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

被引:0
作者
Bi, Suzhen [1 ]
Zhu, Jie [2 ]
Huang, Liting [1 ]
Feng, Wanting [1 ]
Peng, Lulu [1 ]
Leng, Liangqi [1 ]
Wang, Yin [1 ]
Shan, Peipei [1 ]
Kong, Weikaixin [2 ]
Zhu, Sujie [1 ]
机构
[1] Qingdao Univ, Inst Translat Med, Coll Med, Qingdao 266021, Peoples R China
[2] Univ Helsinki, Inst Mol Med Finland FIMM, HiLIFE, Helsinki 00014, Finland
基金
中国国家自然科学基金;
关键词
colon cancer; type 2 bitter taste receptor; immunotherapy; machine learning; BITTER TASTE RECEPTORS; COLORECTAL-CANCER; BREAST-CANCER; EXPRESSION; TRIAL;
D O I
10.3390/ijms25136849
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
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页数:20
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