Identification and validation of potential novel prognostic biomarkers for patients with glioma based on a gene co-expression network

BACKGROUND: Glioma is widely regarded as one of most lethal and challenging diseases of the nervous system. The aim of this study was to identify novel biomarkers that offer better prognosis prediction for Chinese patients with glioma. METHODS: By using systematic approaches, the co-expression modules were identified from the Chinese Glioma Genome Atlas (CGGA) database through weighted gene co-expression network analysis and functional enrichment of essential modules of Kyoto Encyclopedia of Genes and Genomes terms. The co-expression modules were validated using The Cancer Genome Atlas database and the protein-protein interaction (PPI) network. RESULTS: For network construction, 5,374 among 21,494 genes were selected, and an increasing genetic variance was associated with the prognosis of glioma. By using functional enrichment analysis, the involvement of multiple vital processes, including metabolism of fatty acids, was correlated with the patient prognosis. Notably, five hub genes (KCNB1, UST, SOX8, KLHL42, and HDAC4) were identified for these processes. Accordingly, using the Kaplan-Meier method, there was enhanced expression of these genes in patients with significantly lower overall survival rates, especially those from the CGGA database. CONCLUSIONS: This study not only revealed the essential co-expression gene modules in patients with glioma, but it also unraveled the potential signaling pathways underlying these functional processes.