Background Serum proteomic biomarkers offer a promising approach for early detection of cancer. In this study, we aimed to identify proteomic profiles that could distinguish colon cancer cases from controls using serial pre-diagnostic serum samples. Methods This was a nested case-control study of active duty military members. Cases consisted of 264 patients diagnosed with colon cancer between 2001 and 2009. Controls were matched to cases on age, gender, race, serum sample count, and collection date. We identified peaks that discriminated cases from controls using random forest data analysis with a 2/3 training and 1/3 validation data set. We then included epidemiologic data to see if further improvement of model performance was obtainable. Proteins that corresponded to discriminatory peaks were identified. Results Peaks with m/z values of 3119.32, 2886.67, 2939.23, and 5078.81 were found to discriminate cases from controls with a sensitivity of 69% and a specificity of 67% in the year before diagnosis. When smoking status was included, sensitivity increased to 76% while histories of other cancer and tonsillectomy raised specificity to 76%. Peaks at 2886.67 and 3119.32 m/z were identified as histone acetyltransferases while 2939.24 m/z was a transporting ATPase subunit. Conclusion Proteomic profiles in the year before cancer diagnosis have the potential to discriminate colon cancer patients from controls and the addition of epidemiologic information may increase the sensitivity and specificity of discrimination. Impact Our findings indicate the potential value of using serum pre-diagnostic proteomic biomarkers in combination with epidemiologic data for early detection of colon cancer.
Purpose Epidemiologic studies have previously reported an association between high fat intake and colon cancer risk. However, findings have generally been inconclusive. This study aimed to investigate the association between fat as a percentage of energy intake and colon cancer risk. Methods Study subjects included 215 cases and 215 matched controls identified by the Defense Medical Surveillance System. Percentage energy from fat (Pfat) was estimated using a short dietary screener developed by the National Cancer Institute for two time periods: the year before first blood draw and colon cancer diagnosis. Conditional logistic regression analysis was used to assess the relationship between colon cancer risk and Pfat. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Results Compared with the lowest quartile of Pfat, the adjusted odds of having colon cancer were 2.00 (95% CI 0.96–4.18), 2.83 (95% CI 1.41–5.66) and 3.37 (95% CI 1.58–7.17) for the second, third, and highest quartiles in the year before cancer diagnosis. Similar results were observed for Pfat at an earlier time. Conclusion Our findings suggest a positive association between Pfat and colon cancer in the U.S. military population.
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