ArticleAging2023
Machine learning and single cell RNA sequencing analysis identifies regeneration-related hepatocytes and highlights a Birc5-related model for identifying cell proliferative ability.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Molecular Landscape and Predictive Significance of Programmed Cell Death-Related Genes in Sepsis.Human mutation · 2026Article
- Microbiome-derived bile acids as endogenous regenerative mediators in liver repair.Regenerative therapy · 2025Review
- Scaffold-free cell sheet therapies: clinical advances, global approval landscapes, and strategic directions to address regenerative medicine barriers.Stem cell research & therapy · 2025Review
- Influencing factors and mechanism of hepatocyte regeneration.Journal of translational medicine · 2025Review
- Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework.Journal of Cancer · 2024Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPartial hepatectomy (PHx) has been shown to induce rapid regeneration of adult liver under emergency conditions. Therefore, an in-depth investigation of the underlying mechanisms that govern liver regeneration following PHx is crucial for a comprehensive understanding of this process.
methodWe analyzed scRNA-seq data from liver samples of normal and PHx-48-hour mice. Seven machine learning algorithms were utilized to screen and validate a gene signature that accurately identifies and predicts this population. Co-immunostaining of zonal markers with BIRC5 to investigate regional characteristics of hepatocytes post-PHx.
resultsSingle cell sequencing results revealed a population of regeneration-related hepatocytes. Transcription factor analysis emphasized the importance of Hmgb1 transcription factor in liver regeneration. HdWGCNA and machine learning algorithm screened and obtained the key signature characterizing this population, including a total of 17 genes and the function enrichment analysis indicated their high correlation with cell cycle pathway. It is note-worthy that we inferred that Hmgb1 might be vital in the regeneration-related hepatocytes of PHx_48h group. Parallelly, Birc5 might be closely related to the regulation of liver regeneration, and positively correlated with Hmgb1.
conclusionsOur study has identified a distinct population of hepatocytes that are closely associated with liver regeneration. Through machine learning algorithms, we have identified a set of 17 genes that are highly indicative of the regenerative capacity of hepatocytes. This gene signature has enabled us to assess the proliferation ability of
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