Zhang Han, Dai Jiaxing, Mu Qiuqiao, Zhao Xiaojiang, Lin Ziao, Wang Kai, Wang Meng, Sun Daqiang
Tianjin Chest Hospital, Tianjin University, Tianjin, China.
Tianjin Medical College, Tianjin, China.
Front Immunol. 2025 Jan 9;15:1491872. doi: 10.3389/fimmu.2024.1491872. eCollection 2024.
Macrophages play a dual role in the tumor microenvironment(TME), capable of secreting pro-inflammatory factors to combat tumors while also promoting tumor growth through angiogenesis and immune suppression. This study aims to explore the characteristics of macrophages in lung adenocarcinoma (LUAD) and establish a prognostic model based on macrophage-related genes.
We performed scRNA-seq analysis to investigate macrophage heterogeneity and their potential pseudotime evolutionary processes. Specifically, we used scRNA-seq data processing, intercellular communication analysis, pseudotime trajectory analysis, and transcription factor regulatory analysis to reveal the complexity of macrophage subpopulations. Data from The Cancer Genome Atlas (TCGA) was used to assess the impact of various macrophage subtypes on LUAD prognosis. Univariate Cox regression was applied to select prognostic-related genes from macrophage markers. We constructed a prognostic model using Lasso regression and multivariate Cox regression, categorizing LUAD patients into high and low-risk groups based on the median risk score. The model's performance was validated across multiple external datasets. We also examined differences between high and low-risk groups in terms of pathway enrichment, mutation information, tumor microenvironment(TME), and immunotherapy efficacy. Finally, RT-PCR confirmed the expression of model genes in LUAD, and cellular experiments explored the carcinogenic mechanism of COL5A1.
We found that signals such as SPP1 and MIF were more active in tumor tissues, indicating potential oncogenic roles of macrophages. Using macrophage marker genes, we developed a robust prognostic model for LUAD that effectively predicts prognosis and immunotherapy efficacy. A nomogram was constructed to predict LUAD prognosis based on the model's risk score and other clinical features. Differences between high and low-risk groups in terms of TME, enrichment analysis, mutational landscape, and immunotherapy efficacy were systematically analyzed. RT-PCR and cellular experiments supported the oncogenic role of COL5A1.
Our study identified potential oncogenic mechanisms of macrophages and their impact on LUAD prognosis. We developed a prognostic model based on macrophage marker genes, demonstrating strong performance in predicting prognosis and immunotherapy efficacy. Finally, cellular experiments suggested COL5A1 as a potential therapeutic target for LUAD.
巨噬细胞在肿瘤微环境（TME）中发挥双重作用，既能分泌促炎因子对抗肿瘤，又能通过血管生成和免疫抑制促进肿瘤生长。本研究旨在探索肺腺癌（LUAD）中巨噬细胞的特征，并建立基于巨噬细胞相关基因的预后模型。
我们进行了单细胞RNA测序（scRNA-seq）分析，以研究巨噬细胞的异质性及其潜在的伪时间进化过程。具体而言，我们使用scRNA-seq数据处理、细胞间通讯分析、伪时间轨迹分析和转录因子调控分析，以揭示巨噬细胞亚群的复杂性。来自癌症基因组图谱（TCGA）的数据用于评估各种巨噬细胞亚型对LUAD预后的影响。单因素Cox回归用于从巨噬细胞标志物中选择预后相关基因。我们使用套索回归和多因素Cox回归构建了一个预后模型，根据中位风险评分将LUAD患者分为高风险组和低风险组。该模型的性能在多个外部数据集中得到验证。我们还研究了高风险组和低风险组在通路富集、突变信息、肿瘤微环境（TME）和免疫治疗疗效方面的差异。最后，逆转录-聚合酶链反应（RT-PCR）证实了模型基因在LUAD中的表达，细胞实验探索了COL5A1的致癌机制。
我们发现SPP1和MIF等信号在肿瘤组织中更活跃，表明巨噬细胞具有潜在的致癌作用。利用巨噬细胞标志物基因，我们为LUAD开发了一个强大的预后模型，该模型能有效预测预后和免疫治疗疗效。基于模型的风险评分和其他临床特征构建了列线图，以预测LUAD的预后。系统分析了高风险组和低风险组在TME、富集分析、突变图谱和免疫治疗疗效方面的差异。RT-PCR和细胞实验支持COL5A1的致癌作用。
我们的研究确定了巨噬细胞的潜在致癌机制及其对LUAD预后的影响。我们基于巨噬细胞标志物基因开发了一个预后模型，在预测预后和免疫治疗疗效方面表现出强大的性能。最后，细胞实验表明COL5A1是LUAD的一个潜在治疗靶点。
