In silico development and experimental validation of a novel 7-gene signature based on PI3K pathway-related genes in bladder cancer

Abstract

Although bladder cancer (BLCA) is the 10th most common tumor worldwide, particularly practical markers and prognostic models that might guide therapy are needed. We used a non-negative matrix factorization algorithm to classify PI3K pathway-related genes into molecular subtypes. A weighted gene co-expression network analysis (WGCNA) was generated to identify co-expression modules. Univariate Cox regression, least absolute shrinkage sum selection operator-Cox regression, and multivariate Cox regression were utilized to develop a prognostic score model. Kaplan–Meier analysis and receiver operating characteristics were utilized to measure the model’s effectiveness. A nomogram was constructed to improve the predictive ability of the model based on clinical parameters and risk. Decision curve analysis (DCA) was used to evaluate the nomogram. To evaluate the immune microenvironment, an estimate algorithm was used. Drug sensitivity was identified using the R package “pRRophetic.” UM-UC-3 cell line was used to measure the effect of CDK6 in Western blotting, proliferation assay, and 5-ethynyl-20-deoxyuridine assay. Based on PI3K pathway-related genes, The Cancer Genome Atlas (TCGA)-BLCA and GSE32894 patients were divided into two subtypes. Twenty-five co-expression modules were established using the WGCNA algorithm. A seven-gene signature (CDK6, EGFR, IGF1, ITGB7, PDGFRA, RPS6, and VWF) demonstrated robustness in TCGA and GSE32894 datasets. Expression levels of CDK6 and risk positively correlated with M2 macrophages and IgG. Cisplatin, gemcitabine, methotrexate, mitomycin C, paclitaxel, and vinblastine are sensitive to different groups based on the expression of CDK6 and risk. Functional experiments suggested that CDK6 promotes the proliferation of UM-UC-3 cells. We constructed a seven-gene prognostic signature as an effective marker to predict the outcomes of BLCA patients and guide individual treatment.