Methylation Datasets Research Articles

Integrated bioinformatics approach reveals methylation-regulated differentially expressed genes in obesity.

To identify DNA methylation and gene expression profiles involved in obesity by implementing an integrated bioinformatics approach. Gene expression (GSE94752, GSE55200, and GSE48964) and DNA methylation (GSE67024 and GSE111632) datasets were obtained from the GEO database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in subcutaneous adipose tissue of patients with obesity were identified using GEO2R. Methylation-regulated DEGs (MeDEGs) were identified by overlapping DEGs and DMGs. The protein-protein interaction (PPI) network was constructed with the STRING database and analyzed using Cytoscape. Functional modules and hub-bottleneck genes were identified by using MCODE and CytoHubba plugins. Functional enrichment analyses were performed based on Gene Ontology terms and KEGG pathways. To prioritize and identify candidate genes for obesity, MeDEGs were compared with obesity-related genes available at the DisGeNET database. A total of 54 MeDEGs were identified after overlapping the lists of significant 274 DEGs and 11,556 DMGs. Of these, 25 were hypermethylated-low expression genes and 29 were hypomethylated-high expression genes. The PPI network showed three hub-bottleneck genes (PTGS2, TNFAIP3, and FBXL20) and one functional module. The 54 MeDEGs were mainly involved in the regulation of fibroblast growth factor production, the molecular function of arachidonic acid, and ubiquitin-protein transferase activity. Data collected from DisGeNET showed that 11 of the 54 MeDEGs were involved in obesity. This study identifies new MeDEGs involved in obesity and assessed their related pathways and functions. These results data may provide a deeper understanding of methylation-mediated regulatory mechanisms of obesity.

Integrative analysis of gene expression and DNA methylation to identify biomarkers of non-genital warts induced by low-risk human papillomaviruses infection

BackgroundHuman papillomaviruses have been shown to dysregulate the gene expression and DNA methylation profiles of their host cells over the course of infection. However, there is a lack of information on the impact of low-risk HPV infection and wart formation on host cell's expression and methylation patterns. Therefore, the objective of this study is to analyse the genome and methylome of common warts using an integrative approach. MethodsIn the present study, gene expression (GSE136347) and methylation (GSE213888) datasets of common warts were obtained from the GEO database. Identification of the differentially expressed and differentially methylated genes was carried out using the RnBeads R package and the edgeR Bioconductor package. Next, functional annotation of the identified genes was obtained using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Network construction and analyses of the gene-gene, protein-protein, and signaling interactions of the differentially expressed and differentially methylated genes was performed using the GeneMANIA web interface, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the Signaling Network Open Resource 2.0 (SIGNOR 2.0), respectively. Lastly, significant hub genes were identified using the Cytoscape application CytoHubba. ResultsA total of 276 genes were identified as differentially expressed and differentially methylated in common warts, with 52% being upregulated and hypermethylated. Functional enrichment analysis identified extracellular components as the most enriched annotations, while network analyses identified ELN, ITGB1, TIMP1, MMP2, LGALS3, COL1A1 and ANPEP as significant hub genes. ConclusionsTo the best knowledge of the authors, this is the first integrative study to be carried out on non-genital warts induced by low-risk HPV types. Future studies are required to re-validate the findings in larger populations using alternative approaches.

Meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles

BackgroundIdentification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed.ResultsIn this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model’s prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets.ConclusionsFrom the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer.

Chromosome-scale de novo genome assembly and annotation of three representative Casuarina species: C. equisetifolia, C. glauca, and C. cunninghamiana.

