ArticleMolecular medicine reports2019
Investigating age‑induced differentially expressed genes and potential molecular mechanisms in osteosarcoma based on integrated bioinformatics analysis.
Article in Molecular medicine reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Forecasting the Pharmacological Mechanisms ofBiology · 2024Article
- Identification of allograft inflammatory factor-1 suppressing the progression and indicating good prognosis of osteosarcoma.BMC musculoskeletal disorders · 2024Article
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- Sweat and odor in sportswear - A review.iScience · 2023Review
- Article
- Screening and identification of osteoarthritis related differential genes and construction of a risk prognosis model based on bioinformatics analysis.Annals of translational medicine · 2022Article
- Identification and preliminary validation of a four-gene signature to predict metastasis and survival in osteosarcoma.American journal of translational research · 2021Article
- Circ_WWC3 overexpression decelerates the progression of osteosarcoma by regulating miR-421/PDE7B axis.Open life sciences · 2021Article
- Article
- Metastasis-associated gene MAPK15 promotes the migration and invasion of osteosarcoma cells via the c-Jun/MMPs pathway.Oncology letters · 2020Article
- Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy.Frontiers in oncology · 2020Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteosarcoma (OS) is the most common primary bone malignancy. It predominantly occurs in adolescents, but can develop at any age. The age at diagnosis is a prognostic factor of OS, but the molecular basis of this remains unknown. The current study aimed to identify age‑induced differentially expressed genes (DEGs) and potential molecular mechanisms that contribute to the different outcomes of patients with OS. Microarray data (GSE39058 and GSE39040) obtained from the Gene Expression Omnibus database and used to analyze age‑induced DEGs to reveal molecular mechanism of OS among different age groups (<20 and >20 years old). Differentially expressed mRNAs (DEMs) were divided into up and downregulated DEMs (according to the expression fold change), then Gene Ontology function enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. Furthermore, the interactions among proteins encoded by DEMs were integrated with prediction for microRNA‑mRNA interactions to construct a regulatory network. The key subnetwork was extracted and Kaplan‑Meier survival analysis for a key microRNA was performed. DEMs within the subnetwork were predominantly involved in 'ubiquitin protein ligase binding', 'response to growth factor', 'regulation of type I interferon production', 'response to decreased oxygen levels', 'voltage‑gated potassium channel complex', 'synapse part', 'regulation of stem cell proliferation'. In summary, integrated bioinformatics was applied to analyze the potential molecular mechanisms leading to different outcomes of patients with OS among different age groups. The hub genes within the key subnetwork may have crucial roles in the different outcomes associated with age and require further analysis.
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