Sang Mengmeng, Huang Qinhua, Mao Mimi, Yan Haoming, Ge Jia, Zhang Rui, Ju Wenhao, Zhou Xiaorong
Department of Immunology, School of Medicine, Nantong University, Nantong, China.
Department of Radiology, Nantong Tumor Hospital Affiliated to Nantong University, Nantong, China.
FASEB J. 2025 Jun 15;39(11):e70704. doi: 10.1096/fj.202500293RR.
Tumor-associated macrophages (TAMs) are a vital immune component within the tumor microenvironment (TME) of lung adenocarcinoma (LUAD), exerting significant influence on tumor growth, metastasis, and drug resistance. N6-methyladenosine (m6A) modifications regulate gene expression and various facets of cancer biology; nonetheless, the mechanisms by which they modulate gene expression in TAMs and their impact on LUAD progression remain inadequately elucidated. Single-cell transcriptome analysis identified the macrophage m6A-related genes (MMRGs) with high expression in TAMs and linked to m6A modifications in LUAD. The MMRGs were employed to construct 801 prognostic models by 13 different machine learning (ML) algorithms. An integrative multi-omics approach was utilized to analyze the potential biological functions of MMRGs. Five ML algorithms were employed to discover potential biomarkers for patient stratification and precision therapy. Potential drugs for treating LUAD were identified and assessed by molecular docking and molecular dynamics, and ML was employed to determine the most promising candidates. Immunohistochemistry and immunofluorescence staining were conducted to assess MMRG expression in LUAD tissues. Seventeen MMRGs were identified in LUAD and subsequently employed to construct a prognostic model for patient stratification into high-risk and low-risk groups. The impact of MMRG expression on various tumor immune phenotypes, such as tumor stemness, heterogeneity, hallmark pathway enrichment, TAM infiltration, and immune landscape, was thoroughly characterized. PIM3, HMGB2, DUSP2, NR4A2, and others were recognized as promising biomarkers for patient classification and precision therapy. Furthermore, it was predicted that drugs such as BRD9876 and MK-1775 would demonstrate therapeutic efficacy in treating LUAD, and drugs showing potential binding with DUSP2, ZNF331, FLT, and LYZ were identified. Finally, experimental validation was conducted to confirm the protein expression and distribution of DUSP2 and NR4A2 in tissues of LUAD. Our study offers valuable insights into the biological significance of MMRGs, shedding light on novel mechanisms of tumor development and immune evasion in LUAD. Furthermore, our findings have identified potential biomarkers, drug candidates, and therapeutic targets that may improve the management of LUAD in the future.
肿瘤相关巨噬细胞（TAMs）是肺腺癌（LUAD）肿瘤微环境（TME）中的重要免疫成分，对肿瘤生长、转移和耐药性有重大影响。N6-甲基腺苷（m6A）修饰调节基因表达和癌症生物学的各个方面；然而，它们调节TAMs中基因表达的机制及其对LUAD进展的影响仍未得到充分阐明。单细胞转录组分析确定了在TAMs中高表达且与LUAD中m6A修饰相关的巨噬细胞m6A相关基因（MMRGs）。利用MMRGs通过13种不同的机器学习（ML）算法构建了801个预后模型。采用综合多组学方法分析MMRGs的潜在生物学功能。使用五种ML算法发现用于患者分层和精准治疗的潜在生物标志物。通过分子对接和分子动力学鉴定并评估了治疗LUAD的潜在药物，并利用ML确定最有前景的候选药物。进行免疫组织化学和免疫荧光染色以评估LUAD组织中MMRG的表达。在LUAD中鉴定出17个MMRGs，随后用于构建预后模型，将患者分为高风险和低风险组。全面表征了MMRG表达对各种肿瘤免疫表型的影响，如肿瘤干性、异质性、标志性通路富集、TAM浸润和免疫格局。PIM3、HMGB2、DUSP2、NR4A2等被认为是用于患者分类和精准治疗的有前景的生物标志物。此外，预测BRD9876和MK-1775等药物在治疗LUAD方面将显示出治疗效果，并鉴定出与DUSP2、ZNF331、FLT和LYZ有潜在结合的药物。最后，进行实验验证以确认DUSP2和NR4A2在LUAD组织中的蛋白表达和分布。我们的研究为MMRGs的生物学意义提供了有价值的见解，揭示了LUAD肿瘤发生和免疫逃逸的新机制。此外，我们的发现确定了潜在的生物标志物、候选药物和治疗靶点，可能在未来改善LUAD的管理。
