ABSTRACT
Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the “black-box” aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.
ABSTRACT
Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the “black-box” aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.
ABSTRACT
PURPOSE: The high mobility group box 1 (HMGB1) protein has roles in apoptosis and immune responses by acting as a ligand for receptor for advanced glycation end products (RAGE), Toll-like receptors (TLRs), and triggering receptor expressed on myeloid cells 1. In particular, HMGB1/RAGE is involved in tumor metastasis by inducing matrix metalloproteinase 2 (MMP2) and MMP9 expression. We investigated the associations between genetic variations in HMGB1-related genes and disease-free survival (DFS) and overall survival (OS) in Korean female breast cancer patients. METHODS: A total of 2,027 patients in the Seoul Breast Cancer Study were included in the analysis. One hundred sixteen single nucleotide polymorphisms (SNPs) were extracted from eight genes. A multivariate Cox proportional hazards model was used to estimate the hazard ratio and 95% confidence interval (CI) of each SNP. The effects of the SNPs on breast cancer prognosis were assessed at cumulative levels with polygenic risk scores. RESULTS: The SNPs significantly associated with DFS were rs243867 (hazard ratio, 1.26; 95% CI, 1.05–1.50) and rs243842 (hazard ratio, 1.24; 95% CI, 1.03–1.50); both SNPs were in MMP2. The SNPs significantly associated with OS were rs243842 in MMP2 (hazard ratio, 1.33; 95% CI 1.03–1.71), rs4145277 in HMGB1 (hazard ratio, 1.29; 95% CI, 1.00–1.66), rs7656411 in TLR2 (hazard ratio, 0.76; 95% CI, 0.60–0.98), and rs7045953 in TLR4 (hazard ratio, 0.50; 95% CI, 0.29–0.84). The polygenic risk score results for the DFS and OS patients showed third tertile hazard ratios of 1.72 (95% CI, 1.27–2.34) and 2.75 (95% CI, 1.79–4.23), respectively, over their first tertile references. CONCLUSION: The results of the present study indicate that genetic polymorphisms in HMGB1-related genes are related to breast cancer prognosis in Korean women.