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Expression profile analysis identifies key genes as prognostic markers for metastasis of osteosarcoma.

ABSTRACT: Background:OS is the most common malignant tumor of bone which was featured with osteoid or immature bone produced by the malignant cells, and biomarkers are urgently needed to identify patients with this aggressive disease. Methods:We downloaded gene expression profiles from GEO and TARGET datasets for OS, respectively, and performed WGCNA to identify the key module. Whereafter, functional annotation and GSEA demonstrated the relationships between target genes and OS. Results:In this study, we discovered four key genes-ALOX5AP, HLA-DMB, HLA-DRA and SPINT2 as new prognostic markers and confirmed their relationship with OS metastasis in the validation set. Conclusions:In conclusion, ALOX5AP, HLA-DMB, HLA-DRA and SPINT2 were identified by bioinformatics analysis as possible prognostic markers for OS metastasis.

SUBMITTER: Guan X

PROVIDER: S-EPMC7106759 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Expression profile analysis identifies key genes as prognostic markers for metastasis of osteosarcoma.

Guan Xiaoqing X Guan Zhiyuan Z Song Chunli C

Cancer cell international 20200330

<h4>Background</h4>OS is the most common malignant tumor of bone which was featured with osteoid or immature bone produced by the malignant cells, and biomarkers are urgently needed to identify patients with this aggressive disease.<h4>Methods</h4>We downloaded gene expression profiles from GEO and TARGET datasets for OS, respectively, and performed WGCNA to identify the key module. Whereafter, functional annotation and GSEA demonstrated the relationships between target genes and OS.<h4>Results< ...[more]

PMID: 32256213

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Project description:Osteosarcoma (OS) often occurs in children and often undergoes metastasis, resulting in lower survival rates. Information on the complexity and pathogenic mechanism of OS is limited, and thus, the development of treatments involving alternative molecular and genetic targets is hampered. We categorized transcriptome data into metastasis and nonmetastasis groups, and 400 differential RNAs (230 messenger RNAs (mRNAs) and 170 long noncoding RNAs (lncRNAs)) were obtained by the edgeR package. Prognostic genes were identified by performing univariate Cox regression analysis and the Kaplan-Meier (KM) survival analysis. We then examined the correlation between the expression level of prognostic lncRNAs and mRNAs. Furthermore, microRNAs (miRNAs) corresponding to the coexpression of lncRNA-mRNA was predicted, which was used to construct a competitive endogenous RNA (ceRNA) regulatory network. Finally, multivariate Cox proportional risk regression analysis was used to identify hub prognostic genes. Three hub prognostic genes (ABCG8, LOXL4, and PDE1B) were identified as potential prognostic biomarkers and therapeutic targets for OS. Furthermore, transcriptions factors (TFs) (DBP, ESX1, FOS, FOXI1, MEF2C, NFE2, and OTX2) and lncRNAs (RP11-357H14.16, RP11-284N8.3, and RP11-629G13.1) that were able to affect the expression levels of genes before and after transcription were found to regulate the prognostic hub genes. In addition, we identified drugs related to the prognostic hub genes, which may have potential clinical applications. Immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) confirmed that the expression levels of ABCG8, LOXL4, and PDE1B coincided with the results of bioinformatics analysis. Moreover, the relationship between the hub prognostic gene expression and patient prognosis was also validated. Our study elucidated the roles of three novel prognostic biomarkers in the pathogenesis of OS as well as presenting a potential clinical treatment for OS.

Project description:BackgroundOsteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis.MethodsWe performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways.ResultsEighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein-protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein-protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial-mesenchymal transition.ConclusionWe identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

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Expression profile analysis identifies key genes as prognostic markers for metastasis of osteosarcoma.

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Expression profile analysis identifies key genes as prognostic markers for metastasis of osteosarcoma.

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