramatic Suppression of Lipogenesis and No Increase in Beta-Oxidation Gene Expression Are among the Key Effects of Bergamot Flavonoids in Fatty Liver Disease
by Maddalena Parafati, Daniele La Russa, Antonella Lascala, Francesco Crupi, Concetta Riillo, Bartosz Fotschki, Vincenzo Mollace and Elzbieta Janda
Antioxidants 2024, 13(7), 766; https://doi.org/10.3390/antiox13070766 - 25 Jun 2024
Viewed by 924
Abstract
Bergamot flavonoids have been shown to prevent metabolic syndrome, non-alcoholic fatty liver disease (NAFLD) and stimulate autophagy in animal models and patients. To investigate further the mechanism of polyphenol-dependent effects, we performed a RT2-PCR array analysis on 168 metabolism, transport and autophagy-related genes [...] Read more.
Bergamot flavonoids have been shown to prevent metabolic syndrome, non-alcoholic fatty liver disease (NAFLD) and stimulate autophagy in animal models and patients. To investigate further the mechanism of polyphenol-dependent effects, we performed a RT2-PCR array analysis on 168 metabolism, transport and autophagy-related genes expressed in rat livers exposed for 14 weeks to different diets: standard, cafeteria (CAF) and CAF diet supplemented with 50 mg/kg of bergamot polyphenol fraction (BPF). CAF diet caused a strong upregulation of gluconeogenesis pathway (Gck, Pck2) and a moderate (>1.7 fold) induction of genes regulating lipogenesis (Srebf1, Pparg, Xbp1), lipid and cholesterol transport or lipolysis (Fabp3, Apoa1, Lpl) and inflammation (Il6, Il10, Tnf). However, only one β-oxidation gene (Cpt1a) and a few autophagy genes were differentially expressed in CAF rats compared to controls. While most of these transcripts were significantly modulated by BPF, we observed a particularly potent effect on lipogenesis genes, like Acly, Acaca and Fasn, which were suppressed far below the mRNA levels of control livers as confirmed by alternative primers-based RT2-PCR analysis and western blotting. These effects were accompanied by downregulation of pro-inflammatory cytokines (Il6, Tnfa, and Il10) and diabetes-related genes. Few autophagy (Map1Lc3a, Dapk) and no β-oxidation gene expression changes were observed compared to CAF group. In conclusion, chronic BPF supplementation efficiently prevents NAFLD by modulating hepatic energy metabolism and inflammation gene expression programs, with no effect on β-oxidation, but profound suppression of de novo lipogenesis. Full article
(This article belongs to the Special Issue The Role of Oxidative Stress in Non-Alcoholic Fatty Liver Disease)
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<p>The experimental design and screening for genes regulated by BPF in NAFLD rat model in this work: rat division for dietetic treatment and BPF administration for 15 weeks. On the right side, a flow description is exhibited, from the liver sampling to RT2 profiling.</p>
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<p>Bergamot polyphenols prevent CAF diet-induced obesity, hypertriglyceridemia and intracellular fat accumulation in Wistar rats. (<b>A</b>) Final body weight, (<b>B</b>) blood triglycerides, (TGL) (<b>C</b>) blood total cholesterol. Data are presented as the mean ± SD of <span class="html-italic">n</span> = 5 rats. Each dot represents a rat. (<b>D</b>) The total lipid content in 400 mg of liver tissue was determined by Folch’s method. Data are presented as the mean ± SD of <span class="html-italic">n</span> = 4 livers for each group. (<b>E</b>) Representative blots for ADRP/Perilipin 2 and GAPDH as a loading control, showing liver lysates from 3 different rats for each group. (<b>F</b>) OD ratio of ADRP to GAPDH expression levels. Data are expressed as the mean ± SEM of <span class="html-italic">n</span> = 6 rat liver lysates for each group. Statistical analysis in (<b>A</b>–<b>D</b>,<b>F</b>): one-way ANOVA with Tukey’s post-test, * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, ns—not significant change.</p>
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<p>BPF prevents CAF diet-induced hepatic steatosis. Histopathological changes of rat liver tissues between different dietary groups. (<b>A</b>–<b>C</b>) Representative hematoxylin (H) and oil red (ORO) stained liver sections were visualized by confocal microscopy, bar = 25 μm; and (<b>D</b>–<b>F</b>) by bright-field, magnification ×40, bar = 40 μm. Images in the third row show magnified regions of D to F images indicated by blue boxes, bar = 20 μm. (<b>G</b>) LDs size and number quantification on confocal sections (as in <b>A</b>–<b>C</b>). LDs between 1 and 3 µm indicate microsteatosis and 4–7 µm macrosteatosis, respectively. Data are presented as the mean ± SEM (<span class="html-italic">n</span> = 3 livers and 9 images for each group) for LDs between 1 and 3 µm and 4–7 µm, respectively. Statistical analysis: One-way ANOVA with Tukey’s post-test. **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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<p>Differential expression of fatty liver-related transcripts in steatotic livers exposed to CAF diet for 14 weeks as compared to control SC diet (<b>A</b>–<b>C</b>) and the effects of a chronic supplementation of bergamot polyphenols to CAF diet in rats (<b>D</b>–<b>F</b>). (<b>A</b>) CAF vs. SC and (<b>D</b>) CAF + BPF vs. CAF scatter plots for differential expression analysis of 84 fatty liver genes. Below, the lists of genes modulated more than 1.7-fold when CAF livers are compared to SC livers (<b>B</b>) and CAF + BPF (<b>E</b>) are compared to CAF livers. (<b>B</b>,<b>D</b>) The arrays of all fatty liver-associated genes with respective fold regulation values when (<b>C</b>) CAF vs. SC and (<b>F</b>) CAF + BPF vs. CAF livers are compared. Note that bergamot polyphenols strongly suppress lipogenesis-related genes in the liver.</p>
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<p>Differential expression of autophagy-related transcripts in steatotic livers exposed to CAF diet for 14 weeks as compared to control SC diet (<b>A</b>,<b>B</b>) and the effects of chronic supplementation of bergamot polyphenols to CAF diet in rats (<b>C</b>,<b>D</b>). (<b>A</b>) CAF vs. SC and (<b>C</b>) CAF + BPF vs. CAF scatter plots for differential analysis of 84 autophagy genes. (<b>B</b>,<b>D</b>) Below, the lists of genes modulated more than 1.7-fold when CAF livers are compared to SC livers (<b>B</b>) and CAF + BPF are compared to CAF livers (<b>D</b>). Note that CAF diet and bergamot polyphenols have a very modest effect on the expression of autophagy-related genes.</p>
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<p>Differentially expressed hepatic genes in CAF and CAF + BPF groups when compared to control SC group. Fold change expression of selected lipogenesis and lipid transport (<b>left</b>), diabetes-related (<b>center</b>), β-oxidation, inflammation and autophagy-related transcripts (<b>right</b>) in CAF diet- and CAF + BPF-treated livers normalized to control (SC) livers. Data are presented as mean ± SD. See <a href="#sec2-antioxidants-13-00766" class="html-sec">Section 2</a>.</p>
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<p>The expression level of selected genes was analyzed by qRT-PCR with an independent set of primers for each animal separately. The bars represent fold change expression from <span class="html-italic">n</span> = 4 to 5 rats in CAF diet- and CAF + BPF-treated livers normalized to control (SC) livers. Data are presented as mean ± SD. Statistical analysis: one-way ANOVA followed by Tukey’s post-test or uncorrected Fisher’s LSD test, except for <span class="html-italic">Acaca</span>, <span class="html-italic">Fasn</span>, <span class="html-italic">Ins</span> and <span class="html-italic">Il6</span> in which Brown–Forsythe test followed by unpaired <span class="html-italic">t</span> test with Welch’s correction was applied. * <span class="html-italic">p</span> ≤ 0.05, ** <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001, **** <span class="html-italic">p</span> ≤ 0.0001.</p>
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<p>Western blot analysis of protein products of selected genes modulated by CAF diet and BPF. (<b>A</b>) Representative blots showing 3 rat livers, for each group, for ACACA, ACLY, PCK2 and GCK. ADRP/Perilipin 2 has been shown as a marker for steatosis or lipid content and GAPDH as a loading control. (<b>B</b>) OD analysis of expression levels of proteins as in A compared to GAPDH <span class="html-italic">n</span> = 5 to 6 rat livers for each group. (<b>C</b>) Representative blots showing protein lysates from 3 rat livers for each group for autophagy proteins LC3B and ATG16. Alpha-tubulin (TUBA) was used as loading control. (<b>D</b>) OD ratio of proteins as in C compared to TUBA in <span class="html-italic">n</span> = 5 to 6 rat liver samples for each group. Bars show the mean OD ratio ± SEM, normalized to the mean of SC group. Statistical analysis: one-way ANOVA with Tukey’s post-test or with LSD Fisher test for GCK OD analysis. *, **, ***, **** significant difference compared with control SC group at <span class="html-italic">p</span> &lt; 0.5, <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.001 or <span class="html-italic">p</span> &lt; 0.0001, respectively. #, #### significant difference compared with CAF group at <span class="html-italic">p</span> &lt; 0.5 or <span class="html-italic">p</span> &lt; 0.0001, respectively. Numbers on the right of blots indicate the approximate position of molecular weights expressed in kDa.</p>
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<p>Schematic representation of main lipid- and glucose-metabolism genes differentially expressed in CAF + BPF-treated livers. Green rectangles: genes downregulated by BPF; pink rectangles: genes downregulated by CAF diet; light-blue rectangles and outlines: genes related to glucose metabolism; orange rectangles and outlines: genes related to lipid metabolism; violet rectangles and outlines: transcription factors coding genes; blue: intermediaries in gluconeogenesis pathway. Continuous and dotted arrows: direct and indirect connections, respectively.</p>
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11 pages, 452 KiB  
Article
Exploring the DNA Methylation Profile of Genes Associated with Bladder Cancer in Bladder Tissue of Patients with Neurogenic Lower Urinary Tract Dysfunction
by Periklis Koukourikis, Maria Papaioannou, Stavroula Pervana and Apostolos Apostolidis
Int. J. Mol. Sci. 2024, 25(11), 5660; https://doi.org/10.3390/ijms25115660 - 23 May 2024
Viewed by 639
Abstract
DNA methylation is an epigenetic process that commonly occurs in genes’ promoters and results in the transcriptional silencing of genes. DNA methylation is a frequent event in bladder cancer, participating in tumor initiation and progression. Bladder cancer is a major health issue in [...] Read more.
