Wei Su, Guangyao Zhou, Xiangdong Tian, Feng Guo, Lianmin Zhang, Zhenfa Zhang
Department of Endoscopy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Thorac Cancer. 2025 Mar;16(6):e70020. doi: 10.1111/1759-7714.70020.
Lipid metabolic disorders are emerging as a recognized influencing factors of lung adenocarcinoma (LUAD). This study aims to investigate the influence of lipid metabolism-related genes (LMRGs) on the diagnosis and treatment of LUAD and to identify significant biomarkers.
DESeq2 and robust rank aggregation (RRA) analyses were employed to determine the differential expression of LMRGs from TCGA-LUAD and five GEO datasets. Mendelian randomization (MR) was conducted utilizing protein quantitative trait loci (pQTLs) in the deCODE, prot-a, and UKB-PPP Study to estimate causal relationships between plasma proteins and LUAD within the ieu-a-984, ieu-a-965, and FinnGen R10 cohorts as potential drug targets of LUAD. Subsequently, an optimal machine learning model for diagnosing LUAD was established by comparing four models: support vector machine, random forest (RF), glmBoost, and eXtreme Gradient Boosting. Finally, the diagnostic performance of five plasma proteins was validated through nomogram analysis, calibration curve assessment, decision curve analysis (DCA), independent internal and external datasets.
A total of five biomarkers were identified from 1034 LMRGs via MR and differential expression analysis. TNFRSF21 exhibited a positive association with LUAD risk; conversely, BCHE, FABP4, LPL, and PLBD1 demonstrated negative correlations with this risk. The RF machine learning model was determined to be the optimal model for diagnosing LUAD using these five plasma proteins. Ultimately, nomogram construction, calibration curve analysis, DCA, as well as independent internal and external dataset validation confirmed that these biomarkers exhibit excellent diagnostic performance.
BCHE, FABP4, LPL, PLBD1, and TNFRSF21 represent potential novel reliable diagnostic markers as well as therapeutic targets for LUAD.
脂质代谢紊乱正逐渐成为肺腺癌（LUAD）公认的影响因素。本研究旨在探讨脂质代谢相关基因（LMRGs）对LUAD诊断和治疗的影响，并确定重要的生物标志物。
采用DESeq2和稳健秩聚合（RRA）分析来确定来自TCGA-LUAD和五个GEO数据集的LMRGs的差异表达。利用deCODE、prot-a和UKB-PPP研究中的蛋白质定量性状位点（pQTLs）进行孟德尔随机化（MR）分析，以估计ieu-a-984、ieu-a-965和FinnGen R10队列中血浆蛋白与LUAD之间的因果关系，作为LUAD的潜在药物靶点。随后，通过比较支持向量机、随机森林（RF）、glmBoost和极端梯度提升这四种模型，建立了用于诊断LUAD的最优机器学习模型。最后，通过列线图分析、校准曲线评估、决策曲线分析（DCA）以及独立的内部和外部数据集，验证了五种血浆蛋白的诊断性能。
通过MR和差异表达分析，从1034个LMRGs中总共鉴定出五个生物标志物。TNFRSF21与LUAD风险呈正相关；相反，BCHE、FABP4、LPL和PLBD1与该风险呈负相关。确定RF机器学习模型是使用这五种血浆蛋白诊断LUAD的最优模型。最终，列线图构建、校准曲线分析、DCA以及独立的内部和外部数据集验证证实，这些生物标志物具有出色的诊断性能。
BCHE、FABP4、LPL、PLBD1和TNFRSF21代表了LUAD潜在的新型可靠诊断标志物和治疗靶点。
