Network-community analysis of cellular senescence

TL;DR

This study employs network-based analysis of RNA sequencing data to identify genetic markers associated with cellular senescence, revealing both known and novel genes linked to senescence hallmarks.

Contribution

It introduces a network approach combining eigenvector centrality and community detection to identify senescence-related genes from bulk RNA-seq data.

Findings

Identified genetic markers consistent with previous studies.

Discovered novel genes associated with senescence.

Validated findings with independent single-cell RNA-seq data.

Abstract

Most cellular phenotypes are genetically complex. Identifying the set of genes that are most closely associated with a specific cellular state is still an open question in many cases. Here we study the transcriptional profile of cellular senescence using a combination of network-based approaches, which include eigenvector centrality feature selection and community detection. We apply our method to cell-type-resolved RNA sequencing data obtained from injured muscle tissue in mice. The analysis identifies some genetic markers consistent with previous findings, and other previously unidentified ones, which are validated with previously published single-cell RNA sequencing data in a different type of tissue. The key identified genes, both those previously known and the newly identified ones, are transcriptional targets of factors known to be associated with established hallmarks of senescence, and can thus be interpreted as molecular correlates of such hallmarks. The method proposed here could be applied to any complex cellular phenotype even when only bulk RNA sequencing is available, provided the data is resolved by cell type.