Yang Dashuai, Zhao Fangrui, Su Yang, Zhou Yu, Shen Jie, Zhao Kailiang, Ding Youming
Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China.
Front Mol Biosci. 2023 Jul 4;10:1184708. doi: 10.3389/fmolb.2023.1184708. eCollection 2023.
: M2 macrophages perform an influential role in the progression of pancreatic cancer. This study is dedicated to explore the value of M2 macrophage-related genes in the treatment and prognosis of pancreatic cancer. : RNA-Seq and clinical information were downloaded from TCGA, GEO and ICGC databases. The pancreatic cancer tumour microenvironment was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to detect M2 macrophage-associated gene modules. Univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression were applied to develop the prognostic model. The modelling and validation cohorts were divided into high-risk and low-risk groups according to the median risk score. The nomogram predicting survival was constructed based on risk scores. Correlations between risk scores and tumour mutational load, clinical variables, immune checkpoint blockade, and immune cells were further explored. Finally, potential associations between different risk models and chemotherapeutic agent efficacy were predicted. : The intersection of the WGCNA results from the TCGA and GEO data screened for 317 M2 macrophage-associated genes. Nine genes were identified by multivariate COX regression analysis and applied to the construction of risk models. The results of GSEA analysis revealed that most of these genes were related to signaling, cytokine receptor interaction and immunodeficiency pathways. The high and low risk groups were closely associated with tumour mutational burden, immune checkpoint blockade related genes, and immune cells. The maximum inhibitory concentrations of metformin, paclitaxel, and rufatinib lapatinib were significantly differences on the two risk groups. : WGCNA-based analysis of M2 macrophage-associated genes can help predict the prognosis of pancreatic cancer patients and may provide new options for immunotherapy of pancreatic cancer.
M2巨噬细胞在胰腺癌进展中发挥着重要作用。本研究致力于探讨M2巨噬细胞相关基因在胰腺癌治疗和预后中的价值。从TCGA、GEO和ICGC数据库下载RNA测序和临床信息。使用CIBERSORT算法揭示胰腺癌肿瘤微环境。采用加权基因共表达网络分析(WGCNA)检测M2巨噬细胞相关基因模块。应用单因素Cox回归、最小绝对收缩和选择算子(LASSO)回归分析及多因素Cox回归建立预后模型。根据中位风险评分将建模和验证队列分为高风险和低风险组。基于风险评分构建预测生存的列线图。进一步探讨风险评分与肿瘤突变负荷、临床变量、免疫检查点阻断和免疫细胞之间的相关性。最后,预测不同风险模型与化疗药物疗效之间的潜在关联。通过对TCGA和GEO数据的WGCNA结果进行交叉分析,筛选出317个M2巨噬细胞相关基因。通过多因素COX回归分析鉴定出9个基因,并将其应用于风险模型的构建。基因集富集分析(GSEA)结果显示,这些基因大多与信号传导、细胞因子受体相互作用和免疫缺陷途径相关。高风险和低风险组与肿瘤突变负担、免疫检查点阻断相关基因和免疫细胞密切相关。二甲双胍、紫杉醇和拉帕替尼对两组风险患者的最大抑制浓度存在显著差异。基于WGCNA对M2巨噬细胞相关基因进行分析,有助于预测胰腺癌患者的预后,并可能为胰腺癌免疫治疗提供新的选择。