Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor
AIM: To explore the relationship between matrix metalloproteinases (MMPs) expression levels in the tumor and the prognosis of uveal melanoma (UM) and to construct prognostic prediction models. METHODS: Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collecte...
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Press of International Journal of Ophthalmology (IJO PRESS)
2025-05-01
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| Series: | International Journal of Ophthalmology |
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| Online Access: | http://ies.ijo.cn/en_publish/2025/5/20250502.pdf |
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| author | Yu-Ning Chen Jing-Ying Xiu Han-Qing Zhao Jing-Ting Luo Qiong Yang Yang Li Wen-Bin Wei |
| author_facet | Yu-Ning Chen Jing-Ying Xiu Han-Qing Zhao Jing-Ting Luo Qiong Yang Yang Li Wen-Bin Wei |
| author_sort | Yu-Ning Chen |
| collection | DOAJ |
| description | AIM: To explore the relationship between matrix metalloproteinases (MMPs) expression levels in the tumor and the prognosis of uveal melanoma (UM) and to construct prognostic prediction models. METHODS: Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected. Based on the differential gene expression levels and their function, MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning. Tumor microenvironment (TME) analysis was also applied for the impact of immune cell infiltration on prognosis of the disease. RESULTS: Eight MMPs were significantly different expression levels between normal and the tumor tissues. MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high- and low-risk groups. The prediction model based on the risk-score achieved an accuracy of approximately 80% at 1-, 3-, and 5-year after diagnosis. Besides, a Nomogram prognostic prediction model which based on risk-score and pathological type (independent prognostic factors after Cox regression analysis) demonstrated good consistency between the predicted outcomes at 1-, 3-, and 5-year after diagnosis and the actual prognosis of patients. TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages (TAMs) and regulatory T cells compared to the low-risk group. CONCLUSION: Based on MMP-2 and MMP-28 expression levels, our prediction model demonstrates accurate long-term prognosis prediction for UM patients. The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis. |
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| id | doaj-art-eafc419baea44b3d804e32dd80908737 |
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| issn | 2222-3959 2227-4898 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Press of International Journal of Ophthalmology (IJO PRESS) |
| record_format | Article |
| series | International Journal of Ophthalmology |
| spelling | doaj-art-eafc419baea44b3d804e32dd809087372025-08-20T02:18:24ZengPress of International Journal of Ophthalmology (IJO PRESS)International Journal of Ophthalmology2222-39592227-48982025-05-0118576577810.18240/ijo.2025.05.0220250502Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumorYu-Ning Chen0Jing-Ying Xiu1Han-Qing Zhao2Jing-Ting Luo3Qiong Yang4Yang Li5Wen-Bin Wei6Wen-Bin Wei. Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China. weiwenbintr@163.com; Yang Li. Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology; Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, China. liyangtongren@163.comBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, ChinaBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, ChinaBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, ChinaBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, ChinaBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China; Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, ChinaBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, ChinaAIM: To explore the relationship between matrix metalloproteinases (MMPs) expression levels in the tumor and the prognosis of uveal melanoma (UM) and to construct prognostic prediction models. METHODS: Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected. Based on the differential gene expression levels and their function, MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning. Tumor microenvironment (TME) analysis was also applied for the impact of immune cell infiltration on prognosis of the disease. RESULTS: Eight MMPs were significantly different expression levels between normal and the tumor tissues. MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high- and low-risk groups. The prediction model based on the risk-score achieved an accuracy of approximately 80% at 1-, 3-, and 5-year after diagnosis. Besides, a Nomogram prognostic prediction model which based on risk-score and pathological type (independent prognostic factors after Cox regression analysis) demonstrated good consistency between the predicted outcomes at 1-, 3-, and 5-year after diagnosis and the actual prognosis of patients. TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages (TAMs) and regulatory T cells compared to the low-risk group. CONCLUSION: Based on MMP-2 and MMP-28 expression levels, our prediction model demonstrates accurate long-term prognosis prediction for UM patients. The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis.http://ies.ijo.cn/en_publish/2025/5/20250502.pdfuveal melanomamatrix metalloproteinasesprediction modelprognosistumor microenvironment |
| spellingShingle | Yu-Ning Chen Jing-Ying Xiu Han-Qing Zhao Jing-Ting Luo Qiong Yang Yang Li Wen-Bin Wei Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor International Journal of Ophthalmology uveal melanoma matrix metalloproteinases prediction model prognosis tumor microenvironment |
| title | Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor |
| title_full | Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor |
| title_fullStr | Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor |
| title_full_unstemmed | Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor |
| title_short | Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor |
| title_sort | prognostic prediction model for chinese uveal melanoma patients based on matrix metalloproteinase 2 and 28 expression levels in the tumor |
| topic | uveal melanoma matrix metalloproteinases prediction model prognosis tumor microenvironment |
| url | http://ies.ijo.cn/en_publish/2025/5/20250502.pdf |
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