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

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.

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Publication Details

Journal
Scientific Reports
Published
2025-05-27
DOI
https://doi.org/10.1038/s41598-025-01609-8
Citations
1
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
0.94

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article

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

Hongjun Yu, Shounan Lu, Zhanzhi Meng, Bing Yin et al.
1 citations
Scientific Reports
Cancer-related molecular mechanisms research
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article

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

Hongjun Yu, Shounan Lu, Zhanzhi Meng, Bing Yin, Yongliang Hua, Yang Yang, Zhongyu Li, Yong Ma, Xinglong Li, Yao Fu, Baolin Qian, Yongzhi Zhou
article en
1 citations

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.

Scientific ReportsVol. 15(1)
Harbin Medical University (CN), Third Affiliated Hospital of Harbin Medical University (CN), Second Affiliated Hospital of Harbin Medical University (CN), First Affiliated Hospital of Harbin Medical University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Heilongjiang Province
Openalex Percentile: Top 25%
Cancer-related molecular mechanisms research
0.94
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