Analysis of HSD11B2 as a prognostic marker in Melanoma via TCGA data mining

Abstract Background: Hydroxysteroid 11-Beta Dehydrogenase 2 (HSD11B2) expression has been reported to be present in melanoma. We investigated the association of HSD11B2 with melanoma using publicly available data from The Cancer Genome Atlas (TCGA). Methods: The relationship between clinical pathologic features and HSD11B2 were analysed via Wilcoxon signed-rank test and logistic regression. Clinicopathologic characteristics associated with overall survival in melanoma patients were calculated using Cox regression and the Kaplan-Meier method. Gene Set Enrichment Analysis (GSEA) and gene co-association of HSD11B2 were performed using TCGA data set. Results: Reduced HSD11B2 expression was significantly lower in melanoma patients compared to normal patients (p value = 3.004e-122) and also associated with lower survivability. low HSD11B2 expression in melanoma was also significantly associated with cancer stages T (p value = 0.002) and N (p value < 0.001) and age (p value = 0.003). Genes TFCP2L1, PRR15L, ATP6V1B1, C9orf152, AC009948.1, AL391244.1, WDCP, HNRNPCP2 and GTF2E1 were all shown to be co-associated with changes in HSD1B2 expression. Multiple signalling pathway including cytosolic DNA sensing pathway, JAK STAT signalling pathway, NOD like receptor signalling pathway, T cell receptor signalling pathway and Toll like receptor signalling pathway were differentially enriched in low HSD11B2 expression phenotype. Conclusion: Our study revealed that HSD11B2 expression is closely associated with melanoma development and age, as well as multiple cancer related genes and pathways, thus highlighting HSD11B2 as a potential therapeutic marker of melanoma.

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Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

Journal of Gastrointestinal and Liver Diseases ◽

10.15403/jgld-356 ◽

2019 ◽

Vol 28 (4) ◽

pp. 439-447 ◽

Cited By ~ 7

Author(s):

Yan Jiao ◽

Yanqing Li ◽

Bai Ji ◽

Hongqiao Cai ◽

Yahui Liu

Keyword(s):

Liver Cancer ◽

Roc Curve ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Rank Test ◽

P Value ◽

Rna Seq ◽

Cox Regression Analysis

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

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Increased Expression Together with the Gene Regulation Network of NEIL3 Predicts Poor Prognosis and Correlates with Immune Infiltrates in Hepatocellular Carcinoma

10.21203/rs.2.23392/v1 ◽

2020 ◽

Author(s):

Bo Hu ◽

Xiao-Bo Yang ◽

Xinting Sang

Keyword(s):

Hepatocellular Carcinoma ◽

Gene Regulation ◽

Poor Prognosis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Data Set ◽

Gene Markers

Abstract Background: Abnormal Nei endonuclease VIII-like 3 (NEIL3)expression is associated with carcinogenesis. Methods: We used sequencing data from the Cancer Genome Atlas database, analyzed NEIL3 expression, gene regulation networks and the correlation with immune infiltrates in hepatocellular carcinoma (HCC). Clinicopathologic characteristics associated with overall survival in TCGA patients using Cox regression and the Kaplan-Meier method. Gene Set Enrichment Analysis was performed using TCGA data set. LinkedOmics was used to identify differential gene expression with NEIL3 and to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Gene enrichment analysis examined target networks of kinases and transcription factors.Correlations between NEIL3 expression and cancer immune infiltrates and immune gene markers were analyzed by TIMER and GEPIA. Results: We found that overexpressed NEIL3 predicted poor prognosis. Functional network analysis suggested that NEIL3 regulates the DNA replication and cell cycle signaling via pathways involving several cancer-related kinases and E2F Transcription Factor 1.NEIL3 was also found to be associated with the infiltration of several immune cells. Conclusions: Our results demonstrate that data mining efficiently reveals information about NEIL3 expression, potential regulatory networks and the relationship with immune infiltration in HCC, laying a foundation for further study of the role of NEIL3 in carcinogenesis.

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Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

Cancer Cell International ◽

10.1186/s12935-019-1001-0 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 12

Author(s):

Zi-Hao Wang ◽

Yun-Zheng Zhang ◽

Yu-Shan Wang ◽

Xiao-Xin Ma

Keyword(s):

Overall Survival ◽

Endometrial Cancer ◽

Mrna Expression ◽

Cox Regression ◽

Single Gene ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Cox Regression Analysis

Abstract Background Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC. Methods mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan–Meier survival estimates and the log‐rank test were used to validate the significance of risk parameters for prognosis prediction. Result Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors. Conclusion A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.

