Bioinformatics-Led Discovery of Osteoarthritis Biomarkers and Inflammatory Infiltrates

Frontiers in Immunology, Journal Year: 2022, Volume and Issue: 13

Published: June 6, 2022

The molecular mechanisms of osteoarthritis, the most common chronic disease, remain unexplained. This study aimed to use bioinformatic methods identify key biomarkers and immune infiltration in osteoarthritis. Gene expression profiles (GSE55235, GSE55457, GSE77298, GSE82107) were selected from Expression Omnibus database. A protein-protein interaction network was created, functional enrichment analysis genomic performed using Ontology (GO) Kyoto Encyclopedia Genes Genome (KEGG) databases. Immune cell between osteoarthritic tissues control analyzed CIBERSORT method. Identify patterns ConsensusClusterPlus package R software a consistent clustering approach. Molecular biological investigations discover important genes cartilage cells. total 105 differentially expressed identified. Differentially enriched immunological response, chemokine-mediated signaling pathway, inflammatory response revealed by GO KEGG Two distinct (ClusterA ClusterB) identified ConsensusClusterPlus. Cluster patients had significantly lower resting dendritic cells, M2 macrophages, mast activated natural killer cells regulatory T than B patients. levels TCA1, TLR7, MMP9, CXCL10, CXCL13, HLA-DRA, ADIPOQSPP1 higher IL-1β-induced group osteoarthritis an vitro qPCR experiment. Explaining differences normal will contribute understanding development

Language: Английский

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Genistein prevents the production of hypospadias induced by Di-(2-ethylhexyl) phthalate through androgen signaling and antioxidant response in rats

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 466, P. 133537 - 133537

Published: Jan. 17, 2024

Language: Английский

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Recent Trends in Cancer Genomics and Bioinformatics Tools Development

International Journal of Molecular Sciences, Journal Year: 2021, Volume and Issue: 22(22), P. 12146 - 12146

Published: Nov. 10, 2021

We overview recent research trends in cancer genomics, bioinformatics tools development and medical genetics, based on results discussed papers collections "Medical Genetics, Genomics Bioinformatics" (https://www [...].

Language: Английский

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Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage

Bone and Joint Research, Journal Year: 2024, Volume and Issue: 13(2), P. 66 - 82

Published: Feb. 5, 2024

Aims This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at cartilage level find potential biomarkers targets for diagnosing treating OA. Methods Six sets gene expression profiles were obtained from Gene Expression Omnibus database. Differential analysis, weighted coexpression network analysis (WGCNA), multiple machine-learning algorithms used screen crucial osteoarthritic cartilage, genome enrichment functional annotation analyses decipher related categories function. Single-sample set was performed analyze immune cell infiltration. Correlation relationship among hub cells, as well markers articular degradation bone mineralization. Results A total 46 intersection significantly upregulated module screened by WGCNA. Functional revealed that these closely pathological responses associated with OA, such inflammation immunity. Four (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related (ANGPTL2), MAGE family member D1 (MAGED1)) identified after using algorithms. These had high diagnostic value both training cohort external validation (receiver operating characteristic > 0.8). The signified higher levels infiltration metalloproteinases mineralization markers, suggesting harmful alterations indicating play an important role pathogenesis competing endogenous RNA constructed reveal underlying post-transcriptional regulatory mechanisms. Conclusion current explores validates a is capable accurately OA characterizing cartilage; this may become promising indicator decision-making. indicates development progression be therapeutic targets. Cite article: Bone Joint Res 2024;13(2):66–82.

Language: Английский

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Identification of TFRC as a biomarker for pulmonary arterial hypertension based on bioinformatics and experimental verification

Respiratory Research, Journal Year: 2024, Volume and Issue: 25(1)

Published: Aug. 3, 2024

Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there paucity of studies that reflect the available biomarkers from separate gene expression profiles in PAH. The GSE131793 and GSE113439 datasets were combined for subsequent analyses, batch effects removed. Bioinformatic analysis was then performed to identify differentially expressed genes (DEGs). Weighted co-expression network (WGCNA) protein-protein interaction (PPI) used further filter hub genes. Functional enrichment intersection using Gene Ontology (GO), Disease (DO), Kyoto encyclopedia genomes (KEGG) set (GSEA). level diagnostic value pulmonary patients also analyzed validation GSE53408 GSE22356. In addition, target validated lungs monocrotaline (MCT)-induced (PH) rat model serum PAH patients. A total 914 (DEGs) identified, with 722 upregulated 192 downregulated key module relevant selected WGCNA. By combining DEGs WGCNA, 807 selected. Furthermore, protein–protein identified HSP90AA1, CD8A, HIF1A, CXCL8, EPRS1, POLR2B, TFRC, PTGS2 as GSE22356 evaluate which showed robust value. According GSEA analysis, PAH-relevant biological functions pathways enriched high TFRC levels. found be lung tissues our experimental PH compared those controls, same conclusion reached bioinformatics observed increase tissue human patients, indicated by transcriptomic data, consistent alterations rodent models. These data suggest may serve potential biomarker

