背景：基底膜（BMs）参与肿瘤进展，但目前对BM相关基因在肺腺癌（LUAD）中的作用缺乏全面分析。因此，我们旨在开发一种基于BM相关基因谱的LUAD新型预后模型。 方法：从基底膜BASE、癌症基因组图谱（TCGA）和基因表达小组（GEO）数据库获取LUAD BM相关基因谱和相应临床病理数据。使用Cox回归和最小绝对收缩和选择算子（LASSO）方法构建一个基于BM的风险签名。生成协调指数（C-index）、受试者工作特征曲线（ROC）和校准曲线来评估名图。使用GSE72094数据集验证签名的预测能力。根据风险分数比较功能富集、免疫浸润和药物敏感性分析的差异。 结果：在TCGA训练队列中发现10个BMs相关基因，（例如ACAN、ADAMTS15、ADAMTS8、BCAN等）。基于这10个基因的信号签名根据存活差异分成高危和低危组（p＜0.001）。多变量分析表明，联合10个BM相关基因的签名是独立的预后预测因子。GSE72094验证队列的基于BM的签名的预测价值得到进一步验证。GEO验证、C-index和ROC曲线显示名图具有准确的预测性能。功能分析表明BM主要富集于胞外基质-受体（ECM受体）相互作用。此外，BM基础模型与免疫检查点有关。 结论：本研究确定了基于BM的风险签名基因，并证明了它们在预测LUAD患者的预后和指导个体化治疗方面的能力。版权所有©2023 Zhu, Liu, Qiu, Niu, Dong and Song。

Background: The basement membranes (BMs) are involved in tumor progression, while few comprehensive analyses to date are performed on the role of BM-related gene signatures in lung adenocarcinoma (LUAD). Thus, we aimed to develop a novel prognostic model in LUAD based on BMs-related gene profiling. Methods: The LUAD BMs-related gene profiling and corresponding clinicopathological data were obtained from the basement membrane BASE, The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) databases. The Cox regression and least absolute shrinkage and selection operator (LASSO) methods were used to construct a BMs-based risk signature. The concordance index (C-index), receiver operating characteristic (ROC), and calibration curves were generated to evaluate the nomogram. The GSE72094 dataset was used to validate prediction of the signature. The differences in functional enrichment, immune infiltration, and drug sensitivity analyses were compared based on risk score. Results: In TCGA training cohort, 10 BMs-related genes were found, (e.g., ACAN, ADAMTS15, ADAMTS8, BCAN, etc). The signal signature based on these 10 genes was categorized into high- and low-risk groups regarding survival differences (p < 0.001). Multivariable analysis showed that the signature of combined 10 BMs-related genes was an independent prognostic predictor. Such a prognostic value of BMs-based signature in validation cohort of the GSE72094 were further verified. The GEO verification, C-index, and ROC curve showed that the nomogram had accurate prediction performance. The functional analysis suggested that BMs were mainly enriched in extracellular matrix-receptor (ECM-receptor) interaction. Moreover, the BMs-based model was correlated with immune checkpoint. Conclusion: This study identified BMs-based risk signature genes and demonstrated their ability to predict prognosis and guide personalized treatment of patients with LUAD.Copyright © 2023 Zhu, Liu, Qiu, Niu, Dong and Song.