结直肠癌（CRC）的患病率和发病率不断增加，特别是在年轻人中，强调了了解其基本机制、发现新的诊断和预后标志物以及增强治疗策略的必要性。在这里，我们整合了多组学数据，包括基因表达、体细胞突变数据和 DNA 甲基化数据，以揭示结直肠癌中肿瘤微环境 (TME) 的复杂性，并寻找新的预后标志物。通过根据表达谱计算每位患者的免疫评分，我们描绘了差异免疫细胞分数，构建了免疫相关的多组学图谱，并确定了分子特征。整个结直肠数据集 (n = 343) 被随机分为训练数据集 (n = 249) 和测试数据集 (n = 94)。我们筛选了 144 个免疫相关基因、6 个突变基因和 38 个与总生存期 (OS) 相关的甲基化探针。然后使用训练数据集中的 Lasso 和 Cox 回归将这些标记纳入 10 基因预后模型，并在独立的验证数据集中评估模型的性能。该模型表现出令人满意的结果（平均一致性指数 [C-index] = 0.77），训练数据集中的平均 1 年、3 年和 5 年 AUC 分别为 0.79、0.76 和 0.76，平均 1 年、3 年和 5 年 AUC 分别为 0.74、0.80，测试数据集中为 0.90。此外，该预后模型证明了其在指导 CRC 患者化疗方面的适用性，并在风险分层中表现出一定程度的泛癌实用性。总之，我们对多组学数据的综合分析揭示了 TME 的免疫相关遗传和表观遗传特征。我们提出了一种综合预后模型，可以对 CRC 患者进行风险分层并指导化疗。该模型在不同癌症类型风险分层中的普遍性在泛癌症队列中得到了验证。© 2024 作者。由爱思唯尔有限公司出版

The increasing prevalence and incidence of colorectal cancer (CRC), particularly in young adults, underscore the imperative to comprehend its fundamental mechanisms, discover novel diagnostic and prognostic markers, and enhance therapeutic strategies. Here, we integrated multi-omics data, including gene expression, somatic mutation data and DNA methylation data, to unravel the intricacies of tumor microenvironment (TME) in CRC and search for novel prognostic markers. By calculating the immune score for each patient from the expression profile, we delineated the differential immune cell fraction, constructed an immune-related multi-omics atlas, and identified molecular characteristics. The entire colorectal dataset (n = 343) was randomly divided into training (n = 249) and testing datasets (n = 94). We screened 144 immune-related genes, 6 mutant genes, and 38 methylation probes associated with overall survival (OS). These makers were then incorporated into a 10-gene prognostic model using Lasso and Cox regression in the training dataset, and the model's performance was evaluated in an independent validation dataset. The model exhibited satisfactory results (average concordance index [C-index] = 0.77), with the average 1-year, 3-year, and 5-year AUCs being 0.79, 0.76, and 0.76 in the training dataset and 0.74, 0.80, and 0.90 in the testing dataset. Furthermore, the prognostic model demonstrated applicability in guiding chemotherapy for CRC patients and exhibited a degree of pan-cancer utility in risk stratification. In conclusion, our integrated analysis of multi-omics data revealed immune-related genetic and epigenetic characteristics of the TME. We propose an integrative prognostic model that can stratify risk and guide chemotherapy for CRC patients. The generalizability of the model in risk stratification across different cancer types was validated in Pan-Cancer cohort.© 2024 The Authors. Published by Elsevier Ltd.