Language: Английский

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GlucoGenes®, a database of genes and proteins associated with glucose metabolism disorders, its description and applications in bioinformatics research

Vavilov Journal of Genetics and Breeding, Journal Year: 2025, Volume and Issue: 28(8), P. 1008 - 1017

Published: Jan. 26, 2025

Data on the genetics and molecular biology of diabetes are accumulating rapidly. This poses challenge creating research tools for a rapid search for, structuring analysis information in this field. We have developed web resource, GlucoGenes ® , which includes database an Internet portal genes proteins associated with high glucose (hyperglycemia), low (hypoglycemia), both metabolic disorders. The data were collected using text mining publications indexed PubMed Central gene networks hyperglycemia, hypoglycemia variability performed ANDSystems, bioinformatics tool. is freely available at: https://glucogenes.sysbio.ru/genes/main. enables users to access download about risk hyperglycemia hypoglycemia, regulators hyperglycemic antihyperglycemic activity, up-regulated by and/or glucose, down-regulated molecules otherwise metabolism With evolutionary disorders was performed. results revealed significant increase (up 40 %) proportion phylostratigraphic age index (PAI) values corresponding time origin multicellular organisms. Analysis sequence conservation divergence (DI) showed that most highly conserved (DI < 0.6) or conservative 1). When analyzing single nucleotide polymorphism (SNP) proximal regions promoters affecting affinity TATA-binding protein, 181 SNP markers found database, can reduce (45 markers) (136 expression 52 genes. believe resource will be useful tool further field diabetes.

Language: Английский

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Discovery of Endothelial–Monocyte Crosstalk in Ischemic‐Reperfusion Injury Following Liver Transplantation Based on Integration of Single‐Cell RNA and Transcriptome RNA Sequencing

Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(4)

Published: Feb. 1, 2025

ABSTRACT Hepatic ischemia/reperfusion injury (IRI) commonly complicates liver transplantation (LT). However, the precise mechanisms underlying hepatic IRI remain incompletely understood. We acquired single‐cell RNA sequencing (scRNA‐seq) and transcriptome data of LT patients from GEO database. Employing scRNA‐seq, we delved into interplay between non‐immune immune cells in IRI, pinpointing genes exhibiting altered expression patterns. Integrating insights gleaned scRNA‐seq datasets, deepened our comprehension cellular interactions IRI. Additionally, conducted preliminary validation identified gene alterations using immunofluorescence techniques. Using detected significant changes populations sinusoidal endothelial (LSECs) monocytes after ischemia–reperfusion (IRI). By integrating with bulk data, key dysregulated LSECs (ICAM1, SOCS3, NFKBIZ, JUND, TNFRSF12A HSPA6) (SOCS3, FPR2 NR4A2). Our analysis cell communication indicated that ANXA1‐FPR2 axis might be a pivotal signature mediating monocytes. then established mouse model for further analyses flow cytometry showed increase monocyte proportion post‐IR ( p < 0.01). Consistently, Western Blot also revealed upregulation ANXA1 study elucidated signalling pathways following The likely triggers cascade events, promoting infiltration amplifying inflammatory responses, thus worsening deleterious effects

Language: Английский

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Highlighted gene expression alteration in human pancreatic isolated islets in patients with type 2 diabetes

Journal of Diabetes & Metabolic Disorders, Journal Year: 2025, Volume and Issue: 24(1)

Published: April 23, 2025

Language: Английский

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Medical Genetics, Genomics and Bioinformatics—2022

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(10), P. 8968 - 8968

Published: May 18, 2023

The analysis of molecular mechanisms disease progression challenges the development bioinformatics tools and omics data integration [...]

Language: Английский

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Identification of aging-related genes in diagnosing osteoarthritis via integrating bioinformatics analysis and machine learning

Aging, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 3, 2024

Background: Osteoarthritis (OA) is one of the main causes pain and disability in world, it may be caused by many factors. Aging plays a significant role onset progression OA. However, mechanisms underlying remain unknown. Our research aimed to uncover aging-related genes Methods: In Human OA datasets were obtained from GEO database HAGR website, respectively. Bioinformatics methods including Gene Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG) pathway enrichment, Protein-protein interaction (PPI) network analysis used analyze differentially expressed (DEARGs) normal control group group. And then weighted gene coexpression (WGCNA), least absolute shrinkage selection operator (LASSO) regression, Random Forest (RF) machine learning algorithms find hub genes. Results: Four overlapping genes: HMGB2, CDKN1A, JUN, DDIT3 identified. According nomogram model receiver operating characteristic (ROC) curve analysis, four DEARGs had good diagnostic value distinguishing Furthermore, qRT-PCR test demonstrated that mRNA expression levels lower than Conclusion: Finally, these four-hub help us understand mechanism aging osteoarthritis could as possible therapeutic targets.

Language: Английский

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