Preclinical analysis of novel prognostic transcription factors and immune-related gene signatures for bladder cancer via TCGA-based bioinformatic analysis.

Abstract

Bladder cancer (BLCA) is a common malignancy of human urinary tract, whose prognosis is influenced by complex gene interactions. Immune response activity can act as a potential prognostic factor in BLCA. The present study established a prognostic model, based on the identification of tumor transcription factors (TFs) and immune-related genes (IRGs), and further explored their therapeutic potential in BLCA. The enrichment scores of 29 IRG sets, identified in The Cancer Genome Atlas BLCA tumor samples, were quantified by single-sample Gene Set Enrichment Analysis. The abundance of infiltrated immune cells in tumor tissues was determined using the Estimating Relative algorithm. Tumor-related TFs and IRGs signatures were retrieved using Least Absolute Shrinkage and Selection Operator Cox regression analysis. A prognostic gene network was built using Pearson's correlation analysis as a means of predicting the regulatory relationship between prognostic TFs and IRGs. A nomogram was devised to also predict the overall survival (OS) rate of patients with BLCA. Based on the Genomics of Drug Sensitivity in Cancer data, potential therapeutic drugs were identified upon analyzing the relationship between the expression level of prognostic genes and respective IC50 values. In vitro experiments were implemented for further validation. Respective TF binding profiles were acquired from the JASPAR 2020 database. The elevated infiltration of CD8+ T Cells was correlated with an improved OS of patients with BLCA. An innovative prognostic model for BLCA was then constructed that composed of nine putative gene markers: CXCL13, prepronociceptin, microtubule-associated protein tau, major histocompatibility class I polypeptide-related sequence B, prostaglandin E2 receptor EP3 subtype, IL20RA, proepiregulin, early growth response protein 1 and FOS-related antigen 1 (FOSL1). Furthermore, a theoretical basis for the correlation between the prognostic TFs and IRGs was reported. For this, 10 potentially effective drugs targeting the TFs in the present model for patients with BLCA were identified. It was then verified that downregulation of FOSL1 can lead to an enhanced sensitivity of the TW37 in T24 bladder cancer cells. Overall, the present prognostic model demonstrated a robust capability of predicting OS of patients with BLCA. Hence, the gene markers identified could be applied for targeted therapies against BLCA.