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Updated: Jan 31, 2026

Optimized Analysis of DNA Methylation and Gene Expression from Small, Anatomically-defined Areas of the Brain
Published on: July 12, 2012
An 11-gene-based prognostic signature for uveal melanoma metastasis based on gene expression and DNA methylation
Yang Li1, Xuan Yang1, Jingyan Yang1
1Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Abstract:
Uveal melanoma (UM) is the most common intraocular tumor worldwide. We proposed to identify a vital gene signature that has prognostic value for UM metastasis. For this purpose, we obtained a published DNA methylation and gene expression data set associated with UM from the Gene Expression Omnibus. The genes whose aberrant expression significantly associated with UM patients' metastasis-free survival (MFS) were identified by applying a univariate Cox proportional hazards model to the gene expression data set followed by a robust likelihood-based survival analysis to screen the optimal prognostic gene signatures (PGS). A formula for calculating the risk score that represents UM metastasis risk was constructed by including the PGSs' expression values weighted by their regression coefficients, which were obtained by a multivariate Cox regression analysis. As a result, aberrant expression of 2884 genes were found to be significantly associated with UM patients' MFS, which were referred to as MFSGs, and 11 out of those MFSGs, GJC1, TCEA1, MFSD3, FAF2, TLCD1, GPAA1, CYC1, ASAP1, JPH1, LDB3, and KDELR3, were identified as PGSs through which we could accurately separate UM samples with shorter MFS from those with longer MFS. By combining the DNA methylation data set and MFSGs, we further identified 265 MFSGs, which contained CpG sites that significantly hyper- or hypo-methylated in UM samples compared with control samples. Functional enrichment analysis and pathway crosstalk analysis of those genes indicated significant enrichment of cancer-related pathways. In conclusion, we identified an 11-gene-based prognostic signature and several gene biomarkers for UM metastasis, which should be helpful for selecting an appropriate treatment method for specific patients with UM.
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