Identifying Lipid Metabolism-Related Therapeutic Targets and Diagnostic Markers for Lung Adenocarcinoma by Mendelian Randomization and Machine Learning Analysis.

IF 2.3 3区医学 Q3 ONCOLOGY

Thoracic Cancer Pub Date : 2025-03-01 DOI:10.1111/1759-7714.70020

Su Wei, Zhou Guangyao, Tian Xiangdong, Guo Feng, Zhang Lianmin, Zhang Zhenfa

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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Result: 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.

Conclusions: BCHE, FABP4, LPL, PLBD1, and TNFRSF21 represent potential novel reliable diagnostic markers as well as therapeutic targets for LUAD.

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来源期刊

Thoracic Cancer ONCOLOGY-RESPIRATORY SYSTEM

CiteScore

5.20

自引率

3.40%

发文量

439

审稿时长

2 months

期刊介绍： Thoracic Cancer aims to facilitate international collaboration and exchange of comprehensive and cutting-edge information on basic, translational, and applied clinical research in lung cancer, esophageal cancer, mediastinal cancer, breast cancer and other thoracic malignancies. Prevention, treatment and research relevant to Asia-Pacific is a focus area, but submissions from all regions are welcomed. The editors encourage contributions relevant to prevention, general thoracic surgery, medical oncology, radiology, radiation medicine, pathology, basic cancer research, as well as epidemiological and translational studies in thoracic cancer. Thoracic Cancer is the official publication of the Chinese Society of Lung Cancer, International Chinese Society of Thoracic Surgery and is endorsed by the Korean Association for the Study of Lung Cancer and the Hong Kong Cancer Therapy Society. The Journal publishes a range of article types including: Editorials, Invited Reviews, Mini Reviews, Original Articles, Clinical Guidelines, Technological Notes, Imaging in thoracic cancer, Meeting Reports, Case Reports, Letters to the Editor, Commentaries, and Brief Reports.