bioRxivCancer BiologyPreprint
Sex Differences in DNA Methylation and Gene Expression Reveal Distinct Molecular Programs in Muscle-Invasive Bladder Cancer
Bhavisha DoshiiD, Eduardo Cortes GomeziD, Barbara A. FosteriD, Jianmin WangiD, Norbert SüleiD, Wendy J. HussiD, Anna Woloszynska‐ReadiD
Abstract
Sex differences in muscle-invasive bladder cancer (MIBC) incidence and outcomes are well established, but the underlying epigenetic mechanisms remain poorly understood. We integrated genome-wide DNA methylation and RNA sequencing to define sex-associated epigenetic and transcriptional programs in human MIBC and evaluated their conservation in the N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN) mouse model. Male tumors exhibited substantially broader DNA methylation remodeling, whereas female tumors showed prominent epithelial-mesenchymal transition (EMT) and mitotic programs. These sex-associated patterns were recapitulated in BBN tumors, with substantial conservation of human MIBC methylation changes and transcriptional programs. Cross-species integration identified conserved methylation-regulated genes, including ADAMTS12, ONECUT2, and PPP2R2C, whose expression increased following pharmacologic DNA demethylation and was associated with disease progression. Given the potential role of sex chromosomes in sex-associated molecular differences in MIBC, we evaluated X-chromosome inactivation escape and Y-linked gene expression. Reduced expression of an X-chromosome inactivation escape signature was independently associated with shorter overall survival, while reduced Y-linked gene expression was associated with immune activation. Together, these findings identify conserved sex-associated epigenetic programs in MIBC and demonstrate distinct methylation, transcriptional, and sex-chromosome features that may contribute to sex differences in bladder cancer biology and clinical outcomes.
- organism: human
- organism: mouse
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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