ArticleScientific reports2023
Network-level analysis of ageing and its relationship with diseases and tissue regeneration in the mouse liver.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 18 citations in OpenAlex.
- Aging disrupts hepatocyte zonation homeostasis in mice and humans.Hepatology (Baltimore, Md.) · 2026Article
- Aging Promotes Spontaneous Liver Injury: Insights from Metabolic, Inflammatory, and Fibrotic Pathways in C57BL/6 Mice.Biomolecules · 2025Article
- A single-cell network approach to decode metabolic regulation in gynecologic and breast cancers.NPJ systems biology and applications · 2025Article
- Article
- Cgref1 is a CREB-H-regulated hepatokine that promotes hepaticInternational journal of biological sciences · 2025Article
- A biomarker framework for liver aging: the Aging Biomarker Consortium consensus statement.Life medicine · 2024Article
- A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging.PloS one · 2024Article
- Reduced liver damage and fibrosis with combined SCD Probiotics and intermittent fasting in aged rat.Journal of cellular and molecular medicine · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The liver plays a vital role in maintaining whole-body metabolic homeostasis, compound detoxification and has the unique ability to regenerate itself post-injury. Ageing leads to functional impairment of the liver and predisposes the liver to non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Mapping the molecular changes of the liver with ageing may help to understand the crosstalk of ageing with different liver diseases. A systems-level analysis of the ageing-induced liver changes and its crosstalk with liver-associated conditions is lacking. In the present study, we performed network-level analyses of the ageing liver using mouse transcriptomic data and a protein-protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To gain further insights, we also integrated the differentially expressed genes (DEGs) between young and different age groups with the PPI network and identified core modules and nodes associated with ageing. Finally, we computed the network proximity of the ageing network with different networks of liver diseases and regeneration to quantify the effect of ageing. Our analysis revealed the complex interplay of immune, cancer signalling, and metabolic genes in the ageing liver. We found significant network proximities between ageing and NAFLD, HCC, liver damage conditions, and the early phase of liver regeneration with common nodes including NLRP12, TRP53, GSK3B, CTNNB1, MAT1 and FASN. Overall, our study maps the network-level changes of ageing and their interconnections with the physiology and pathology of the liver.
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