ArticleJournal of inflammation research2026
Machine Learning, scRNA-Seq and Biological Experiments Identify Hub Genes Responsible for Ischemia-Reperfusion Injury in Steatotic Liver Allografts.
Article in Journal of inflammation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Liver transplantation is the standard treatment for end-stage liver disease, but donor organ shortage has increased the use of marginal donor livers (eg, steatotic grafts). These livers are highly sensitive to liver ischemia/reperfusion injury (LIRI), worsening transplant outcomes, with unclear molecular mechanisms limiting targeted therapies. Methods: We adopted an integrative bioinformatics approach combining transcriptomic analysis of public datasets (GSE151648, GSE135251, GSE15480, GSE23649 and GSE193764), machine learning (LASSO, SVM-RFE), and scRNA-Seq analysis (GSE171539, CRA004061) to identify hub genes and pathways. Key findings were validated in vitro (AML12 hepatocytes under hypoxia/reoxygenation) and in vivo (murine model of steatotic liver under I/R). Results: Weighted gene co-expression network analysis (WGCNA) identified LIRI-, NAFLD-, and NASH-associated modules enriched in immune/inflammatory pathways. Machine learning pinpointed distinct hub genes for NAFLD-related LIRI (JUN, CCL2) and NASH-related LIRI (PHLDA1, PNRC1, GADD45B, NFKBIA, JUND; all AUC > 0.7). CCL2 emerged as the top predictor via SHAP analysis. scRNA-seq revealed cell-type-specific expression patterns and distinct immune infiltration signatures between NAFLD and NASH cohorts, with remodeled cell-cell communication via CCL/CXCL pathways. In vitro/in vivo models confirmed the upregulation of key genes under LIRI, and CCR2 inhibition significantly attenuated liver injury, pathological damage, and pro-inflammatory cytokine (IL-1β, TNF-α, IL-6) expression. Conclusion: Our study identifies distinct molecular signatures and hub genes for LIRI in NAFLD versus NASH allografts, revealing differential immune landscapes between these two conditions. The findings further support the CCL2-CCR2 axis as a therapeutically targetable pathway driving LIRI in steatotic livers and provide potential biomarkers to improve marginal liver transplantation outcomes.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.