Characterization of an Autophagy-Immune Related Genes Score Signature and Prognostic Model and its Correlation with Immune Response for Bladder Cancer

Abstract

JunJie Yu1 *, WeiPu Mao1 *, Si Sun,1 Qiang Hu,1 Can Wang,1 ZhiPeng Xu,1 RuiJi Liu,1 SaiSai Chen,1 Bin Xu,2,3 Ming Chen2,4 1Medical College, Southeast University, Nanjing, 210009, People’s Republic of China; 2Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, 210009, People’s Republic of China; 3Institute of Urology, Southeastern University, Nanjing, People’s Republic of China; 4Department of Urology, Affiliated Lishui People’s Hospital of Southeast University, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bin Xu; Ming ChenDepartment of Urology, Affiliated Zhongda Hospital of Southeast University, No. 87 Dingjiaqiao, Hunan Road, Gulou District, Nanjing, 210009, Jiangsu, People’s Republic of ChinaTel/Fax [phone]Email [email protected]; [email protected]: The study aimed to identify an autophagy-related molecular subtype and characterize a novel defined autophagy-immune related genes score (AI-score) signature and prognosis model in bladder cancer (BLCA) patients using public databases.Methods: The transcriptome cohorts downloaded from TCGA and GEO database were carried out with genomic analysis and unsupervised methods to obtain autophagy-related molecular subtypes. The single-sample gene-set enrichment analysis (ssGSEA) was utilized to perform immune subtype clustering. We defined a novel autophagy subtype and evaluated the role in TME cell infiltration. Then, the principal-component analysis (PCA) was applied to construct an AI-score signature. Subsequently, two immunotherapeutic cohorts were used to evaluate the predictive value in immunotherapeutic benefits and immune response. Finally, univariate, Lasso and multivariate Cox regression algorithm were used to construct and evaluate an autophagy-immune-related genes prognosis model. Also, qRT-PCR and IHC was applied to validate the expression of the 6 genes in the model.Results: Three distinct autophagy clusters and immune-related clusters were identified, and a novel autophagy-related molecular subtypes were defined. Furthermore, the roles in TME cell infiltration and clinical traits for the autophagy subtypes were characterized. Meanwhile, we constructed an AI-score signature and demonstrated it could predict genetic mutation, clinicopathological traits, prognosis, and TME stromal activity. We found that it could accurately predict the clinicopathological characteristics and immune response of individual BLCA patients and provide guidance for selecting immunotherapy. Ultimately, we constructed and verified an autophagy-immune-related prognostic model of BLCA patients and established a prognostic nomogram with a good prediction accuracy.Conclusion: We constructed AI-score signatures and prognosis risk model to characterize their role in clinical features and TME immune cell infiltration. It revealed that the AI-score signature and prognosis model could be a valid predictive tool, which could accurately predict the prognosis of BLCA patients and contribute to choosing effective personalized immunotherapy strategies.Keywords: bladder cancer, The Cancer Genome Atlas, Gene Expression Omnibus, tumor microenvironment, immunotherapy, mutation burden

Keywords

• bladder cancer
• the cancer genome atlas
• gene expression omnibus
• tumor microenvironment
• immunotherapy
• mutation burden