PURPOSE

We aimed to study the role of anoikis-related genes (ARGs) in colorectal cancer (CRC) using bioinformatics.

METHODS

GSE39582 and GSE39084, which collectively contain 363 CRC samples, were downloaded from the NCBI Gene Expression Omnibus (GEO) database as a test set. TCGA-COADREAD, with 376 CRC samples, was downloaded from the UCSC database as a validation set. Univariate Cox regression analysis was used to screen for ARGs that were significantly associated with prognosis. The top 10 ARGs were used to classify the samples into different subtypes based on unsupervised cluster analysis. The immune environments of the different subtypes were analyzed. ARGs that were significantly associated with CRC prognosis were used to construct a risk model. Univariate and multivariate Cox regression analyses were used to screen independent prognostic factors and construct a nomogram.

RESULTS

Four anoikis-related subtypes (ARSs) with differential prognoses and immune microenvironments were identified. KRAS and epithelial-mesenchymal transition pathways were enriched in subtype B, which had the worst prognosis. Three ARGs (DLG1, AKT3, and LPAR1) were used to construct the risk model. Both the test and validation sets showed worse outcomes for patients in the high-risk group than those in the low-risk group. Risk score was found to be an independent prognostic factor for CRC. Moreover, there was a difference in drug sensitivity between the high- and low-risk groups.

CONCLUSION

The identified ARGs and risk scores were associated with CRC prognosis and could predict the responses of patients with CRC to immunotherapy strategies.