Australian pine (Casuarina spp.) is extensively planted in tropical and subtropical regions for wood production, shelterbelts, environmental protection, and ecological restoration. To characterize Casuarina genomic diversity, we sequenced and constructed de novo genome assemblies of the three most widely planted species: C. equisetifolia, C. glauca, and C. cunninghamiana. We generated chromosome-scale genome sequences using both Pacific Biosciences (PacBio) Sequel sequencing and chromosome conformation capture technology (Hi-C). The total genome sizes for C. equisetifolia, C. glauca, and C. cunninghamiana are 268,942,579 bp, 296,631,783 bp, and 293,483,606 bp, respectively, of which 25.91%, 27.15%, and 27.74% were annotated as repetitive sequences. We annotated 23,162, 24,673, and 24,674 protein-coding genes in C. equisetifolia, C. glauca, and C. cunninghamiana, respectively. We then collected branchlets from male and female individuals for whole-genome bisulfite sequencing (BS-seq) to explore the epigenetic regulation of sex determination in these three species. Transcriptome sequencing (RNA-seq) revealed differential expression of phytohormone-related genes between male and female plants. In summary, this study generated three chromosome-level genome assemblies and comprehensive DNA methylation and transcriptome datasets from both male and female material for three Casuarina species, providing a basis for the comprehensive investigation of genomic diversity and functional gene discovery of Casuarina in the future.

Abstract 3345: Tissue of origin (TOO) localization using targeted methylation biomarkers discovered by whole-genome bisulfite sequencing

Abstract DNA methylation is a promising biomarker for tissue of origin (TOO) localization, which is important for classification of cancer of unknown primary site (CUP) in multiple cancer early detection. The current most of CpG sites of TOO focused on CpG islands based on The Cancer Genome Atlas (TCGA) data sets, but remains largely under-explored across the whole genome. In this study, we conducted the whole-genome bisulfite sequencing (WGBS) in 481 tumor samples and 361 matched adjacent normal tissue samples for 9 common types of cancers, including lung, breast, colon, liver, stomach, lymph, pancreas, esophageal and ovarian. After adjusted the tumor cells’ DNA methylation rates, 3,000 CpG sites, 60% of which were not on CpG islands, were selected through Random Forest classifier (multi-class) to identify cancer type. To evaluate these TOO methylation biomarkers, we used Random Forest Model to predict TOO of the methylation data sets in The Cancer Genome Atlas (TCGA), including 1,704 tumor samples. The results showed that accuracy of TOO could achieve 98.5% for these tumor samples of TCGA. Among these samples, the accuracy of TOO of breast, liver, lung, colorectal and pancreas were 99.6%, 99.9%, 97.7%, 99.2% and 99.3% respectively. Several samples from Esophagus and Stomach were classified to each other, and their accuracy of TOO was relatively low, 97.6% and 96.7%. It demonstrated that these methylation biomarkers could detect multiple cancers, and the multi-class model had good performance for TOO localization. Citation Format: Qiang Wei, Wanglong Deng, Zhihui Xu, Yihui Han, Yuan Yang, Chao Zhang, Xiaoqiang Wang, Xuan Tang, Chao Song, Qing Xu, Yong Ren. Tissue of origin (TOO) localization using targeted methylation biomarkers discovered by whole-genome bisulfite sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3345.

Co-expression network of mRNA and DNA methylation in first-episode and drug-naive adolescents with major depressive disorder.

We explored the DNA methylation and messenger RNA (mRNA) co-expression network and hub genes in first-episode, drug-naive adolescents with major depressive disorder (MDD). To preliminarily explore whether adolescent MDD has unique mechanisms compared with adult MDD. We compared DNA methylation and mRNA profiles of peripheral blood mononuclear cells from four first-episode and drug-naive adolescents with MDD and five healthy adolescent controls (HCs). We performed differential expression analysis, constructed co-expression network, and screened the hub genes. And enrichment analysis was performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We also downloaded DNA methylation and mRNA datasets of adults with MDD (GSE113725/GSE38206) from the GEO database, and performed differential expression and enrichment analysis. Our clinical data showed that 3034 methylation sites and 4190 mRNAs were differentially expressed in first-episode, drug-naive adolescents MDD patients compared with HCs. 19 hub genes were screened out according to the high degree value in the co-expression network. The results from the GEO database showed that compared with adult HCs, there were 290 methylation sites and 127 mRNAs were differentially expressed in adult MDD patients. Compared with adolescent HCs and adult MDD patients, the DNA methylation and mRNA expression patterns of first-episode, drug-naive adolescent MDD patients were different. The co-expression network of DNA methylation and mRNA and the screened hub genes may play an important role in the pathogenesis of MDD in first-episode, drug-naive adolescents. Compared with adult MDD, adolescent MDD is more enriched in metabolism in terms of function and pathways.