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Increased expression of RUNX1 in clear cell renal cell carcinoma predicts poor prognosis

PeerJ ◽

10.7717/peerj.7854 ◽

2019 ◽

Vol 7 ◽

pp. e7854 ◽

Cited By ~ 1

Author(s):

Yang Fu ◽

Shanshan Sun ◽

Xiaojun Man ◽

Chuize Kong

Keyword(s):

Cox Regression ◽

Histological Grade ◽

Clinical Stage ◽

Cell Receptor ◽

Signalling Pathway ◽

High Expression ◽

Rank Test ◽

Cell Renal Cell Carcinoma ◽

Receptor Signalling ◽

Runx1 Expression

Background Runt-related transcription factor 1 (RUNX1) was previously reported to play a dual role in promoting or suppressing tumorigenesis in various malignancies. A public dataset from The Cancer Genome Atlas (TCGA) was used to evaluate the role of RUNX1 in clear cell renal cell carcinoma (ccRCC). Methods The Wilcoxon signed-rank test was used to compare the expression of RUNX1 in ccRCC tissues and normal tissues. The Wilcoxon signed-rank test and logistic regression were utilized to investigate the relationship between clinicopathological factors and RUNX1 expression. Additionally, we analysed the differences in prognosis between patients with high and low expression of RUNX1 via the Kaplan–Meier method and Cox regression. Gene set enrichment analysis (GSEA) was performed to explore the mechanisms of RUNX1 in ccRCC. Results The expression of RUNX1 in ccRCC tissues was significantly higher than that in normal tissues. High expression of RUNX1 was significantly associated with gender (p = 0.003), clinical stage (p < 0.001), tissue infiltration (p < 0.001), lymph node metastasis (p = 0.037) and histological grade (p < 0.001). Logistic regression analysis showed that high RUNX1 expression was significantly correlated with gender (OR = 1.71 for male vs. female, p = 0.004), histological grade (OR = 11.61 for grade IV vs. I, p < 0.001), clinical stage (OR = 1.55 for stage III/IV vs. I/II, p = 0.014) and tissue infiltration (OR = 1.54 for positive vs. negative, p = 0.018). Kaplan–Meier survival curves revealed that the prognosis of patients with ccRCC with high RUNX1 expression was worse than that of patients with ccRCC with low RUNX1 expression (p < 0.001). Univariate Cox regression analysis showed that high RUNX1 expression was strongly correlated with poor prognosis (HR = 1.60, 95% CI [1.31–1.97], p < 0.001). In addition, high expression of RUNX1 was an independent prognostic factor for poor overall survival (OS), with an HR of 1.50 (95% CI [1.20–1.87], p < 0.001) in multivariate Cox analysis. GSEA showed that the apoptosis, B cell receptor signalling pathway, calcium signalling pathway, chemokine signalling pathway, JAK/STAT signalling pathway, MAPK signalling pathway, p53 signalling pathway, pathways in cancer, T cell receptor signalling pathway, Toll-like receptor signalling pathway, VEGF signalling pathway, and Wnt signalling pathway were significantly enriched in the RUNX1 high-expression phenotype. In conclusion, RUNX1 can be used as a novel prognostic factor and therapeutic target in ccRCC.

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Immune prognostic implications of PSMD14 and its associated genes signatures in hepatocellular carcinoma

10.21203/rs.3.rs-20812/v1 ◽

2020 ◽

Author(s):

Chuan Tian ◽

Mubalake Abudoureyimu ◽

Xinrong Lin ◽

Hao Zhou ◽

Xiaoyuan Chu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Clinicopathologic Characteristics ◽

Data Set ◽

Cox Regression Analysis ◽

Prognostic Implications

Abstract Background PSMD14 played a vital roles initiation and progression of hepatocellular carcinoma (HCC). However, PSMD14 and its-related genes for the immune prognostic implications of HCC patients have rarely been analyzed. Therefore, we aimed to explore gene signatures and immune prognostic values of PSMD14 and its-related genes in HCC. Method Analyzed the expression of PSMD14 in multiple databases, and clinicopathologic characteristics associated with PSMD14 overall survival using Wilcoxon signed-ranktest, logistic and Cox regression, Kaplan-Meier method. An immune prognostic signature (including RBM45, PSMD1, OLA1, CCT6A, LCAT and IVD) was constructed and validated using the co-expression and cox regression analyses in TCGA, ICGC and TIMER datasets. Gene Set Enrichment Analysis (GSEA) was performed using TCGA data set. Results Increased PSMD14 expression in HCC was significantly associated with poor prognosis and clinicopathologic characteristics (grade, histologic stage, surgical approach and T stage, all p-values < 0.05). A total of six PSMD14-related genes were detected, which markedly related to overall survival and immune infiltrating levels in HCC patients. Using cox regression analysis, the PSMD14 and its-related genes were found to be an independent prognostic factor for HCC survival. Calibration curves confirmed good consistency between clinical nomogram prediction and actual observation. Immune prognostic model suggests that patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. Conclusion We screened potential immune prognostic genes and constructed and verified a novel PSMD14-based prognostic model of HCC, which provides new potential prognostic biomarkers and therapeutic targets and lays a theoretical foundation for immunotherapy of HCC.

