Prediction of the survival status and tumor microenvironment in colorectal cancer through genotyping analysis based on toll-like receptors

Abstract

Background:
Colorectal cancer (CRC) ranks third in both the incidence and mortality rates among male and female cancers, and it is the leading digestive system cancer. Due to the inter- and intratumor heterogeneity of cancer, the TNM system is insufficient for predicting prognosis, necessitating the use of molecular biomarkers for prognostic prediction. Toll-like receptors (TLRs) have been associated with CRC survival rates. This study focused on the investigation of the role and potential value of TLRs in CRC genotyping to aid in immunotherapy for CRC patients.


Methods:
Differential gene expression analysis was performed on CRC transcriptomic data from The Cancer Genome Atlas database. TLRs were referred from the literature, and their intersection with differentially expressed genes (DEGs) in CRC yielded TLR-DEGs. The expression patterns of TLR-DEGs were predicted using the STRING website, and copy number variations of TLR-DEGs were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on TLR-DEGs. ConsensusClusterPlus R package was used for clustering CRC patients, and ESTIMATE and GSEAbase were employed to analyze immune characteristics of different subtypes. Immune phenotyping scores and tumor immune dysfunction and exclusion scores were evaluated. DEGs of different subtypes were analyzed, followed by GO and KEGG enrichment analyses, the protein–protein interaction (PPI) network analysis, and further selection of hub genes. The sensitivity of drugs was assessed using the identified hub genes.


Results:
We identified 37 TLR-DEGs, and the PPI analysis revealed their coexpression, although they were distributed on different chromosomes. Enrichment analyses indicated that the 37 TLR-DEGs were linked to cancer cell immune response. Based on these TLR-DEGs, CRC patients were classified into three subtypes. Cluster2 exhibited lower survival rates and higher immune infiltration levels and predicted poorer response to immune checkpoint inhibitor therapy. The intersection of DEGs from cluster2 and cluster1 with DEGs from cluster2 and cluster3 yielded a set of 426 commonly shared DEGs. Enrichment analyses revealed that these shared DEGs might regulate immune cell viability. Eight common hub genes for different subtypes were further identified to predict drug-related correlations.


Conclusion:
The developed TLR genotyping was used to predict the survival status and tumor microenvironment of CRC, providing a foundation for understanding the molecular mechanisms of TLR signaling and deepening its clinical significance.