Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer

Jing Sun, Yue Liu, Jianhui Zhao, Bin Lu, Siyun Zhou, Wei Lu, Jingsun Wei, Yeting Hu, Xiangxing Kong, Junshun Gao, Hong Guan, Junli Gao, Qian Xiao () and Xue Li
Additional contact information
Jing Sun: The Second Affiliated Hospital, Zhejiang University School of Medicine
Yue Liu: The Second Affiliated Hospital, Zhejiang University School of Medicine
Jianhui Zhao: The Second Affiliated Hospital, Zhejiang University School of Medicine
Bin Lu: The Second Affiliated Hospital of Zhejiang University School of Medicine
Siyun Zhou: The Second Affiliated Hospital, Zhejiang University School of Medicine
Wei Lu: The Second Affiliated Hospital, Zhejiang University School of Medicine
Jingsun Wei: The Second Affiliated Hospital, Zhejiang University School of Medicine
Yeting Hu: The Second Affiliated Hospital, Zhejiang University School of Medicine
Xiangxing Kong: The Second Affiliated Hospital, Zhejiang University School of Medicine
Junshun Gao: Cosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School
Hong Guan: Cosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School
Junli Gao: Cosmos Wisdom Mass Spectrometry Center of Zhejiang University Medical School
Qian Xiao: The Second Affiliated Hospital, Zhejiang University School of Medicine
Xue Li: The Second Affiliated Hospital, Zhejiang University School of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract This study aims to identify colorectal cancer (CRC)-related proteomic profiles and develop a prediction model for CRC onset by integrating proteomic profiles with genetic and non-genetic factors (QCancer-15) to improve the risk stratification and estimate of personalized initial screening age. Here, using a two-stage strategy, we prioritize 15 protein biomarkers as predictors to construct a protein risk score (ProS). The risk prediction model integrating proteomic profiles with polygenic risk score (PRS) and QCancer-15 risk score (QCancer-S) shows improved performance (C-statistic: 0.79 vs. 0.71, P = 4.94E–03 in training cohort; 0.75 vs 0.69, P = 5.49E–04 in validation cohort) and net benefit than QCancer-S alone. The combined model markedly stratifies the risk of CRC onset. Participants with high ProS, PRS, or combined risk score are proposed to start screening at age 46, 41, or before 40 years old. In this work, the integration of blood proteomics with PRS and QCancer-15 demonstrates improved performance for risk stratification and clinical implication for the derivation of risk-adapted starting ages of CRC screening, which may contribute to the decision-making process for CRC screening.

Date: 2024
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DOI: 10.1038/s41467-024-52894-2

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