Six-gene-based prognostic model predicts overall survival in patients with uveal melanoma

Qi Wan, Jing Tang, Jianqun Lu, Lin Jin, Yaru Su, Shoubi Wang, Yaqi Cheng, Ying Liu, Chaoyang Li, Zhichong Wang

Research output: Contribution to journalArticlepeer-review


BACKGROUND: Uveal melanoma (UM) is the most common primary intraocular tumor in adults, which has a high mortality rate and worse prognosis. Therefore, early potential molecular detection and prognostic evaluation seem more important for early diagnosis and treatment. METHODS: Gene expression data were obtained from The Cancer Genome Atlas-Uveal melanomas database. Survival genes were identified by univariate analysis and were regarded to be associated with the overall survival of UM patients. Then, pathway enrichment analysis of these survival genes was performed. Robust likelihood-based survival model and multivariate survival analysis were conducted to identify more reliable genes and the prognostic signature for UM survival prediction. Two internal datasets and another two UM datasets from Gene Expression Omnibus (GEO) were used for the validation of prognostic signature. RESULTS: Firstly, 2,010 survival genes were screened by univariate survival analysis. GO and KEGG analysis revealed that these genes were mainly involved in pathways such as mRNA processing, RNA splicing, spliceosome and ubiquitin mediated proteolysis. Secondly, a six-gene signature was identified by Robust likelihood-based survival model approach. The gene expression of the six genes can successfully divide UM samples into high-and low-risk groups and have strong survival prediction ability. What's more, the expression of six genes was compared in 80 healthy adipose tissue samples obtained from GTEx (Genotype-Tissue Expression) database and further validated in internal datasets and GEO datasets, which also can predict UM patient survival. CONCLUSIONS: The six genes (SH2D3A, TMEM201, LZTS1, CREG1, NIPA1 and HIST1H4E) model might play a vital role in prognosis of UM, which should be helpful for further insight into the treatment of uveal melanoma.

Original languageEnglish (US)
Pages (from-to)343-356
Number of pages14
JournalCancer Biomarkers
Issue number3
StatePublished - 2020
Externally publishedYes


  • GEO
  • survival analysis
  • TCGA
  • Uveal melanoma

ASJC Scopus subject areas

  • Oncology
  • Genetics
  • Cancer Research


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