Network analysis of aging acceleration reveals systematic properties of 11 types of cancers

Abstract

Cancers are known to be associated with accelerated aging, but to date, there has been a paucity of systematic and in‐depth studies of the correlation between aging and cancer. DNA methylation (DNAm) profiles can be used as aging markers and utilized to construct aging predictors. In this study, we downloaded 333 paired samples of DNAm, expression and mutation profiles encompassing 11 types of tissues from The Cancer Genome Atlas public access portal. The DNAm aging scores were calculated using the Support Vector Machine regression model. The DNAm aging scores of cancers revealed significant aging acceleration compared to adjacent normal tissues. Aging acceleration‐associated mutation modules and expression modules were identified in 11 types of cancers. In addition, we constructed bipartite networks of mutations and expression, and the differential expression modules related to aging‐associated mutations were selected in 11 types of cancers using the expression quantitative trait locus method. The results of enrichment analyses also identified common functions across cancers and cancer‐specific characteristics of aging acceleration. The aging acceleration interaction network across cancers suggested a core status of thyroid carcinoma and neck squamous cell carcinoma in the aging process. In summary, we have identified correlations between aging and cancers and revealed insights into the biological functions of the modules in aging and cancers.

Keywords

• accelerated aging
• cancer
• DNA methylation
• network analysis
• pan‐cancer