列线图
比例危险模型
基因签名
肿瘤科
Lasso(编程语言)
医学
接收机工作特性
基因表达谱
内科学
生存分析
微阵列分析技术
肺癌
微阵列
预测模型
腺癌
图谱
转录组
弗雷明翰风险评分
组织微阵列
免疫疗法
计算生物学
生物信息学
基因
癌症
DNA微阵列
肿瘤微环境
总体生存率
TNM分期系统
基因调控网络
基因表达
作者
Weiwei Gu,Yahua Wu,Rongqi Jiang,Mingliang Shi,Jiude Qi,Jinhuo Lai
标识
DOI:10.3389/fimmu.2026.1693469
摘要
Background Lung adenocarcinoma (LUAD) exhibits high mortality and heterogeneity. While immune-related signatures show prognostic potential, robust models validated through both computational screening and experimental methods are lacking. Methods Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) database and three Gene Expression Omnibus (GEO) cohorts (GSE3141, GSE30219, and GSE50081) were analyzed. A 12-gene immune-related prognostic signature was constructed using LASSO Cox regression. The model was subsequently validated using three independent external cohorts. Its prognostic performance was comprehensively assessed using time-dependent receiver operating characteristic (ROC) curves. Functional enrichment analyses (GO, KEGG, and GSEA), tumor microenvironment (TME) profiling (via CIBERSORT and ESTIMATE algorithms), and drug sensitivity analyses were conducted. Protein-protein interaction (PPI) network analysis identified KRT6B as a central hub gene. KRT6B expression and its functional role were further validated through tissue microarray immunohistochemistry (IHC), as well as in vitro and in vivo experiments. Results We developed a prognostic model for LUAD based on 12 immune-related genes and derived a risk score via LASSO regression. High-risk patients exhibited significantly worse overall survival compared to low-risk patients in both the training set (TCGA) and the three independent validation cohorts (GSE3141, GSE30219, and GSE50081) (all P < 0.05). Time-dependent ROC analysis confirmed the model’s predictive accuracy for 1-, 2-, and 3-year survival (AUC: 0.624–0.788). A nomogram incorporating the risk score and key clinical indicators further enhanced prognostic performance (AUC: 0.753 to 0.763). PPI network analysis pinpointed KRT6B as a core hub gene within the signature. Subsequent experimental validation confirmed the overexpression of KRT6B in LUAD tumor cells and demonstrated its tumor-promoting functions both in vitro and in vivo . Conclusion We established and validated an immune-related gene signature for prognostic prediction and identified KRT6B as a promising prognostic biomarker and potential therapeutic target in LUAD.
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