DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk

Ning Wang; Jing Sun; Tao Pang; Haohao Zheng; Fengji Liang; Xiayue He; Danian Tang; Tao Yu; Jianghui Xiong; Suhua Chang

doi:10.3389/fnmol.2022.845212

DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk

Front Mol Neurosci. 2022 Feb 23:15:845212. doi: 10.3389/fnmol.2022.845212. eCollection 2022.

Authors

Ning Wang¹, Jing Sun², Tao Pang³, Haohao Zheng³, Fengji Liang⁴, Xiayue He¹, Danian Tang⁵, Tao Yu⁶, Jianghui Xiong^{4

7

8}, Suhua Chang³

Affiliations

¹ Affective Disorder Department, Beijing Huilongguan Hospital, Beijing, China.
² Department of Biobank, Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China.
³ NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Peking University Institute of Mental Health, Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing, China.
⁴ State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China.
⁵ Gastrointestinal Surgery Department, Beijing Hospital, Beijing, China.
⁶ Department of Medical Imaging, Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China.
⁷ Deepome. Inc., Beijing, China.
⁸ Lab of Epigenetics and Advanced Health Technology, Space Science and Technology Institute, Shenzhen, China.

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.

Keywords: DNA methylation; breast cancer; mDI; major depressive disorder; prediction model.