DNA methylation is an epigenetic process that commonly occurs in genes’ promoters and results in the transcriptional silencing of genes. DNA methylation is a frequent event in bladder cancer, participating in tumor initiation and progression. Bladder cancer is a major health issue in patients suffering from neurogenic lower urinary tract dysfunction (NLUTD), although the pathogenetic mechanisms of the disease remain unclear. In this population, bladder cancer is characterized by aggressive histopathology, advanced stage during diagnosis, and high mortality rates. To assess the DNA methylation profiles of five genes’ promoters previously known to be associated with bladder cancer in bladder tissue of NLUTD patients, we conducted a prospective study recruiting NLUTD patients from the neuro-urology unit of a public teaching hospital. Cystoscopy combined with biopsy for bladder cancer screening was performed in all patients following written informed consent being obtained. Quantitative methylation-specific PCR was used to determine the methylation status of RASSF1, RARβ, DAPK, hTERT, and APC genes’ promoters in bladder tissue samples. Twenty-four patients suffering from mixed NLUTD etiology for a median duration of 10 (IQR: 12) years were recruited in this study. DNA hypermethylation was detected in at least one gene of the panel in all tissue samples. RAR-β was hypermethylated in 91.7% samples, RASSF and DAPK were hypermethylated in 83.3% samples, APC 37.5% samples, and TERT in none of the tissue samples. In 45.8% of the samples, three genes of the panel were hypermethylated, in 29.2% four genes were hypermethylated, and in 16.7% and in 8.3% of the samples, two and one gene were hypermethylated, respectively. The number of hypermethylated genes of the panel was significantly associated with recurrent UTIs (p = 0.0048). No other significant association was found between DNA hypermethylation or the number of hypermethylated genes and the clinical characteristics of the patients. Histopathological findings were normal in 8.3% of patients, while chronic inflammation was found in 83.3% of patients and squamous cell metaplasia in 16.7% of patients. In this study, we observed high rates of DNA hypermethylation of genes associated with bladder cancer in NLUTD patients, suggesting an epigenetic field effect and possible risk of bladder cancer development. Recurrent UTIs seem to be associated with increased DNA hypermethylation. Further research is needed to evaluate the impact of recurrent UTIs and chronic inflammation in DNA hypermethylation and bladder cancer etiopathogenesis in NLUTD patients. Full article
(This article belongs to the Special Issue Circulating Biomarkers for the Diagnosis of Neurobiological Diseases)
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<p>Distribution of hypermethylated genes of the panel.</p>
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15 pages, 6203 KiB  
Article
Rapamycin as a Potential Alternative Drug for Squamous Cell Gingiva Carcinoma (Ca9-22): A Focus on Cell Cycle, Apoptosis and Autophagy Genetic Profile
by Sofia Papadakos, Hawraa Issa, Abdulaziz Alamri, Abdullah Alamri and Abdelhabib Semlali
Pharmaceuticals 2024, 17(1), 131; https://doi.org/10.3390/ph17010131 - 19 Jan 2024
Viewed by 1738
Abstract
Oral cancer is considered as one of the most common malignancies worldwide. Its conventional treatment primarily involves surgery with or without postoperative adjuvant therapy. The targeting of signaling pathways implicated in tumorigenesis is becoming increasingly prevalent in the development of new anticancer drug [...] Read more.
Oral cancer is considered as one of the most common malignancies worldwide. Its conventional treatment primarily involves surgery with or without postoperative adjuvant therapy. The targeting of signaling pathways implicated in tumorigenesis is becoming increasingly prevalent in the development of new anticancer drug candidates. Based on our recently published data, Rapamycin, an inhibitor of the mTOR pathway, exhibits selective antitumor activity in oral cancer by inhibiting cell proliferation and inducing cancer cell apoptosis, autophagy, and cellular stress. In the present study, our focus is on elucidating the genetic determinants of Rapamycin’s action and the interaction networks accountable for tumorigenesis suppression. To achieve this, gingival carcinoma cell lines (Ca9-22) were exposed to Rapamycin at IC50 (10 µM) for 24 h. Subsequently, we investigated the genetic profiles related to the cell cycle, apoptosis, and autophagy, as well as gene–gene interactions, using QPCR arrays and the Gene MANIA website. Overall, our results showed that Rapamycin at 10 µM significantly inhibits the growth of Ca9-22 cells after 24 h of treatment by around 50% by suppression of key modulators in the G2/M transition, namely, Survivin and CDK5RAP1. The combination of Rapamycin with Cisplatin potentializes the inhibition of Ca9-22 cell proliferation. A P1/Annexin-V assay was performed to evaluate the effect of Rapamycin on cell apoptosis. The results obtained confirm our previous findings in which Rapamycin at 10 μM induces a strong apoptosis of Ca9-22 cells. The live cells decreased, and the late apoptotic cells increased when the cells were treated by Rapamycin. To identify the genes responsible for cell apoptosis induced by Rapamycin, we performed the RT2 Profiler PCR Arrays for 84 apoptotic genes. The blocked cells were believed to be directed towards cell death, confirmed by the downregulation of apoptosis inhibitors involved in both the extrinsic and intrinsic pathways, including BIRC5, BNIP3, CD40LG, DAPK1, LTA, TNFRSF21 and TP73. The observed effects of Rapamycin on tumor suppression are likely to involve the autophagy process, evidenced by the inhibition of autophagy modulators (TGFβ1, RGS19 and AKT1), autophagosome biogenesis components (AMBRA1, ATG9B and TMEM74) and autophagy byproducts (APP). Identifying gene–gene interaction (GGI) networks provided a comprehensive view of the drug’s mechanism and connected the studied tumorigenesis processes to potential functional interactions of various kinds (physical interaction, co-expression, genetic interactions etc.). In conclusion, Rapamycin shows promise as a clinical agent for managing Ca9-22 gingiva carcinoma cells. Full article
(This article belongs to the Section Pharmacology)
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<p>Effect of Rapamycin on Ca9-22 cell viability, apoptosis, and autophagy. (<b>A</b>) Effect of Rapamycin on cell growth evaluated by MTT cell proliferation and viability assay. The presented data are expressed as mean ± SEM values of 6 independent experiments. *** <span class="html-italic">p</span> &lt; 0.001 is considered statistically significant. (<b>B</b>) Synergistic effect of Rapamycin and Cisplatin combinations on cell growth revealed by MTT assay (<span class="html-italic">n</span> = 4). (<b>C</b>) Apoptosis assessment conducted using the PI and Annexin markers. Results are expressed as mean ± SEM of 4 independent experiments. (<b>D</b>) Flow cytometry analysis showing the percentage of autophagic cells. Results are expressed as mean ± SEM of 4 independent experiments.</p>
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<p>Effect of Rapamycin on Ca9-22 cell cycle arrest markers. (<b>A</b>) Differences in the expression of cell cycle-related genes. QPCR array screening for 84 markers after incubation with 10 µM Rapamycin (<span class="html-italic">n</span> = 3). (<b>B</b>–<b>D</b>) Summary of positively and negatively modulated genes. Only fold regulation values above 2 were considered (<span class="html-italic">n</span> = 3). (<b>E</b>) Interaction networks of Rapamycin-regulated genes involved in the cell cycle.</p>
