Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UMLS:C0279530 (bone cancer)
 1,036 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

BACKGROUND Osteosarcoma is the most frequent primary bone cancer derived from primitive mesenchymal cells. The aim of this study was to explore the molecular mechanism of the development and progression of osteosarcoma. MATERIAL AND METHODS The gene expression profiles of osteosarcoma from 17 specimens (3 normal and 14 osteosarcoma) were downloaded from the GEO database. The differentially expressed genes were identified by use of the Limma package. DAVID and Enrichment Map were used to perform GO and KEGG pathways enrichment analysis and to integrate enrichment results of differentially expressed genes (DEGs). Protein-protein interaction network was constructed and analyzed to screen out the potential regulatory proteins using the STRING online tools. RESULTS A total of 417 DEGs were screened, including 215 up-regulated and 202 down-regulated ones, accounting for 51.56% and 48.4%, respectively. In GO term, a total of 12 up-regulated expression genes were enriched in Cellular Component. The up-regulated DEGs were enriched in 6 KEGG pathways while the down-regulated expression genes were enriched in 2 KEGG pathways. The constructed PPI network was aggregated with 1006 PPI relationships and 238 nodes, accounting for 57.07% of DEGs. We found that CD20, MCM, and CCNB1 (down-regulated) in cell cycle and ECM, ITGA, RTKin (up-regulated) in focal adhesion had important roles in the progression of osteosarcoma. CONCLUSIONS The identified DEGs and their enriched pathways provide references for the exploration of the molecular mechanism of the development and progression of osteosarcoma. Moreover, the key genes (CD20, ECM, and ITGA) may be useful in treatment and diagnosis of osteosarcoma.
...
PMID:Identification of CD20, ECM, and ITGA as Biomarkers for Osteosarcoma by Integrating Transcriptome Analysis. 2731 45

BACKGROUND Osteosarcoma (OS), an aggressive malignant neoplasm, is the most common primary bone cancer mainly in adolescents and young adults. Differentially expressed modules tend to distinguish differences integrally. Identifying modules individually has been crucial for understanding OS mechanisms and applications of custom therapeutic decisions in the future. MATERIAL AND METHODS Samples came from individuals were used from control group (n=15) and OS group (n=84). Based on clique-merging, module-identification algorithm was used to identify modules from OS PPI networks. A novel approach - the individualized module aberrance score (iMAS) was performed to distinguish differences, making special use of accumulated normal samples (ANS). We performed biological process ontology to classify functionally modules. Then Support Vector Machine (SVM) was used to test distribution results of normal and OS group with screened modules. RESULTS We identified 83 modules containing 2084 genes from PPI network in which 61 modules were significantly different. Cluster analysis of OS using the iMAS method identified 5 modules clusters. Specificity=1.00 and Sensitivity=1.00 proved the distribution outcomes of screened modules were mainly consistent with that of total data, which suggested the efficiency of 61 modules. CONCLUSIONS We conclude that a novel pipeline that identified the dysregulated modules in individuals of OS. The constructed process is expected to aid in personalized health care, which may present fruitful strategies for medical therapy.
 ...
PMID:Personalized Identification of Differentially Expressed Modules in Osteosarcoma. 2819 21