Evidence map›Paper›PMID 42399771›Full record

ArticleBMC gastroenterology2026

Integrating WGCNA and machine learning to identify and validate key biomarkers in MASLD.

Jianfeng Zhang, Qi Liang, Xiaona Xu, Peilong Wang, Baoguo Yan

Abstract read
In one paragraph

Article in BMC gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jianfeng ZhangDepartment of Pharmacy, Eighth Hospital of Xi'an City, Xi'an, Shaanxi, 710061, China.
Qi LiangShaanxi Provincial Drug Evaluation Center, No. 56, Gaoxin Sixth Road, Xi'an, Shaanxi, 710065, China.
Xiaona XuDepartment of Pharmacy, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710004, China.
Peilong WangDepartment of Pharmacy, Eighth Hospital of Xi'an City, Xi'an, Shaanxi, 710061, China.
Baoguo YanDepartment of Pharmacy, Eighth Hospital of Xi'an City, Xi'an, Shaanxi, 710061, China. 13709188719@163.com.

Funding

Natural Science Foundation of Shaanxi Province 2023-JC-QN-0159
6 · The paper itself

Abstract

backgroundMetabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) represents the most prevalent chronic liver disease worldwide. The absence of approved pharmacotherapies is largely attributed to their profound molecular heterogeneity. The identification of novel hub genes in MASLD is therefore critical for unraveling the complex molecular mechanisms driving disease pathogenesis and progression.

methodsWe employed an integrative systems-biology approach utilizing Weighted Gene Co-expression Network Analysis (WGCNA) followed by multi-algorithm machine learning (LASSO, Random Forest, SVM-RFE) across GEO datasets (GSE89632, GSE63067) to identify hub genes. Key hub genes were validated in vitro with FFA-treated HepG2 cells and in vivo with an HFD-fed mouse model.

resultsBioinformatic analyses revealed two distinct pathological networks. First, a lipogenesis-associated cluster revealed FMO1 and C10orf140 as upregulated, alongside JUNB downregulation. In vitro, the expression of these genes was significantly associated with the activation of the SREBP-1c/FASN lipogenic pathway. Second, WGCNA revealed a co-expression module that exhibited high correlation with inflammation (R = 0.59, p = 4e-05), from which 11 hub genes relating to inflammation (such as MAP3K8, PFKFB3) were identified. In vivo, HFD mice developed severe steatosis and demonstrated a key pathological paradox: high-level activation of both the pro-lipogenic p-AKT and the inhibitory p-AMPK pathways.

conclusionOur study identified the hub genes FMO1, C10orf140, and JUNB as novel regulators of MASLD lipogenesis through the SREBP-1c pathway. Additionally, we showed that co-activated p-AKT and p-AMPK in steatotic livers indicates "AMPK Resistance". Ultimately, we describe a mechanism of pro-lipogenic signaling that is not curtailed and is accompanied by the inability to compensate for the inhibitory path. All findings reveal potential therapeutic targets for MASLD.

Indexed as

Fatty LiverGene Regulatory NetworksMachine LearningNon-alcoholic Fatty Liver DiseaseAnimalsBiomarkersDisease Models, AnimalGene Expression ProfilingHep G2 CellsHumansLipogenesisMaleMiceMice, Inbred C57BLSterol Regulatory Element Binding Protein 1Systems BiologyBiomarkersSterol Regulatory Element Binding Protein 1AMPK resistanceHub genesLipogenesisMachine learningMetabolic dysfunction-associated steatotic liver disease (MASLD)WGCNA

Identifiers

PMID42399771
PMCPMC13625418

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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.