Prognostic value and underlying mechanism of autophagy-related genes in bladder cancer

• Published in: Scientific reports Vol. 12; no. 1; p. 2219
• Main Authors: Peng, Shiyuan, Ma, Shanjin, Yang, Fa, Xu, Chao, Li, Hongji, Lu, Shiqi, Zhang, Jingliang, Jiao, Jianhua, Han, Donghui, Shi, Changhong, Zhang, Rui, Yang, An-Gang, Zhang, Keying, Wen, Weihong, Qin, Weijun
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 09.02.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Accuracy; Autophagy; Autophagy - genetics; Biomarkers, Tumor - genetics; Bladder cancer; Cancer; Correlation of Data; Databases, Genetic; Energy balance; Female; Gene Expression Regulation, Neoplastic; Genomes; Homeostasis; Humans; Invasiveness; Kaplan-Meier Estimate; Male; Malignancy; Mitochondria; Multivariate Analysis; Nomograms; Patients; Prognosis; Regression analysis; Risk Factors; Therapeutic applications; Therapeutic targets; Tumor microenvironment; Tumor Microenvironment - genetics; Tumors; Urinary Bladder Neoplasms - diagnosis; Urinary Bladder Neoplasms - genetics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-06334-0

Bladder cancer (BLCA) is the most common malignancy whose early diagnosis can ensure a better prognosis. However, the predictive accuracy of commonly used predictors, including patients' general condition, histological grade, and pathological stage, is insufficient to identify the patients who need invasive treatment. Autophagy is regarded as a vital factor in maintaining mitochondrial function and energy homeostasis in cancer cells. Whether autophagy-related genes (ARGs) can predict the prognosis of BLCA patients deserves to be investigated. Based on BLCA data retrieved from the Cancer Genome Atlas and ARGs list obtained from the Human Autophagy Database website, we identified prognosis-related differentially expressed ARGs (PDEARGs) through Wilcox text and constructed a PDEARGs-based prognostic model through multivariate Cox regression analysis. The predictive accuracy, independent forecasting capability, and the correlation between present model and clinical variables or tumor microenvironment were evaluated through R software. Enrichment analysis of PDEARGs was performed to explore the underlying mechanism, and a systematic prognostic signature with nomogram was constructed by integrating clinical variables and the aforementioned PDEARGs-based model. We found that the risk score generated by PDEARGs-based model could effectively reflect deteriorated clinical variables and tumor-promoting microenvironment. Additionally, several immune-related gene ontology terms were significantly enriched by PDEARGs, which might provide insights for present model and propose potential therapeutic targets for BLCA patients. Finally, a systematic prognostic signature with promoted clinical utility and predictive accuracy was constructed to assist clinician decision. PDEARGs are valuable prognostic predictors and potential therapeutic targets for BLCA patients.