Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15
Abstract Osteosarcoma has the worst prognosis among malignant bone tumors, and effective biomarkers are lacking. Our study aims to explore m6A-related and immune-related biomarkers. Gene expression profiles of osteosarcoma and healthy controls were downloaded from multiple public databases, and thei...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-28739-1 |
| _version_ | 1797853881901776896 |
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| author | Jie Jiang Haishun Qu Xinli Zhan Dachang Liu Tuo Liang Liyi Chen Shengsheng Huang Xuhua Sun Jiarui Chen Tianyou Chen Hao Li Yuanlin Yao Chong Liu |
| author_facet | Jie Jiang Haishun Qu Xinli Zhan Dachang Liu Tuo Liang Liyi Chen Shengsheng Huang Xuhua Sun Jiarui Chen Tianyou Chen Hao Li Yuanlin Yao Chong Liu |
| author_sort | Jie Jiang |
| collection | DOAJ |
| description | Abstract Osteosarcoma has the worst prognosis among malignant bone tumors, and effective biomarkers are lacking. Our study aims to explore m6A-related and immune-related biomarkers. Gene expression profiles of osteosarcoma and healthy controls were downloaded from multiple public databases, and their m6A-based gene expression was utilized for tumor typing using bioinformatics. Subsequently, a prognostic model for osteosarcoma was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression analysis, and its immune cell composition was calculated using the CIBERSORTx algorithm. We also performed drug sensitivity analysis for these two genes. Finally, analysis was validated using immunohistochemistry. We also examined the RBM15 gene by qRT-PCR in an in vitro experiment. We collected routine blood data from 1738 patients diagnosed with osteosarcoma and 24,344 non-osteosarcoma patients and used two independent sample t tests to verify the accuracy of the CIBERSORTx analysis for immune cell differences. The analysis based on m6A gene expression tumor typing was most reliable using the two typing methods. The prognostic model based on the two genes constituting RNA-binding motif protein 15 (RBM15) and YTDC1 had a much lower survival rate for patients in the high-risk group than those in the low-risk group (P < 0.05). CIBERSORTx immune cell component analysis demonstrated that RBM15 showed a negative and positive correlation with T cells gamma delta and activated natural killer cells, respectively. Drug sensitivity analysis showed that these two genes showed varying degrees of correlation with multiple drugs. The results of immunohistochemistry revealed that the expression of these two genes was significantly higher in osteosarcoma than in paraneoplastic tissues. The results of qRT-PCR experiments showed that the expression of RBM15 was significantly higher in both osteosarcomas than in the control cell lines. Absolute lymphocyte value, lymphocyte percentage, hematocrit and erythrocyte count were lower in osteosarcoma than in the control group (P < 0.001). RBM15 and YTHDC1 can serve as potential prognostic biomarkers associated with m6A in osteosarcoma. |
| first_indexed | 2024-04-09T19:57:58Z |
| format | Article |
| id | doaj.art-d6a4709033e2481d81f042e733cc80d4 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T19:57:58Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d6a4709033e2481d81f042e733cc80d42023-04-03T05:24:08ZengNature PortfolioScientific Reports2045-23222023-03-0113111910.1038/s41598-023-28739-1Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15Jie Jiang0Haishun Qu1Xinli Zhan2Dachang Liu3Tuo Liang4Liyi Chen5Shengsheng Huang6Xuhua Sun7Jiarui Chen8Tianyou Chen9Hao Li10Yuanlin Yao11Chong Liu12The First Clinical Affiliated Hospital of Guangxi Medical UniversityDepartment of Traditional Chinese Medicine, The People’s Hospital of Guangxi Zhuang Autonmous RegionThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityThe First Clinical Affiliated Hospital of Guangxi Medical UniversityAbstract Osteosarcoma has the worst prognosis among malignant bone tumors, and effective biomarkers are lacking. Our study aims to explore m6A-related and immune-related biomarkers. Gene expression profiles of osteosarcoma and healthy controls were downloaded from multiple public databases, and their m6A-based gene expression was utilized for tumor typing using bioinformatics. Subsequently, a prognostic model for osteosarcoma was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression analysis, and its immune cell composition was calculated using the CIBERSORTx algorithm. We also performed drug sensitivity analysis for these two genes. Finally, analysis was validated using immunohistochemistry. We also examined the RBM15 gene by qRT-PCR in an in vitro experiment. We collected routine blood data from 1738 patients diagnosed with osteosarcoma and 24,344 non-osteosarcoma patients and used two independent sample t tests to verify the accuracy of the CIBERSORTx analysis for immune cell differences. The analysis based on m6A gene expression tumor typing was most reliable using the two typing methods. The prognostic model based on the two genes constituting RNA-binding motif protein 15 (RBM15) and YTDC1 had a much lower survival rate for patients in the high-risk group than those in the low-risk group (P < 0.05). CIBERSORTx immune cell component analysis demonstrated that RBM15 showed a negative and positive correlation with T cells gamma delta and activated natural killer cells, respectively. Drug sensitivity analysis showed that these two genes showed varying degrees of correlation with multiple drugs. The results of immunohistochemistry revealed that the expression of these two genes was significantly higher in osteosarcoma than in paraneoplastic tissues. The results of qRT-PCR experiments showed that the expression of RBM15 was significantly higher in both osteosarcomas than in the control cell lines. Absolute lymphocyte value, lymphocyte percentage, hematocrit and erythrocyte count were lower in osteosarcoma than in the control group (P < 0.001). RBM15 and YTHDC1 can serve as potential prognostic biomarkers associated with m6A in osteosarcoma.https://doi.org/10.1038/s41598-023-28739-1 |
| spellingShingle | Jie Jiang Haishun Qu Xinli Zhan Dachang Liu Tuo Liang Liyi Chen Shengsheng Huang Xuhua Sun Jiarui Chen Tianyou Chen Hao Li Yuanlin Yao Chong Liu Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15 Scientific Reports |
| title | Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15 |
| title_full | Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15 |
| title_fullStr | Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15 |
| title_full_unstemmed | Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15 |
| title_short | Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15 |
| title_sort | identification of osteosarcoma m6a related prognostic biomarkers using artificial intelligence rbm15 |
| url | https://doi.org/10.1038/s41598-023-28739-1 |
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