Transcriptomic Identification of Diagnostic Biomarkers for Alcohol-Associated Liver Cirrhosis: Integration of Population-Level Epidemiology with Multi-Cohort Transcriptomic Analysis.

Hao Wang, Wenzhang Ding, Linjie Zhang, Muyang Xu, Jing Sui

Abstract read

In one paragraph

Article in International journal of molecular sciences, 2026. 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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hao WangSchool of Electronic and Information Engineering, Anhui Jianzhu University, Heifei 230601, China.ORCID 0009-0001-4573-2833

Wenzhang DingSchool of Clinical Medicine, Wannan Medical University, Wuhu 241002, China.

Linjie ZhangSchool of Public Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0009-0008-2929-7385

Muyang XuSchool of Electronic and Information Engineering, Anhui Jianzhu University, Heifei 230601, China.

Jing SuiSchool of Public Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China.ORCID 0000-0001-8223-9872

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alcohol-associated liver cirrhosis (ALC) lacks aetiology-specific molecular diagnostic biomarkers. This study aims to quantify the association between alcohol and cirrhosis risk, and to identify transcriptomic diagnostic biomarkers and candidate therapeutics. Methods: Survey-weighted logistic regression was applied to 17,007 adults from NHANES (2017-2023) to quantify alcohol-cirrhosis associations. ALC transcriptomic data from four GEO datasets were analysed using weighted gene co-expression network analysis (WGCNA) and three parallel machine learning algorithms (LASSO, Random Forest, SVM-RFE). External validation was performed in an independent cohort of 93 samples. Candidate therapeutics were identified via drug signature database querying and validated by molecular docking. Heavy drinking conferred a 5.14-fold increased cirrhosis risk (95% CI: 2.60-10.20,

Indexed as

BiomarkersGene Expression ProfilingLiver Cirrhosis, AlcoholicTranscriptomeAdultCohort StudiesFemaleHumansMaleMiddle AgedRNA, Long NoncodingBiomarkersRNA, Long Noncodingalcohol-associated liver cirrhosisdiagnostic biomarkersdrug repurposinghepatic fibrosisImmune microenvironmentmachine learningweighted gene co-expression network analysis

Identifiers

PMID42450082

DOI10.3390/ijms27135809

PMCPMC13360815

What Socratic holds

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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.