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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-12-01
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| Series: | Cancer Cell International |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12935-021-02308-w |
| _version_ | 1818401522079236096 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T07:53:48Z |
| format | Article |
| id | doaj.art-70b140854d40456bbb1c0e6a559732ab |
| institution | Directory Open Access Journal |
| issn | 1475-2867 |
| language | English |
| last_indexed | 2024-12-14T07:53:48Z |
| publishDate | 2021-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj.art-70b140854d40456bbb1c0e6a559732ab2022-12-21T23:10:36ZengBMCCancer Cell International1475-28672021-12-0121111410.1186/s12935-021-02308-wIdentification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformaticsFu-peng Ding0Jia-yi Tian1Jing Wu2Dong-feng Han3Ding Zhao4Department of Orthopedics Surgery, The First Hospital of Jilin UniversityDepartment of Reproductive Medicine and Center for Prenatal Diagnosis, The First Hospital of Jilin UniversityDepartment of General Practice, The First Hospital of Jilin UniversityDepartment of Emergency Medicine, The First Hospital of Jilin UniversityDepartment of Orthopedics Surgery, The First Hospital of Jilin UniversityAbstract 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.https://doi.org/10.1186/s12935-021-02308-wOsteosarcomaMetastasisARHGAP25TCGAWGCNA |
| spellingShingle | 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 Cancer Cell International Osteosarcoma Metastasis ARHGAP25 TCGA WGCNA |
| title | 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_short | 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 |
| topic | Osteosarcoma Metastasis ARHGAP25 TCGA WGCNA |
| url | https://doi.org/10.1186/s12935-021-02308-w |
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