DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk.
DNA Methylation Markers and Prediction Model for Depression and Their Contribution for Breast Cancer Risk.
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DNA 甲基化标记物和抑郁症预测模型及其对乳腺癌风险的贡献
DOI：
10.3389/fnmol.2022.845212
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发表时间：
2022
影响因子：
4.8
通讯作者：
Chang S
中科院分区：
文献类型：
--
作者：
Wang N;Sun J;Pang T;Zheng H;Liang F;He X;Tang D;Yu T;Xiong J;Chang S
重度抑郁症（MDD）已成为全球残疾的主要原因。然而，疾病的诊断依赖于临床经验和库存。目前，还没有可靠的生物标志物来帮助诊断和治疗。DNA甲基化模式可能是阐明MDD病因和预测患者易感性的一种有前途的方法。我们的首要目标是基于DNA甲基化识别生物标志物，然后用它来提出MDD的甲基化预测评分，我们希望这将有助于我们评估乳腺癌的风险。从基因表达综合数据库（GEO）中提取来自533个样本的甲基化数据，其中324个个体被诊断为MDD。基于DNA甲基化数据计算每个基因的启动子和其它体区之间的DNA甲基化（SIMPO）评分的统计学差异。基于SIMPO评分，我们选择了与MDD相关性最高的基因，然后通过结合所选基因的SIMPO来预测MDD，从而提出了甲基化衍生的抑郁指数（mDI）。然后使用从GEO数据库提取的194个样品的额外DNA甲基化数据进行验证分析。此外，我们应用mDI构建了一个预测模型，使用逐步回归和随机森林方法的乳腺癌的风险。最佳mDI来自426个基因，其中包括245个正相关和181个负相关。它被构建为在发现数据集中以高预测能力（AUC为0.88）预测MDD。此外，我们在验证数据集中观察到mDI的中等功效，OR为1.79。对426个基因的生物学功能评估表明，它们在Eph Ephrin信号通路和β-连环蛋白Wnt信号通路中功能富集。然后使用mDI构建乳腺癌的预测模型，其AUC范围为0.70至0.67。结果表明，DNA甲基化有助于解释MDD的发病机制，并有助于MDD的诊断。
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. 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. 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. Our results indicated that DNA methylation could help to explain the pathogenesis of MDD and assist with its diagnosis.
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