Heliyon (Aug 2023)

Nomogram of uveal melanoma as prediction model of metastasis risk

  • Yimin Wang,
  • Minyue Xie,
  • Feng Lin,
  • Xiaonan Sheng,
  • Xiaohuan Zhao,
  • Xinyue Zhu,
  • Yuwei Wang,
  • Bing Lu,
  • Jieqiong Chen,
  • Ting Zhang,
  • Xiaoling Wan,
  • Wenjia Liu,
  • Xiaodong Sun

Journal volume & issue
Vol. 9, no. 8
p. e18956

Abstract

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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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