Abstract
Background Inflammation can influence the development of CRC as well as immunotherapy and plays a key role in CRC. Therefore, this study aimed to investigate the potential of inflammation-related genes in CRC risk prediction.
Methods The transcriptomic and clinical information of colorectal cancer patients was obtained from The Cancer Genome Atlas (TCGA) database and externally validated with the GSE39582 dataset. Consistency clustering was used to molecularly typify and genotype patients. Genes for model construction were screened using univariate Cox, LASSO Cox, and multivariate Cox regression, and model validation was performed by K‒M survival analysis and receiver operating characteristic (ROC) curve analysis. In addition, we combined nomograms for further prediction of patient prognosis. Finally, the possible mechanisms of inflammation-related genes in CRC were explored by functional enrichment analysis, immune microenvironment analysis and immune checkpoint analysis.
Results We identified two molecular subtypes and three genetic subtypes, two risk subgroups according to median risk values, constructeda prognostic model including thirteen genes (TIMP1, GDF15, UCN, KRT4, POU4F1, NXPH1, SIX2, NPC1L1, KLK12, IGFL1, FOXD1, ASPG, and CYP4F8), and validated the performance of each aspect of the model in an external database. Patients in the high-risk group had worse survival with reduced immune cell infiltration and a greater tumor mutational load. The risk score correlated strongly with the immune checkpoints PD1, PDL1, PDL2, and CTLA4, and it is possible that high-risk patients are more sensitive to treatment involving immune checkpoints. qPCR further verified that ASPG expression in the CRC tumors of our patients was significantly lower than that in the normal tissues and was a protective factor.
Conclusion In summary, we developed a prognostic marker associated with inflammatory genes to provide new directions for subsequent studies and to help clinicians assess the prognosis of CRC patients as well as to guide clinical treatment with different sensitive drugs.