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<p>Effect of Rapamycin on Ca9-22 apoptosis markers. (<b>A</b>) Variations in the apoptosis-related gene expression was detected in Ca9-22 cells, both in untreated conditions and those treated with Rapamycin, as determined by QPCR array. A total of 84 markers were included in the screening (<span class="html-italic">n</span> = 3). (<b>B</b>,<b>C</b>) Visualization of log2 fold changes following exposure to Rapamycin relative to untreated cells (<span class="html-italic">n</span> = 3). (<b>D</b>) Table showing the list of genes modulated by Rapamycin as well as the associated fold change (<span class="html-italic">n</span> = 3). (<b>E</b>) Network of gene interactions influenced by Rapamycin, specifically those associated with apoptosis.</p>
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<p>Effect of Rapamycin on Ca9-22 autophagy markers. (<b>A</b>) Discrepancies in the expression genes related to autophagy were investigated in Ca9-22 cells under untreated conditions and after treatment with Rapamycin. QPCR array screening for 84 markers was conducted following incubation with 10 µM of Rapamycin (<span class="html-italic">n</span> = 3). (<b>B</b>,<b>C</b>) Visualization of significant changes induced by Rapamycin as compared to untreated cells (<span class="html-italic">n</span> = 3). (<b>D</b>) Display of the genes downregulated by Rapamycin along with the change magnitude (<span class="html-italic">n</span> = 3). (<b>E</b>) Graphical representation of the autophagy gene interaction networks, particularly those linked to the modulated markers.</p>
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24 pages, 1142 KiB  
Review
Potential Implications of miRNAs in the Pathogenesis, Diagnosis, and Therapeutics of Alzheimer’s Disease
by Long Wang, Xindong Shui, Yuelin Diao, Duoting Chen, Ying Zhou and Tae Ho Lee
Int. J. Mol. Sci. 2023, 24(22), 16259; https://doi.org/10.3390/ijms242216259 - 13 Nov 2023
Cited by 9 | Viewed by 2680
Abstract
Alzheimer’s disease (AD) is a complex multifactorial disorder that poses a substantial burden on patients, caregivers, and society. Considering the increased aging population and life expectancy, the incidence of AD will continue to rise in the following decades. However, the molecular pathogenesis of [...] Read more.
Alzheimer’s disease (AD) is a complex multifactorial disorder that poses a substantial burden on patients, caregivers, and society. Considering the increased aging population and life expectancy, the incidence of AD will continue to rise in the following decades. However, the molecular pathogenesis of AD remains controversial, superior blood-based biomarker candidates for early diagnosis are still lacking, and effective therapeutics to halt or slow disease progression are urgently needed. As powerful genetic regulators, microRNAs (miRNAs) are receiving increasing attention due to their implications in the initiation, development, and theranostics of various diseases, including AD. In this review, we summarize miRNAs that directly target microtubule-associated protein tau (MAPT), amyloid precursor protein (APP), and β-site APP-cleaving enzyme 1 (BACE1) transcripts and regulate the alternative splicing of tau and APP. We also discuss related kinases, such as glycogen synthase kinase (GSK)-3β, cyclin-dependent kinase 5 (CDK5), and death-associated protein kinase 1 (DAPK1), as well as apolipoprotein E, that are directly targeted by miRNAs to control tau phosphorylation and amyloidogenic APP processing leading to Aβ pathologies. Moreover, there is evidence of miRNA-mediated modulation of inflammation. Furthermore, circulating miRNAs in the serum or plasma of AD patients as noninvasive biomarkers with diagnostic potential are reviewed. In addition, miRNA-based therapeutics optimized with nanocarriers or exosomes as potential options for AD treatment are discussed. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Brain Aging and Alzheimer’s Disease)
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<p>A summary of miRNAs directly targeting MAPT transcripts and related kinases, including GSK-3β, CDK5, DYRK1A, and DAPK1.</p>
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<p>A summary of miRNAs directly targeting APP transcripts, BACE1 transcripts, and related kinases, including GSK-3β, CDK5, JNK, and DAPK1.</p>
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13 pages, 1314 KiB  
Article
Diagnostic Value of DAPK Methylation for Nasopharyngeal Carcinoma: Meta-Analysis
by Thuan Duc Lao, Phuong Kim Truong and Thuy Ai Huyen Le
Diagnostics 2023, 13(18), 2926; https://doi.org/10.3390/diagnostics13182926 - 12 Sep 2023
Viewed by 1060
Abstract
Background: Methylation of DAPK has been reported to play a key role in the initiation and progression of nasopharyngeal cancer. However, there are differences between the studies on it. This meta-analysis was performed to evaluate the diagnostic value of DAPK promoter methylation for [...] Read more.
Background: Methylation of DAPK has been reported to play a key role in the initiation and progression of nasopharyngeal cancer. However, there are differences between the studies on it. This meta-analysis was performed to evaluate the diagnostic value of DAPK promoter methylation for NPC. Method: The study method involves the systematic research of eligible studies based on criteria. The frequency, odds ratios (OR), sensitivity as well as specificity with the corresponding 95% confidence intervals (CIs) were used to assess the effect sizes. Results: A total of 13 studies, including 1048 NPC samples and 446 non-cancerous samples, were used for the meta-analysis. The overall frequencies of DAPK methylation were 56.94% and 9.28% in NPC samples and non-cancerous samples, respectively. The association between DAPK methylation and risk of NPC was also confirmed by calculating the OR value which was 13.13 (95%CI = 54.24–40.72) based on a random-effect model (Q = 64.74; p < 0.0001; I2 = 81.47% with 95%CI for I2 = 69.39–88.78). Additionally, the study results suggest that testing for DAPK methylation in tissue samples or brushing may provide a promising method for diagnosing NPC. Conclusion: This is the first meta-analysis that provided scientific evidence that methylation of the DAPK gene could serve as a potential biomarker for diagnosis, prognosis, and early screening of NPC patients. Full article
(This article belongs to the Special Issue Histopathology in Cancer Diagnosis and Prognosis)
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<p>Flow chart of study selection in the meta-analysis.</p>
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<p>Forest plot of <span class="html-italic">DAPK</span> gene’s methylation frequency in (<b>A</b>) NPC samples, (<b>B</b>) non-cancerous samples.</p>
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<p>Forest plot of <span class="html-italic">DAPK</span> promoter methylation associated with NPC by evaluating odds ratio risk using random-effect model.</p>
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<p>Forest plot of sensitivity and specificity of <span class="html-italic">DAPK</span> promoter methylation associated with NPC: (<b>A</b>) Tissue samples; (<b>B</b>) brushing samples; (<b>C</b>) plasma samples.</p>
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13 pages, 863 KiB  
Article
A Study of DNA Methylation of Bladder Cancer Biomarkers in the Urine of Patients with Neurogenic Lower Urinary Tract Dysfunction
by Periklis Koukourikis, Maria Papaioannou, Petros Georgopoulos, Ioannis Apostolidis, Stavroula Pervana and Apostolos Apostolidis
Biology 2023, 12(8), 1126; https://doi.org/10.3390/biology12081126 - 12 Aug 2023
Cited by 1 | Viewed by 1369
Abstract
Background: Bladder cancer (BCa) in patients suffering from neurogenic lower urinary tract dysfunction (NLUTD) is a significant concern due to its advanced stage at diagnosis and high mortality rate. Currently, there is a scarcity of specific guidelines for BCa screening in these patients. [...] Read more.
