ArticleBioscience reports2019
Screening of disorders associated with osteosarcoma by integrated network analysis.
Article in Bioscience reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Uncovering the heterogeneity of NK cells on the prognosis of HCC by integrating bulk and single-cell RNA-seq data.Frontiers in oncology · 2025Article
- Tubulin alpha-1b chain was identified as a prognosis and immune biomarker in pan-cancer combing with experimental validation in breast cancer.Scientific reports · 2024Article
- Integrated Omics Reveal the Pathogenic Potential ofTransboundary and emerging diseases · 2024Article
- Long non-coding RNA MALAT1 promotes cell proliferation, migration and invasion by targeting miR-590-3p in osteosarcoma.Experimental and therapeutic medicine · 2022Article
- Tubulin Alpha 1b Is Associated with the Immune Cell Infiltration and the Response of HCC Patients to Immunotherapy.Diagnostics (Basel, Switzerland) · 2022Article
- Circular RNA hsa_circ_0002052 promotes osteosarcoma via modulating miR-382/STX6 axis.Human cell · 2020Article
- Differential expression of AURKA/PLK4 in quiescence and senescence of osteosarcoma U2OS cells.Cell cycle (Georgetown, Tex.) · 2020Article
- Tubulin Alpha-1b as a Potential Biomarker for Lung Adenocarcinoma Diagnosis and Prognosis.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteosarcoma is a common malignant bone tumor in children and adolescents under the age of 20. However, research on the pathogenesis and treatment of osteosarcoma is still insufficient. In the present study, based on gene-phenotype correlation network, an analysis was performed to screen disorders related to osteosarcoma. First, we analyzed the differential expression of osteosarcoma in two groups according to different types of osteosarcoma and screened the differentially expressed genes (DEGs) related to osteosarcoma. Further, these DEG coexpression modules were obtained. Finally, we identified a series of regulatory factors, such as endogenous genes, transcription factors (TFs), and ncRNAs, which have potential regulatory effects on osteosarcoma, based on the prediction analysis of related network of gene phenotypes. A total of 3767 DEGs of osteosarcoma were identified and clustered them into 20 osteosarcoma-related dysfunction modules. And there were 38 endogenous genes (including ARF1, HSP90AB1, and TUBA1B), 53 TFs (including E2F1, NFKB1, and EGR1), and 858 ncRNAs (including MALAT1, miR-590-3p, and TUG1) were considered as key regulators of osteosarcoma through a series of function enrichment analysis and network analysis. Based on the results of the present study, we can show a new way for biologists and pharmacists to reveal the potential molecular mechanism of osteosarcoma typing, and provide valuable reference for different follow-up treatment options.
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