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dc.contributor.authorPattaralawan Sittijuen_US
dc.contributor.authorParunya Chaiyawaten_US
dc.contributor.authorDumnoensun Pruksakornen_US
dc.contributor.authorJeerawan Klangjorhoren_US
dc.contributor.authorWeerinrada Wongrinen_US
dc.contributor.authorPhichayut Phinyoen_US
dc.contributor.authorRawikant Kamolphiwongen_US
dc.contributor.authorAreerak Phanphaisarnen_US
dc.contributor.authorPimpisa Teeyakasemen_US
dc.contributor.authorPrachya Kongtawelerten_US
dc.contributor.authorPeraphan Pothacharoenen_US
dc.date.accessioned2022-05-27T08:24:16Z-
dc.date.available2022-05-27T08:24:16Z-
dc.date.issued2022-05-01en_US
dc.identifier.issn20797737en_US
dc.identifier.other2-s2.0-85129928895en_US
dc.identifier.other10.3390/biology11050698en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85129928895&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/72240-
dc.description.abstractA liquid biopsy is currently an interesting tool for measuring tumor material with the advantage of being non-invasive. The overexpression of vimentin and ezrin genes was associated with epithelial-mesenchymal transition (EMT), a key process in metastasis and progression in osteosarcoma (OS). In this study, we identified other OS-specific genes by calculating differential gene expression using the Gene Expression Omnibus (GEO) database, confirmed by using quantitative reverse transcription-PCR (qRT-PCR) to detect OS-specific genes, including VIM and ezrin in the buffy coat, which were obtained from the whole blood of OS patients and healthy donors. Furthermore, the diagnostic model for OS detection was generated by utilizing binary logistic regression with a multivariable fractional polynomial (MFP) algorithm. The model incorporating VIM, ezrin, and COL5A2 genes exhibited outstanding discriminative ability, as determined by the receiver operating characteristic curve (AUC = 0.9805, 95% CI 0.9603, 1.000). At the probability cut-off value of 0.3366, the sensitivity and the specificity of the model for detecting OS were 98.63% (95% CI 90.5, 99.7) and 94.94% (95% CI 87.5, 98.6), respectively. Bioinformatic analysis and qRT-PCR, in our study, identified three candidate genes that are potential diagnostic and prognostic genes for OS.en_US
dc.subjectAgricultural and Biological Sciencesen_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectImmunology and Microbiologyen_US
dc.titleOsteosarcoma-Specific Genes as a Diagnostic Tool and Clinical Predictor of Tumor Progressionen_US
dc.typeJournalen_US
article.title.sourcetitleBiologyen_US
article.volume11en_US
article.stream.affiliationsFaculty of Medicine, Chiang Mai Universityen_US
article.stream.affiliationsFaculty of Medicine, Prince of Songkia Universityen_US
article.stream.affiliationsChiang Mai Universityen_US
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