ArticleFrontiers in molecular neuroscience2022
DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk.
Article in Frontiers in molecular neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches.Translational psychiatry · 2024Pooled it
- Integrating DNA methylation biomarkers for breast cancer risk prediction using artificial intelligence.Scientific reports · 2026Article
- The Co-Delivery of Natural Products and Small RNAs for Cancer Therapy: A Review.Molecules (Basel, Switzerland) · 2025Review
- Rapid genome-wide profiling of DNA methylation and genetic variation using guide positioning sequencing (GPS).Frontiers in cell and developmental biology · 2024Article
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Authors and funding
10 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Major 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. Methods: Methylation 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. Results: The 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. Conclusion: Our results indicated that DNA methylation could help to explain the pathogenesis of MDD and assist with its diagnosis.
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