Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China.
Inflamm Res. 2024 May;73(5):841-866. doi: 10.1007/s00011-024-01871-y. Epub 2024 Mar 20.
BACKGROUND: Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures. METHODS: In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells. RESULTS: Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients. CONCLUSIONS: ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.
背景：先前的研究在很大程度上忽视了 ADCC 在 LUAD 中的作用，并且没有研究系统地编译与 ADCC 相关的基因来创建预后标志物。
方法：本研究纳入了 1564 名 LUAD 患者、2057 名 NSCLC 患者和来自多个队列的 5000 多名患有各种癌症类型的患者。使用 R 包 ConsensusClusterPlus 将患者分为不同的亚型。使用多种机器学习算法构建 ADCCRS。使用 GSVA 和 ClusterProfiler 进行富集分析，使用 IOBR 量化免疫细胞浸润水平。使用 GISTIC2.0 和 maftools 分析 CNV 和 SNV 数据。使用 Oncopredict 包基于 GDSC1 预测药物信息。使用三个免疫治疗队列评估患者对免疫治疗的反应。使用 Seurat 包处理单细胞数据，使用 AUCell 包计算细胞的基因集活性评分，并使用 Scissor 算法识别 ADCCRS 相关细胞。
结果：通过无监督聚类，鉴定出两种截然不同的 LUAD 亚型，每个亚型均具有独特的临床特征。通过集成机器学习方法构建了由 16 个基因组成的 ADCCRS。ADCCRS 的预后能力在 28 个独立数据集得到验证。此外，ADCCRS 在预测 LUAD 患者的生存方面优于 102 个先前发表的标志物。构建了包含 ADCCRS 和临床特征的列线图，显示出较高的预测性能。ADCCRS 与患者的基因突变呈正相关，对批量和单细胞转录组数据的综合分析表明，ADCCRS 与 TME 调节剂相关。代表高 ADCCRS 表型的细胞表现出更多恶性特征。具有高 ADCCRS 水平的 LUAD 患者对化疗和靶向治疗敏感，而对免疫治疗有抵抗性。在泛癌分析中，ADCCRS 仍具有显著的预后价值，并且被发现是大多数癌症患者的危险因素。
结论：ADCCRS 为 LUAD 患者提供了重要的预后见解，揭示了肿瘤微环境并预测了治疗反应性。
Transl Lung Cancer Res. 2023-7-31
Transl Lung Cancer Res. 2023-3-31
Zotero 插件