Evidence map›Paper›PMID 40943308›Full record

ArticleInternational journal of molecular sciences2025

Identification of a 13-Gene Immune Signature in Liver Fibrosis Reveals GABRE as a Novel Candidate Biomarker.

Wei-Lu Wang, Haoran Lian, Yiling Chen, Zhejun Song, Paul Kwong Hang Tam, Yan Chen

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Wei-Lu WangSchool of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
Haoran LianSchool of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.ORCID 0009-0002-9693-2234
Yiling ChenSchool of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
Zhejun SongSchool of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
Paul Kwong Hang TamSchool of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.ORCID 0000-0001-6231-3035
Yan ChenSchool of Pharmacy, Faculty of Medicine, Macau University of Science and Technology, Macau, China.

Funding

Macau Science and Technology Development Fund 0011/2023/AKPMacau Science and Technology Development Fund 0097/2022/A2Macau Science and Technology Development Fund FRG-22-088-FMD
6 · The paper itself

Abstract

Liver fibrosis (LF) poses significant challenges in diagnosis and treatment. This study aimed to identify effective biomarkers for diagnosis and therapy, as well as to gain deeper insights into the immunological features associated with LF. LF-related datasets were retrieved from the Gene Expression Omnibus (GEO) database. Two datasets were merged to generate a metadata cohort for bioinformatics analysis and machine learning, while another dataset was reserved for external validation. Seventy-eight machine learning algorithms were employed to screen signature genes. The diagnostic performance of these genes was evaluated using receiver operating characteristic (ROC) curves, and their expression levels were validated via qRT-PCR experiments. The R language was utilized to delineate the immune landscape. Finally, correlation analysis was conducted to investigate the relationship between the signature genes and immune infiltration. Through the intersection of GEO datasets and Weighted Gene Co-expression Network Analysis (WGCNA), 42 genes were identified. Machine learning methods further narrowed down 13 signature genes (alpha-2-macroglobulin (

Indexed as

Liver CirrhosisTranscriptomeBiomarkersComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningBiomarkersbiomarkersGABREimmune landscapeliver fibrosismachine learningqRT-PCR

Identifiers

PMID40943308
PMCPMC12429109

What OpenQuestion 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 OpenQuestion graph.