Background: Bladder cancer (BCa) in patients suffering from neurogenic lower urinary tract dysfunction (NLUTD) is a significant concern due to its advanced stage at diagnosis and high mortality rate. Currently, there is a scarcity of specific guidelines for BCa screening in these patients. The development of urine biomarkers for BCa seems to be an attractive non-invasive method of screening or risk stratification in this patient population. DNA methylation is an epigenetic modification, resulting in the transcriptional silencing of tumor suppression genes, that is frequently detected in the urine of BCa patients. Objectives: We aimed to investigate DNA hypermethylation in five gene promoters, previously associated with BCa, in the urine of NLUTD patients, and in comparison with healthy controls. Design, setting and participants: This was a prospective case–control study that recruited neurourology outpatients from a public teaching hospital who had suffered from NLUTD for at least 5 years. They all underwent cystoscopy combined with biopsy for BCa screening following written informed consent. DNA was extracted and DNA methylation was assessed for the RASSF1, RARβ, DAPK, TERT and APC gene promoters via quantitative methylation-specific PCR in urine specimens from the patients and controls. Results: Forty-one patients of mixed NLUTD etiology and 35 controls were enrolled. DNA was detected in 36 patients’ urine specimens and in those of 22 controls. In the urine specimens, DNA was hypermethylated in at least one of five gene promoters in 17/36 patients and in 3/22 controls (47.22% vs. 13.64%, respectively, p = 0.009). RASSF1 was hypermethylated in 10/17 (58.82%) specimens with detected methylation, APC in 7/17 (41.18%), DAPK in 4/17 (23.53%), RAR-β2 in 3/17 (17.56%) and TERT in none. According to a multivariate logistic regression analysis, NLUTD and male gender were significantly associated with hypermethylation (OR = 7.43, p = 0.007 and OR = 4.21; p = 0.04, respectively). In the tissue specimens, histology revealed TaLG BCa in two patients and urothelial squamous metaplasia in five patients. Chronic bladder inflammation was present in 35/41 bladder biopsies. Conclusions: DNA hypermethylation in a panel of five BCa-associated genes in the urine was significantly more frequent in NLUTD patients than in the controls. Our results warrant further evaluation in longitudinal studies assessing the clinical implications and possible associations between DNA hypermethylation, chronic inflammation and BCa in the NLUTD population. Full article
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<p>Distribution of hypermethylated genes of panel in NLUTD group.</p>
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<p>ROC curve and AUC of the gene panel for BCa diagnosis in NLUTD group.</p>
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14 pages, 2061 KiB  
Review
The Role of Death-Associated Protein Kinase-1 in Cell Homeostasis-Related Processes
by Lilian Makgoo, Salerwe Mosebi and Zukile Mbita
Genes 2023, 14(6), 1274; https://doi.org/10.3390/genes14061274 - 16 Jun 2023
Cited by 2 | Viewed by 1498
Abstract
Tremendous amount of financial resources and manpower have been invested to understand the function of numerous genes that are deregulated during the carcinogenesis process, which can be targeted for anticancer therapeutic interventions. Death-associated protein kinase 1 (DAPK-1) is one of the [...] Read more.
Tremendous amount of financial resources and manpower have been invested to understand the function of numerous genes that are deregulated during the carcinogenesis process, which can be targeted for anticancer therapeutic interventions. Death-associated protein kinase 1 (DAPK-1) is one of the genes that have shown potential as biomarkers for cancer treatment. It is a member of the kinase family, which also includes Death-associated protein kinase 2 (DAPK-2), Death-associated protein kinase 3 (DAPK-3), Death-associated protein kinase-related apoptosis-inducing kinase 1 (DRAK-1) and Death-associated protein kinase-related apoptosis-inducing kinase 2 (DRAK-2). DAPK-1 is a tumour-suppressor gene that is hypermethylated in most human cancers. Additionally, DAPK-1 regulates a number of cellular processes, including apoptosis, autophagy and the cell cycle. The molecular basis by which DAPK-1 induces these cell homeostasis-related processes for cancer prevention is less understood; hence, they need to be investigated. The purpose of this review is to discuss the current understanding of the mechanisms of DAPK-1 in cell homeostasis-related processes, especially apoptosis, autophagy and the cell cycle. It also explores how the expression of DAPK-1 affects carcinogenesis. Since deregulation of DAPK-1 is implicated in the pathogenesis of cancer, altering DAPK-1 expression or activity may be a promising therapeutic strategy against cancer. Full article
(This article belongs to the Special Issue Genotyping and Prognostic Markers in Cancers)
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<p>DAPK-1 interacting partners predicted using the STRING online tool.</p>
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<p>(<b>A</b>) DAPK-1 family members and their respective structures showing the kinase domain in all members of the DAPK family. The degree of amino acid identity with DAPK-1 is determined by the number of amino acids within the kinase domains and CaM regulatory domains. Adapted from Farag and Roh, [<a href="#B50-genes-14-01274" class="html-bibr">50</a>], Shiloh et al. [<a href="#B39-genes-14-01274" class="html-bibr">39</a>] and Shoval et al. [<a href="#B51-genes-14-01274" class="html-bibr">51</a>]. (<b>B</b>) Sequence alignment of the protein kinase domain found in all members of the DAPK family, the domain was aligned using Multiple sequence alignment tool. The “*” indicates positions where there is one fully conserved residue, while “:” indicates conservation between groups with strongly similar properties (<b>C</b>) Phylogenetic tree depicting the relationship between DRAKs and DAPKs in <span class="html-italic">Homo sapiens</span>.</p>
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<p>A schematic representation of <span class="html-italic">s-DAPK-1</span> mRNA (<b>A</b>) and protein (<b>B</b>) in relation to DAPK-1. s-DAPK-1′s mRNA begins in intron 13–14 of the DAPK-1 gene, its coding region ends at 126 base pairs in intron 20–21 of DAPK-1. There are 295 amino acids in s-DAPK-1 that are identical to amino acids 447–743 in full-length DAPK-1; however, the last 42 amino acids are unique. Adapted from Lin et al. [<a href="#B36-genes-14-01274" class="html-bibr">36</a>].</p>
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<p>Different types of cell death based on morphological characteristics.</p>
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<p>DAPK-1 mediates TNF-α-related apoptosis.</p>
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13 pages, 295 KiB  
Article
Helicobacter pylori and Epstein–Barr Virus Co-Infection in Gastric Disease: What Is the Correlation with p53 Mutation, Genes Methylation and Microsatellite Instability in a Cohort of Sicilian Population?
by Anna Giammanco, Rita Anzalone, Nicola Serra, Giuseppa Graceffa, Salvatore Vieni, Nunzia Scibetta, Teresa Rea, Giuseppina Capra and Teresa Fasciana
Int. J. Mol. Sci. 2023, 24(9), 8104; https://doi.org/10.3390/ijms24098104 - 30 Apr 2023
Viewed by 1585
Abstract
Genetic predisposition, environmental factors, and infectious agents interact in the development of gastric diseases. Helicobacter pylori (Hp) and Epstein–Barr virus (EBV) infection has recently been shown to be correlated with these diseases. A cross-sectional study was performed on 100 hospitalized Italian patients with [...] Read more.
