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Supplementary Material for: Analysis of lifestyle and genetic risk factors in urban women in China who had malignant or suspected malignant breast nodules identified via breast cancer screening

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posted on 2025-03-19, 12:55 authored by figshare admin kargerfigshare admin karger, Mo J., Lei L., Yin J., Li X., Zhou H., Zhang L.-J., Hong W.
Objective: Breast cancer seriously endangers women's health. It is very important to analyze the lifestyle and genetic risk factors for people with malignant or suspected malignant nodules in breast cancer screening for the prevention of breast cancer. Methods: A total of 3142 urban female residents in southern China completed a clinical screening for breast cancer. The participants completed questionnaires on living environmental factors and underwent clinical imaging examinations and genetic testing of 73 SNP loci. According to the BI-RADS classification results, the population was divided into positive and negative groups. Key factors were identified through intergroup differences and association analysis. Lifestyle models, SNP models, and lifestyle+PRS models were constructed. ROC curves and nomograms were used to evaluate the classification effect of the model. Results: There were 10 lifestyle factors that were significantly different between the groups, 4 of which were significantly associated with positive breast imaging results (P<0.05), including age (OR=0.972, 95% CI: 0.957-0.988), duration of breastfeeding (0.982, 0.970-0.994), history of benign breast disease (1.838, 1.299-2.599), and high-fat diet (1.507, 1.166-1.947). There were 5 significant SNPs, including BRCA2-rs1799955, TLR1-rs4833095, ZNF365-rs10822013, SLC4A7-rs4973768, and BRCA2-rs144848. The AUC values for the lifestyle, SNP and lifestyle+PRS models were 0.625, 0.598 and 0.633, respectively. The C-index of the lifestyle+PRS model was 0.633. Conclusion: Advocating breastfeeding, reducing the intake of high-fat diets, and protecting breast health may help to lower the risk of positive results in breast screenings. The combination of lifestyle factors and genetic factors can enhance the predictive power of the model.

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