Identification of a two-gene prognostic model associated with cytolytic activity for colon cancer

鉴定与结肠癌细胞溶解活性相关的双基因预后模型

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作者:Xiaoye Jiang, Zhongxiang Jiang, Lichun Xiang, Xuenuo Chen, Jiao Wu, Zheng Jiang

Background

Increasing evidence has shown that cytolytic activity (CYT) is a new immunotherapy biomarker that characterises the antitumour immune activity of cytotoxic T cells and macrophages. In this study, we established a prognostic model associated with CYT.

Conclusions

Overall, our model may precisely predict the overall survival of CC and reflect the strength of antitumour immune activity in the CC microenvironment. Furthermore, the model may be a predictive factor for the response to immunotherapy.

Methods

A prognostic model based on CYT-related genes was developed. Furthermore, aberrant expression of genes of the model in colon cancer (CC) was identified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays. Next, the correlation between the model and T-cell infiltration in the CC microenvironment was analysed. The Tumour Immune Dysfunction and Exclusion (TIDE) algorithm and subclass mapping were used to predict clinical responses to immune checkpoint inhibitors.

Results

In total, 280 of the 1418 genes were differentially expressed based on CYT. A prognostic model (including HOXC8 and MS4A2) was developed based on CYT-related genes. The model was validated using the testing set, the whole set and a Gene Expression Omnibus (GEO) cohort (GSE41258). Gene set enrichment analysis (GSEA) and other analyses showed that the levels of immune infiltration and antitumour immune activation in low-risk-score tumours were greater than those in high-risk-score tumours. CC patients with a low-risk-score showed more promise in the response to anti-immune checkpoint therapy. Conclusions: Overall, our model may precisely predict the overall survival of CC and reflect the strength of antitumour immune activity in the CC microenvironment. Furthermore, the model may be a predictive factor for the response to immunotherapy.

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