Genetic predisposition, environmental factors, and infectious agents interact in the development of gastric diseases. Helicobacter pylori (Hp) and Epstein–Barr virus (EBV) infection has recently been shown to be correlated with these diseases. A cross-sectional study was performed on 100 hospitalized Italian patients with and without gastric diseases. The patients were stratified into four groups. Significant methylation status differences among CDH1, DAPK, COX2, hMLH1 and CDKN2A were observed for coinfected (Hp-EBV group) patients; particularly, a significant presence of COX2 (p = 0.0179) was observed. For microsatellite instability, minor stability was described in the Hp-HBV group (69.23%, p = 0.0456). Finally, for p53 mutation in the EBV group, exon 6 was, significantly, most frequent in comparison to others (p = 0.0124), and in the Hp-EBV group exon 8 was, significantly, most frequent in comparison to others (p < 0.0001). A significant positive relationship was found between patients with infection (Hp, EBV or both) and p53 mutation (rho = 0.383, p = 0.0001), methylation status (rho = 0.432, p < 0.0001) and microsatellite instability (rho = 0.285, p = 0.004). Finally, we observed among infection and methylation status, microsatellite instability, and p53 mutation a significant positive relationship only between infection and methylation status (OR = 3.78, p = 0.0075) and infection and p53 mutation (OR = 6.21, p = 0.0082). According to our analysis, gastric disease in the Sicilian population has different pathways depending on the presence of various factors, including infectious agents such as Hp and EBV and genetic factors of the subject. Full article
(This article belongs to the Special Issue New Advances on Helicobacter pylori Research)
15 pages, 3511 KiB  
Article
Ablation of Death-Associated Protein Kinase 1 Changes the Transcriptomic Profile and Alters Neural-Related Pathways in the Brain
by Ruomeng Li, Shuai Zhi, Guihua Lan, Xiaotong Chen, Xiuzhi Zheng, Li Hu, Long Wang, Tao Zhang, Tae Ho Lee, Shitao Rao and Dongmei Chen
Int. J. Mol. Sci. 2023, 24(7), 6542; https://doi.org/10.3390/ijms24076542 - 31 Mar 2023
Cited by 2 | Viewed by 2037
Abstract
Death-associated protein kinase 1 (DAPK1), a Ca2+/calmodulin-dependent serine/threonine kinase, mediates various neuronal functions, including cell death. Abnormal upregulation of DAPK1 is observed in human patients with neurological diseases, such as Alzheimer’s disease (AD) and epilepsy. Ablation of DAPK1 expression and suppression [...] Read more.
Death-associated protein kinase 1 (DAPK1), a Ca2+/calmodulin-dependent serine/threonine kinase, mediates various neuronal functions, including cell death. Abnormal upregulation of DAPK1 is observed in human patients with neurological diseases, such as Alzheimer’s disease (AD) and epilepsy. Ablation of DAPK1 expression and suppression of DAPK1 activity attenuates neuropathology and behavior impairments. However, whether DAPK1 regulates gene expression in the brain, and whether its gene profile is implicated in neuronal disorders, remains elusive. To reveal the function and pathogenic role of DAPK1 in neurological diseases in the brain, differential transcriptional profiling was performed in the brains of DAPK1 knockout (DAPK1-KO) mice compared with those of wild-type (WT) mice by RNA sequencing. We showed significantly altered genes in the cerebral cortex, hippocampus, brain stem, and cerebellum of both male and female DAPK1-KO mice compared to those in WT mice, respectively. The genes are implicated in multiple neural-related pathways, including: AD, Parkinson’s disease (PD), Huntington’s disease (HD), neurodegeneration, glutamatergic synapse, and GABAergic synapse pathways. Moreover, our findings imply that the potassium voltage-gated channel subfamily A member 1 (Kcna1) may be involved in the modulation of DAPK1 in epilepsy. Our study provides insight into the pathological role of DAPK1 in the regulatory networks in the brain and new therapeutic strategies for the treatment of neurological diseases. Full article
(This article belongs to the Special Issue Molecular Mechanisms of Neurodegeneration 2023)
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<p>Transcriptional profiling of tissues from different brain regions in DAPK1-KO mice. (<b>A</b>) DAPK1 protein levels in tissues from four different brain regions of WT and DAPK1-KO male and female mice by immunoblotting analysis. (<b>B</b>) Differential gene expression volcano plots of tissues from each brain region of male mice by the edgeR method. The horizontal dotted line refers to the threshold of statistical significance with log, while the vertical dotted line refers to the threshold of the differential expressed ratio. (<b>C</b>) Number of DEGs in tissues of four brain regions for male mice.</p>
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<p>Chromosome distribution of significantly regulated genes in different regions of male DAPK1-KO mouse brain tissues. Chromosome distribution of DEGs in the cerebral cortex (<b>A</b>), hippocampus (<b>B</b>), brain stem (<b>C</b>) and cerebellum (<b>D</b>).</p>
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<p>Venn analysis of the differentially expressed genes in different brain region tissues of male (<b>A</b>) and female (<b>B</b>) DAPK1-KO mice.</p>
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<p>Gene ontology enrichment analysis of the DEGs in the cerebral cortex (<b>A</b>) and hippocampus (<b>B</b>) of male DAPK1-KO mice. The GO categories were biological process (BP), cellular component (CC), and molecular function (MF).</p>
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<p>Gene ontology enrichment analysis of the DEGs in the brain stem (<b>A</b>) and cerebellum (<b>B</b>) of male DAPK1-KO mice. The GO categories were biological process (BP), cellular component (CC), and molecular function (MF).</p>
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<p>KEGG pathway analysis of the DEGs in the cerebral cortex (<b>A</b>) and hippocampus (<b>B</b>) of male DAPK1-KO mice.</p>
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<p>KEGG pathway analysis of the DEGs in the brain stem (<b>A</b>) and cerebellum (<b>B</b>) of male DAPK1-KO mice.</p>
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<p>Validation of gene expression by qRT-PCR in DAPK1-KO mice. qRT-PCR analysis of Aff2, Zkscan16, Kcna1, Pcdhac2, and Pcdhga8 in the cerebral cortex, hippocampus, brain stem, and cerebellum for males (<b>A</b>) and females (<b>B</b>). Each data point represents the mean ± standard deviation (SD) of three mice.</p>
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19 pages, 9450 KiB  
Article
Identification of an Autophagy-Related Signature for Prognosis and Immunotherapy Response Prediction in Ovarian Cancer
by Jinye Ding, Chunyan Wang, Yaoqi Sun, Jing Guo, Shupeng Liu and Zhongping Cheng
Biomolecules 2023, 13(2), 339; https://doi.org/10.3390/biom13020339 - 9 Feb 2023
Cited by 13 | Viewed by 3711
Abstract
Background: Ovarian cancer (OC) is one of the most malignant tumors in the female reproductive system, with a poor prognosis. Various responses to treatments including chemotherapy and immunotherapy are observed among patients due to their individual characteristics. Applicable prognostic markers could make it [...] Read more.
Background: Ovarian cancer (OC) is one of the most malignant tumors in the female reproductive system, with a poor prognosis. Various responses to treatments including chemotherapy and immunotherapy are observed among patients due to their individual characteristics. Applicable prognostic markers could make it easier to refine risk stratification for OC patients. Autophagy is closely implicated in the occurrence and development of tumors, including OC. Whether autophagy -related genes can be used as prognostic markers for OC patients remains unclear. Methods: The gene transcriptome data of 374 OC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between the autophagy levels and outcomes of OC patients was identified through the single sample gene set enrichment analysis (ssGSEA). Recognized molecular markers of autophagy in different clinical specimens were detected by immunohistochemistry (IHC) assay. The gene set enrichment analysis (GSEA), ESTIMATE, and CIBERSORT analysis were applied to explore the correlation of autophagy with the tumor immune microenvironment (TIME). Single-cell RNA-sequencing (scRNA-seq) data from seven OC patients were included for characterizing cell-cell interaction patterns of autophagy-high or low tumor cells. Machine learning, Stepwise Cox regression and LASSO-Cox analysis were used to screen autophagy hub genes, which were used to establish an autophagy-related signature for prognosis evaluation. Four tumor immunotherapy cohorts were obtained from the GEO (Gene Expression Omnibus) database and the literature for autophagy risk score validation. Results: The autophagy levels were closely related to the prognosis of the OC patients. Additionally, the autophagy levels were correlated with TIME status including immune score, and immune-cell infiltration. The scRNA-seq analysis found that tumor cells with high or low autophagy levels had different interactions with immune cells, especially macrophages. Eight autophagy-hub genes (ZFYVE1, AMBRA1, LAMP2, TRAF6, PDPK1, ATG2B, DAPK1 and TP53INP2) were screened for an autophagy-related signature. According to this signature, higher risk score was correlated with poor prognosis and better immunotherapy response in the OC patients. Conclusions: The autophagy-related signature is applicable to predict the prognosis and immune checkpoint inhibitors (ICIs) therapy efficiency in OC patients. It is possible to identify OC patients who will respond to ICIs therapy and have a favorable prognosis, although more verification is needed. Full article
(This article belongs to the Section Bioinformatics and Systems Biology)
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<p>Identification and validation of the correlation between autophagy levels and prognosis of OC patients: (<b>A</b>) PCA on the correlation of the OC patients with different levels of autophagy, apoptosis, necroptosis and ferroptosis. (<b>B</b>) Kaplan-Meier analysis of the survival outcomes of patients (TCGA) with high and low autophagy levels. (<b>C</b>) The proportion of lymphatic metastases in patients with high and low autophagy levels. (<b>D</b>) Detection of the expression levels of LC3B, p62 and Beclin1 in OC and normal ovary tissues by IHC assay. (<b>E</b>) Detection of the expression levels of LC3B, p62 and Beclin1 in cisplatin-sensitive and cisplatin-resistant OC tissues by IHC assay. (Scale bar, 200 μm and 50 μm, * <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>The relationship between autophagy and immune-related signaling in OC: (<b>A</b>) GSEA enrichment analysis showed immune-related signals were positively enriched in the autophagy high group. Volcano plot (<b>B</b>) and box plot (<b>C</b>) showed the expression levels of immune-related genes (CD28, CD4, CD274, CTLA4, TLR2, TLR4, TICAM2, LCP2, ITK, PTPRC, EGF, JAK1 and mTOR) in autophagy high and low group (Two-tailed student’s <span class="html-italic">t</span> test, <span class="html-italic">p</span> &lt; 0.05 was considered to be significant).</p>