Comparative and integrative analysis of transcriptomic and epigenomic-wide DNA methylation changes in African American prostate cancer

ABSTRACT African American (AA) men have the highest incidence and mortality rate from Prostate cancer (PCa) than any other racial/ethnic group. To date, PCa genomic studies have largely under-represented tumour samples from AA men. We measured genome-wide DNA methylation in benign and tumor prostate tissues from AA men using the Illumina Infunium 850 K EPIC array. mRNA expression database from a subset of the AA biospecimen were used to assess correlation of transcriptome and methylation datasets. Genome-wide methylation analysis identified 11,460 probes that were significant (p < 0.01) and differentially methylated in AA PCa compared to normal prostate tissues and showed significant (p < 0.01) inverse-correlation with mRNA expression. Ingenuity pathway analysis and Gene Ontology analysis in our AA dataset compared with TCGA dataset showed similarities in methylation patterns: top candidate genes with significant hypermethylation and corresponding down-regulated gene expression were associated with biological pathways in hemidesmosome assembly, mammary gland development, epidermis development, hormone biosynthesis, and cell communication. In addition, top candidate genes with significant hypomethylation and corresponding up-regulated gene expression were associated with biological pathways in macrophage differentiation, cAMP-dependent protein kinase activity, protein destabilization, transcription co-repression, and fatty acid biosynthesis. In contrast, differences in genome-wide methylation in our AA dataset compared with TCGA dataset were enriched for genes in steroid signalling, immune signalling, chromatin structure remodelling and RNA processing. Overall, differential methylation of AMIGO3, IER3, UPB1, GRM7, TFAP2C, TOX2, PLSCR2, ZNF292, ESR2, MIXL1, BOLL, and FGF6 were significant and uniquely associated with PCa progression in our AA cohort.

DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence.

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that the contribution of methylation entropy to time- and tissue-specific patterns of development is comparable to the contribution of methylation mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.

Abstract WP60: Greater Enrichment Of Neuronal Function Can Be Reflected In Blood Plasma Samples Following The Stroke.

Acute ischemic stroke induces widespread cellular necrosis and apoptosis and activation of inflammation. These mediate release of cell-free DNA (cfDNA) into the circulation. Although increased cfDNA concentrations have been associated with clinical outcome in stroke, data concerning neuronal cfDNA is rare. In this study, we assessed the association of cfDNA with neuron biomarkers in patients with acute ischemic stroke. Methods: Thirteen patients without stroke and four patients with middle cerebral artery occlusion were recruited at UT Memorial Hermann Hospital. Blood plasma samples were collected and cfDNA samples were prepared for whole-genome bisulfite sequencing (WGBS) in order to generate quantitative DNA methylation datasets. The resulting WGBS data were analyzed for epigenetic biomarker identification using bioinformatic statistical approaches. CelFiE was used for cell deconvolution, designed to accurately estimate the relative abundances of cell types and tissues present in plasma cfDNA from methylation sequencing, a technique frequently referred to as cell deconvolution. Results: Differential methylation statistical analysis comparing the stroke against the non-stroke group of samples found 3493 DNA methylation differences. Pathway Analysis discovered greater enrichment for neuronal function with the top hit being “Neuronal System” followed by activity surrounding synapses, demonstrating that a clear enrichment of methylation change in neuronal pathways can be reflected from blood plasma samples following the stroke. Cell deconvolution showed trends of increased megakaryocyte and neutrophils which are involved in the post-stroke immune response. It may be that the secreted neutrophil nuclear DNA in blood plasma drives the increase in the observed proportion of neutrophil cell type in the stroke group samples. Principal Component Analysis demonstrated good separation between the stroke and normal groups. Conclusion: Blood sample can be used for circulating cfDNA analysis and neuron biomarker discovery for acute ischemic stroke prediction/recovery.

