Home > Archive>Volume 18, Issue 5, 2025 >765-778. DOI:10.18240/ijo.2025.05.02
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Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor
Author:
  • Yu-Ning Chen

    Yu-Ning Chen

    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 Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
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  • Jing-Ying Xiu

    Jing-Ying Xiu

    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 Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
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  • Han-Qing Zhao

    Han-Qing Zhao

    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 Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
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  • Jing-Ting Luo

    Jing-Ting Luo

    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 Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
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  • Qiong Yang

    Qiong Yang

    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 Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
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  • Yang Li

    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 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, China
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  • Wen-Bin Wei

    Wen-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 Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
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Corresponding Author:

Wen-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.com

Fund Project:

Supported by the National Natural Science Foundation of China (No.82220108017; No.82141128; No.82101180); Beijing Natural Science Foundation (No.Z220012); The Capital Health Research and Development of Special (No.2020-1-2052); Science & Technology Project of Beijing Municipal Science & Technology Commission (No.Z201100005520045); Sanming Project of Medicine in Shenzhen (No.SZSM202311018); Beijing Science & Technology Development of TCM (No.BJZYYB-2023-17).

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    Abstract:

    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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Yu-Ning Chen, Jing-Ying Xiu, Han-Qing Zhao,/et al.Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor. Int J Ophthalmol, 2025,18(5):765-778

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Publication History
  • Received:February 07,2025
  • Revised:February 25,2025
  • Online: April 21,2025
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