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.2021 Jan 27;12(2):178.
doi: 10.3390/genes12020178.

Integrated Analysis of Methylomic and Transcriptomic Data to Identify Potential Diagnostic Biomarkers for Major Depressive Disorder

Affiliations

Integrated Analysis of Methylomic and Transcriptomic Data to Identify Potential Diagnostic Biomarkers for Major Depressive Disorder

Yinping Xie et al. Genes (Basel)..

Abstract

Major depressive disorder (MDD) is a mental illness with high incidence and complex etiology, that poses a serious threat to human health and increases the socioeconomic burden. Currently, high-accuracy biomarkers for MDD diagnosis are urgently needed. This paper aims to identify novel blood-based diagnostic biomarkers for MDD. Whole blood DNA methylation data and gene expression data from the Gene Expression Omnibus database are downloaded. Then, differentially expressed/methylated genes (DEGs/DMGs) are identified. In addition, we made a systematic analysis of the DNA methylation on 5'-C-phosphate-G-3' (CpGs) in all of the gene regions, as well as different gene regions, and then we defined a "dominant" region. Subsequently, integrated analysis is employed to identify the robust MDD-related blood biomarkers. Finally, a gene expression classifier and a methylation classifier are constructed using the random forest algorithm and the leave-one-out cross-validation method. Our results demonstrate that DEGs are mainly involved in the inflammatory response-associated pathways, while DMGs are primarily concentrated in the neurodevelopment- and neuroplasticity-associated pathways. Our integrated analysis identified 46 hypo-methylated and up-regulated (hypo-up) genes and 71 hyper-methylated and down-regulated (hyper-down) genes. One gene expression classifier and two DNA methylation classifiers, based on the CpGs in all of the regions or in the dominant regions are constructed. The gene expression classifier possessed the best predictive ability, followed by the DNA methylation classifiers, based on the CpGs in both the dominant regions and all of the regions. In summary, the integrated analysis of DNA methylation and gene expression has identified 46 hypo-up genes and 71 hyper-down genes, which could be used as diagnostic biomarkers for MDD.

Keywords: DNA methylation; diagnostic biomarkers; leave-one-out cross-validation; mRNA expression; major depressive disorder; random forest algorithm.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the analysis process. CpGs, 5′-C-phosphate-G-3′; GO, Gene Ontology; HC, healthy control; hyper-down, hyper-methylated and down-regulated; hypo-up, hypo-methylated and up-regulated; KEGG, Kyoto Encyclopedia of Genes and Genomes; MDD, major depressive disorder; ROC, receiver operating characteristic; TSS, transcriptional start site; UTR, untranslated region.N, number of MDD or HC.
Figure 2
Figure 2
Identification of the DEGs in GES98793. (A) Volcano plots of differentially expressed genes (DEGs) in the MDD patients and healthy controls. The red and blue dots represent the up-regulated and down-regulated genes, respectively, while the black dots refer to the non-DEGs. (B) Heat map of the top 50 DEGs in the MDD patients and healthy controls.
Figure 3
Figure 3
Top 10 GO and KEGG terms from the pathway enrichment analysis of the DEGs: (A) Biological process (BP); (B) molecular function (MF); (C) cellular component (CC); (D) KEGG pathway.
Figure 4
Figure 4
Top 10 GO and KEGG terms of the analysis of the differentially methylated genes (DMGs): (A) Biological process; (B) molecular function; (C) cellular component; (D) KEGG pathway.
Figure 5
Figure 5
Identification of the hypo-up genes and hyper-down genes. Venn diagram of (A) the hypo-up genes and (B) the hyper-down genes. Heatmap of (C) the hypo-up genes and (D) the hyper-down genes between the MDD patients and healthy controls.
Figure 6
Figure 6
Integrated analysis of the DEGs and DMGs based on the CpGs in different regions. (A) Barplot for the CpG distribution of the DMGs. (B) Barplot for the overlapped genes between the DEGs and the different regions of the DMGs. The y-axis stands for the overlapped gene numbers. The x-axis represents different gene regions: TSS1500, TSS200, 5′UTR, Exon1st, body, and 3′UTR. (C) Barplot of the four groups that overlap in each region. Hyper-up represents the hyper-methylated and up-regulated genes. The y-axis is the number of genes. The x-axis represents different gene regions: TSS1500, TSS200, 5′UTR, Exon1st, body, and 3′UTR.
Figure 7
Figure 7
Top 20 genes and importance scores of the hypo-up genes and hyper-down genes in each classifier. Top 20 genes and importance scores of the hypo-up genes in (A) the gene expression classifier, (B) the gene methylation classifier based on CpGs in all of the regions, and (C) the gene methylation classifier based on the CpGs in the dominant regions. Top 20 genes and importance scores of the hyper-down genes in (D) the gene expression classifier, (E) the gene methylation classifier based on the CpGs in all of the regions, and (F) gene methylation classifier based on the CpGs in the dominant regions.
Figure 8
Figure 8
Scatter plots diagrammatizing the relationship between the prediction ability and the number of hypo-up genes and hyper-down genes in each classifier. Classifiers of the hypo-up genes: (A) The gene expression classifier, (B) the gene methylation classifier based on the CpGs in all of the regions, and (C) the gene methylation classifier based on the CpGs in the dominant regions. Classifiers of hyper-down genes: (D) The gene expression classifier, (E) the gene methylation classifier based on the CpGs in all of the regions, and (F) the gene methylation classifier based on the CpGs in the dominant regions. ROC, receiver operating characteristic; AUC, area under the curve; y-axis, AUC value of the ROC curve for the classifier; x-axis, the number of genes in the classifier.
Figure 9
Figure 9
ROC curves for the hypo-up genes and hyper-down gene classifiers. (A) Top 25 hypo-up gene expression classifier. (B) Top 12 hypo-up gene methylation classifier based on the CpGs in all of the regions. (C) Top 23 hypo-up gene methylation classifier based on the CpGs in the dominant regions. (D) Top 31 hyper-down gene expression classifier. (E) Top 2 hyper-down gene methylation classifier based on the CpGs in all of the regions. (F) Top 18 hyper-down gene methylation classifier based on the CpGs in the dominant regions. ROC, receiver operating characteristic; AUC, area under the curve.
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