Genome-wide DNA methylation and transcription analysis in psoriatic epidermis.

Aim: To identify DNA methylation and transcription biomarkers in the psoriatic epidermis. Materials & methods: Gene transcription and DNA methylation datasets of psoriatic epidermal tissue were obtained from the Gene Expression Omnibus. Machine learning algorithm analysis and weighted gene coexpression network analysis were carried out to screen hub genes. Results: Differentially methylated and expressed genes were identified in the psoriatic epidermis. Six hub genes were selected - GZMB, CRIP1, S100A12, ISG15, CRABP2 and VNN1- whose transcript levels showed a significant correlation with Psoriasis Area and Severity Index scores and immune infiltration. Conclusion: Psoriatic epidermis is primarily in a hypermethylated status. Epidermis-specific hub differentially methylated and expressed genes are potential biomarkers to help judge the condition of psoriasis.

Research on the pathological mechanism of rectal adenocarcinoma based on DNA methylation.

Colorectal cancer is one of the 3 most common cancers worldwide. In this study, a weighted network-based analysis method was proposed to explore the pathological mechanisms and prognostic targets of rectal adenocarcinoma (READ) at the deoxyribonucleic acid (DNA) methylation level. In this study, we downloaded clinical information and DNA methylation data from The Cancer Genome Atlas database. Differentially methylated gene analysis was used to identify the differential methylated genes in READ. Canonical correlation analysis was used to construct the weighted gene regulatory network for READ. Multilevel analysis and association analyses between gene modules and clinical information were used to mine key modules related to tumor metastasis evaluation. Genetic significance analysis was used to identify methylation sites in key modules. Finally, the importance of these methylation sites was confirmed using survival analysis. DNA methylation datasets from 90 cancer tissue samples and 6 paracancerous tissue samples were selected. A weighted gene regulatory network was constructed, and a multilevel algorithm was used to divide the gene co-expression network into 20 modules. From gene ontology enrichment analysis, characteristic M was related to biological processes such as the chemotaxis of fibroblast growth factors and the activation and regulation of immune cells etc and characteristic N was associated with the regulation of cytoskeleton formation, mainly microtubules and flagella, regulation of synapses, and regulation of cell mitosis. Based on the results of survival analysis, 7 key methylation sites were found closely correlated to the survival rate of READ, such as cg04441191 (microtubule-associated protein 4 [MAP4]), cg05658717 (KSR2), cg09622330 (GRIN2A), cg10698404 (YWHAG), cg17047993 (SPAG9), cg24504843 (CEP135), and cg24531267 (CEP250). Mutational and transcriptomic level studies revealed significant differences in DNA methylation, single nucleotide polymorphism, and transcript levels between YWHAG and MAP4 in normal tissues compared to tumor tissues, and differential expression of the 2 proteins in immunohistochemistry. Therefore, potential targeting drugs were screened for these 2 proteins for molecular docking, and artenimol was found to bind to MAP4 protein and 27-hydroxycholesterol to YWHAG. Our study found that key methylation sites played an important role in tumor metastasis and were associated with the prognosis of READ. Mutations and methylation may jointly regulate the transcription and translation of related genes, which in turn affect cancer progression. This may provide some new potential therapeutic targets and thoughts for the prognosis of READ.

