Xie Yuwen, Guan Shenyuan, Li Zhenkang, Cai Guohao, Liu Yuechen, Li Guoxin, Huang Ping, Lin Mingdao
Department of Radiation Oncology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, 570311, Hainan, China.
Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
Clin Exp Med. 2025 Jan 24;25(1):46. doi: 10.1007/s10238-025-01566-6.
The role of metabolic reprogramming of the tumor immune microenvironment in cancer development and immune escape has increasingly attracted attention. However, the predictive value of differences in metabolism-immune microenvironment on the prognosis of colon cancer (CC) and the response to immunotherapy have not been elucidated. The aim of this study was to investigate changes in metabolism and immune profile of CC and to identify a reliable signature for predicting prognosis and therapeutic response. The metabolism and immune-related differential genes in CC were screened out by differential gene expression analysis. A metabolism and immune related prognostic signature was established by the least absolute shrinkage and selection operator (LASSO) Cox algorithm. The training cohort with 417 patients from The Cancer Genome Atlas (TCGA) database and the validation cohort of 232 patients from GSE17538 were used to confirm the robustness of the prognostic signature. Immunohistochemical staining scores were used to assess gene expression levels in our clinical samples. Gene ontology (GO) analysis, gene set enrichment analysis (GSEA), single nucleotide variation (SNV) analysis, immune infiltration and immune factors analysis were used to explore the characteristics of patients with different subtypes. Multiple cancer immunotherapy datasets were used to assess the response of patients with different subtypes to immune checkpoint inhibitors. We established the Metabolism and Immune-Related Prognostic Score (MIRPS) based on six genes (CD36, PCOLCE2, SCG2, CALB2, STC2, CLDN23) to predict the prognosis of CC patients. We found a correlation between MIRPS and the malignant phenotype, microsatellite subtype, mutation load, and immune escape in CC. Tumors with high MIRPS presented a higher tumor mutation load and a more prominent immunosuppressive microenvironment. This subset of patients may potentially respond well to immune checkpoint inhibitor therapy. MIRPS may be used as a novel prognostic tool for CC and have potential value for immunotherapy response prediction.
肿瘤免疫微环境的代谢重编程在癌症发展和免疫逃逸中的作用日益受到关注。然而，代谢-免疫微环境差异对结肠癌（CC）预后及免疫治疗反应的预测价值尚未阐明。本研究旨在探讨CC的代谢和免疫特征变化，并确定一个可靠的特征用于预测预后和治疗反应。通过差异基因表达分析筛选出CC中代谢和免疫相关的差异基因。采用最小绝对收缩和选择算子（LASSO）Cox算法建立代谢和免疫相关的预后特征。使用来自癌症基因组图谱（TCGA）数据库的417例患者的训练队列和来自GSE17538的232例患者的验证队列来确认预后特征的稳健性。免疫组织化学染色评分用于评估我们临床样本中的基因表达水平。基因本体（GO）分析、基因集富集分析（GSEA）、单核苷酸变异（SNV）分析、免疫浸润和免疫因子分析用于探索不同亚型患者的特征。使用多个癌症免疫治疗数据集评估不同亚型患者对免疫检查点抑制剂的反应。我们基于六个基因（CD36、PCOLCE2、SCG2、CALB2、STC2、CLDN23）建立了代谢和免疫相关预后评分（MIRPS）来预测CC患者的预后。我们发现MIRPS与CC的恶性表型、微卫星亚型、突变负荷和免疫逃逸之间存在相关性。高MIRPS的肿瘤呈现出更高的肿瘤突变负荷和更突出的免疫抑制微环境。这部分患者可能对免疫检查点抑制剂治疗有较好反应。MIRPS可作为CC的一种新型预后工具，对免疫治疗反应预测具有潜在价值。
