Revelation of prognosis and tumor microenvironment of colorectal cancer based on genes related to antibody-dependent cellular phagocytosis and single-cell landscape.

Abstract

Background: Increasing evidence highlights the crucial role of antibody-dependent cellular phagocytosis (ADCP) in colorectal cancer (CRC). However, how to use ADCP-related genes to predict prognosis in CRC and guide treatment remains unelucidated.

Methods: Gene expression profiles and clinical data information on CRC were sourced from the Cancer Genome Atlas (TCGA) database. We obtained the validation set GSE29621 and CRC single-cell dataset GSE178341 from the Gene Expression Omnibus (GEO) database and the ADCP-related gene set from the literature. Based on the TCGA-CRC cohort, univariate Cox and LASSO Cox regression analyses were employed to screen for ADCP-related genes linked with prognosis. Then a prognostic model was set up through multivariate Cox regression analysis. We further graphed a nomogram based on clinical information and risk scoring and evaluated its prognostic value using Kaplan-Meier (K-M) survival curves and receiver operation characteristic (ROC) curves. Based on the single-cell data analysis model, the expression levels of genes in different cell clusters were evaluated by scoring individual cells using the AUCell R package. Finally, functional enrichment, immune infiltration, and somatic mutation analyses were performed on the high- and low-ADCP-related risk score (ADCPRS) groups clustered by the median value of the ADCPRS. In addition, small molecular drugs for the treatment of CRC patients were analyzed using drug sensitivity analysis of IC₅₀ and molecular docking.

Results: This project created a prognostic model based on 7 feature genes using the TCGA training set. The K-M survival curves and ROC curves indicated that the model, as well as the nomogram, was capable of accurately predicting prognosis for CRC patients. Based on scRNA-seq data analysis, the 7 feature genes were examined to be expressed across 8 cell clusters (Monocytes, CD8 + T cells, Epithelial cells, B cells, Macrophages, HSC, Endothelial cells, and Fibroblasts). We scored individual cells and revealed that cells with higher scores were mainly concentrated in B cells and macrophages. Functional enrichment analysis manifested that the upregulated differentially expressed genes (DEGs) in the high-ADCPRS group were mainly enriched in signaling pathways such as the Drug metabolism cytochrome P450, Neuroactive ligand-receptor interaction, and Calcium signaling pathway. Immune infiltration analysis manifested that Th1 cells, iDCs, and Th2 cells had higher abundance in the low-ADCPRS group. Gene mutation analysis uncovered that both high- and low-ADCPRS groups had high mutation rates, with APC and TP53 being the top two genes with the highest mutation rates. Moreover, the drug sensitivity analysis and molecular docking uncovered that Dasatinib, Benzaldehyde, and Tegafur may aid in treating CRC patients.

Conclusion: The prognostic model developed in this project functioned as a potential tool for risk assessment. The 7 model genes may serve as prognostic biomarkers for CRC, which can guide treatment decisions for CRC patients.