Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C1522084 (Osteosarcoma)
 2,200 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Osteosarcoma is a primary malignant tumor of bone arising from primitive bone-forming mesenchymal cells and accounts for approximately 60% of malignant bone tumors. Our comparative genomic hybridization (CGH) studies have identified frequent amplification at 6p12-p21, 12q13-q15, and 17p11.2 in osteosarcoma. Of these amplified regions, 6p12-p21 is particularly interesting because of its association with progression and poor prognosis in patients with osteosarcoma. In an attempt to identify aberrantly expressed gene(s) mapping to the 6p12-p21 amplicon, a region-specific array was generated using 108 overlapping BAC and P1 clones covering a 28.8-Mb region at 0.26-Mb intervals. Based on array CGH analysis, the 6p amplicon was refined to 7.9 Mb between the clones RP11-91E11 and RP1-244F2 and 10 amplified clones, with possible target genes, were identified. To study the expression pattern of the target genes from the hotspot amplicon and known candidate genes from 6p12-21, we did quantitative reverse transcription-PCR analysis of MAPK14, MAPK13, CDKN1A, PIM1, MDGA1, BTB9, DNAH8, CCND3, PTK7, CDC5L, and RUNX2 on osteosarcoma patient samples and seven cell lines. The combined array CGH and quantitative reverse transcription-PCR analysis identified amplification and overexpression of CDC5L, CCND3, and RUNX2. We screened these three genes for protein expression by Western blotting and immunohistochemistry and detected overexpression of CDC5L. Furthermore, we used an in vivo assay to show that CDC5L possesses potential oncogenic activity. These results indicate that CDC5L, a cell cycle regulator important for the G2-M transition, is the most likely candidate oncogene for the 6p12-p21 amplicon found in osteosarcoma.
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PMID:Cell cycle regulator gene CDC5L, a potential target for 6p12-p21 amplicon in osteosarcoma. 1856 98

Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a prognostic pseudogene signature of osteosarcoma by machine learning. A sample of 94 osteosarcoma patients' RNA-Seq data with clinical follow-up information was involved in the study. The survival-related pseudogenes were screened and related signature model was constructed by cox-regression analysis (univariate, lasso, and multivariate). The predictive value of the signature was further validated in different subgroups. The putative biological functions were determined by co-expression analysis. In total, 125 survival-related pseudogenes were identified and a four-pseudogene (RPL11-551L14.1, HR: 0.65 (95% CI: 0.44-0.95); RPL7AP28, HR: 0.32 (95% CI: 0.14-0.76); RP4-706A16.3, HR: 1.89 (95% CI: 1.35-2.65); RP11-326A19.5, HR: 0.52(95% CI: 0.37-0.74)) signature effectively distinguished the high- and low-risk patients, and predicted prognosis with high sensitivity and specificity (AUC: 0.878). Furthermore, the signature was applicable to patients of different genders, ages, and metastatic status. Co-expression analysis revealed the four pseudogenes are involved in regulating malignant phenotype, immune, and DNA/RNA editing. This four-pseudogene signature is not only a promising predictor of prognosis and survival, but also a potential marker for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.
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PMID:A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma. 3114 89