Prognostic value of autophagy-related genes based on single-cell RNA-sequencing in colorectal cancer

Background: Colorectal cancer (CRC) is the second most common cancer in China. Autophagy plays an important role in the initiation and development of CRC. Here, we assessed the prognostic value and potential functions of autophagy-related genes (ARGs) using integrated analysis using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA). Methods: We analyzed GEO-scRNA-seq data from GEO using various single-cell technologies, including cell clustering, and identification of differentially expressed genes (DEGs) in different cell types. Additionally, we performed gene set variation analysis (GSVA). The differentially expressed ARGs among different cell types and those between CRC and normal tissues were identified using TCGA-RNA-seq data, and the hub ARGs were screened. Finally, a prognostic model based on the hub ARGs was constructed and validated, and patients with CRC in TCGA datasets were divided into high- and low-risk groups based on their risk-score, and immune cells infiltration and drug sensitivity analyses between the two groups were performed. Results: We obtained single-cell expression profiles of 16,270 cells, and clustered them into seven types of cells. GSVA revealed that the DEGs among the seven types of cells were enriched in many signaling pathways associated with cancer development. We screened 55 differentially expressed ARGs, and identified 11 hub ARGs. Our prognostic model revealed that the 11 hub ARGs including CTSB, ITGA6, and S100A8, had a good predictive ability. Moreover, the immune cell infiltrations in CRC tissues were different between the two groups, and the hub ARGs were significantly correlated with the enrichment of immune cell infiltration. The drug sensitivity analysis revealed that the patients in the two risk groups had difference in their response to anti-cancer drugs. Conclusion: We developed a novel prognostic 11-hub ARG risk model, and these hubs may act as potential therapeutic targets for CRC.