Abstract
BackgroundMajor depressive disorder (MDD) has become a leading cause of disability worldwide. However, the diagnosis of the disorder is dependent on clinical experience and inventory. At present, there are no reliable biomarkers to help with diagnosis and treatment. DNA methylation patterns may be a promising approach for elucidating the etiology of MDD and predicting patient susceptibility. Our overarching aim was to identify biomarkers based on DNA methylation, and then use it to propose a methylation prediction score for MDD, which we hope will help us evaluate the risk of breast cancer.MethodsMethylation data from 533 samples were extracted from the Gene Expression Omnibus (GEO) database, of which, 324 individuals were diagnosed with MDD. 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, then proposed a methylation-derived Depression Index (mDI) by combining the SIMPO of the selected genes to predict MDD. A validation analysis was then performed using additional DNA methylation data from 194 samples extracted from the GEO database. Furthermore, we applied the mDI to construct a prediction model for the risk of breast cancer using stepwise regression and random forest methods.ResultsThe optimal mDI was derived from 426 genes, which included 245 positive and 181 negative correlations. It was constructed to predict MDD with high predictive power (AUC of 0.88) in the discovery dataset. In addition, we observed moderate power for mDI in the validation dataset with an OR of 1.79. Biological function assessment of the 426 genes showed that they were functionally enriched in Eph Ephrin signaling and beta-catenin Wnt signaling pathways. The mDI was then used to construct a predictive model for breast cancer that had an AUC ranging from 0.70 to 0.67.ConclusionOur results indicated that DNA methylation could help to explain the pathogenesis of MDD and assist with its diagnosis.
Highlights
Major depressive disorder (MDD) is a mental disease characterized by pervasive and persistent low mood with loss of pleasure, feelings of guilt, and inferiority
We investigated the association of DNA methylation at the gene level with depression and proposed a methylation-derived depression index to predict depression
We observed that the optimal methylation-derived depression index (mDI) model was when the number of genes was 426, with coefficient = 0.59 and p-value = 2.06e-51 for the correlation between the mDI and case–control phenotype (Figure 1A)
Summary
Major depressive disorder (MDD) is a mental disease characterized by pervasive and persistent low mood with loss of pleasure, feelings of guilt, and inferiority. A previous epidemiological study using a large patient cohort identified adverse life events, in childhood, that were highly associated with the onset of MDD, with its effects persisting beyond childhood (Kessler et al, 1997). A previous meta-analysis demonstrated that the heritability of MDD was approximately 31–42% (Sullivan et al, 2000) This is much lower compared to other mental diseases, such as schizophrenia, which is estimated to be approximately 70% (Sullivan et al, 2003). The interaction of gene and the environment has drawn increasing attention Life event such as having a stressful life have been highly correlated with MDD and are partly influenced by genetic factors (Kessler, 1997; Kendler et al, 1999). Our overarching aim was to identify biomarkers based on DNA methylation, and use it to propose a methylation prediction score for MDD, which we hope will help us evaluate the risk of breast cancer
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