| Title: | Pre-diagnostic plasma proteomics profile uncovers new biomarkers and mechanistic insights for incident kidney cancer |
| Journal: | International Journal of Surgery |
| Published: | 10 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40928391/ |
| DOI: | https://doi.org/10.1097/js9.0000000000003474 |
Publication 19523
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Abstract
BACKGROUND: The pathophysiological changes driving incident kidney cancer remain unclear. This study aimed to identify protein biomarkers and underlying mechanisms using pre-diagnostic plasma proteomics.</p>
MATERIALS AND METHODS: Among 48 851 UK Biobank participants, 165 were diagnosed with kidney cancer, and 2911 plasma proteins were analyzed. Dynamic changes in significant proteins were assessed up to 15 years before diagnosis using locally estimated scatterplot smoothing method. A mediation analysis using a four-component framework was conducted to evaluate the mediating role of proteomic features in the associations of body mass index (BMI) and smoking with kidney cancer risk. Additionally, an absolute shrinkage and selection operator regression model was developed for proteomics-based risk prediction.</p>
RESULTS: Over a follow-up period exceeding 11 years, 24 proteins were significantly associated with kidney cancer risk ( P < 0.05, Bonferroni-corrected for 2911), with Hepatitis A Virus Cellular Receptor 1 (HAVCR1) exhibiting the most statistically significant association (HR = 3.18, 95% CI: 2.70-3.74, P = 1.11 × 10 -40 ). Trajectory modeling revealed that HAVCR1 exhibited the most significant fluctuations, with abnormal expression detectable up to 15 years before diagnosis. Unsupervised clustering identified four distinct protein trajectory patterns, suggesting different mechanisms may drive kidney cancer progression at various stages. Proteomic data mediated the effects of BMI and smoking on cancer risk, contributing 38.6% and 9.2% to the risk, respectively. The proteomic model significantly improved kidney cancer risk prediction compared to the clinical model (concordance index [C-index]: 0.811 vs. 0.713, P = 0.029), with HAVCR1 alone demonstrating comparable discriminative ability (C-index: 0.754).</p>
CONCLUSIONS: This large-scale plasma proteomics study highlights the potential of biomarkers, particularly HAVCR1, for early detection and insight into kidney cancer pathophysiology.</p>
14 Keywords
- Aged
- Biomarkers, Tumor
- Blood Proteins
- Body Mass Index
- Female
- Hepatitis A Virus Cellular Receptor 1
- Humans
- Incidence
- Kidney Neoplasms
- Male
- Middle Aged
- Proteomics
- Risk Factors
- United Kingdom
5 Authors
- Wen Liu
- Wei Chen
- Dahai Dong
- Guiming Zhang
- Nianzeng Xing
1 Application
| Application ID | Title |
|---|---|
| 184070 | Multimodal Analysis for Predicting Occurrence, Development, and Identifying Therapeutic Targets in Aging and Aging-Related Disorders |
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