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Construction and Evaluation of an M2 Macrophage-Related Prognostic Model for Colon Cancer
Applied Biochemistry and Biotechnology ( IF 3.1 ) Pub Date : 2023-11-21 , DOI: 10.1007/s12010-023-04789-z
Min Ji 1 , Yanping Chen 1, 2 , Lu Zhang 1 , Leqian Ying 1 , Chunchun Huang 1 , Lin Liu 1, 2
Affiliation  

Colon cancer (CC) is a primary human malignancy. Recently, the mechanism of the tumor microenvironment (TME) in CC has been a hot topic of research. However, there is uncertainty regarding the contribution of M2 macrophages and related genes to the prognosis for CC. M2 macrophage-related genes (M2RGs) were obtained from The Cancer Genome Atlas (TCGA) database. Immune cell infiltration in CC tissue was assessed by Cibersort. Based on the TCGA-COAD training set, a Least Absolute Shrinkage and Selection Operator (LASSO) Cox risk model was constructed and its efficiency was evaluated by analyzing risk profiles and survival profiles. Using gene set enrichment analysis (GSEA), the functional distinctions between high-risk and low-risk categories were further investigated. Finally, potential immune checkpoints, immunotherapy efficiency, and clinical treatment of high-risk patients were evaluated. A total of 1063 M2RGs were identified in TCGA-COAD, 32 of these were confirmed to be strongly related to overall survival (OS), and 14 of these were picked to construct an OS-oriented prognostic model in CC patients. The M2RG signature had a positive correlation with unfavorable prognosis according to the survival analysis. Correlation analysis revealed that the risk model was positively associated with clinicopathological characteristics, immune cell infiltration, immune checkpoint inhibitor targets, the risk of immune escape, and the efficiency of anti-cancer medications. The risk model created using M2RGs may be useful in predicting the prognosis of CC.



中文翻译:


M2巨噬细胞相关结肠癌预后模型的构建和评估



结肠癌(CC)是一种主要的人类恶性肿瘤。近年来,肿瘤微环境(TME)在CC中的作用机制成为研究热点。然而,M2 巨噬细胞和相关基因对 CC 预后的贡献尚不确定。 M2 巨噬细胞相关基因 (M2RG) 获自癌症基因组图谱 (TCGA) 数据库。通过 Cibersort 评估 CC 组织中的免疫细胞浸润。基于TCGA-COAD训练集,构建了最小绝对收缩和选择算子(LASSO)Cox风险模型,并通过分析风险概况和生存概况评估其效率。使用基因集富集分析(GSEA),进一步研究了高风险和低风险类别之间的功能差异。最后,评估潜在的免疫检查点、免疫治疗效率以及高危患者的临床治疗。 TCGA-COAD 共鉴定出 1063 个 M2RG,其中 32 个被证实与总生存期 (OS) 密切相关,并挑选其中 14 个构建 CC 患者的 OS 导向的预后模型。根据生存分析,M2RG 特征与不良预后呈正相关。相关分析显示,风险模型与临床病理特征、免疫细胞浸润、免疫检查点抑制剂靶点、免疫逃逸风险以及抗癌药物的有效性呈正相关。使用 M2RG 创建的风险模型可能有助于预测 CC 的预后。

更新日期:2023-11-22
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