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A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

Frontiers in Oncology ◽

10.3389/fonc.2020.580263 ◽

2020 ◽

Vol 10 ◽

Author(s):

Huaide Qiu ◽

Yongqiang Li ◽

Shupeng Cheng ◽

Jiahui Li ◽

Chuan He ◽

...

Keyword(s):

Immune Cells ◽

Cox Regression ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Data Sets ◽

Data Set ◽

Cox Regression Analysis ◽

Immune Signature ◽

Genome Atlas

ObjectiveIn the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.MethodsStromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.ResultsWe obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.ConclusionsThe PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

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The YTH Domain Family of N6-Methyladenosine “Readers” in the Diagnosis and Prognosis of Colonic Adenocarcinoma

BioMed Research International ◽

10.1155/2020/9502560 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Dian Xu ◽

Jun Shao ◽

Huan Song ◽

Jianming Wang

Keyword(s):

Cox Regression ◽

Expression Patterns ◽

Prognostic Significance ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Colonic Adenocarcinoma ◽

Domain Family ◽

Rna Methylation ◽

Yth Domain

To profile the landscape of methylation N6 adenosine (m6A) RNA regulators in colonic adenocarcinoma (COAD) and to explore potential diagnostic and prognostic biomarkers, we assessed the differential expression patterns of m6A RNA methylation regulators between 418 COAD patients and 41 controls based on profiling from The Cancer Genome Atlas (TCGA) database. We plotted the receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC) values to estimate the discrimination ability. The relationship between the expression of m6A RNA methylation regulators and clinicopathological characteristics was explored. Kaplan-Meier plotter, log-rank test, and Cox regression were used and a nomogram was created to explore the prognostic significance of m6A-related genes in overall survival at the mRNA level. Pathway analysis was performed by gene set enrichment analysis (GSEA) using TCGA dataset, and a coexpression network was built based on the STRING database. We observed that YTHDF1, METTL3, and KIAA1429 were significantly upregulated, while YTHDF3, YTHDC2, METTL14, and ALKBH5 were significantly downregulated in COAD samples compared to normal samples. YTHDF1 had the highest diagnostic value. Low expression of YTHDF3 predicted a poor survival rate in COAD patients. YTHDC2 was related to sex and showed a downward trend as clinical stage increased. Our results indicate that the YT521-B homology (YTH) domain family (“readers”), especially YTHDF1, YTHDF3, and YTHDC2, might play a significant role in the detection, progression, and prognosis of COAD, indicating that they are promising cancer biomarkers.

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Overexpression of MATN3 predicts poor prognosis of gastric adenocarcinoma: based on TCGA database

10.21203/rs.2.23505/v1 ◽

2020 ◽

Author(s):

GuoLiang Zheng ◽

Yan Zhao ◽

Zhichao Zheng

Keyword(s):

Signaling Pathway ◽

Gastric Adenocarcinoma ◽

Cox Regression ◽

Univariate Analysis ◽

Mtor Signaling ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Mtor Signaling Pathway ◽

Kaplan Meier

Abstract Background Matrilin-3 (MATN3) was previously reported to in the cartilage extracellular matrix and had a role in the development and homeostasis of cartilage and bone. We evaluated the role of MATN3 in gastric adenocarcinoma (GAC) using publicly available data from The Cancer Genome Atlas (TCGA). Methods The relationship between clinicapathologic features and MATN3 were analyzed with the Wilcoxon signed-rank test and logistic regression. Clinicopathologic variables associated with overall survival (OS) in TCGA patients using Cox regression and the Kaplan-Meier method. Gene Set Enrichment Analysis (GSEA) was performed using TCGA cohort. Results MATN3 overexpressed in GAC than that in normal tissues (p﹤0.05), and MATN3 overexpression was significantly associated with TNM stage (p= 0.012), and T stage (p﹤0.01). Kaplan-Meier survival analysis showed that GAC with MATN3 - high had a worse prognosis than that with MATN3- low (p﹤0.01). The univariate analysis revealed that MATN3- high correlated significantly with a poor OS (HR: 1.86; 95% confidence interval [CI]: 0.82- 2.01; p = 0.014). The multivariate analysis revealed that MATN3 remained independently associated with OS, with a HR of 2.68 (CI: 0.74- 2.13; p﹤0.01). GSEA showed that NOTCH, WNT, and MTOR signaling pathway were differentially enriched in MANT3 high expression phenotype. Conclusion Overexpression of MATN3 may be a potential prognostic biomarker of poor survival in gastric cancer, Moreover, NOTCH, WNT, and MTOR signaling pathway may be the key pathway regulated by MATN3 in GAC.

