Dataset Information

Bioinformatics Analysis of the Molecular Mechanism of Aging on Fracture Healing.

ABSTRACT: Increasing age negatively affects different phases of bone fracture healing. The present study aimed to explore underlying mechanisms related to bone fracture repair in the elderly. GSE17825 public transcriptome data from the Gene Expression Omnibus database were used for analysis. First, raw data were normalized and differentially expressed genes (DEGs) were identified. Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were implemented to evaluate pathways and DEGs. A protein-protein interaction (PPI) network was then constructed. A total of 726, 861, and 432 DEGs were identified between the young and elderly individuals at 1, 3, and 5 days after fracture, respectively. The results of GO, KEGG, and PPI network analyses suggested that the inflammatory response, Wnt signaling pathway, vascularization-associated processes, and synaptic-related functions of the identified DEGs are markedly enriched, which may account for delayed fracture healing in the elderly. These findings provide valuable clues for investigating the effects of aging on fracture healing but should be validated through further experiments.

SUBMITTER: Zhao SJ

PROVIDER: S-EPMC6311305 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Bioinformatics Analysis of the Molecular Mechanism of Aging on Fracture Healing.

Zhao Shu-Jie SJ Kong Fan-Qi FQ Fan Jin J Chen Ying Y Zhou Shuai S Xue Ming-Xin MX Yin Guo-Yong GY Yin Guo-Yong GY

BioMed research international 20181216

Increasing age negatively affects different phases of bone fracture healing. The present study aimed to explore underlying mechanisms related to bone fracture repair in the elderly. GSE17825 public transcriptome data from the Gene Expression Omnibus database were used for analysis. First, raw data were normalized and differentially expressed genes (DEGs) were identified. Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were implemented to evaluate pathways and ...[more]

PMID: 30643820

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Project description:The mechanism of brain aging is not fully understood. Few studies have attempted to identify molecular changes using bioinformatics at the subregional level in the aging brain. This study aimed to identify the molecular signatures and key genes involved in aging, depending on the brain region. Differentially expressed genes (DEGs) associated with aging of the cerebral cortex (CX), hippocampus (HC), and cerebellum (CB) were identified based on five datasets from the Gene Expression Omnibus (GEO). The molecular signatures of aging were explored using functional and pathway analyses. Hub genes of each brain region were determined by protein-protein interaction network analysis, and commonly expressed DEGs (co-DEGs) were also found. Gene-microRNAs (miRNAs) and gene-disease interactions were constructed using online databases. The expression levels and regional specificity of the hub genes and co-DEGs were validated using animal experiments. In total, 32, 293, and 141 DEGs were identified in aging CX, HC, and CB, respectively. Enrichment analysis indicated molecular changes related to leukocyte invasion, abnormal neurotransmission, and impaired neurogenesis due to inflammation as the major signatures of the CX, HC, and CB. Itgax is a hub gene of cortical aging. Zfp51 and Zfp62 were identified as hub genes involved in hippocampal aging. Itgax and Cxcl10 were identified as hub genes involved in cerebellar aging. S100a8 was the only co-DEG in all three regions. In addition, a series of molecular changes associated with inflammation was observed in all three brain regions. Several miRNAs interact with hub genes and S100a8. The change in gene levels was further validated in an animal experiment. Only the upregulation of Zfp51 and Zfp62 was restricted to the HC. The molecular signatures of aging exhibit regional differences in the brain and seem to be closely related to neuroinflammation. Itgax, Zfp51, Zfp62, Cxcl10, and S100a8 may be key genes and potential targets for the prevention of brain aging.

Dataset Information

Bioinformatics Analysis of the Molecular Mechanism of Aging on Fracture Healing.

Publications

Bioinformatics Analysis of the Molecular Mechanism of Aging on Fracture Healing.

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