Developing a male-specific age predictive model based on Y-CpGs for forensic analysis

In forensic work, predicting the age of the criminal suspect or victim could provide beneficial clues for investigation. Epigenetic age estimation based on age-correlated DNA methylation has been one of the most widely studied methods of age estimation. However, almost all available epigenetic age prediction models are based on autosomal CpGs, which are only applicable to single-source DNA samples. In this study, we screened the available methylation data sets to identify loci with potential to meet the objectives of this study and then established a male-specific age prediction model based on 2 SNaPshot systems that contain 13 Y-CpGs and the mean absolute deviation (MAD) values were 4–6 years. The multiplex methylation SNaPshot systems and age-predictive model have been validated for sensitivity (the DNA input could be as low as 0.5 ng) and male specificity. They are supposed to have feasibility in forensic practice. In addition, it demonstrated that the method was also applicable to bloodstains, which were commonly found at crime scenes. The results showed good performance (the training set: R2 = 0.9341, MAD = 4.65 years; the test set: R2 = 0.8952, MAD = 5.73 years) in case investigation for predicting male age. For mixtures, when the male to female DNA ratio is 1:1, 1:10, the deviation between the actual age and the predicted age obtained by the model was less than 8 years, which offers great hope for future prediction of the age of males in mixtures and will be a powerful tool for special cases, such as sexual assault. Furthermore, the work provides a basis for the application of Y-CpGs in forensic science.

Identification of potential biomarkers for systemic lupus erythematosus by integrated analysis of gene expression and methylation data.

Systemic lupus erythematosus (SLE) is a heterogeneous and chronic autoimmune disease. Aberrant DNA methylation occurs during various processes of SLE development regulating the mRNA expression of interrelated genes. This study aims to screen potential DNA methylation markers for SLE. Gene expression and methylation datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between SLE patients and healthy controls were screened using the limma R package, and differentially methylated positions (DMPs) and regions (DMRs) were identified using dmpfinder and bumphunter (minfi). Additionally, the DNA methylation markers to distinguish SLE patients from healthy controls were explored through receiver operating characteristic (ROC) curves and logistic regression analyses. Finally, we validated the results of the bioinformatic analysis by pyrosequencing. In total, 91 DEGs, 90,092 DMPs, 15 DMRs, and 13 DMR-associated genes were identified. Through the integrative analysis of DEG- and DMR-associated genes, we identified five type I interferon (IFN)-related genes as key epigenetic-driven genes in SLE. GO enrichment analysis showed that the five SLE-associated epigenetic-driven genes were mainly enriched in the type I IFN signaling pathway involved in immune response and defense response to virus. Moreover, we identified two SLE-specific DNA methylation markers, three SLE without lupus nephritis (SLE-LN-)-specific DNA methylation markers, and two SLE with lupus nephritis (SLE-LN+)-specific DNA methylation markers by stepwise logistic regression. Overall, our study demonstrates potential DNA methylation markers of SLE, SLE-LN-, and SLE-LN+, which may help the diagnosis, boost the development of new epigenetic therapy, and contribute to individualized treatment. Key Points • This study identified five type I IFN-related genes as key epigenetic-driven genes in SLE, which support the importance of the type I IFN pathway in the pathogenesis of SLE • We identified novel DNA methylation biomarkers in SLE, SLE-LN-, and SLE-LN+ by a comprehensive analysis of bioinformatics methods and executed experimental validation, and binary logistic regression analysis showed that they have excellent potential • These results may provide new insights into the biological mechanisms of SLE, and identify reliable biomarkers for SLE, SLE-LN-, and SLE-LN+, which may contribute to diagnosis and individualized treatment.

Insight into the mechanism of DNA methylation and miRNA-mRNA regulatory network in ischemic stroke.

