Diabetes is a rising global metabolic disorder and leads to long-term consequences. As a multifactorial disease, the gene-associated mechanisms are important to know. This study applied a bioinformatics approach to explore the molecular underpinning of type 2 diabetes mellitus through differential gene expression analysis. We used microarray datasets GSE16415 and GSE29226 to identify differentially expressed genes between type 2 diabetes and normal samples using R software. Following that, using the STRING database, the protein-protein interaction network was constructed and further analyzed by Cytoscape software. The EnrichR database was used for Gene Ontology and pathway enrichment analysis to explore key pathways and functional annotations of hub genes. We also used miRTarBase and TargetScan databases to predict miRNAs targeting hub genes. We identified 21 hub genes in type 2 diabetes, some showing more significant changes in the PPI network. Our results revealed that GLUL, SLC32A1, PC, MAPK10, MAPT, and POSTN genes are more important in the PPI network and can be experimentally investigated as therapeutic targets. Hsa-miR-492 and hsa-miR-16-5p are suggested for diagnosis and prognosis by targeting GLUL, SLC32A1, PC, MAPK10, and MAPT genes involved in the insulin signaling pathway. Insight: Type 2 diabetes, as a rising global and multifactorial disorder, is important to know the gene-associated mechanisms. In an integrative bioinformatics analysis, we integrated different finding datasets to put together and find valuable diagnostic and prognostic hub genes and miRNAs. In contrast, genes, RNAs, and enzymes interact systematically in pathways. Using multiple databases and software, we identified differential expression between hub genes of diabetes and normal samples. We explored different protein-protein interaction networks, gene ontology, key pathway analysis, and predicted miRNAs that target hub genes. This study reported 21 significant hub genes and some miRNAs in the insulin signaling pathway for innovative and potential diagnostic and therapeutic purposes.
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http://dx.doi.org/10.1093/intbio/zyae002 | DOI Listing |
Sci Rep
December 2024
Division of Joint Surgery and Sports Medicine, Department of Orthopedic Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Lines of evidence have indicated that type 2 diabetes mellitus (T2DM) is an independent risk factor for osteoarthritis (OA) progression. However, the study focused on the relationship between T2DM and OA at the transcriptional level remains empty. We downloaded OA- and T2DM-related bulk RNA-sequencing and single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) dataset.
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December 2024
Department of Orthopaedics, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China.
Osteosarcoma (OS) is the most prevalent secondary sarcoma associated with retinoblastoma (RB). However, the molecular mechanisms driving the interactions between these two diseases remain incompletely understood. This study aims to explore the transcriptomic commonalities and molecular pathways shared by RB and OS, and to identify biomarkers that predict OS prognosis effectively.
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December 2024
Department of Breast Surgery, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China.
Breast cancer is a leading cause of cancer-related deaths among women globally. It is imperative to explore novel biomarkers to predict breast cancer treatment response as well as progression. Here, we collected six breast cancer samples and paired normal tissues for high-throughput sequencing.
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December 2024
Department of Medical Genetics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Alzheimer's disease (AD) is a degenerative illness that accounts for the common type of dementia among adults over the age of 65. Despite extensive studies on the pathogenesis of the disease, early diagnosis of AD is still debatable. In this research, we performed bioinformatics approaches on the AD-related E-MTAB 6094 dataset to uncover new potential biomarkers for AD diagnosis.
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December 2024
Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China; Key laboratory of Myopia and Related Eye Diseases, NHC, Chinese Academy of Medical Sciences, 83 Fenyang Road, Shanghai, 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, 83 Fenyang Road, Shanghai, 200031, China. Electronic address:
Choroid neovascularization (CNV) is a distinct type of age-related macular degeneration (AMD) with a poor prognosis and responsible for the majority of vision loss in the elderly population. The laser-induced CNV model is a well-established animal model frequently used to study CNV. In this study, we performed an integrated analysis of metabolomic and transcriptomic data from CNV samples, utilizing multiple approaches including single-sample gene set enrichment analysis (ssGSEA), correlation analysis, and weighted gene co-expression network analysis (WGCNA), alongside various bioinformatics platforms, to identify key metabolic and immune signatures and to investigate their interplay during angiogenesis.
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