Lung cancer (Amsterdam, Netherlands) · 2026
A dynamic proteomic signature for the prediction of lung Cancer: a longitudinal analysis in the UK Biobank cohort.
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Liang Jie, Feng Yi, Gui Daying, Zheng Xiangyuan, Gao Rui, Zhou Jingdong, Liang Jiekai, Diao Jingwen, He Juan, Zhao Yilin, He Jianxing, Wu Xuan, Cheng Bo, Liang Wenhua
Аннотация
BACKGROUND: To understand the molecularly obscure pre-diagnostic phase of lung cancer, we mapped the temporal evolution of the plasma proteome for new biological insights and improved risk prediction.
METHODS: Leveraging the UK Biobank prospective cohort, we analyzed 2,921 plasma proteins from 37,759 participants, including 342 incident lung cancer cases identified over a median follow-up of 11.7 years. We employed time-stratified Cox models, locally weighted scatterplot smoothing (LOESS) trajectory modeling, and hierarchical clustering to characterize protein dynamics relative to the time of diagnosis. A multi-algorithm machine learning pipeline was used to develop a predictive signature, and two-sample Mendelian randomization was performed to infer causal relationships.
RESULTS: We identified 340 risk-associated proteins showing significant temporal heterogeneity. Long-term risk (>5 years pre-diagnosis) was linked to proteins like CEACAM5, indicating early dysregulation of cell adhesion. Imminent risk (<5 years) was marked by a surge in inflammatory proteins like IL6. These dynamics were resolved into four distinct trajectory patterns, creating a molecular timeline of carcinogenesis. A machine learning-derived 28-protein signature, integrated with clinical factors and Polygenic Risk Score (PRS), achieved outstanding predictive performance (AUC = 0.830). Mendelian randomization also suggested a causal role for some proteins of 340 risk-associated proteins in lung cancer development.
CONCLUSION: Our findings establish that lung cancer evolves through a dynamic sequence of protein changes. This provides a new model for understanding pre-diagnostic disease, and our 28-protein signature is a powerful tool for precision screening to identify individuals with active disease progression.
METHODS: Leveraging the UK Biobank prospective cohort, we analyzed 2,921 plasma proteins from 37,759 participants, including 342 incident lung cancer cases identified over a median follow-up of 11.7 years. We employed time-stratified Cox models, locally weighted scatterplot smoothing (LOESS) trajectory modeling, and hierarchical clustering to characterize protein dynamics relative to the time of diagnosis. A multi-algorithm machine learning pipeline was used to develop a predictive signature, and two-sample Mendelian randomization was performed to infer causal relationships.
RESULTS: We identified 340 risk-associated proteins showing significant temporal heterogeneity. Long-term risk (>5 years pre-diagnosis) was linked to proteins like CEACAM5, indicating early dysregulation of cell adhesion. Imminent risk (<5 years) was marked by a surge in inflammatory proteins like IL6. These dynamics were resolved into four distinct trajectory patterns, creating a molecular timeline of carcinogenesis. A machine learning-derived 28-protein signature, integrated with clinical factors and Polygenic Risk Score (PRS), achieved outstanding predictive performance (AUC = 0.830). Mendelian randomization also suggested a causal role for some proteins of 340 risk-associated proteins in lung cancer development.
CONCLUSION: Our findings establish that lung cancer evolves through a dynamic sequence of protein changes. This provides a new model for understanding pre-diagnostic disease, and our 28-protein signature is a powerful tool for precision screening to identify individuals with active disease progression.