Trans-omics analyses revealed key epigenetic genes associated with overall survival in secondary progressive multiple sclerosis.

BACKGROUND

Secondary progressive multiple sclerosis (SPMS) is the second most common presentation of multiple sclerosis (MS) and is characterized by a gradually deteriorating disease with or without relapses. Approximately 80% of patients with relapsing-remitting MS (RRMS) develop SPMS within 20 years. Epidemiological investigations have revealed an average 7-year life expectancy decrease (more severe in progressive subtypes) in patients with MS. Studies have focused on the neurodegenerative pathogenesis of SPMS; and epigenetic changes have been associated with disease progression in neurodegenerative disorders. However, the evidence for the association between epigenetic changes and SPMS is scarce. Thus, in this study we aimed to identify the key epigenetic genes in SPMS.

METHODS

We downloaded DNA methylation and gene expression matrices from the Gene Expression Omnibus (GEO) database. We used bioinformatic analyses to identify key epigenetic genes associated with overall survival (OS) in patients with SPMS.

RESULTS

We found 49 differentially methylated positions (DMPs) between the SPMS and control GSE40360 datasets. We used the wANNOVAR server to obtain 64 methylated genes. We merged the gene expression datasets (GSE131282 and GSE135511) in the NetworkAnalyst platform and found 12,442 differentially-expressed genes (DEGs) between SPMS and controls using the Fisher's method, fixed effect model, Vote counting, and direct merging methods. Moreover, we identified 21 epigenetic genes (all hyper-methylated) after an integrating analysis of DMPs and DEGs of patients with SPMS. We established an epigenetic gene signature associated with the OS of patients with SPMS including six hyper-methylated genes (ITGA6, PPP1R16B, RNF126, ABHD8, FOXK1, and SLC6A19) based on the LASSO-Cox method. The calculated individual risk scores were associated with Oss, and we divided patients into high- and low-risk groups on the basis of the mean cut-off value. The six key epigenetic genes were significantly associated with gender, disease duration, and age at death via Spearman correlation analyses. In addition, survival analyses revealed a significant OS difference between high- and low-risk groups. The ROC curves indicated good performance for this predictive model.

CONCLUSION

We identified 21 hyper-methylated genes in patients with SPMS via an integrated analysis of DNA methylation and gene expression datasets. We identified a six-epigenetic gene signature that predicts the individual OS with good accuracy. These results indicated that epigenetic modifications play a vital role in the disease progression of SPMS.