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16 March 2020 : Clinical Research  

Identification of Biomarkers for Osteosarcoma Based on Integration Strategy

Junjie Bao1BCE, Zhaona Song2CEF, Chunyu Song1BC, Yahui Wang2CE, Wan Li2E, Wei Mai1B, Qingyu Shi1D, Hongwei Yu1B, Linying Ni1F, Yishu Liu1D, Xiaolin Lu1F, Chuan He1C, Lina Chen2A*, Guofan Qu1G

DOI: 10.12659/MSM.920803

Med Sci Monit 2020; 26:e920803


BACKGROUND: Osteosarcoma (OS) is the most common primary malignant tumor of bone. The identification of novel biomarkers is necessary for the diagnosis and treatment of osteosarcoma.

MATERIAL AND METHODS: We obtained 11 paired fresh-frozen OS samples and normal controls from patients between September 2015 and February 2017. We used an integration strategy that analyzes next-generation sequencing data by bioinformatics methods based on the pathogenesis of osteosarcoma.

RESULTS: One susceptibility lncRNA and 7 susceptibility genes regulated by the lncRNA for osteosarcoma were effectively identified, and real-time PCR and clinical index ALP data were used to test their effectiveness.

CONCLUSIONS: The results showed that the expression levels of the 7 genes were highly consistent in the training and test sample sets, especially between the expression value of the gene ALPL and the plasma detection value of its encoded protein ALP. In particular, both the expression of gene ALPL and the plasma detection values of protein ALP encoded by gene ALPL showed a high degree of consistency among different data types. The identified lncRNA and genes effectively classified the samples proved so that they could be used as potential biomarkers of osteosarcoma. Our strategy may also be helpful for the identification of biomarkers for other diseases.

Keywords: Biological Markers, High-Throughput Nucleotide Sequencing, Osteosarcoma, Adolescent, Alkaline Phosphatase, Biomarkers, Tumor, Bone Neoplasms, Child, Gene Expression Regulation, Neoplastic, RNA, Long Noncoding, RNA-Seq, Real-Time Polymerase Chain Reaction, young adult

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750