Vasudevan Karthick, T Dhanushkumar, Hebbar Sripad Rama, Selvam Prasanna Kumar, Rambabu Majji, Anbarasu Krishnan, Rohini Karunakaran
Manipal Academy of Higher Education (MAHE), Manipal, 576104, India.
Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India.
Sci Rep. 2025 Jun 2;15(1):19333. doi: 10.1038/s41598-025-01680-1.
Colorectal cancer (CRC) presents significant challenges due to limited targeted therapeutic options. This study integrates multi-omics analysis and AI to identify tumor antigens and immune gene targets for personalized immunotherapy. Using TCGA, differential expression and mutation analysis, we identified overexpressed and mutated genes in CRC. Among these, 62 neoantigens were shortlisted as potential tumor antigens. Survival analysis highlighted prognostic antigens, while their correlation with immune gene expression suggested these antigens could trigger immune activation. Three key neoantigens (TTK, EZH2, and KIF4A) emerged as promising candidates for immunotherapy. Based on immune gene activity, patients were categorized into three Immune Subtypes (IS). IS groups 1 and 2, characterized by high immune gene expression and immune activation markers, exhibited better survival outcomes, while IS 3, with low immune gene expression, showed poor survival and immune unresponsiveness. Neoantigen-based vaccines could potentially boost tumor recognition and improve survival for patients in immune-cold subtypes. Machine learning models like LightGBM, XGBoost, and XGBRF predicted optimal immune targets for vaccine design, validated through SHAP analysis. This study provides a machine learning- driven framework to identify tumor antigens and immune targets, offering a promising strategy for CRC immunotherapy tailored to immune subtype-specific responses.
由于靶向治疗选择有限，结直肠癌（CRC）带来了重大挑战。本研究整合多组学分析和人工智能，以识别用于个性化免疫治疗的肿瘤抗原和免疫基因靶点。利用TCGA进行差异表达和突变分析，我们在CRC中鉴定出了过表达和突变的基因。其中，62种新抗原被列为潜在的肿瘤抗原。生存分析突出了预后抗原，而它们与免疫基因表达的相关性表明这些抗原可触发免疫激活。三种关键新抗原（TTK、EZH2和KIF4A）成为免疫治疗的有希望的候选者。基于免疫基因活性，患者被分为三种免疫亚型（IS）。以高免疫基因表达和免疫激活标志物为特征的IS 1组和IS 2组表现出更好的生存结果，而免疫基因表达低的IS 3组则显示出生存率低和免疫无反应性。基于新抗原的疫苗可能会增强肿瘤识别并改善免疫冷亚型患者的生存。像LightGBM、XGBoost和XGBRF这样的机器学习模型预测了疫苗设计的最佳免疫靶点，并通过SHAP分析进行了验证。本研究提供了一个由机器学习驱动的框架来识别肿瘤抗原和免疫靶点，为根据免疫亚型特异性反应量身定制的CRC免疫治疗提供了一种有前景的策略。
