Evidence map›Paper›PMID 40425614›Full record

ArticleScientific reports2025

Identifying shared hub genes in LIRI and MASLD through bioinformatics analysis and machine learning.

Yongzhi Zhou, Bing Yin, Yang Yang, Zhongyu Li, Zhanzhi Meng, Shounan Lu, Baolin Qian, Xinglong Li, Yongliang Hua, Hongjun Yu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yongzhi Zhou *Department of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Bing Yin *Department of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Yang Yang *Department of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Zhongyu LiDepartment of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Zhanzhi MengDepartment of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Shounan LuDepartment of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Baolin QianDepartment of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Xinglong LiDepartment of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Yongliang HuaDepartment of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Hongjun YuDepartment of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Yao FuDepartment of Ultrasound, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.
Yong MaDepartment of Minimally Invasive Hepatic Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China. mayong@ems.hrbmu.edu.cn.

Funding

Chen Xiaoping Foundation for the Development of Science and Technology of Hubei Province CXPJJH11900001-2019349Natural Science Foundation of Heilongjiang Province of China LC2018037Research Fund of the National Natural Science Foundation of China 82370643
6 · The paper itself

Abstract

Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) are more susceptible to liver ischemia-reperfusion injury (LIRI), complicating liver surgery outcomes. This study aimed to uncover shared hub genes and mechanisms linking LIRI and MASLD to enhance donor liver utilization and improve prognosis. Using liver transplantation and MASLD datasets from the Gene Expression Omnibus, we applied Linear Models for Microarray Data and weighted gene co-expression analysis to identify differentially expressed genes and key module genes. Further analysis involved Gene Ontology, KEGG, and machine learning to pinpoint common hub genes and pathways. We identified 5,920 differentially expressed genes in liver datasets and 8,978 across LIRI and MASLD datasets. 71 shared hub genes were associated with pathways like MAPK signaling. Key genes, ADRB2 and CCL2, exhibited correlated mRNA expression in both datasets and human liver tissues. Hypoxia-reoxygenation in MASLD models elevated CCL2 levels and reduced ADRB2 expression. These genes showed strong diagnostic potential (AUC, 0.97). CCL2 knockdown reduced, while ADRB2 knockdown increased, MASLD cells' H/R injury sensitivity. Immune infiltration analysis revealed increased immune cell activity, particularly correlations between M0/M2 macrophages and NK cells/mast cells. ADRB2 and CCL2 were identified as crucial biomarkers, potentially explaining MASLD patients' heightened vulnerability to LIRI during liver transplantation.

Indexed as

Computational BiologyMachine LearningNon-alcoholic Fatty Liver DiseaseReperfusion InjuryChemokine CCL2Gene Expression ProfilingGene Regulatory NetworksHumansLiverLiver TransplantationReceptors, Adrenergic, beta-2ADRB2 protein, humanCCL2 protein, humanChemokine CCL2Receptors, Adrenergic, beta-2Bioinformatic analysisImmune infiltrationLiver ischemia-reperfusion injuryMachine learningMetabolic dysfunction-associated steatotic liver disease

Identifiers

PMID40425614
PMCPMC12117116

What Socratic holds

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Registered trials

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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.