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<p>The correlation between autophagy and the levels of immune cell infiltration in OC: (<b>A</b>) Estimate analysis of the immune score in autophagy high and low group. (<b>B</b>) CIBERSORT analysis of the infiltration proportion of twenty−two immune cell types in the TIME of OC. (<b>C</b>) Correlation analysis of the differential infiltration of twenty−two immune cell types in the TIME of OC. (<b>D</b>) Detection of the expression levels of iNOS and CD163 in OC and normal ovary tissues by IHC assay. (<b>E</b>) Detection of the expression levels of CD86 and CD163 in cisplatin-sensitive and cisplatin−resistant OC tissues by IHC assay. (Scale bar, 200 μm and 50 μm; Two−tailed student’s t test, *** <span class="html-italic">p</span> &lt; 0.001, ns, no statistical difference).</p>
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<p>Single-cell RNA sequencing analysis of the cell-cell communication between autophagy high/low tumor cells and macrophages: UMAP plots (<b>A</b>) and Box plots (<b>B</b>) showed differential autophagy scores in different tumor cell clusters. (<b>C</b>) The ligand-receptor interactions between autophagy high/low tumor cells and macrophages. (<b>D</b>) Description of two clusters of macrophages (M1/M2) by UMAP plots. (<b>E</b>) The expression levels of the classical genes which correlated with M1/M2-like macrophages. (<b>F</b>) Different intensity of interactions between autophagy high/low tumor cells and M1/M2-like macrophages.</p>
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<p>Development of an autophagy-related signature: (<b>A</b>) Ranking of top thirty autophagy hub genes based on their importance in the autophagy process. (<b>B</b>) Selection of eight risk genes by stepwise multivariate Cox proportional risk regression analysis. (<b>C</b>) Kaplan-Meier analysis of the survival outcomes of patients (TCGA) with high or low autophagy risk scores. (<b>D</b>) Time-dependent ROC curves analysis of the prognostic accuracy of the autophagy-related signature at 3 years, 5 years and 10 years. (<b>E</b>) Kaplan-Meier analysis of the survival outcomes of patients with high or low autophagy risk scores in the validation cohorts from GEO. Left: GSE14407; Right: GSE38666. (<b>F</b>) The distribution of the autophagy risk scores in OC patients from TCGA cohort. (<b>G</b>) The survival status of the OC patients in autophagy-high/low risk group. (<b>H</b>) The expression levels of the eight risk genes in autophagy-high/low risk group.</p>
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<p>Assessment of the prognostic value of autophagy-related signature: Univariate (<b>A</b>) and multivariate (<b>B</b>) Cox regression analyses of age, FIGO stage and autophagy risk score in the TCGA cohort. (<b>C</b>) A nomogram integrating the autophagy-related signature risk score with the clinical characteristics to quantify risk evaluation for each patient. (<b>D</b>) The calibration curves for the nomogram in the OC cohorts from TCGA.</p>
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<p>Evaluation of the ICIs therapy efficiency based on autophagy-related signature: Autophagy risk score in the patients with different responses to immunotherapy in the IMvigor210 cohort (<b>A</b>), an institutional cohort (<b>B</b>), GSE176307 (<b>C</b>) and GSE78220 cohort (<b>D</b>). TMB (<b>E</b>) and TNB (<b>F</b>) values (IMvigor210) of the patients with autophagy high/low risk score (ns, no statistical difference).</p>
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13 pages, 1717 KiB  
Article
Circulating Chromosome Conformation Signatures Significantly Enhance PSA Positive Predicting Value and Overall Accuracy for Prostate Cancer Detection
by Dmitri Pchejetski, Ewan Hunter, Mehrnoush Dezfouli, Matthew Salter, Ryan Powell, Jayne Green, Tarun Naithani, Christina Koutsothanasi, Heba Alshaker, Jiten Jaipuria, Martin J. Connor, David Eldred-Evans, Francesca Fiorentino, Hashim Ahmed, Alexandre Akoulitchev and Mathias Winkler
Cancers 2023, 15(3), 821; https://doi.org/10.3390/cancers15030821 - 29 Jan 2023
Cited by 3 | Viewed by 19536
Abstract
Background: Prostate cancer (PCa) has a high lifetime prevalence (one out of six men), but currently there is no widely accepted screening programme. Widely used prostate specific antigen (PSA) test at cut-off of 3.0 ng/mL does not have sufficient accuracy for detection of [...] Read more.
Background: Prostate cancer (PCa) has a high lifetime prevalence (one out of six men), but currently there is no widely accepted screening programme. Widely used prostate specific antigen (PSA) test at cut-off of 3.0 ng/mL does not have sufficient accuracy for detection of any prostate cancer, resulting in numerous unnecessary prostate biopsies in men with benign disease and false reassurance in some men with PCa. We have recently identified circulating chromosome conformation signatures (CCSs, Episwitch® PCa test) allowing PCa detection and risk stratification in line with standards of clinical PCa staging. The purpose of this study was to determine whether combining the Episwitch PCa test with the PSA test will increase its diagnostic accuracy. Methods: n = 109 whole blood samples of men enrolled in the PROSTAGRAM screening pilot study and n = 38 samples of patients with established PCa diagnosis and cancer-negative controls from Imperial College NHS Trust were used. Samples were tested for PSA, and the presence of CCSs in the loci encoding for of DAPK1, HSD3B2, SRD5A3, MMP1, and miRNA98 associated with high-risk PCa identified in our previous work. Results: PSA > 3 ng/mL alone showed a low positive predicted value (PPV) of 0.14 and a high negative predicted value (NPV) of 0.93. EpiSwitch alone showed a PPV of 0.91 and a NPV of 0.32. Combining PSA and Episwitch tests has significantly increased the PPV to 0.81 although reducing the NPV to 0.78. Furthermore, integrating PSA, as a continuous variable (rather than a dichotomised 3 ng/mL cut-off), with EpiSwitch in a new multivariant stratification model, Prostate Screening EpiSwitch (PSE) test, has yielded a remarkable combined PPV of 0.93 and NPV of 0.95 when tested on the independent combined cohort. Conclusions: Our results demonstrate that combining the standard PSA readout with circulating chromosome conformations (PSE test) allows for significantly enhanced PSA PPV and overall accuracy for PCa detection. The PSE test is accurate, rapid, minimally invasive, and inexpensive, suggesting significant screening diagnostic potential to minimise unnecessary referrals for expensive and invasive MRI and/or biopsy testing. Further extended prospective blinded validation of the new combined signature in a screening cohort with low cancer prevalence would be the recommended step for PSE adoption in PCa screening. Full article
(This article belongs to the Section Cancer Causes, Screening and Diagnosis)
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<p>Scheme of chromatin conformation assay and microarray analysis. A three-dimensional structure of chromosomes contains loops with an enhancer and promoter regions, whereas the enhancer increases target gene promoter activity. CC capture assay: DNA is cross-linked using formaldehyde, digested, and ligated with the preference of cross-linked fragments. New sequences are formed where the loops have been. These new sequences are predicted via relevance machine vector algorithm. Specific primers to these sequences are synthesised and placed on the DNA microarray, which detects whether the loop was present or not. Resulting markers are analysed using multivariate analysis yielding specific epigenetic signatures for selected patient cohorts.</p>
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<p>Batch adjustment by reference alignment (BARA) procedure. The scheme for batch adjustment by reference alignment (BARA) procedure [<a href="#B27-cancers-15-00821" class="html-bibr">27</a>] that was implemented in results analysis for better control of batch effects (non-biological variation).</p>
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<p>SHapley Additive exPlanations (SHAP) values of each marker contribution in the PSE model. SHAP values (26) demonstrating the contribution of each marker in the model are shown for the binary PSA (<b>A</b>) and continuous PSA (<b>B</b>). Each dot represents one sample.</p>
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<p>Schematic depiction of the overlap between the anchors of Episwitch loops and known PCa related SNPs on chromosome 8 from GWAS. The image shows the overlap of the anchoring sites of EpiSwitch 3D genomic markers, identified in early whole genome screening stage of EpiSwitch biomarker development [<a href="#B21-cancers-15-00821" class="html-bibr">21</a>,<a href="#B22-cancers-15-00821" class="html-bibr">22</a>], with positions of PCa SNPs from GWAS Catalogue on 8q24 chromosome region.</p>
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<p>Schematic depiction of the genes involved in the PSE signature. The network was built using the five model markers and PSA, using the STRING DB (<a href="https://string-db.org/" target="_blank">https://string-db.org/</a>, accessed between 1 July and 1 October 2022). The network was generated by five additional entities that have a high confidence in interaction (data supporting the connections) suggesting high connectability at the protein level. <span class="html-italic">DAPK1</span>, <span class="html-italic">HSD3B2</span>, <span class="html-italic">SRD5A3</span> and <span class="html-italic">MMP1</span> model markers are circled in blue, <span class="html-italic">PSA (KLK3)</span> in red. miRNA98 is not in STRING.</p>
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17 pages, 2605 KiB  
Article
Stellettin B Induces Cell Death in Bladder Cancer Via Activating the Autophagy/DAPK2/Apoptosis Signaling Cascade
by Chun-Han Chang, Bo-Jyun Lin, Chun-Han Chen, Nham-Linh Nguyen, Tsung-Han Hsieh, Jui-Hsin Su and Mei-Chuan Chen
Mar. Drugs 2023, 21(2), 73; https://doi.org/10.3390/md21020073 - 21 Jan 2023
Cited by 8 | Viewed by 2447
Abstract
Bladder cancer (BC) is one of the most prevalent cancers worldwide. However, the recurrence rate and five-year survival rate have not been significantly improved in advanced BC, and new therapeutic strategies are urgently needed. The anticancer activity of stellettin B (SP-2), a triterpene [...] Read more.
