Integrative Bioinformatics Analysis of Transcriptomic Data Reveals Hub Genes as Diagnostic Biomarkers for Non-Muscle vs. Muscle Invasive Bladder Cancer

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high socioeconomic impact. BCa can manifest in the form of nonaggressive and usually non-muscle invasive (NMIBC) tumors that recur and require chronic invasive surveillance, or aggressive and muscle invasive (MIBC) tumors with high associated mortality. These two subtypes exhibit distinct prognosis and require different therapeutic approaches. In the present study, we conducted an integrative bioinformatics analysis, combining transcriptomic data from various microarray experiments, in order to reveal a common signature of differentially expressed genes (DEGs) between the two subtypes. Subsequently, we constructed the protein-protein interaction (PPI) network of the DEGs and defined the hub genes based on 11 topological analysis methods. Then, the most significant hub genes were identified using LASSO logistic regression algorithm. The selected genes were finally used as features in supervised classification algorithms, namely support vector machines and random forests, for BCa subtype discrimination. The models' evaluation showed area under the curve (AUC) values up to 96% as regards separating NMIBC from MIBC tumors. Genes driving the separation between tumor subtypes may prove to be important biomarkers for BCa development and progression, and eventually candidates for therapeutic targeting.