Identification of hub genes and biological pathways in glioma via integrated bioinformatics analysis

Abstract

Objective Glioma is the most common intracranial primary malignancy, but its pathogenesis remains unclear. Methods We integrated four eligible glioma microarray datasets from the gene expression omnibus database using the robust rank aggregation method to identify a group of significantly differently expressed genes (DEGs) between glioma and normal samples. We used these DEGs to explore key genes closely associated with glioma survival through weighted gene co-expression network analysis. We then constructed validations of prognosis and survival analyses for the key genes via multiple databases. We also explored their potential biological functions using gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Results We selected DLGAP5, CDCA8, NCAPH, and CCNB2, as four genes that were abnormally up-regulated in glioma samples, for verification. They showed high levels of isocitrate dehydrogenase gene mutation and tumor grades, as well as good prognostic and diagnostic value for glioma. Their methylation levels were generally lower in glioma samples. GSEA and GSVA analyses suggested the genes were closely involved with glioma proliferation. Conclusion These findings provide new insights into the pathogenesis of glioma. The hub genes have the potential to be used as diagnostic and therapeutic markers.

Quantitative study of bioinformatics analysis on glioma: a bibliometric analysis

“…Omics analyses are considered the key to promoting the development of precision medicine (5). In recent years, scholars have used bioinformatics methods to analyse omics data and search for glioma-related pathways and prognostic markers (6)(7)(8)(9); predict the influence of biological process-related markers, such as ferroptosis-and pyroptosis-related markers on the prognosis of glioma (10)(11)(12)(13); predict the mechanism of action of individual genes (14)(15)(16), including noncoding RNA (17,18); guide the design of glioma-related experiments (19)(20)(21); search for prognostic markers in cerebrospinal fluid circulation to guide personalized treatment for clinical patients (22)(23)(24); predict the mechanism of anticancer drugs (25)(26)(27)(28), and search for potential antiglioma drugs (16,(29)(30)(31).…”