Epigenetic changes, such as DNA methylation and miRNA-target gene mechanisms, have recently emerged as key provokers in Ischemic stroke (IS) onset. However, cellular and molecular events harboring these epigenetic alterations are poorly understood. Therefore, the present study aimed to explore the potential biomarkers and therapeutic targets for IS. miRNAs, mRNAs and DNA methylation datasets of IS were derived from the GEO database and normalized by PCA sample analysis. Differentially expressed genes (DEGs) were identified, and GO and KEGG enrichment analyses were performed. The overlapped genes were utilized to construct a protein-protein interaction network (PPI). Meanwhile, differentially expressed mRNAs and miRNAs interaction pairs were obtained from the miRDB, TargetScan, miRanda, miRMap and miTarBase databases. We constructed differential miRNA-target gene regulatory networks based on mRNA-miRNA interactions. A total of 27 up-regulated and 15 down-regulated differential miRNAs were identified. Dataset analysis identified 1053 and 132 up-regulated and 1294 and 9068 down-regulated differentially expressed genes in the GSE16561 and GSE140275 datasets, respectively. Moreover, 9301 hypermethylated and 3356 hypomethylated differentially methylated sites were also identified. Moreover, DEGs were enriched in terms related to translation, peptide biosynthesis, gene expression, autophagy, Th1 and Th2 cell differentiation, primary immunodeficiency, oxidative phosphorylation and T cell receptor signaling pathway. MRPS9, MRPL22, MRPL32 and RPS15 were identified as hub genes. Finally, a differential miRNA-target gene regulatory network was constructed. RPS15, along with hsa-miR-363-3p and hsa-miR-320e have been identified in the differential DNA methylation protein interaction network and miRNA-target gene regulatory network, respectively. These findings strongly posit the differentially expressed miRNAs as potential biomarkers to improve ischemic stroke diagnosis and prognosis.

Reference on copy number variations in pleomorphic xanthoastrocytoma: Implications for diagnostic approach.

Pleomorphic xanthoastrocytoma (PXA) poses a diagnostic challenge. The present study relies on methylation-based predictions and focuses on copy number variations (CNV) in PXA. We identified 551 tumors from patients having received the histologic diagnosis or differential diagnosis pleomorphic xanthoastrocytoma (PXA) uploaded to the web page www.molecularneuropathology.org. Of these 551 tumors, 165 received the prediction "methylation class (anaplastic) pleomorphic xanthoastrocytoma" with a calibrated score >=0.9 by the brain tumor classifier version v12.8 and, therefore, were defined the PXA reference set designated mcPXAref. In addition to these 165 mcPXAref, 767 other tumors received the prediction mcPXA with a calibrated score >=0.9 but without a histological PXA diagnosis. The total number of individual tumors predicted by histology and/or by methylome based classification as PXA, mcPXA or both was 1318, and these were designated the study cohort. The selection of a control cohort was guided by methylation-based predictions recurrently observed for the other 386/551 tumors diagnosed as histologic PXA. 131/386 received predictions for another entity besides PXA with a score >=0.9. Control tumors corresponding to the 11 most common other predictions were selected, adding up to 1100 reference cases. CNV profiles were calculated from all methylation datasets of the study and control cohorts. Special attention was given to the 7/10 signature, gene amplifications and homozygous deletion of CDKN2A/B. Comparison of CNV in the subsets of the study cohort and the control cohort were used to establish relations independent of histological diagnoses. Tumors in mcPXA were highly homogenous in regard to CNV alterations, irrespective of the histological diagnoses. The 7/10 signature commonly present in glioblastoma, IDH-wildtype, was present in 15-20% of mcPXA, whereas amplification of oncogenes (likewise common in glioblastoma) was very rare in mcPXA (<1%). In contrast, the histology-based PXA group exhibited high variance in regard to methylation classes as well as to CNVs. Our data add to the notion, that histologically defined PXA likely only represent a subset of the biological disease.

