Comprehensive analysis of autophagy related long non-coding RNAs in prognosis, immunity, and treatment of muscular invasive bladder cancer

Abstract

AbstractTo predict disease outcome in muscle-invasive bladder cancer (MIBC), we constructed a prognostic autophagy-related (PAR) lncRNA signature. Comprehensive bioinformatics analyses were performed using data from TCGA and GTEx databases. Univariate Cox, and least absolute shrinkage and selection operator regression analyses were also performed, based on differentially expressed genes, to identify PAR-related lncRNAs to establish the signature. Furthermore, the Kaplan–Meier OS curve and receiver operating characteristic curve analyses were performed and a nomogram was constructed, all of which together confirmed the strong predictive ability of the constructed signature. Patients with MIBC were then divided into high- and low-risk groups. Gene enrichment and immune infiltration analyses revealed the potential mechanisms in MIBC. We also further evaluated the signature of molecules related to immune checkpoints and the sensitivity toward chemotherapeutic agents and antitumor-targeted drugs to find better treatment prescriptions. We identified a number of PAR-related lncRNA signatures, including HCP5, AC024060.1, NEAT1, AC105942.1, XIST, MAFG-DT, and NR2F1-AS1, which could be valuable prognostic tools to develop more efficient, individualized drug therapies for MIBC patients.