Proteomic markers of colorectal cancer risk: results from the European Prospective Investigation into Cancer and Nutrition
Lee, M. A.; Viallon, V.; Muller, D. C.; Wang, Z.; Murphy, N.; Yarmolinsky, J.; Cross, A. J.; Ferrari, P.; Jakszyn, P.; Jenab, M.; Kolijn, P. M.; Masala, G.; Pali, D.; Panico, S.; Peters, U.; Sacerdote, C.; Smith-Byrne, K.; Travis, R. C.; Tsilidis, K. K.; Tumino, R.; Van-Guelpen, B.; Vermeulen, R.; Verschuren, M.; Wareham, N.; Chatterjee, N.; Platz, E.; Riboli, E.; Gunter, M. J.
Show abstract
Background: Circulating proteins are promising biomarkers of colorectal cancer risk. Most studies combine colon and rectal cancer despite their biological differences. Objective: To identify proteins associated with colorectal, colon, and rectal cancer risk and triangulate findings using cross-cohort confirmation and Mendelian randomisation (MR). Design: We profiled 6,402 proteins, using the SomaScan 7K assay in a case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC; 658 colon and 319 rectal cancers). Cox models identified proteins associated with cancer risk at a false discovery rate <5%. Findings were confirmed in the Atherosclerosis Risk in Communities study (ARIC; SomaScan; 235 colon, 36 rectal) and UK Biobank (Olink Explore; 321 colon, 164 rectal). MR and genetic colocalisation evaluated potential causality. Results: We identified 36 proteins associated with colorectal cancer, 50 with colon cancer, and 2 with rectal cancer; 20 of the 36 were also associated with colon cancer and none with rectal cancer, and no protein was associated with both sites. Seven of the 40 colon cancer proteins available in ARIC were confirmed, including COMP, MIC-1/GDF15, TFF3, and THIK. Of these, MIC-1/GDF15 and TFF3 were also confirmed in UK Biobank. COMP was inversely associated with colon cancer risk observationally and in MR analyses. Conclusion: Colon and rectal cancers have different pre-diagnostic proteomic profiles, underlining the need for site-specific analyses. COMP, MIC-1/GDF15, and TFF3 were the most robustly supported biomarkers of colon cancer risk. Future work should prioritise experimental validation of these associations and use of repeated measures to define timing.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study 93%
- Risk of cancer in regular and low meat-eaters, fish-eaters, and vegetarians: a prospective analysis of UK Biobank participants 93%
- Separating the effects of early and later life adiposity on colorectal cancer risk: a Mendelian randomization study 92%
Similar papers in this journal
- Diagnostic performance of the faecal immunochemical test for patients with low-risk symptoms of colorectal cancer in primary care: a service evaluation in the South West of England 92%
- Molecular pathways in post-colonoscopy versus detected colorectal cancers: results from a nested case-control study 91%
- Lipoprotein and metabolite associations to breast cancer risk in the HUNT2 study 90%
Similar papers in this journal
- Red meat intake interacts with a TGF-β-pathway-based polygenic risk score to impact colorectal cancer risk: Application of a novel approach for polygenic risk score construction. 94%
- A new colorectal cancer risk prediction model incorporating family history, personal and environmental factors 91%
- The Multiethnic Cohort: A Resource for the study of Genetic and non-Genetic Cancer Risk Across Populations 90%
Similar papers in this journal
- Modifiable lifestyle factors and genetic risk of obesity in Indians 92%
- Increased adiposity is protective for breast and prostate cancer: a Mendelian randomisation study using up to 132,413 breast cancer cases and 85,907 prostate cancer cases. 92%
- Modulation of intestinal bile acids influences colonic mucosal responses 91%
Similar papers in this journal
- Deep plasma proteomics identifies and validates an eight-protein biomarker panel that separate benign from malignant tumors in ovarian cancer 90%
- Machine learning-based proteogenomic data modeling identifies circulating plasma biomarkers for early detection of lung cancer 88%
- Comparing DXA and MRI body composition measurements in cross-sectional and longitudinal cohorts 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.