Identification of four hub genes as promising biomarkers to evaluate the prognosis of ovarian cancer in silico

Abstract

Background: Ovarian cancer (OvCa) is one of the most fatal cancers among females in the world. With growing numbers of individuals diagnosed with OvCa ending in deaths, it is urgent to further explore the potential mechanisms of OvCa oncogenesis and development and related biomarkers. Methods: The gene expression profiles of GSE49997 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to explore the most potent gene modules associated with the overall survival (OS) and progression-free survival (PFS) events of OvCa patients, and the prognostic values of these genes were exhibited and validated based on data from training and validation sets. Next, protein-protein interaction (PPI) networks were built by GeneMANIA. Besides, enrichment analysis was conducted using DAVID website. Results: According to the WGCNA analysis, a total of eight modules were identified and four hub genes (MM > 0.90) in the blue module were reserved for next analysis. Kaplan-Meier analysis exhibited that these four hub genes were significantly associated with worse OS and PFS in the patient cohort from GSE49997. Moreover, we validated the short-term (4-years) and long-term prognostic values based on the GSE9891 data, respectively. Last, PPI networks analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed several potential mechanisms of four hub genes and their co-operators participating in OvCa progression. Conclusion: Four hub genes (COL6A3, CRISPLD2, FBN1 and SERPINF1) were identified to be associated with the prognosis in OvCa, which might be used as monitoring biomarkers to evaluate survival time of OvCa patients.

Efficacy of Compound Herbal Medicine Tong-Xie-Yao-Fang for Acute Radiation Enteritis and Its Potential Mechanisms: Evidence from Transcriptome Analysis

“…In the past decades, vigorously developing high-throughput sequencing technology and computer-aided analytical methods have largely promoted the flourish of big data applications [ 20 – 22 ]. As an effective research strategy, transcriptome analysis has been widely used in several aspects of clinical or basic medical research [ 23 ].…”

Low Expression of RILPL2 Predicts Poor Prognosis and Correlates with Immune Infiltration in Endometrial Carcinoma

“…WGCNA analysis First, as precviously described, differentially expressed genes (DEGs) between EC and normal specimens in the TCGA dataset were screened out using the "limma" package with threshold of adjusted P-value < 0.05 and |log2FC| >1 [21]. Next the expression pro le of these DEGs was sent to establish a gene coexpression network by the weighted gene co-expression network analysis (WGCNA) package in R language as precviously described [22]. To matching the correlations with the nature regulation relationship, we built a scale-free topology to emphasize the strong correlations and attenuate the weak correlations with the soft threshold power of β = 3 (scale free R 2 = 0.85).…”

The Expression and Prognostic Values of Epidermal Growth Factor Receptor Family in Glioma

“…In recent years, due to the continuous development and application of bioinformatics databases, more and more tumor biomarkers have been discovered [13][14][15][16]. At present, more and more studies have shown that EGFR family can be used as potential targets for the treatment of glioma [17,18].…”