Bioinformatics analysis based on immune-autophagy-related lncRNAs combined with immune infiltration in bladder cancer

BACKGROUND: To construct a prognostic model based on immune-autophagy-related long noncoding RNA (IArlncRNAs), mainly to predict the overall survival rate (OS) of bladder cancer patients and investigate its possible mechanisms. METHODS: Transcriptome and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We identified the IArlncRNA by co-expression analysis, differential expression analysis, and Venn analysis. Then, we identified the independent prognostic IArlncRNAs by univariate Cox regression, LASSO regression, and multivariate Cox regression analysis. Moreover, we constructed the prognostic model based on the independent prognostic IArlncRNAs and clinical features. The proportion of 22 immune cell subtypes was analyzed by the CIBERSORT algorithm. Besides, we identified the differential proportion of 22 immune cell subtypes between the high- and low-risk groups. In addition, we identified the correlation between immune-infiltrating cells (screened by univariate Cox regression and multivariate Cox regression analysis) and IArlncRNAs by Pearson correlation analysis. Finally, we estimated the half-maximal inhibitory concentration (IC(50)) of chemotherapeutic drugs in patients with bladder cancer based on the pRRophetic algorithm. RESULTS: Four IArlncRNAs were identified as independent prognostic factors, including AL136084.3, AC006270.1, Z84484.1, and AL513218.1. The OS of patients in the high-risk group was significantly worse compared to the low-risk group. The nomogram showed an excellent predictive effect with the C-index of 0.64. The calibration chart showed a good actual vs. predicted probability. B cells naïve, T cells CD8, T cells CD4 memory resting, T cells follicular helper, macrophages M1, dendritic resting and activated cells had higher infiltrations in the low-risk group and lower infiltration of macrophages M2. The fraction of macrophages M2 was positively associated with AL136084.3. The fraction of T cells CD8 was positively associated with Z84484.1. The fraction of M + macrophages M0 was negatively associated with Z84484.1. Further, we identified the differential IC(50) of 24 chemotherapeutic drugs between the high- and low-risk groups. CONCLUSIONS: The prognostic model based on 4 IArlncRNAs showed an excellent predictive effect. Furthermore, we reasonably speculated that IArlncRNAs are directly or indirectly involved in the immune regulation of the tumor microenvironment (TME), as well as autophagy.