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An apoptosis-related gene signature for the prognosis of hepatocellular carcinoma

10.21203/rs.3.rs-19086/v1 ◽

2020 ◽

Author(s):

Pengfei Zhu ◽

Zhang Lei ◽

Du Zhicheng ◽

Liao Yuan ◽

Yan Lei ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Data Set ◽

Public Health Burden ◽

Apoptotic Genes

Abstract Hepatocellular carcinoma (HCC) is a major public health burden worldwide owning to high incidence and poor prognosis. Although a mushrooming number of apoptosis-related genes had been disclosed in HCC, the prognostic value and clinical utility of them remain to be illustrated. Here, we defined the data from Gene Expression Omnibus (GEO) as a training cohort and data from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma data set (TCGA-LIHC) as a validation cohort. The apoptosis-related differentially expressed genes (AR-DEGs) were identified with the two cohorts and the Gene Set Enrichment Analysis. Then, we constructed a Lasso-penalized Cox regression model using AR-DEGs and conducted a signature including 14 apoptotic genes to calculate the risk score. Patients with a high risk score indicated worse overall survival than those with low risk. Besides, the 3-year and 5-year area under curve (AUC) values of the signature were above 0.7 in both training and validation cohorts (0.762, 0.818, 0.717, 0.745, respectively). Moreover, a nomogram containing the signature and clinical characteristics presented reliable net benefits for the survival prediction. And the nomogram was tested by probability calibration curves and Decision Curve Analysis (DCA). Furthermore, protein-protein interaction (PPI) and Gene Ontology (GO) enrichment analysis disclosed several noticeable pathways that might clarify the hidden mechanism. Collectively, the present study formed a novel signature based on the 14 apoptotic genes and this possibly predicted prognosis and strengthened the communication with HCC patients about the likely treatment.

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Overexpression of RIPK4 Predicts Poor Prognosis and Promotes Metastasis in Ovarian Cancer

BioMed Research International ◽

10.1155/2021/6622439 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Shaoqiu Liu ◽

Lewei He ◽

Chenchen Sheng ◽

Rongjia Su ◽

Xiaomei Wu ◽

...

Keyword(s):

Ovarian Cancer ◽

Wnt Signaling ◽

Signaling Pathway ◽

Cox Regression ◽

Wnt Signaling Pathway ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Cell Transfection ◽

The Relationship

This study was conducted to evaluate the prognostic value of receptor-interacting protein kinase 4 (RIPK4) in ovarian cancer (OC) and its role in tumorigenesis. RNA expression and the corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The relationship between clinical-pathological characteristics and RIPK4 expression was analyzed using the Wilcoxon signed-rank test and logistic regression. The Cox regression and the Kaplan-Meier method were used to evaluate the relationship between clinicopathological features and overall survival (OS). Gene set enrichment analysis (GSEA) was performed using Molecular Signatures Database. Scratch assay, transwell assay, and cell transfection were used to verify the function of RIPK4. Overexpression of RIPK4 was associated with the stage of OC and distant metastasis. Survival analysis revealed that patients with OC and higher expression of RIPK4 had a poorer prognosis. Univariate and multivariate analyses indicated that high expression of RIPK4 was associated with poor OS, as well as age and stage of OC. The areas under the curve (AUC) at 1, 4, and 8 years were 0.737, 0.634, and 0.669, respectively, according to the established OS prediction model. GSEA revealed that adherens junction, cadherin binding, and Wnt signaling pathway were enriched in the high RIPK4 expression group. Cell transfection confirmed RIPK4 was involved in the Wnt signaling pathway. RIPK4 can act as a potential prognostic molecular marker for poor survival in OC. Moreover, RIPK4 is associated with tumor metastasis and implicated in the regulation of the Wnt signaling pathway.

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