Zheng Jinyu, Xu Xiaoyi, Chen Xianguo, Li Xianshuai, Fu Miao, Zheng Yiping, Yang Jie
Department of Cardiothoracic Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
Front Oncol. 2025 Jul 22;15:1630663. doi: 10.3389/fonc.2025.1630663. eCollection 2025.
Lung adenocarcinoma is a leading subtype of lung cancer with high rates of recurrence and metastasis. Identifying novel prognostic biomarkers is essential for improving patient outcomes.
Transcriptomic and clinicopathological data from TCGA (55 tumor samples and 38 normal samples) were used to construct a prognostic model, with 30 samples for internal validation. An external validation cohort (10 tumor-normal pairs) was obtained from the First Affiliated Hospital of Wenzhou Medical University. Differentially expressed genes and immune-related genes from the IMMPORT database were integrated using WGCNA. Three machine learning algorithms-Random Forest, LASSO, and SVM-RFE-were applied to identify key hub genes. A multivariate Cox regression model was built to predict survival. Model performance was assessed by time-dependent ROC and ANN models. Immune infiltration was analyzed using TIMER and ssGSEA, with consensus clustering performed to explore immune subtypes. Protein expression and biological functions of hub genes were validated using the HPA database and GSEA.
A total of 1,822 DEGs were identified, with 68 immune-related genes significantly associated with LUAD prognosis. Four hub genes-CBLC, GDF10, LTBP4, and FABP4-were selected to construct the prognostic model, which showed strong predictive performance in both ROC and ANN analyses. Immune profiling revealed elevated CD4⁺ T cells, macrophages, and dendritic cells in LUAD. Consensus clustering identified two immune subtypes with distinct prognoses and immune landscapes.
This study established a robust immune-related prognostic model for LUAD and identified key biomarkers associated with immune infiltration and survival. These findings offer valuable insights for personalized diagnosis and treatment strategies in LUAD.
肺腺癌是肺癌的主要亚型，具有较高的复发和转移率。识别新的预后生物标志物对于改善患者预后至关重要。
利用来自TCGA的转录组和临床病理数据（55个肿瘤样本和38个正常样本）构建预后模型，其中30个样本用于内部验证。从温州医科大学附属第一医院获得一个外部验证队列（10对肿瘤-正常样本）。使用加权基因共表达网络分析（WGCNA）整合来自IMMPORT数据库的差异表达基因和免疫相关基因。应用三种机器学习算法——随机森林、套索回归和支持向量机递归特征消除（SVM-RFE）来识别关键枢纽基因。构建多变量Cox回归模型来预测生存。通过时间依赖的ROC和人工神经网络（ANN）模型评估模型性能。使用TIMER和单样本基因集富集分析（ssGSEA）分析免疫浸润，并进行一致性聚类以探索免疫亚型。使用人类蛋白质图谱（HPA）数据库和基因集富集分析（GSEA）验证枢纽基因的蛋白质表达和生物学功能。
共鉴定出1822个差异表达基因，其中68个免疫相关基因与肺腺癌预后显著相关。选择四个枢纽基因——CBLC、GDF10、LTBP4和FABP4——构建预后模型，该模型在ROC和ANN分析中均显示出强大的预测性能。免疫图谱显示肺腺癌中CD4⁺T细胞、巨噬细胞和树突状细胞升高。一致性聚类确定了两种具有不同预后和免疫格局的免疫亚型。
本研究建立了一个强大的肺腺癌免疫相关预后模型，并识别出与免疫浸润和生存相关的关键生物标志物。这些发现为肺腺癌的个性化诊断和治疗策略提供了有价值的见解。
