Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

Abstract Background Osteosarcoma (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. Methods We performed weighted gene co-expression ne...

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Autores principales:	Fu-peng Ding, Jia-yi Tian, Jing Wu, Dong-feng Han, Ding Zhao
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Osteosarcoma Metastasis ARHGAP25 TCGA WGCNA Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Cytology QH573-671
Acceso en línea:	https://doaj.org/article/70b140854d40456bbb1c0e6a559732ab
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spelling	oai:doaj.org-article:70b140854d40456bbb1c0e6a559732ab2021-12-05T12:23:39ZIdentification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics10.1186/s12935-021-02308-w1475-2867https://doaj.org/article/70b140854d40456bbb1c0e6a559732ab2021-12-01T00:00:00Zhttps://doi.org/10.1186/s12935-021-02308-whttps://doaj.org/toc/1475-2867Abstract Background Osteosarcoma (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. Methods We 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. Results Eighteen 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. Conclusion We 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.Fu-peng DingJia-yi TianJing WuDong-feng HanDing ZhaoBMCarticleOsteosarcomaMetastasisARHGAP25TCGAWGCNANeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282CytologyQH573-671ENCancer Cell International, Vol 21, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Osteosarcoma Metastasis ARHGAP25 TCGA WGCNA Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Cytology QH573-671
spellingShingle	Osteosarcoma Metastasis ARHGAP25 TCGA WGCNA Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Cytology QH573-671 Fu-peng Ding Jia-yi Tian Jing Wu Dong-feng Han Ding Zhao Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics
description	Abstract Background Osteosarcoma (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. Methods We 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. Results Eighteen 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. Conclusion We 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.
format	article
author	Fu-peng Ding Jia-yi Tian Jing Wu Dong-feng Han Ding Zhao
author_facet	Fu-peng Ding Jia-yi Tian Jing Wu Dong-feng Han Ding Zhao
author_sort	Fu-peng Ding
title	Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics
title_short	Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics
title_full	Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics
title_fullStr	Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics
title_full_unstemmed	Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics
title_sort	identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics
publisher	BMC
publishDate	2021
url	https://doaj.org/article/70b140854d40456bbb1c0e6a559732ab
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Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

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