协和医学杂志2026，Vol.17Issue(3)：652-662,11.DOI:10.12290/xhyxzz.2026-0217
脂质组学联合机器学习筛选老年早期肺癌标志物
Lipidomics Combined with Machine Learning for Screening Biomarkers of Early-Stage Lung Cancer in the Elderly
摘要
Abstract
Objective Based on plasma lipidomics combined with machine learning approaches,this study aimed to screen molecular biomarkers for the diagnosis of early-stage lung cancer in elderly patients and to evaluate their diagnostic performance.Methods This was a retrospective diagnostic study consisting of two parts.The first part involved molecular biomarker screening.Elderly patients with early-stage lung cancer(early lung cancer group),patients with benign pulmonary nodules(benign nodule group),and contemporaneous healthy in-dividuals undergoing physical examinations(healthy control group)were enrolled from Peking University People's Hospital between November 2023 and November 2024.In addition,early-stage lung cancer patients and healthy controls meeting the inclusion criteria from a previous study of our research group were included as an independent validation cohort.Plasma samples were collected from all subjects,and untargeted lipidomics analysis was per-formed using high-performance liquid chromatography-mass spectrometry.Principal component analysis and or-thogonal partial least squares discriminant analysis were used to evaluate metabolic differences between groups.L1-regularized support vector machine combined with incremental feature selection was employed to screen diag-nostic biomarkers for early-stage lung cancer.Model performance was assessed using receiver operating character-istic curves,calibration curves,Brier scores,and decision curve analysis.The second part involved functional validation of the molecular biomarkers using the human lung adenocarcinoma cell line A549,with palmitoylcarni-tine(CAR 16:0)selected as a representative biomarker for functional validation via CCK-8 and cell scratch as-says.Results A total of 36 patients in the early lung cancer group,35 patients in the benign nodule group,and 41 healthy controls were enrolled,along with an independent validation cohort of 110 individuals(59 patients with early-stage lung cancer and 51 healthy controls).The principal component analysis results demonstrated that quality control samples were tightly aggregated at the centroid of all samples,reflecting robust instrument perform-ance and dependable data quality.Orthogonal partial least squares discriminant analysis revealed significant meta-bolic differences between the early lung cancer group and the control group(benign nodule group+healthy con-trol group)(R2X = 0.406,R2Y = 0.529,Q2Y = 0.44).L1-regularized support vector machine identified five carnitine-related lipids-palmitoleoylcarnitine(CAR 16:1),palmitoylcarnitine,α-linolenoylcarnitine(CAR 18:3),linoleoylcarnitine(CAR 18:2),and oleoylcarnitine(CAR 18:1)-as diagnostic biomarkers for early-stage lung cancer,all with stability values>98％.In the screening scenario(early lung cancer group vs.be-nign nodule group+healthy control group),the model based on these five biomarkers achieved an area under the curve(AUC)of 0.895(95％CI:0.700-1.000)for diagnosing early-stage lung cancer,with a sensitivity of 98.4％,specificity of 63.9％,and accuracy of 75.0％.For differentiating early-stage lung cancer from benign pulmonary nodules,the model yielded an AUC of 0.877(95％CI:0.797-0.965),sensitivity of 86.1％,and specificity of 80.0％.For differentiating early-stage lung cancer from healthy controls,the model yielded an AUC of 0.929(95％CI:0.877-0.988),sensitivity of 94.4％,and specificity of 85.4％.Calibration and decision curve analyses demonstrated good model calibration and overall net benefit for patients with early-stage lung canc-er.In the independent validation cohort,the model achieved an AUC of 0.874(95％CI:0.781-0.940)for di-agnosing early-stage lung cancer,with a sensitivity of 86.4％,specificity of 82.4％,and accuracy of 84.5％.In vitro experiments showed that palmitoylcarnitine inhibited the proliferation and migration of A549 cells,with a half-maximal inhibitory concentration of 57.12 μmol/L.Conclusions The five plasma carnitine-related lipids screened based on untargeted lipidomics and machine learning may serve as potential molecular biomarkers for the diagnosis of early-stage lung cancer in elderly patients.The high-sensitivity characteristic of the model makes it particularly suitable for screening scenarios in early-stage lung cancer.关键词
早期肺癌/脂质组学/肉碱/机器学习/诊断标志物Key words
early-stage lung cancer/lipidomics/carnitine/machine learning/diagnostic biomarker分类
医药卫生引用本文复制引用
王晴,何越,刘旭,李紫凡,陈克终..脂质组学联合机器学习筛选老年早期肺癌标志物[J].协和医学杂志,2026,17(3):652-662,11.基金项目
雄安新区科技创新专项(2023XAGG0071) （2023XAGG0071）
北京市自然科学基金-海淀原始创新联合基金(L222021) （L222021）
四大慢病重大专项(2023ZD0501900) （2023ZD0501900）
研究型病房卓越临床研究计划项目(BRWEP2024W034080200) （BRWEP2024W034080200）
国家自然科学基金(82525052,82388102) （82525052,82388102）
中国医学科学院医学与健康科技创新工程(2021RU002,2021-I2M-5-002) （2021RU002,2021-I2M-5-002）
北京市自然科学基金(Z240013) Xiong'an New Area Science and Technology Innovation Special Project(2023XAGG0071) （Z240013）
Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund(L222021) （L222021）
National Key Research and Development Program for Major Chronic Diseases(2023ZD0501900) （2023ZD0501900）
Research-Oriented Ward Outstanding Clinical Research Program(BRWEP2024W034080200) （BRWEP2024W034080200）
National Natural Science Foundation of China(82525052,82388102) （82525052,82388102）
Chinese Academy of Medical Sciences Innovation Fund for Medical Sci-ences(2021RU002,2021-I2M-5-002) （2021RU002,2021-I2M-5-002）
Beijing Natural Science Foundation(Z240013) （Z240013）
