Abstract
Objectives: Bladder carcinoma (BLCA) is one of the most common malignant diseases of urinary system. Our study aimed to investigate the autophagy-related signatures in the tumor immune microenvironment and construct effective prognosis prediction model.Methods: RNA sequencing data and corresponding clinical information of BLCA were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Autophagy-related genes were extracted from TCGA dataset for consensus clustering analysis, and differences in survival rate were analyzed. STIMATE algorithm was used to analyze the tumor microenvironment (TME) and immune cell infiltration was compared between different clusters. Differentially expressed genes (DEGs) between different clusters were identified, followed by function annotation. Independent prognostic signatures were further revealed to construct prognostic prediction model.Results: We identified 35 autophagy-related genes associated with prognosis. Survival rate of samples in cluster 1 was significant lower than that in cluster 2. Cluster 2 had markedly lower tumor purity and significantly higher estimate score and stromal score than cluster 1. The proportions of T cells CD8, macrophages M1, T cells CD4 memory activated, NK cells activated, and dendritic cells activated were higher in cluster 1. There were 1,275 DEGs which were mainly enriched in functions and pathways related to immune response and cancer. Seven genes (ATF6, CAPN2, NAMPT, NPC1, P4HB, PIK3C3, and RPTOR) were further identified as the independent prognostic signatures to construct risk score prediction model, which had good prediction performance.Conclusion: Prognosis prediction model based on 7 autophagy-related genes may have great value in BLCA prognosis prediction.