Evidence map›Paper›PMID 41331566›Full record

ArticleBMC ophthalmology2025

A prognostic model for uveal melanoma in Asian populations: a comparative analysis of clinical features and gene expression patterns using the TRACE and TCGA data.

Jingting Luo, Yuning Chen, Zhaoxun Feng, Haowen Li, Jingying Xiu, Yangxu Tao, Qiong Yang, Yueming Liu, Yang Li, Wenbin Wei

Abstract readComparative Study
In one paragraph

Article in BMC ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jingting 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 Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China.
Yuning 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 Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China.
Zhaoxun FengDepartment of Ophthalmology, University of Ottawa, Ottawa, ON, Canada.
Haowen LiBeijing 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, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China.
Jingying XiuBeijing 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, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China.
Yangxu TaoBeijing 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, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China.
Qiong YangBeijing 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, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China.
Yueming LiuBeijing 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, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China.
Yang LiBeijing 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, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China. liyangtongren@163.com.
Wenbin WeiBeijing 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, No. 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, P. R. China. weiwenbintr@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUveal melanoma (UM), the most common primary intraocular malignancy in adults, shows racial disparities in incidence, genetic drivers, and clinical outcomes. While most prognostication models are based on Caucasian populations, Asians demonstrate distinct molecular profiles, necessitating population-specific risk stratification.

methodsThis study analyzed 53 Asian UM tumors, 17 normal choroidal tissues (TRACE database), and 80 Caucasian UM samples (TCGA database). Differential gene analysis, immune microenvironment profiling, and survival modeling were performed. A 7-gene prognostic signature was developed by LASSO regression and validated across cohorts, with drug sensitivity predicted using GDSC2 data.

resultsThe Asian UM exhibited 3,827 tumor-specific differentially expressed genes (DEGs) compared to the normal choroid, with enrichment in PI3K-Akt signalling, and 3,814 race-specific DEGs compared to Caucasians, suggesting specific disease pathways and variations in the tumor microenvironment. The tumor microenvironment in Asian UM exhibited increased immunological activation (M1 macrophages, PD-L1, CD8; p < 0.05), while Caucasian uveal melanoma was marked by immunosuppressive predominance (M2 macrophages, MDSCs). The seven-gene prognostic model (MMP2, LRAT, NOG, IHH, CDH18, MYH11, and SELE) exhibited strong predictive efficacy in Asians (AUC: 0.979, 0.924, and 0.984 for 1, 3, and 5-year survival) but was less successful in Caucasians. High-risk scores correlated with metastasis (12/26 vs. 4/27; p = 0.02) and had independent prognostic value.

conclusionsThis cross-racial UM study reveals significant molecular and immune differences, indicating that Asian UM may be more responsive to immunotherapy The population-specific prognostic model improves our understanding of molecular differences in Asian and Caucasian UM, warranting further validation in multiethnic cohorts.

Indexed as

Asian PeopleBiomarkers, TumorGene Expression Regulation, NeoplasticMelanomaUveal NeoplasmsAdultAgedFemaleGene Expression ProfilingHumansMaleMiddle AgedPrognosisTumor MicroenvironmentUveal MelanomaBiomarkers, TumorImmune microenvironmentPrognostic modelRacial disparitiesUveal melanoma

Identifiers

PMID41331566
PMCPMC12781388

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.