In silico Evaluation of WWC1 in Melanoma Using Bioinformatic Analyses

Objective: It is suggested that WWC1 has an active role in melanoma progression. Therefore, it was aimed to evaluate the WWC1 gene expression profiles in melanoma, an aggressive malignant skin tumor. Materials and Methods: Quantitative data from melanoma samples (n=592) were clinically evaluated using cBioPortal. Gene expression (GSE65904 and GSE22155) and gene methylation datasets (GSE120878) were retrieved from the Gene Expression Omnibus (GEO) database. Using the GeneMANIA database, the functions of given genes and pathways were evaluated. The STRING database achieved a protein-protein interaction (PPI) network was used to visualize it. Results: Mutations in the WWC1 were found in 6.7% of all melanoma samples, 8% of skin cutaneous melanoma, and 2.8% of metastatic melanoma. When the GeneMANIA platform was used to analyze gene interactions, it was determined that the WWC1 gene shared common protein domains with three genes, was co-expressed with five genes, and interacted with 17 other genes. According to the function analysis results, the most effective of the ten functions of WWC1 was Hippo signaling, with a coverage value of 0.16 (p=0.009). In addition, it then played a role in Notch signaling and organ growth. When the protein-protein interactions were examined, it was determined that it interacted with ten proteins and was co-expressed with nine. Conclusion: The findings demonstrated the potential of WWC1 to be effective in the progression of melanoma. Further research is needed to provide a more accurate analysis of WWC1 expression and methylation.

Identification of candidate aberrant differentially methylated/expressed genes in asthma

BackgroundAsthma is an important non-communicable disease worldwide. DNA methylation is associated with the occurrence and development of asthma. We are aimed at assuring differential expressed genes (DEGs) modified by aberrantly methylated genes (DMGs) and pathways related to asthma by integrating bioinformatics analysis.MethodsOne mRNA dataset (GSE64913) and one gene methylation dataset (GSE137716) were selected from the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed using GeneCodies 4.0 database. All gene expression matrices were analyzed by Gene set enrichment analysis (GSEA) software. STRING was applied to construct a protein-protein interaction (PPI) network to find the hub genes. Then, electronic validation was performed to verify the hub genes, followed by the evaluation of diagnostic value. Eventually, quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to detect the expression of hub genes.ResultsIn total, 14 hypomethylated/high-expression genes and 10 hypermethylated/low-expression genes were obtained in asthma. Among them, 10 hub genes were identified in the PPI network. Functional analysis demonstrated that the differentially methylated/expressed genes were primarily associated with the lung development, cytosol and protein binding. Notably, HLA-DOA was enriched in asthma. FKBP5, WNT5A, TM4SF1, PDK4, EPAS1 and GMPR had potential diagnostic value for asthma.ConclusionThe project explored the pathogenesis of asthma, which may provide a research basis for the prediction and the drug development of asthma.

Meta-Analysis of DNA Methylation Datasets Shows Aberrant DNA Methylation of Thyroid Development or Function Genes in Down Syndrome.

Background: In Down syndrome (DS), there is high occurrence of congenital hypothyroidism (CH) and subclinical hypothyroidism (SH) early in life. The etiology of CH and early SH in DS remains unclear. Previous research has shown genome-wide transcriptional and epigenetic alterations in DS. Thus, we hypothesized that CH and early SH could be caused by epigenetically driven transcriptional downregulation of thyroid-related genes, through promoter region hypermethylation. Methods: We extracted whole blood DNA methylation (DNAm) profiles of DS and non-DS individuals from four independent Illumina array-based datasets (252 DS individuals and 519 non-DS individuals). The data were divided into discovery and validation datasets. Epigenome-wide association analysis was performed using a linear regression model, after which we filtered results for thyroid-related genes. Results: In the discovery dataset, we identified significant associations for DS in 18 thyroid-related genes. Twenty-one of 30 significant differentially methylated positions (DMPs) were also significant in the validation dataset. A meta-analysis of the discovery and validation datasets detected 31 DMPs, including 29 promoter-associated cytosine-guanine dinucleotides (CpG) with identical direction of effect across the datasets, and two differentially methylated regions. Twenty-seven DMPs were hypomethylated and promoter associated. The mean methylation difference of hypomethylated thyroid-related DMPs decreased with age. Conclusions: Contrary to our hypothesis of generalized hypermethylation of promoter regions of thyroid-related genes-indicative of epigenetic silencing of promoters and subsequent transcriptional downregulation, causing biochemical thyroid abnormalities in DS-we found an enrichment of hypomethylated DMPs annotated to promoter regions of these genes. This suggests that CH and early SH in DS are not caused by differential methylation of thyroid-related genes. Considering that epigenetic regulation is dynamic, we hypothesize that the observed thyroid-related gene DNAm changes could be a rescue phenomenon in an attempt to ameliorate the thyroid phenotype, through epigenetic upregulation of thyroid-related genes. This hypothesis is supported by the finding of decreasing methylation difference of thyroid-related genes with age. The prevalence of early SH declines with age, so hypothetically, epigenetic upregulation of thyroid-related genes also diminishes. While this study provides interesting insights, the exact origin of CH and early SH in DS remains unknown.

