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High Expression of PQBP1 and Low Expression of PCK2 are Associated with Metastasis and Recurrence of Osteosarcoma and Unfavorable Survival Outcomes of the Patients.

Yong Zhang | Haiyue Zhao | Wu Xu | Dinghua Jiang | Lixin Huang | Lisong Li
Journal of Cancer | 2019

Osteosarcoma (OS) is the most common primary bone malignancy, predominately affecting children and adolescents. Due to the introduction of chemotherapy, the 5-year survival rate of OS patients has dramatically improved to 60-70%. Unfortunately, OS patients with recurrence or metastatic disease have less than a 20% chance of long-term survival, despite aggressive therapies. In this study, we aimed to identify gene expression patterns associated with metastasis and recurrence in order to identify potential biomarkers with prognostic power. We found that high expression of polyglutamine tract-binding protein 1 (PQBP1) and low expression of phosphoenolpyruvate carboxykinase 2 (PCK2) were related to a high probability of recurrence and metastasis in OS patients and also predicted shorter recurrence-free survival (RFS) and metastasis-free survival (MFS) after adjustment for other clinical variables. Prediction models based on the combination of PQBP1 and PCK2 expression had good and robust predictive power for recurrence and metastasis. A PQBP1 and PCK2-centered protein interaction network was built, and the hypothetical regulatory path between them was identified and termed the PQBP1-SF3A2-UBA52-PCK2 axis. Gene enrichment analysis indicated that aberrations of metabolism might play an important role in recurrence and metastasis in OS patients. Accordingly, PQBP1 and PCK2 are crucial for recurrence and metastasis in OS, and these findings provide a molecular basis for the exploitation of diagnostic and therapeutic strategies for overcoming recurrence and metastasis in OS.

Pubmed ID: 31205570

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STRING (tool)

RRID:SCR_005223

Database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)

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Bioconductor (tool)

RRID:SCR_006442

Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.

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