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Query: UMLS:C0029463 (osteosarcoma)
 16,637 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Osteosarcoma (OS) is a common primary malignancy in children and adolescents with relative high survival rate after chemotherapy. While, the toxicity of chemotherapy and personalized different response to chemotherapy makes it difficult for the selection of therapeutics and improvement of diagnosis. In this study, we conducted a combined analysis of two types of microarray datasets (gene expression and DNA methylation) from the Gene Expression Omnibus (GEO). Differential methylation sites (DMS) were identified by the IMA package and differential expression genes (DEGs) were screened out via the limma package. A total of 11,242 DMS (corresponding to 3080 genes (DMGs)) and 337 DEGs, with 40 overlaps (OS genes) between DEGs and DMGs, were identified. Enriched functions of OS genes were obtained through the Database for Annotation, Visualization and Integrated Discovery (DAVID). The OS genes were mainly enriched in the biological processes related to inflammatory/immune response and Pertussis pathways and Hematopoietic cell lineage pathways. Besides, OS-specific disease network was obtained, and found that UBS and NRF1 were regulated by multiple OS genes. Kaplan Meier analysis of OS genes identified BHMT2, DOCK2, DNALI1 and RIPK3 as significant OS survival-related genes. SEMA3A and PRAME are included in the 40 OS genes and within the top 10 most up-regulated DEGs. Their expression changes were further validated in U2OS osteosarcoma cell lines and hOB normal cell lines through quantitative PCR (qPCR) and consistent result with microarray analysis was obtained. Based on this study, some novel targets were identified for OS, which would be helpful in its early diagnosis and treatment.
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PMID:Combined analysis of DNA methylation and gene expression profiles of osteosarcoma identified several prognosis signatures. 2940 29

Osteosarcoma is the most common type of primary malignant bone tumor observed in children and adolescents. The aim of the present study was to identify an osteosarcoma-related gene module (OSM) by looking for a dense module following the integration of signals from genome-wide association studies (GWAS) into the human protein-protein interaction (PPI) network. A dataset of somatic mutations in osteosarcoma was obtained from the dbGaP database and their testing P-values were incorporated into the PPI network from a recent study using the dmGWAS bioconductor package. An OSM containing 201 genes (OS genes) and 268 interactions, which were closely associated with immune response, intracellular signal transduction and cell activity was identified. Topological analysis of the OSM identified 11 genes, including APP, APPBP2, ATXN1, HSP90B1, IKZF1, KRTAP10-1, PAK1, PDPK1, SMAD4, SUZ12 and TP53 as potential diagnostic biomarkers for osteosarcoma. The overall survival analysis of osteosarcoma for those 11 genes based on a dataset from the Cancer Genome Atlas, identified APP, HSP90B1, SUZ12 and IKZF1 as osteosarcoma survival-related genes. The results of the present study should be helpful in understanding the diagnosis and treatment of osteosarcoma and its underlying mechanisms. In addition, the methodology used in the present study may be suitable for the analysis of other types of disease.
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PMID:Analyzing the disease module associated with osteosarcoma via a network- and pathway-based approach. 3021 Jun 6

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

Osteosarcoma represents one of the most aggressive tumors of bone among adolescents and young adults. Despite improvements in treatment, osteosarcoma has a grave prognosis. The identification of prognostic factors is still in its infancy. Weighted gene correlation network analysis (WGCNA) was conducted on mRNA-sequencing and clinical information (gender, survival and metastasis) of osteosarcoma patients from the TARGET database to obtain genes in modules associated with metastasis of osteosarcoma. The Cox regression analysis was then performed on the gene expression profile from TARGET to screen genes associated with patients' survival. Known genes related to osteosarcoma were obtained by intersecting osteosarcoma-related genes from DisGeNET and DiGSeE, followed by the construction of PPI network of osteosarcoma-related genes and survival-related genes in modules. The screened key genes were subject to multi-factor Cox proportional hazards model, and osteosarcoma patients were classified into high- and low- risk groups according to the risk score to evaluate the potential of key genes to predict the survival of osteosarcoma patients. The WGCNA showed that 4 genes in tan and 19 genes in pink modules were related to the survival of osteosarcoma patients. Osteosarcoma-related known genes (9) were obtained in intersection of DisGeNET and DiGSeE. PPI network identified 4 key genes (KRT5, HIPK2, MAP3K5 and CD5) closely associated with survival of osteosarcoma patients. HIPK2, MAP3K5 and CD5 expression was inversely correlated with survival risk, while KRT5 expression was positively correlated with survival risk. These results show KRT5, HIPK2, MAP3K5 and CD5 serve as prognostic factors of osteosarcoma patients.
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PMID:Four genes predict the survival of osteosarcoma patients based on TARGET database. 3251 76

Osteosarcoma is the most common primary malignant bone tumour predominantly occurring in children and adolescents with a high tendency of local invasion and early metastases. Currently, tumour immune microenvironment (TME) is becoming the focus of studying of malignant tumours.. However, no sound evidence shows a specific immune molecular target in osteosarcoma. We downloaded the gene expression profile and clinical data of osteosarcoma from the TARGET portal, and extracted and normalized via R software. Then, the immune cell infiltration assessed by CIBERSORT and ESTIMATE algorithms. Three survival-related immune cells and immune score were obtained via Kaplan-Meier survival analysis, and 232 immune-related genes were obtained as candidate genes. Enrichment and protein-protein interaction co-expression analyses were performed to identify 13 hub genes. Lastly, a seven gene prognostic signature was identified by univariate and multivariate Cox regression analyses. More importantly, our validations and TIMER algorithm suggested this immune-related prognostic signature a good predictive tool. Our findings have provided novel insights that could demonstrate new targets of immunotherapy in osteosarcoma.
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PMID:Development of a prognostic gene signature based on an immunogenomic infiltration analysis of osteosarcoma. 3282 Jun 15