Circulating cell-free DNA methylation mirrors alterations in cerebral patterns in epilepsy

BackgroundDNA methylation profiling of circulating cell-free DNA (cfDNA) has rapidly become a promising strategy for biomarker identification and development. The cell-type-specific nature of DNA methylation patterns and the direct relationship between cfDNA and apoptosis can potentially be used non-invasively to predict local alterations. In addition, direct detection of altered DNA methylation patterns performs well as a biomarker. In a previous study, we demonstrated marked DNA methylation alterations in brain tissue from patients with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE–HS).ResultsWe performed DNA methylation profiling in cfDNA isolated from the serum of MTLE patients and healthy controls using BeadChip arrays followed by systematic bioinformatic analysis including deconvolution analysis and integration with DNase accessibility data sets. Differential cfDNA methylation analysis showed an overrepresentation of gene ontology terms and transcription factors related to central nervous system function and regulation. Deconvolution analysis of the DNA methylation data sets ruled out the possibility that the observed differences were due to changes in the proportional contribution of cortical neurons in cfDNA. Moreover, we found no overrepresentation of neuron- or glia-specific patterns in the described cfDNA methylation patterns. However, the MTLE–HS cfDNA methylation patterns featured a significant overrepresentation of the epileptic DNA methylation alterations previously observed in the hippocampus.ConclusionsOur results support the use of cfDNA methylation profiling as a rational approach to seeking non-invasive and reproducible epilepsy biomarkers.

Evaluation of obstructive sleep apnea: an analysis based on aberrant genes.

Obstructive sleep apnea (OSA) is an upstream disorder that frequently causes multisystem disorders. Much research has revealed the pathogenesis of OSA, but there is still a lack of research on the complications caused by OSA. The mRNA expression and methylation dataset based on peripheral blood mononuclear cells (PBMCs) were downloaded from the Gene Expression Omnibus (GEO) database. All differential expressed genes (DEGs) were ranked using the Robust Rank Aggregation (RRA) algorithm. A weighted gene co-expression network analysis (WGCNA) was constructed. Subsequently, we used immune infiltration, enrichment analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis for apnea and hypopnea index (AHI) and hypertension and excessive daytime sleepiness (EDS) and constructed diagnostic model using random forest algorithm. In the present study, we identified 318 DEGs in PBMCs involved in pathogenesis or continuous positive airway pressure (CPAP) therapy. Pathway enrichment identified DEGs associated with protein regulation and metabolism. Notably, through intra group analysis, we found that the immune disorder was more significant for OSAin males, non-daytime sleepy, or non-hypertensive OSA. The area under the ROC curve of model for EDS prediction is 0.889 and 0.852 for hypertension. Notably, we found that the diagnostic model had a high linear predictive value for AHI. Our results indicate that PBMCs are a significant component of alterations in OSA and are expected to explain the mechanism of multisystem diseases caused by OSA. The present study provides new insights for symptom evaluation, classification and treatment of OSA from the molecular level.