Robust machine−learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer

Abstract

Abstract Background A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC). Methods 101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross−validation framework, and patients with CRC were separated into high− and low−CERPI groups. Relationship between immune cell infiltration levels, immune−related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in−vitro verified. Results The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low−CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single−cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration. Conclusion We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.

Keywords

• Machine learning
• Immune evasion
• Cytotoxic T lymphocyte
• Prognosis
• Colorectal cancer