Weighted Gene Coexpression Network Analysis Reveals Cancer Stem Cell-Associated Metabolic Gene Signature in Glioma

Background: Isocitrate dehydrogenase (IDH) mutant glioma had favorable prognosis, with metabolism alterations and glioma cell dedifferentiation. Recently, evidence suggest that mRNA expression-based stemness index (mRNAsi) serves as a pivotal role in tumor prognosis. Thus, we aimed to establish a cancer stem cell-associated metabolic gene signature for risk stratification of glioma. Methods: Two cohorts were separately downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Next, we screened the differentially expressed genes (DEGs) between IDH mutant and IDH wild-type glioma samples for performing weighted gene correlation network analysis (WGCNA). Then multivariate cox regression analysis with Akaike information criterion (AIC) algorithm was employed to establish a stemness-related metabolic gene signature, being validated via TCGA and CGGA cohorts, respectively. Findings: IDH wild-type glioma presented lower mRNAsi, closely correlated with worse survival. Blue module and its genes were found to be pertinent to mRNAsi trait via WGCNA method. Seven cancer stem cell-associated metabolic genes were identified, designated as core genes. Next, Kaplan-Meier survival curves denoted that a significant link between the influence of these core genes on glioma survival. Subsequently, univariate and multivariate cox regression analyses successively were implemented, then a four gene signature (LCAT, UST, GALNT13, SMPD3) was established. Notably, the prognostic model presented a superior predictive value for glioma survival via validation. Interpretation: The four gene signature can be functioned as an independent predicting indicator of glioma survival. Funding Statement: This research was supported by Youth Fund of Southwest Medical University (no. 2018-ZRQN-125). Declaration of Interests: The authors declare that they have no competing interests.