Nomogram of uveal melanoma as prediction model of metastasis risk

BACKGROUND: Since the poor prognosis of uveal melanoma with distant metastasis, we intended to screen out possible biomarkers for uveal melanoma metastasis risk and establish a nomogram model for predicting the risk of uveal melanoma (UVM) metastasis. METHODS: Two datasets of UVM (GSE84976, GSE22138...

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Detalles Bibliográficos
Autores principales: Wang, Yimin, Xie, Minyue, Lin, Feng, Sheng, Xiaonan, Zhao, Xiaohuan, Zhu, Xinyue, Wang, Yuwei, Lu, Bing, Chen, Jieqiong, Zhang, Ting, Wan, Xiaoling, Liu, Wenjia, Sun, Xiaodong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440531/
https://www.ncbi.nlm.nih.gov/pubmed/37609406
http://dx.doi.org/10.1016/j.heliyon.2023.e18956
Descripción
Sumario:BACKGROUND: Since the poor prognosis of uveal melanoma with distant metastasis, we intended to screen out possible biomarkers for uveal melanoma metastasis risk and establish a nomogram model for predicting the risk of uveal melanoma (UVM) metastasis. METHODS: Two datasets of UVM (GSE84976, GSE22138) were selected. Data was analyzed by R language, CTD database and GEPIA. RESULTS: The co-upregulated genes of two datasets, HTR2B, CHAC1, AHNAK2, and PTP4A3 were identified using a Venn diagram. These biomarkers are combined with clinical characteristics, and Lasso regression was conducted to filter the metastasis-related biomarkers. HTR2B, CHAC1, AHNAK2, PTP4A3, tumor thickness, and retinal detachment (RD) were selected to establish the nomogram. CONCLUSION: Our study provides a comprehensive predictive model and personalized risk estimation tool for assessment of 3-year metastasis risk of UVM with a better accuracy.

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