Prognostic prediction model for Chinese uveal melanoma patients based on matrix metalloproteinase-2 and -28 expression levels in the tumor
Year: 2025
Type: article
Study design: Observational Study
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.
Authors: Yu-Ning Chen, Han-Qing Zhao, Jing-Ting Luo, Qiong Yang, Yang Li, Wen-Bin Wei
Language: English
Subfield: Ophthalmology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,800
OpenAlex ID: W4409663348