Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer

Abstract

In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.

Keywords: colon cancer; immunotherapy; machine learning; type 2 bitter taste receptor.

Figure 1

Expression of the TAS2Rs gene family in colon cancer tissues and cell lines. (A) Expression of 25 TAS2Rs gene family members in normal colon tissues and colon cancer tissues in TCGA-CDAD. (B–G) Expression of TAS2R4, TAS2R5, TAS2R14, TAS2R19, TAS2R20, and TAS2R31 in human normal colonic epithelial cell lines and different types of colon cancer cell lines. (H–N) Expression of TAS2R4, TAS2R5, TAS2R14, TAS2R19, TAS2R20, TAS2R31, and TAS2R38 in colon cancer tissues compared to normal colon tissues. The upper and lower ends of these boxes represent the interquartile range of the values. The line inside the box represents the median. Asterisks indicate statistical significance (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

Figure 2

Identification and biological characteristics of two molecular subtypes of colon cancer. (A) Consensus matrix of TCGA-COAD for k = 2. (B) CDF curves in consensus clustering analysis. CDF curves representing consensus scores for different subtype numbers (k = 2–9). (C) Survival curves of TCGA-COAD patients among different subtypes. Survival rate differences were evaluated using log-rank test, p < 0.05. (D) Heatmap of seven TAS2Rs prognostic genes in the TCGA-COAD dataset. (E) GSEA enrichment analysis showing the activation status of biological pathways in two molecular subtypes of colon cancer, red boxs indicates an important signaling pathway.

Figure 3

Immune cell infiltration characteristics and immune therapy prediction in the two molecular subtypes of colon cancer. (A) Analysis of immune cell content in the TCGA-COAD dataset. (B) Analysis of differential expression of chemokines in the TCGA-COAD dataset. Intergroup comparisons were performed using one-way analysis of variance (* p < 0.05, ** p < 0.01, *** p < 0.001). (C–F) Relationship between immune therapy-related scores and patient subtypes in the TCGA-COAD dataset. The Wilcoxon test was used to conduct the intergroup comparisons between the different subtypes.

Figure 4

Construction and biological processes in colon cancer genomic subtypes. (A) Venn diagram of 6541 TAS2Rs phenotype genes. (B) Survival curve of patients with colon cancer between TAS2Rs genomic subtypes geneCluster A and geneCluster B; 197 patients belong to geneCluster A and 251 patients belong to geneCluster B (p < 0.05). (C) Expression of the seven TAS2Rs prognostic genes in the two genome subtype clusters. (D) Functional annotation of TAS2Rs phenotypic genes using GO enrichment analysis, red boxs indicates an important signaling pathway. (E) Enrichment analysis of signal pathways for TAS2Rs phenotypic genes using KEGG enrichment analysis, red boxs indicates an important signaling pathway. (F) Abundance of immune cells in the two genome subtype clusters (* p < 0.05, ** p < 0.01, *** p < 0.001).

Figure 5

Construction of the GBM scoring model based on gradient boosting machine (GBM) learning. (A) Coefficients of the 16 gene pairs used to build the GBM scoring model (* p < 0.05, ** p < 0.01, *** p < 0.001). (B–E) Survival curves of the GBM scoring model in the TCGA-COAD training set, TCGA-COAD test set, GSE17538, and GSE29623 (log-rank test). (F–I) ROC curves for 1, 3, and 5 years GBM scoring models in the TCGA-COAD training set, TCGA-COAD test set, GSE17538, and GSE29623.

Figure 6

Generation and verification of the nomogram. (A) A three-factor nomogram with high and low risk scores (* p < 0.05, ** p < 0.01, *** p < 0.001). (B) ROC curve of the TCGA-COAD training set. (C) ROC curve of the TCGA-COAD test set. (D) ROC curve of GSE17538. (E) ROC curve of GSE29623.