Dataset Information

Methylation factors as biomarkers of fibromyalgia.

ABSTRACT:

Background

Fibromyalgia (FM) is a common and intractable chronic musculoskeletal pain syndrome, but its exact underlying mechanisms are unknown. This study sought to identify biomarkers of FM and the underlying molecular mechanisms of the disease.

Methods

FM-related gene expression profiles (GSE67311) and methylation profiles (GSE85506) were obtained from the Gene Expression Omnibus database, and a differential expression analysis was performed to identify the methylation factors. Subsequently, an enrichment analysis and gene set enrichment analysis (GSEA) were conducted to examine the methylation factors. In addition, the transcriptional regulators of the methylation factors were predicted, and key methylation factors were identified by a receiver operating characteristic curve analysis and nomogram models. Finally, the relationship between FM and cell death (pyroptosis, necroptosis, and cuproptosis) was assessed by a GSEA and gene set variation analysis.

Results

A total of 455 methylation factors were identified. The enrichment analysis and GSEA results showed that methylation factors were clearly involved in the biological functions and signaling pathways related to neural, immune inflammation, and pain responses. The transcriptional regulator specificity protein 1 (SP1) may have a broad regulatory role. Finally, seven key methylation factors were identified, of which amino beta (A4) precursor protein binding family B member 2 (APBB2), A-kinase anchor protein 12 (AKAP12), and cluster of differentiation 38 (CD38) had strong clinical diagnostic power. In addition, AKAP12 and CD38 were significantly and negatively associated with sepsis, necrotizing sepsis, and cupular sepsis.

Conclusions

Our study suggests that FM is associated with deoxyribonucleic acid methylation. The methylation factors APBB2, AKAP12, and CD38 may be potential biomarkers and should be further examined to provide a new biological framework of the possible disease mechanisms underlying FM.

SUBMITTER: Huang C

PROVIDER: S-EPMC10009573 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Methylation factors as biomarkers of fibromyalgia.

Huang Chengyu C Zhang Nan N Wei Mengxin M Pan Qinchun Q Cheng Chunyan C Lu Ke-Er KE Mo Jianwen J Chen Yixuan Y

Annals of translational medicine 20230221 4

<h4>Background</h4>Fibromyalgia (FM) is a common and intractable chronic musculoskeletal pain syndrome, but its exact underlying mechanisms are unknown. This study sought to identify biomarkers of FM and the underlying molecular mechanisms of the disease.<h4>Methods</h4>FM-related gene expression profiles (GSE67311) and methylation profiles (GSE85506) were obtained from the Gene Expression Omnibus database, and a differential expression analysis was performed to identify the methylation factors. ...[more]

PMID: 36923073

Similar Datasets

Project description:BackgroundCoping strategies and their efficacy vary greatly in patients suffering from fibromyalgia syndrome (FMS).ObjectiveWe aimed to identify somatic and psychosocial factors that might contribute to different coping strategies and resilience levels in FMS.Subjects and methodsStandardized questionnaires were used to assess coping, pain, and psychological variables in a cohort of 156 FMS patients. Quantitative real-time polymerase chain reaction (qRT-PCR) determined gene expression of selected cytokines in white blood cells of 136 FMS patients and 25 healthy controls. Data of skin innervation, functional and structural sensory profiles of peripheral nociceptive nerve fibers of a previous study were included into the statistics. An exploratory factor analysis was used to define variance explaining factors, which were then included into cluster analysis.Results54.9% of the variance was explained by four factors which we termed (1) affective load, (2) coping, (3) pain, and (4) pro-inflammatory cytokines (p < 0.05). Considering differences in the emerged factors, coping strategies, cytokine profiles, and disability levels, 118 FMS patients could be categorized into four clusters which we named "maladaptive", "adaptive", "vulnerable", and "resilient" (p < 0.05). The adaptive cluster had low scores in disability and in all symptom categories in contrast to the vulnerable cluster, which was characterized by high scores in catastrophizing and disability (p < 0.05). The resilient vs. the maladaptive cluster was characterized by better coping and a less pro-inflammatory cytokine pattern (p < 0.05).ConclusionOur data suggest that problem- and emotion-focused coping strategies and an anti-inflammatory cytokine pattern are associated with reduced disability and might promote resilience. Additional personal factors such as low anxiety scores, ability of acceptance, and persistence further favor a resilient phenotype. Individualized therapy should take these factors into account.

Project description:BackgroundAberrant methylation of DNA plays an important role in the pathogenesis of nasopharyngeal carcinoma (NPC). In the current study, we aimed to integrate three cohorts profile datasets to identify abnormally methylated-differentially expressed genes and pathways associated with NPC.MethodsData of gene expression microarrays (GSE53819, GSE412452) and gene methylation microarrays (GSE52068) obtained from the GEO database. Aberrantly methylated differentially expressed genes (DEGs) were obtained by GEO2R. The David database was utilized to perform enrichment and functional analysis regarding selected genes. To create a protein-protein interaction (PPI), STRING and Cytoscape software were utilized. The MCODE was used for module analysis of the PPI network.ResultsIn total, 181 hypomethylation-high expression genes were identified, which were enriched in the biological mechanisms involved in the differentiation of endodermal cell, mitotic nuclear division, mitotic cell cycle process, chromosome segregation and cell cycle phase transition, etc. Pathway enrichment showed ECM-receptor interaction, PI3K-Akt signaling pathway, Focal adhesion, Protein digestion and absorption and Amoebiasis, etc. The top 3 hub genes of PPI network were FANCI, POSTN, and IFIH1. Additionally, 210 hypermethylation-low expression genes were identified, and our data revealed enrichment in biological processes including axoneme assembly, micro tubular formation, assembly of axonemal dynein complex, cilium movement and cilium organization, etc. Pathway analysis indicated enrichment in B cell receptor signaling pathway, Hematopoietic cell lineage, Leukocyte transendothelial migration, Complement and coagulation cascades and Fc gamma R-mediated phagocytosis, etc. The ZMYND10, PACRG and POU2AF1 were identified as the top three hub genes of PPI network. After validation in TCGA and GEPIA database, most hub genes remained significant. Patients with high expression of POSTN found to have shorter overall survival, while in patients with high expression of ZMYND10 and POU2AF1 longer overall survival was identified.ConclusionsThe data revealed novel aberrantly methylated-differentially expressed genes and pathways in NPC by bioinformatics analysis, potentially providing novel insights for the molecular mechanisms governing NPC progression. Hub genes including FANCI, POSTN, IFIH1, ZMYND10, PACRG and POU2AF1 might serve as novel biomarkers for precision diagnosis and providing medical treatment for patient with NPC.

Dataset Information

Methylation factors as biomarkers of fibromyalgia.

Background

Methods

Results

Conclusions

Publications

Methylation factors as biomarkers of fibromyalgia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets