DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk - Wang, Ning; Sun, Jing; Pang, Tao; Zheng, Haohao; Liang, Fengji; He, Xiayue; Tang, Danian; Yu, Tao; Xiong, Jianghui; Chang, Suhua (Frontiers in Molecular Neuroscience, 2022)
Major depression disorder (MDD) has become a leading cause of disability worldwide. However, the diagnosis of MDD remains dependent on the clinical view and inventory, without effective biomarkers to help diagnosis and treatment. DNA methylation is promising for elucidating the etiology of MDD and predicting susceptibility of MDD. Our overarching aim is to identify biomarkers based on DNA methylation, use it to propose a methylation prediction score for MDD and apply it to evaluate the risk of breast cancer. The methylation data of 533 samples were obtained from the Gene Expression Omnibus (GEO) database, of which 324 individuals were diagnosed with MDD. The Statistical difference of DNA Methylation between Promoter and Other body region (SIMPO) score for each gene was calculated based on the DNA methylation data. Based on SIMPO scores, we selected the top genes that showed a correlation with MDD in random resampling and then proposed a methylation-derived Depression Index (mDI) by combining the SIMPO of selected genes to predict MDD. A validation analysis was carried out using other DNA methylation data of 194 samples derived from the GEO database. Furthermore, we applied it to construct a prediction model for the risk of breast cancer using a stepwise regression method. The best mDI composed of 426 genes, including 245 positive and 181 negative correlations, was constructed to predict MDD and obtained high predictive power with an AUC of 0.88 in the discovery data. We also observed moderate power of the mDI in the validation data with an OR of 1.79. Biological function exploration of the 426 genes showed that they were functionally enriched in Eph Ephrin signaling, beta-catenin Wnt signaling pathways. Finally, the mDI was further used to construct a predictive model for breast cancer and yielded AUCs ranging from 0.70 to 0.67. The results indicate that DNA methylation can help to explain the pathogenesis of MDD and can be a promising avenue to facilitate diagnose MDD in the future.