Background Ovarian cancer is one of the three major malignant tumors of the female reproductive system, and the mortality associated with ovarian cancer ranks first among gynecologic malignant tumors. The pathogenesis of ovarian cancer is not yet clearly defined but elucidating this process would be of great significance for clinical diagnosis, prevention, and treatment. For this study, we used bioinformatics to identify the key pathogenic genes and reveal the potential molecular mechanisms of ovarian cancer; we used immunohistochemistry to validate them. Methods We analyzed and integrated four gene expression profiles (GSE14407, GSE18520, GSE26712, and GSE54388), which were downloaded from the Gene Expression Omnibus (GEO) database, with the aim of obtaining a common differentially expressed gene (DEG). Then, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We then established a protein–protein interaction (PPI) network of the DEGs through the Search Tool for the Retrieval of Interacting Genes (STRING) database and selected hub genes. Finally, survival analysis of the hub genes was performed using a Kmplotter online tool. Results A total of 226 DEGs were detected after the analysis of the four gene expression profiles; of these, 87 were upregulated genes and 139 were downregulated. GO analysis results showed that DEGs were significantly enriched in biological processes including the G2/M transition of the mitotic cell cycle, the apoptotic process, cell proliferation, blood coagulation, and positive regulation of the canonical Wnt signaling pathway. KEGG analysis results showed that DEGs were particularly enriched in the cell cycle, the p53 signaling pathway, the Wnt signaling pathway, the Ras signaling pathway, the Rap1 signaling pathway, and tyrosine metabolism. We selected 50 hub genes from the PPI network, which had 147 nodes and 655 edges, and 30 of them were associated with the prognosis of ovarian cancer. We performed immunohistochemistry on phosphoserine aminotransferase 1 (PSAT1). PSAT1 was highly expressed in cancer tissues, and its expression level was related to clinical stage and tissue differentiation in ovarian cancer. A Cox proportional risk model suggested that high expression of PSAT1 and late clinical stage were independent risk factors for survival and prognosis of ovarian cancer patients. Conclusion The detection of DEGs using bioinformatics analysis might be crucial to understanding the pathogenesis of ovarian cancer, especially the molecular mechanisms of its development. The association between PSAT1 expression and the occurrence, development, and prognosis of ovarian cancer was further verified by immunohistochemistry. The PSAT1 expression can be used as a prognostic marker to provide a potential target for the diagnosis and treatment of ovarian cancer.
Ovarian carcinoma has the highest mortality among the malignant tumours in gynaecology, and new treatment strategies are urgently needed to improve the clinical status of ovarian carcinoma patients. The Cancer Genome Atlas (TCGA) cohort were performed to explore the immune function of the internal environment of tumours and its clinical correlation with ovarian carcinoma. Finally, four molecular subtypes were obtained based on the global immune‐related genes. The correlation analysis and clinical characteristics showed that four subtypes were all significantly related to clinical stage; the immune scoring results indicated that most immune signatures were upregulated in C3 subtype, and the majority of tumour‐infiltrating immune cells were upregulated in both C3 and C4 subtypes. Compared with other subtypes, C3 subtype had a higher BRCA1 mutation, higher expression of immune checkpoints, and optimal survival prognosis. These findings of the immunological microenvironment in tumours may provide new ideas for developing immunotherapeutic strategies for ovarian carcinoma.
Background Annexins are involved in vesicle trafficking, cell proliferation and apoptosis, but their functional mechanisms in ovarian cancer remain unclear. In this study, we analyzed Annexins in ovarian cancer using different databases and selected Annexin A8 (ANXA8), which showed the greatest prognostic value, for subsequent validation in immunohistochemical (IHC) assays. Methods The mRNA expression levels, genetic variations, prognostic values and gene–gene interaction network of Annexins in ovarian cancer were analyzed using the Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), cBioPortal, Kaplan–Meier plotter and GeneMANIA database. ANXA8 was selected for analyzing the biological functions and pathways of its co-expressed genes, and its correlation with immune system responses via the Database for Annotation, Visualization, and Integrated Discovery (DAVID) and the TISIDB database, respectively. We validated the expression of ANXA8 in ovarian cancer via IHC assays and analyzed its correlation with clinicopathological parameters and prognosis. Results ANXA2/3/8/11 mRNA expression levels were significantly upregulated in ovarian cancer, and ANXA5/6/7 mRNA expression levels were significantly downregulated. Prognostic analysis suggested that significant correlations occurred between ANXA2/4/8/9 mRNA upregulation and poor overall survival, and between ANXA8/9/11 mRNA upregulation and poor progression-free survival in patients with ovarian serous tumors. Taken together, results suggested that ANXA8 was most closely associated with ovarian cancer tumorigenesis and progression. Further analyses indicated that ANXA8 may be involved in cell migration, cell adhesion, and vasculature development, as well as in the regulation of PI3K-Akt, focal adhesion, and proteoglycans. Additionally, ANXA8 expression was significantly correlated with lymphocytes and immunomodulators. The IHC results showed that ANXA8 expression was higher in the malignant tumor group than in the borderline and benign tumor groups and normal ovary group, and high ANXA8 expression was an independent risk factor for survival and prognosis of ovarian cancer patients ( P = 0.013). Conclusions Members of the Annexin family display varying degrees of abnormal expressions in ovarian cancer. ANXA8 was significantly highly expressed in ovarian cancer, and high ANXA8 expression was significantly correlated with poor prognosis. Therefore, ANXA8 is a high candidate as a novel biomarker and therapeutic target for ovarian cancer.
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