Construction of a gene signature associated with anoikis to evaluate the prognosis and immune infiltration in patients with colorectal cancer.

Background: Colorectal cancer (CRC) is characterized by a high metastasis rate, leading to poor prognosis and increased mortality. Anoikis, a physiological process, serves as a crucial barrier against metastasis. The objective of this research is to construct a prognostic model for CRC based on genes associated with anoikis. Methods: The study involved differential analysis and univariate Cox analysis of anoikis-related genes (ARGs), resulting in the selection of 47 genes closely associated with prognosis. Subsequently, unsupervised k-means clustering analysis was conducted on all patients to identify distinct clusters. Survival analysis, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) analysis were performed on the different clusters to investigate associations within the clusters. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were utilized to assess metabolic pathway enrichment between the identified clusters. Furthermore, single-sample GSEA (ssGSEA) was applied to explore variations in immune infiltration. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk model based on ten signatures, which enabled the grouping of all samples according to their risk scores. The prognostic value of the model was validated using receiver operating characteristic (ROC) curves, area under the curve (AUC) calculations, and survival curves. Additionally, the expression of candidate genes was validated using quantitative real-time polymerase chain reaction (qRT-PCR). Results: Forty-seven survival-related ARGs were screened out. Somatic mutation analysis showed that these genes revealed a high mutation rate. Based on their expression, two clusters were identified. Cluster B patients exhibited a shortened overall survival and higher immune infiltration. A risk scoring model including ten genes was subsequently developed, which exhibited excellent prognostic predictive ability for CRC, as evidenced by the survival curve, ROC curve, and AUC curve. In addition, a nomogram was developed for predicting 3- and 5-year survival probabilities. The qRT-PCR results indicated the dissimilarities among the ten signatures in the tumor tissues and adjacent tissues of patients with CRC were fundamentally consistent with the analytical findings. Conclusions: This study comprehensively evaluated the prognostic significance of ARGs in CRC. It identified two distinct anoikis-related clusters and examined their respective immune microenvironments. Furthermore, an ARGs signature was developed to effectively predict the prognosis of CRC, thereby establishing a solid foundation for investigating the clinical prognostic role of anoikis in CRC.