Department of Colorectal Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China.
Shanghai Colorectal Cancer Research Center, Shanghai, 200092, China.
J Cancer Res Clin Oncol. 2023 Jun;149(6):2393-2416. doi: 10.1007/s00432-022-04070-6. Epub 2022 Jun 22.
Colon cancer presents challenges to clinical diagnosis and management due to its high heterogeneity. For more efficient and convenient diagnosis and treatment of colon cancer, we are committed to characterizing the molecular features of colon cancer by pioneering a classification system based on metabolic pathways.
Based on the 113 metabolic pathways and genes collected in the previous stage, we scored and filtered the metabolic pathways of each sample in the training set by ssGSEA, and obtained 16 metabolic pathways related to colon cancer recurrence. In consistent clustering of training set samples with recurrence-related metabolic pathway scores, we identified two robust molecular subtypes of colon cancer (MC1 and MC2). Furthermore, we performed multi-angle analysis on the survival differences of subtypes, metabolic characteristics, clinical characteristics, functional enrichment, immune infiltration, differences with other subtypes, stemness indices, TIDE prediction, and drug sensitivity, and finally constructed colon cancer prognostic model.
The results showed that the MC1 subtype had a poor prognosis based on higher immune activity and immune checkpoint gene expression. The MC2 subtype is associated with high metabolic activity and low expression of immune checkpoint genes and a better prognosis. The MC2 subtype was more responsive to PD-L1 immunotherapy than the MC1 subclass. However, we did not observe significant differences in tumor mutational burden between the two.
Two molecular subtypes of colon cancer based on metabolic pathways have distinct immune signatures. Constructing prognostic models based on subtype differential genes provides valuable reference for personalized therapy targeting unique tumor metabolic signatures.
由于结肠癌具有高度异质性，因此对其临床诊断和治疗提出了挑战。为了更有效地对结肠癌进行诊断和治疗，我们致力于通过开拓性地基于代谢途径建立分类系统来描述结肠癌的分子特征。
基于前一阶段收集的 113 条代谢途径和基因，我们通过 ssGSEA 对训练集中每个样本的代谢途径进行评分和筛选，得到与结肠癌复发相关的 16 条代谢途径。在对具有复发相关代谢途径评分的训练集样本进行一致聚类后，我们确定了两种稳健的结肠癌分子亚型（MC1 和 MC2）。此外，我们对亚型的生存差异、代谢特征、临床特征、功能富集、免疫浸润、与其他亚型的差异、干性指数、TIDE 预测和药物敏感性进行了多角度分析，最终构建了结肠癌预后模型。
结果表明，基于较高的免疫活性和免疫检查点基因表达，MC1 亚型预后较差。MC2 亚型与高代谢活性和低免疫检查点基因表达相关，预后较好。与 MC1 亚类相比，MC2 亚类对 PD-L1 免疫治疗的反应更好。然而，我们没有观察到两种亚型之间肿瘤突变负担的显著差异。
基于代谢途径的结肠癌两种分子亚型具有不同的免疫特征。基于亚型差异基因构建预后模型为针对独特肿瘤代谢特征的个体化治疗提供了有价值的参考。
