Table 2_Integrated multi-omics and machine learning reveal an immunogenic cell death-related signature for prognostic stratification and therapeutic optimization in colorectal cancer.xlsx

Colorectal cancer (CRC) continues to rise in global incidence and remains a leading cause of cancer-related mortality. Immunogenic cell death (ICD) has emerged as a critical modulator of tumor microenvironment (TME) dynamics; however, its prognostic implications and therapeutic potential in CRC require systematic characterization. Through the integrative analysis of single-cell RNA sequencing and bulk transcriptomic data, 11 ICD-related genes with prognostic significance were identified in CRC. A comprehensive computational framework was then employed to evaluate 101 machine learning combinations, ultimately constructing an optimized 11-gene ICD-related signature (ICDRS) by integrating StepCox [forward] and RSF. The ICDRS exhibited strong predictive performance for overall survival in CRC patients across the training and validation datasets. Notably, the ICDRS-based nomogram achieved outstanding time-dependent AUCs (>0.90) for 1- to 3-year survival prediction. Multidimensional analysis revealed significant associations between ICDRS-derived risk score and distinct immune infiltration patterns, immunotherapy response and TME characteristics. Furthermore, a novel macrophage subtype, SPP1+/SLC11A1+, was discovered and characterized by high infiltration levels. Drug repurposing analysis indicated Olaparib as a potential therapeutic candidate for high-risk CRC patients. Therefore, this study establishes ICDRS as a promising tool for CRC prognosis and immunotherapy, with future validation studies planned to guide personalized treatment strategies.

结直肠癌（Colorectal cancer, CRC）的全球发病率持续攀升，仍是癌症相关死亡的首要致死原因。免疫原性细胞死亡（Immunogenic cell death, ICD）已被证实是调控肿瘤微环境（tumor microenvironment, TME）动态变化的关键因子，但其在结直肠癌中的预后价值与治疗潜力仍有待系统解析。本研究通过整合分析单细胞RNA测序与批量转录组数据（bulk transcriptomic data），在结直肠癌样本中筛选出11个具有预后价值的ICD相关基因。随后构建了一套完整的计算分析框架，对101种机器学习模型组合进行性能评估，最终结合向前法逐步Cox回归（StepCox [forward]）与随机生存森林（Random Survival Forest, RSF），构建了优化的11基因ICD相关特征标记（ICDRS）。该特征标记在训练集与验证数据集的结直肠癌患者队列中，均展现出优异的总生存期预测性能。尤为值得注意的是，基于ICDRS构建的列线图，在1至3年生存期预测中，其时间依赖性受试者工作特征曲线下面积（time-dependent AUCs）均超过0.90。多维度分析显示，ICDRS衍生的风险评分与独特的免疫浸润模式、免疫治疗响应及肿瘤微环境特征存在显著关联。此外，本研究还发现并鉴定了一种新型巨噬细胞亚型SPP1+/SLC11A1+，该亚型呈现高浸润水平。药物重定位分析结果显示，奥拉帕利（Olaparib）可作为高风险结直肠癌患者的潜在治疗候选药物。综上，本研究构建的ICDRS有望成为结直肠癌预后评估与免疫治疗指导的有效工具，后续将开展验证研究以推动个体化治疗策略的临床应用。