Xu Chaojie, Song Lishan, Yang Yubin, Liu Yi, Pei Dongchen, Liu Jiabang, Guo Jianhua, Liu Nan, Li Xiaoyong, Liu Yuchen, Li Xuesong, Yao Lin, Kang Zhengjun
The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Peking University First Hospital, Peking University, Beijing, China.
Front Oncol. 2022 Jul 22;12:919899. doi: 10.3389/fonc.2022.919899. eCollection 2022.
Numerous studies have found that infiltrating M2 macrophages play an important role in the tumor progression of lung adenocarcinoma (LUAD). However, the roles of M2 macrophage infiltration and M2 macrophage-related genes in immunotherapy and clinical outcomes remain obscure.
Sample information was extracted from TCGA and GEO databases. The TIME landscape was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to find M2 macrophage-related gene modules. Through univariate Cox regression, lasso regression analysis, and multivariate Cox regression, the genes strongly associated with the prognosis of LUAD were screened out. Risk score (RS) was calculated, and all samples were divided into high-risk group (HRG) and low-risk group (LRG) according to the median RS. External validation of RS was performed using GSE68571 data information. Prognostic nomogram based on risk signatures and other clinical information were constructed and validated with calibration curves. Potential associations of tumor mutational burden (TMB) and risk signatures were analyzed. Finally, the potential association of risk signatures with chemotherapy efficacy was investigated using the pRRophetic algorithm.
Based on 504 samples extracted from TCGA database, 183 core genes were identified using WGCNA. Through a series of screening, two M2 macrophage-related genes ( and ) strongly correlated with LUAD prognosis were finally selected. RS was calculated, and prognostic risk nomogram including gender, age, T, N, M stage, clinical stage, and RS were constructed. The calibration curve shows that our constructed model has good performance. HRG patients were suitable for new ICI immunotherapy, while LRG was more suitable for CTLA4-immunosuppressive therapy alone. The half-maximal inhibitory concentrations (IC50) of the four chemotherapeutic drugs (metformin, cisplatin, paclitaxel, and gemcitabine) showed significant differences in HRG/LRG.
In conclusion, a comprehensive analysis of the role of M2 macrophages in tumor progression will help predict prognosis and facilitate the advancement of therapeutic techniques.
大量研究发现,浸润性M2巨噬细胞在肺腺癌(LUAD)的肿瘤进展中起重要作用。然而,M2巨噬细胞浸润和M2巨噬细胞相关基因在免疫治疗及临床结局中的作用仍不明确。
从TCGA和GEO数据库中提取样本信息。使用CIBERSORT算法揭示肿瘤免疫微环境(TIME)景观。采用加权基因共表达网络分析(WGCNA)寻找M2巨噬细胞相关基因模块。通过单因素Cox回归、lasso回归分析和多因素Cox回归,筛选出与LUAD预后密切相关的基因。计算风险评分(RS),并根据RS中位数将所有样本分为高风险组(HRG)和低风险组(LRG)。利用GSE68571数据信息对RS进行外部验证。构建基于风险特征和其他临床信息的预后列线图,并用校准曲线进行验证。分析肿瘤突变负荷(TMB)与风险特征的潜在关联。最后,使用pRRophetic算法研究风险特征与化疗疗效的潜在关联。
基于从TCGA数据库中提取的504个样本,通过WGCNA鉴定出183个核心基因。经过一系列筛选,最终选择了两个与LUAD预后密切相关的M2巨噬细胞相关基因(和)。计算RS,并构建了包括性别、年龄、T、N、M分期、临床分期和RS的预后风险列线图。校准曲线表明我们构建的模型具有良好的性能。HRG患者适合新的免疫检查点抑制剂(ICI)免疫治疗,而LRG患者更适合单独使用CTLA4免疫抑制治疗。四种化疗药物(二甲双胍顺铂、紫杉醇和吉西他滨)的半数最大抑制浓度(IC50)在HRG/LRG中显示出显著差异。
总之,全面分析M2巨噬细胞在肿瘤进展中的作用将有助于预测预后并促进治疗技术的进步。