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SIRT4 and SIRT6 Serve as Novel Prognostic Biomarkers With Competitive Functions in Serous Ovarian Cancer.

ABSTRACT: Sirtuins (SIRTs) are class III histone deacetylases (HDACs) that include seven members and are widely expressed in mammals. Accumulating evidence shows that sirtuins may have contradictory roles in various malignancies. They mainly participate in metabolic homeostasis, DNA damage repair, cell survival, and differentiation, as well as other cancer-related biological processes. To better understand their prognostic role and biological functions, we used comprehensive bioinformatic analyses to demonstrate the expression and mutation of sirtuin family member genes in ovarian cancer (OC), with a detailed focus on prognostic prediction, including the effectiveness of anti-OC drugs. Furthermore, the co-expression genes of SIRT4 and SIRT6 with contradictory survival prediction values in both overall and progression-free survival (PFS) times were further analyzed through Gene Ontology enrichment and Kyoto Encyclopedia annotation. Additionally, we performed and obtained the immunohistochemical staining patterns of these two biomarkers from the serous OC patient database and clinical patient samples to demonstrate their potential applicability in clinical pathology. According to our findings, SIRT4 and SIRT6 are novel prognostic biomarkers that may serve as contradictory competitors for OC cell survival. They are also sensitive biomarkers for the prediction of Avastin's anticancer effect. While SIRT4 is related to the immune response during oocyte maturation, SIRT6 participates in immune-related disease pathways and mitochondrial metabolism-mediated DNA translation. These findings contribute to the novel hypothesis that SIRT4 and SIRT6 act as contradictory competitors in the regulation of OC behavior. Further studies are required to validate our hypothesis.

SUBMITTER: Wang H

PROVIDER: S-EPMC8320514 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Purpose: Considerable evidence suggests that autophagy plays a crucial role in the biological processes of ovarian cancer. The aim of this study was to develop a novel autophagy-related prognostic signature for serous ovarian cancer. Methods: A univariate Cox proportional regression model was used to analyze mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) for the purpose of selecting autophagy-related prognostic genes. A multivariate Cox proportional regression model and the survival analysis were used to develop an eight-gene prognostic signature. The multivariate Cox and stratification analysis suggested that this signature was an independent prognostic factor for serous ovarian cancer patients. Bioinformatics functions were investigated by a principal components analysis and gene set enrichment analysis (GSEA). Finally, the correlation between the prognostic signature and gene mutation status was further analyzed in serous ovarian cancer, and especially with regard to the mutation status of BRCA1 and BRCA2 (BRCA1/2) genes. Results: Distinctly different autophagy-related gene expression profiles were identified in normal ovarian tissues and serous ovarian cancer tissues. We profiled an autophagy-related gene set and identified eight genes with significant prognostic values for serous ovarian cancer. Subsequently, an autophagy-related ovarian cancer risk signature was constructed, and patients at a high-risk or low-risk for poor prognosis were identified based on their signature. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) times than low-risk patients. GSEA results suggested an enhanced intensity of autophagy regulation in high-risk patients when compared with low-risk patients. When studied as an independent prognostic factor for serous ovarian cancer, the significant prognostic value of this signature could be seen in the stratified cohorts. For clinical use, we developed a nomogram that included the prognostic classifier and seven clinical risk factors. Additionally, we identified the 10 most frequently mutated genes found in serous ovarian cancer patients, and analyzed them for their differences in high-risk and low-risk patients. Among 293 patients, 62 had BRCA1/2 gene mutations, and this result was significantly correlated with the autophagy-related prognostic signature. Conclusions: Our findings suggest that the eight-gene autophagy-related signature could serve as an independent prognostic indicator for cases of serous ovarian cancer.

Project description:Ovarian cancer (OC) is a major health threat to females, as it has high morbidity and mortality. Evidence has increasingly demonstrated that long non-coding RNAs (lncRNAs) regulate OC progression and they may have value as early diagnostic biomarkers, prognostic biomarkers and/or therapeutic targets. In the present study, the regulatory mechanisms and prognosis associated with cancer-specific lncRNAs and their related competing endogenous (ce)RNA network in OC were investigated. The differential expression profiles and prognostic significance of lncRNAs and mRNAs were systematically explored based on data from 359 OC cases from The Cancer Genome Atlas and 180 healthy individuals from the Genotype-Tissue Expression database. Functional enrichment analyses, RNA-RNA interactome prediction, ceRNA network analysis, correlation analysis and survival analysis were utilized to identify hub lncRNAs and biomarkers associated with OC diagnosis or prognosis. A total of 1,049 differentially expressed lncRNAs and 6,516 differentially expressed mRNAs between OC and healthy tissues were detected. An lncRNA-micro (mi)RNA-mRNA regulatory network in OC was further established, containing 91 lncRNAs, 23 miRNAs and 179 mRNAs. After survival analysis based on the expression of the RNAs in the ceRNA network, 8 lncRNAs, 4 miRNAs and 11 mRNAs that were significantly associated with OC patient survival (P<0.05) were obtained. Using least absolute shrinkage and selection operator-penalized Cox regression, an eight-lncRNA risk score model was generated, which was able to readily discriminate between OC and healthy individuals and predict the survival of patients with OC. In addition, the differential expression of several key lncRNAs and mRNAs was verified by reverse transcription-quantitative PCR and western blot analysis. The current study presents a novel lncRNA-miRNA-mRNA network, which provides insight into the potential pathogenesis of OC and allows the identification of prognostic biomarkers and treatment strategies for OC.

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SIRT4 and SIRT6 Serve as Novel Prognostic Biomarkers With Competitive Functions in Serous Ovarian Cancer.

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