Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme - Yang, Ji’an; Wang, Long; Xu, Zhou; Wu, Liquan; Liu, Baohui; Wang, Junmin; Tian, Daofeng; Xiong, Xiaoxing; Chen, Qianxue (Frontiers in Genetics, 2020)
Background: Gliomas are the most common intracranial tumors and are classified as I–IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research reveals four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unravels new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients. Materials and Methods: To identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. Result: After differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes (DEGs) were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly rich subclasses of the GO classification of these genes include mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and a gene co-expression network was constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazard regression model using the top 20 genes significantly correlated with survival time. We identified a four-protein prognostic signature that could divide patients into high-risk and low-risk. Increased expression of SLC12A5, CCL2, IGFBP2 and PDPN was associated with increased risk scores. Finally, the K-M curves confirmed that these four genes could be used as independent predictors of survival in patients with glioblastoma. Conclusion: Our analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.