Bladder cancer (BC) is one of the most prevalent cancers worldwide. However, the recurrence rate and five-year survival rate have not been significantly improved in advanced BC, and new therapeutic strategies are urgently needed. The anticancer activity of stellettin B (SP-2), a triterpene isolated from the marine sponge Rhabdastrella sp., was evaluated with the MTT assay as well as PI and Annexin V/7-AAD staining. Detailed mechanisms were elucidated through an NGS analysis, protein arrays, and Western blotting. SP-2 suppressed the viability of BC cells without severe toxicity towards normal uroepithelial cells, and it increased apoptosis with the activation of caspase 3/8/9, PARP, and γH2AX. The phosphorylation of FGFR3 and its downstream targets were downregulated by SP-2. Meanwhile, it induced autophagy in BC cells as evidenced by LC3-II formation and p62 downregulation. The inhibition of autophagy using pharmacological inhibitors or through an ATG5-knockout protected RT-112 cells from SP-2-induced cell viability suppression and apoptosis. In addition, the upregulation of DAPK2 mRNA and protein expression also contributed to SP-2-induced cytotoxicity and apoptosis. In RT-112 cells, an FGFR3-TACC3-knockout caused the downregulation of DAPK2, autophagy, and apoptosis. In conclusion, this is the first study demonstrating that SP-2 exhibits potent anti-BC activity by suppressing the FGFR3-TACC3/Akt/mTOR pathway, which further activates a novel autophagy/DAPK2/apoptosis signaling cascade. Full article
(This article belongs to the Special Issue Development and Application of Marine-Derived Anti-cancer Agents)
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<p>SP-2 selectively suppressed cell viability in bladder cancer (BC) cells. BC cells (RT-112, J82, UMUC3, and RT4) and normal uroepithelial cells (SV-HUC-1) were treated with different concentrations of SP-2 for 48 h (<b>A</b>) and 72 h (<b>B</b>), and cell viability was determined through MTT assay. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001 compared with control group.</p>
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<p>SP-2 induced accumulation of cells in the sub-G1 phase and apoptosis in RT-112 cells. (<b>A</b>–<b>C</b>) SP-2 induced sub-G1 cell population in RT-112 cells. Cells were treated with SP-2 at different concentrations for 48 h then were stained with PI, and cell cycle distribution was detected through flow cytometry (<b>A</b>). Quantitative data (<b>B</b>,<b>C</b>) are based on flow cytometry histograms and are presented as mean ± S.D. (<b>D</b>,<b>E</b>) SP-2 induced annexin-V-positive apoptotic cells in RT-112 cells. Quantitative data of apoptotic cells are presented as mean ± S.D. (<b>E</b>). Cells were treated with SP-2 at different concentrations for 48 h and stained with Annexin V/7-AAD. Apoptotic cells were detected through flow cytometry. (<b>F</b>) SP-2 increased levels of the cleaved forms of PARP; caspase 3, 8, and 9; and γH2AX in a concentration-dependent manner. Cells were treated with the indicated concentrations of SP-2 for 48 h, and cell lysates were immunoblotted using the indicated antibodies. * <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 compared with control group.</p>
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<p>The effects of SP-2 on the phosphorylation profiles in RT-112 cells. (<b>A</b>,<b>B</b>) SP-2 affected the phosphorylation of various receptor tyrosine kinases (RTKs) (<b>A</b>) and protein kinases (<b>B</b>) in RT-112 cells. Cells were treated with SP-2 (0.5 μM) for 36 h, and cell lysates were applied to phosphoprotein array analysis. Protein dots in the blue box indicate increased phosphorylation, and protein dots in the red box indicate decreased phosphorylation after SP-2 treatment. (<b>C</b>) SP-2 significantly downregulated p-FGFR3 and its downstream signaling pathways. Cells were treated with different concentrations of SP-2 for the indicated times, and cell lysates were immunoblotted using the indicated antibodies.</p>
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<p>SP-2 induced autophagy in RT-112 cells. (<b>A</b>) SP-2 upregulated autophagy and downregulated steroid biosynthesis according to NGS-based pathway analysis. Cells were treated with 0.5 μM SP-2 for 24 h. The data were analyzed through NGS as described in Materials and Methods. (<b>B</b>,<b>C</b>) Expression levels of mRNAs (<b>B</b>) and proteins (<b>C</b>) of ATG9B and DAPK2 with SP-2 treatment. Cells were treated with various concentrations of SP-2 for 24 h, and mRNA levels and proteins were analyzed through RT-qPCR and Western blotting. (<b>D</b>) Concentration-dependent effect of SP-2 on the conversion of endogenous LC3-I to LC3-II. Cells were treated with different concentrations of SP-2 for 24 h and 48 h, and cell lysates were immunoblotted using the indicated antibodies. * <span class="html-italic">p</span> &lt; 0.05 and **** <span class="html-italic">p</span> &lt; 0.0001 compared with the control (CTL) group.</p>
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<p>Knockout of ATG5 reduced SP-2-induced sub-G1 accumulation and apoptosis in RT-112 cells. (<b>A</b>,<b>B</b>) Inhibition of autophagy with either CQ (<b>A</b>) or 3-MA (<b>B</b>) reduced SP-2-induced apoptosis in RT-112 cells. The cells were treated with SP-2 for 48 h in the presence or absence of an autophagy inhibitor (CQ or 3-MA), and cell lysates were analyzed through Western blotting. (<b>C</b>–<b>E</b>) ATG5-knockout reduced SP-2-induced cytotoxicity (<b>C</b>), apoptosis (<b>D</b>), and accumulation in the sub-G1 phase (<b>E</b>) in RT-112 cells. Cells were treated with indicated concentrations of SP-2 for 48 h, and cell viability (<b>C</b>) and cell cycle distribution (<b>E</b>) were determined with MTT assay and through flow cytometry, respectively. * <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. Cell lysates from 48 h SP-2 treatment were analyzed through Western blotting (<b>D</b>).</p>
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<p>DAPK2 potentiated SP-2-induced apoptosis in RT-112 cells. (<b>A</b>) SP-2 induced autophagy and the DAPK2 level in a time-dependent manner. (<b>B</b>) ATG5-knockout reduced the upregulation of DAPK2 in RT-112 cells. Cells were treated with different concentrations of SP-2 for 48 h, and cell lysates were immunoblotted using the indicated antibodies. (<b>C</b>–<b>E</b>) DAPK2-knockdown protected SP-2-induced cytotoxicity (<b>C</b>) and apoptosis (<b>D</b>,<b>E</b>) in RT-112 cells. Cells were treated with different concentrations of SP-2 for 48 h, and cell viability and apoptosis were determined with MTT assay and through Western blotting, respectively. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>FGFR3-TACC3 fusion was essential to SP-2-induced autophagy and apoptosis in RT-112 cells. (<b>A</b>) FGFR3-TACC3 knockout significantly reversed SP-2-reduced cell viability. Cells were treated with different concentrations of SP-2 for 48 h, and cell viability was determined with MTT assay. * <span class="html-italic">p</span> &lt; 0.05 and *** <span class="html-italic">p</span> &lt; 0.001. (<b>B</b>) FGFR3-TACC3 knockout decreased SP-2-indcued autophagy. Cells were treated with different concentrations of SP-2 for 48 h, and cell lysates were immunoblotted using the indicated antibodies. (<b>C</b>) FGFR3-TACC3 knockout protected RT-112 cells from SP-2-induced apoptosis. Cells were treated with different concentrations of SP-2 for 48 h, and cell lysates were immunoblotted using the indicated antibodies.</p>
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16 pages, 3696 KiB  
Article
High DAPK1 Expression Promotes Tumor Metastasis of Gastric Cancer
by Qingshui Wang, Shuyun Weng, Yuqin Sun, Youyu Lin, Wenting Zhong, Hang Fai Kwok and Yao Lin
Biology 2022, 11(10), 1488; https://doi.org/10.3390/biology11101488 - 11 Oct 2022
Cited by 8 | Viewed by 2148
Abstract
Gastric cancer (GC) is a common upper gastrointestinal tumor. Death-associated protein kinase (DAPK1) was found to participate in the development of various malignant tumors. However, there are few reports on DAPK1 in gastric cancer. In this study, the TCGA and GEO datasets were [...] Read more.
