title: Bioinformatics analysis of differentially expressed pathways related to the metastatic characteristics of osteosarcoma.
abstract: In this study, gene expression data of osteosarcoma (OSA) were analyzed to identify metastasis-related biological pathways. Four gene expression data sets (GSE21257, GSE9508, GSE49003 and GSE66673) were downloaded from Gene Expression Omnibus (GEO). An analysis of differentially expressed genes (DEGs) was performed using the Significance Analysis of Microarray (SAM) method. Gene expression levels were converted into scores of pathways by the Functional Analysis of Individual Microarray Expression (FAIME) algorithm and the differentially expressed pathways (DEPs) were then disclosed by a t-test. The distinguishing and prediction ability of the DEPs for metastatic and non-metastatic OSA was further confirmed using the principal component analysis (PCA) method and 3 gene expression data sets (GSE9508, GSE49003 and GSE66673) based on the support vector machines (SVM) model. A total of 616 downregulated and 681 upregulated genes were identified in the data set, GSE21257. The DEGs could not be used to distinguish metastatic OSA from non-metastatic OSA, as shown by PCA. Thus, an analysis of DEPs was further performed, resulting in 14 DEPs, such as NRAS signaling, Toll-like receptor (TLR) signaling, matrix metalloproteinase (MMP) regulation of cytokines and tumor necrosis factor receptor-associated factor (TRAF)-mediated interferon regulatory factor 7 (IRF7) activation. Cluster analysis indicated that these pathways could be used to distinguish between metastatic OSA from non-metastatic OSA. The prediction accuracy was 91, 66.7 and 87.5% for the data sets, GSE9508, GSE49003 and GSE66673, respectively. The results of PCA further validated that the DEPs could be used to distinguish metastatic OSA from non-metastatic OSA. On the whole, several DEPs were identified in metastatic OSA compared with non-metastatic OSA. Further studies on these pathways and relevant genes may help to enhance our understanding of the molecular mechanisms underlying metastasis and may thus aid in the development of novel therapies.
authors.affiliation: Department of Orthopedics, Shanghai General Hospital, Nanjing Medical University, Shanghai 200072, P.R. China.
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.affiliation: Department of Orthopedics, Shanghai General Hospital, School of Medicine Shanghai Jiao Tong University, Shanghai 200080, P.R. China.
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Ma
authors.affiliation: Department of Orthopedics, Shanghai General Hospital, School of Medicine Shanghai Jiao Tong University, Shanghai 200080, P.R. China.
authors.forename: Jiakang
authors.initials: J
authors.lastname: Shen
authors.affiliation: Department of Orthopedics, Shanghai General Hospital, School of Medicine Shanghai Jiao Tong University, Shanghai 200080, P.R. China.
authors.forename: Fei
authors.initials: F
authors.lastname: Yin
authors.affiliation: Tongji University, Shanghai 200092, P.R. China.
authors.forename: Chongren
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Orthopedics, Shanghai General Hospital, Nanjing Medical University, Shanghai 200072, P.R. China.
authors.forename: Zhengdong
authors.initials: Z
authors.lastname: Cai
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001859:Bone Neoplasms / Q000235:genetics* / Q000473:pathology*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D009362:Neoplasm Metastasis; D012516:Osteosarcoma / Q000235:genetics* / Q000473:pathology*; D025341:Principal Component Analysis; D055815:Young Adult