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作者

Fei Wang Zeming Guo Wei Tang Wei Cao Xuesi Dong Yongjie Xu Chenran Wang Jiaxin Xie Xiaoyue Shi Zilin Luo Yadi Zheng Guochao Zhang Na Ren Nan Zhang Donghua Wei Lingbin Du Fengwei Tan Ni Li

作者单位

²Contributed equally to this work. ⁸Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China ⁵Department of Cancer Prevention, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences ³Department of Diagnostic Radiology, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China ⁶Office for Cancer Prevention and Control, Anhui Provincial Cancer Hospital, Hefei 230031, China ⁴Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China ⁷Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)/ Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China ¹Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

刊名

Journal of Advanced Research

年份

2025

ISSN

2090-1232

关键词

Early Onset Lung Cancer Lipidomics Prediction model Early detection Biomarker

摘要

Introduction Lung cancer is the leading cause of cancer-related mortality worldwide. While traditionally associated with older adults, early-onset lung cancer is rising, particularly in Asia, which accounts for 75.9% of global cases. Existing lung cancer screening guidelines primarily focus on older populations, which may result in missed opportunities for early detection in younger individuals. Given its distinct clinical characteristics, EOLC warrants dedicated research and targeted intervent...更多

Introduction Lung cancer is the leading cause of cancer-related mortality worldwide. While traditionally associated with older adults, early-onset lung cancer is rising, particularly in Asia, which accounts for 75.9% of global cases. Existing lung cancer screening guidelines primarily focus on older populations, which may result in missed opportunities for early detection in younger individuals. Given its distinct clinical characteristics, EOLC warrants dedicated research and targeted interventions. Objectives This study aims to characterize the lipidomic profiles specific to EOLC patients and develop a biomarker-based predictive model to improve risk assessment and early detection. Methods The discovery and validation sets included 111 EOLC cases and 127 non-EOLC controls, all aged 18–49 years. Targeted lipidomics analysis, combined with logistic regression, was performed on plasma samples to identify differentially expressed lipids species. Clustering and pathway analyses were conducted to uncover and visualize the internal signatures of the identified lipids. Key lipids were refined using the LASSO-bootstrap regression method combined with the Boruta algorithm. A random forest model was subsequently employed to develop a robust prediction model for EOLC. Results A total of 843 lipids were identified, with 60 differentially expressed lipids detected, of which 33 were validated in the validation set. Cluster analysis revealed that passive smoking and current smoking were associated with elevated lipid metabolite profiles in EOLC patients. The validated lipids were further refined using LASSO and Boruta methods, which ultimately selected 6 lipids for inclusion in a prediction model constructed with random forest. This model achieved an area under the curve of 0.874 in the validation set. Conclusion Our study identified lipidomic signatures associated with the risk of EOLC, offering potential translational implications for lung cancer prevention strategies.收起

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