Evidence map›Paper›PMID 42415271›Full record

ArticleCanadian journal of gastroenterology & hepatology2026

Integrative Transcriptomics Across Etiologies Reveals Common and Disease-Specific Fibrogenic Signatures in Liver Fibrosis.

Wenyan Yang, Long Li, Yamei Ye, Chun Lin, Cheng Zhang, Haibin Tu

Abstract read
In one paragraph

Article in Canadian journal of gastroenterology & hepatology, 2026. 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. Article
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

6 authors.

Wenyan YangDepartment of Hepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China, fjmu.edu.cn.ORCID https://orcid.org/0009-0003-8077-6668
Long LiDistrict 1, Intensive Care Unit (ICU), Fujian Provincial Governmental Hospital, Fuzhou, China.ORCID https://orcid.org/0009-0004-2531-3190
Yamei YeDepartment of Hepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China, fjmu.edu.cn.ORCID https://orcid.org/0009-0004-2456-6698
Chun LinDepartment of Hepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China, fjmu.edu.cn.ORCID https://orcid.org/0009-0002-7524-7879
Cheng ZhangBlood Purification Center, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China, fjmu.edu.cn.ORCID https://orcid.org/0009-0003-9796-7954
Haibin TuDepartment of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China, fjmu.edu.cn.ORCID https://orcid.org/0000-0003-4540-9937

Funding

Fujian Medical University 2025-LCY-05Fujian Provincial Health Commission Science and Technology Plan Project 2024QNA079Natural Science Foundation of Fujian Province 2023J011480Natural Science Foundation of Fujian Province 2024J011240Project of the Science and Technology Bureau of Fuzhou City 2025-S-001
6 · The paper itself

Abstract

backgroundChronic liver diseases caused by metabolic, viral, and mixed etiologies frequently converge on fibrosis and cirrhosis; however, the extent to which fibrogenic mechanisms are shared across etiologies versus disease-specific remains incompletely defined.

methodsFour GEO datasets were analyzed: GSE135251 (NAFLD-related fibrosis), GSE84044 (HBV-related fibrosis), GSE197112 (mixed-etiology fibrosis), and GSE14323 (cirrhosis versus normal). Differential expression analysis was performed separately within each dataset using DESeq2 for RNA-seq and limma for microarray data. Shared genes were identified by cross-dataset intersection. For downstream network and ordination analyses, gene-level matrices were harmonized across platforms and batch-adjusted using ComBat, with PCA before and after correction provided in the supporting information. WGCNA and random forest were then applied to the integrated matrix, and the final seven hub genes were defined as genes supported by recurrent differential expression, co-expression prioritization, and random forest feature importance. The final seven-gene hub panel was further used for exploratory age-stratified visualization and regression analysis. Functional enrichment, miRNA-mRNA mapping, PPI analysis, PCoA, UMAP, RT-qPCR, and western blotting were performed.

resultsAcross the four cohorts, 434-787 differentially expressed genes were identified per dataset, and 26 genes were consistently upregulated across all etiologies. Enrichment analyses converged on extracellular matrix organization, TGF-β, PI3K-Akt, MAPK, and Wnt-related signaling. The final seven hub genes were MAOA, LOC102724200, SLC16A3, GPM6B, CST7, MT3, and ZNF142. Exploratory age analyses in the age-annotated GSE84044 cohort suggested that a subset of the final seven hub genes varied with age; however, these findings should be interpreted cautiously because age metadata were not uniformly available across all public cohorts. RT-qPCR in 10 fibrotic and 10 nonfibrotic liver tissues confirmed upregulation of the seven hub genes, and western blotting supported increased protein abundance of CST7, MT3, SLC16A3, and MAOA.

conclusionsThis integrative analysis identifies both shared and etiology-associated transcriptional programs in liver fibrosis and defines a multistep strategy for prioritizing conserved hub genes. The seven validated hub genes represent candidate biomarkers for fibrotic liver injury, whereas the exploratory age-related findings based on this seven-gene panel remain hypothesis-generating. This workflow may support future cross-platform transcriptomic studies of hepatic fibrosis.

Indexed as

Gene Expression ProfilingLiver CirrhosisTranscriptomeHumansbiomarker discoverycirrhosisliver fibrosisRT-qPCR validationtranscriptomicsWGCNA

Identifiers

PMID42415271
PMCPMC13341641

What Socratic holds

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LicenceCC BY
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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.