Gastric cancer (GC) is a common upper gastrointestinal tumor. Death-associated protein kinase (DAPK1) was found to participate in the development of various malignant tumors. However, there are few reports on DAPK1 in gastric cancer. In this study, the TCGA and GEO datasets were used to explore the expression and role of DAPK1 in gastric cancer. The functions of DAPK1 in gastric cancer were determined by proliferation, migration and invasion assays. In addition, genes co-expressed with DAPK1 in gastric cancer were estimated through the WGCNA and correlation analysis. A DAPK1-related gene prognostic model was constructed using the Cox regression and lasso analyses. The expression of DAPK1 was significantly up-regulated in gastric cancer tissues. Kaplan–Meier analysis showed that low expression of DAPK1 was a favorable prognostic factor of overall survival and disease-free survival for gastric cancer patients. Functional experiments demonstrated that DAPK1 can promote the migration and invasion of gastric cancer cells. WGCNA, correlation analysis, Cox regression, and lasso analyses were applied to construct the DAPK1-related prognostic model. The prognostic value of this prognostic model of DAPK1-related genes was further successfully validated in an independent database. Our results indicated that DAPK1 can promote gastric cancer cell migration and invasion and established four DAPK1-related signature genes for gastric cancer that could independently predict the survival of GC patients. Full article
(This article belongs to the Section Cancer Biology)
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<p>DAPK1 expression in GC. (<b>A</b>–<b>C</b>) The expression of DAPK1 in GC based on GSE13195 (<b>A</b>), GSE63089 (<b>B</b>), and GSE33335 (<b>C</b>). (<b>D</b>) Boxplots of data distribution before and after batch effect removal for GSE13195, GSE63089, and GSE33335 datasets. (<b>E</b>,<b>F</b>) The expression of DAPK1 in GC based on 3GEOs (<b>E</b>) and TCGA (<b>F</b>). * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>The prognosis of DAPK1 in GC patients. (<b>A</b>) Survival analysis of DAPK1 on the OS of GC patients based on the GSE62254 dataset; (<b>B</b>) survival analysis of DAPK1 on the DFS of GC patients based on the GSE62254 dataset; (<b>C</b>) survival analysis of DAPK1 on the OS of GC patients based on the TCGA dataset.</p>
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<p>The correlation between the level of DAPK1 and methylation of DAPK1 DNA CpG sites and TF. (<b>A</b>)The expression value of 24 methylation sites of DAPK1 promoter. (<b>B</b>–<b>F</b>) The correlation of DAPK1 with (<b>B</b>) cg13527872, (<b>C</b>) cg2474277, (<b>D</b>) cg19734228, (<b>E</b>) cg14014720 and (<b>F</b>) cg15746719. (<b>G</b>) Top 20 TFs that potentially regulate DAPK1 in different human cancers. (<b>H</b>) Correlation between DAPK1 and TF mRNA expression.</p>
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<p>Knockdown DAPK1 inhibits GC cell migration and invasion. (<b>A</b>) Western blot was used to detect the levels of DAPK1 in MGC-803 cells transduced by DAPK1-shRNA; (<b>B</b>) DAPK1 knockdown did not significantly affect MGC-803 cell proliferation by using CCK8; (<b>C</b>) DAPK1 knockdown inhibited MGC-803 cell migration; (<b>D</b>) DAPK1 knockdown inhibited MGC-803 cell invasion. ** <span class="html-italic">p</span> &lt; 0.01. Please find the original western blot of (<b>A</b>) in the <a href="#app1-biology-11-01488" class="html-app">Figure S1</a>.</p>
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<p>Overexpression of DAPK1 promotes GC cell migration and invasion. (<b>A</b>) Western blot was used to detect the levels of DAPK1 in MGC-803 cells transduced by HA-DAPK1; (<b>B</b>) DAPK1 did not significantly affect MGC-803 cell proliferation by using CCK8; (<b>C</b>) DAPK1 promoted MGC-803 cell migration; (<b>D</b>) DAPK1 promoted MGC-803 cell invasion. ** <span class="html-italic">p</span> &lt; 0.01. Please find the original western blot of (<b>A</b>) in the <a href="#app1-biology-11-01488" class="html-app">Figure S2</a>.</p>
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<p>WGCNA was used to identify co-expression module genes relating to DAPK1. (<b>A</b>) Association between power and scale-free topology model fit in GSE62254 database. (<b>B</b>) Association between power and mean connectivity in GSE62254 dataset. (<b>C</b>) Dendrogram of modules identified by WGCNA in the GSE62254 dataset. (<b>D</b>) GO-BP analysis of the genes in the blue module. (<b>E</b>) KEGG-pathway analysis of the genes in the blue module.</p>
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<p>Construction of survival risk score system based on DAPK1-related genes. (<b>A</b>) The correlation between DAPK1 and DAPK1-related genes. (<b>B</b>) Survival analysis of the DAPK1-related genes. (<b>C</b>) Partial likelihood deviance of overall survival for LASSO coefficient profiles. (<b>D</b>) LASSO coefficient profiles of 4 genes for overall survival. (<b>E</b>) The distribution of survival status, risk scores, and the expression of four genes of GC patients. (<b>F</b>) Survival analysis of risk score module.</p>
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<p>Survival analysis of GC patients in different subgroups. (<b>A</b>,<b>B</b>) Survival analysis of the GC patients with (<b>A</b>) sex = female and (<b>B</b>) GC patients with sex = male subgroup. (<b>C</b>,<b>D</b>) Survival analysis of the GC patients with age &lt; 60 (<b>C</b>) and GC patients with age ≥ 60 (<b>D</b>) subgroup. (<b>E</b>,<b>F</b>) Survival analysis of the GC patients with stage = I &amp; II (<b>E</b>) and GC patients with stage = III &amp; IV (<b>F</b>) subgroup.</p>
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<p>Validation of the risk score model based on TCGA database.</p>
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<p>Nomogram for the prediction of outcomes of GC patients. (<b>A</b>) Nomogram for predicting survival that combines clinical data. (<b>B</b>) The calibration plots for predicting OS.</p>
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