Research Paper Advance Articles
A combination of differential expression and network connectivity analyses identifies a common set of RNA splicing and processing genes altered with age across human tissues
- 1 Department of Biochemistry, Chemistry Institute, University of São Paulo, São Paulo, São Paulo 05508-000, Brazil
- 2 Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, São Paulo 05508-000, Brazil
- 3 Division of Network AI Statistics, Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582, Japan
Received: March 6, 2025 Accepted: November 20, 2025 Published: December 22, 2025
https://doi.org/10.18632/aging.206347How to Cite
Copyright: © 2025 Batalha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Although transcriptomic changes are known to occur with age, the extent to which these are conserved across tissues is unclear. Previous studies have identified little conservation in age-modulated genes in different tissues. Here, we sought to identify common transcriptional changes with age in humans (aged 20 to 70) across tissues using differential network analysis, assuming that differential expression analysis alone cannot detect all changes in the transcriptional landscape that occur in tissues with age. Our results demonstrate that differential connectivity analysis reveals significant transcriptional alterations that are not detected by differential expression analysis. Combining the two analyses, we identified gene sets modulated by age across all tissues that are highly enriched in terms related to “RNA splicing” and “RNA processing”. The identified genes are also highly interconnected in protein-protein interaction networks. Co-expression module analyses demonstrated that other genes that show tissue-specific variations with age are enriched in pathways that combat the accumulation of aberrant RNAs and proteins, likely caused by defective splicing. Additionally, with convergent connectivity patterns, most tissues significantly reorganized their gene connectivity with age. Our results identified genes and processes whose age-associated transcriptional changes are conserved across tissues, demonstrating a central role for RNA splicing and processing genes and highlighting the importance of differential network analysis for understanding the ageing transcriptome.