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pmid:40141450

Identification and Assessment of lncRNAs and mRNAs in PM2.5-Induced Hepatic Steatosis.

Peixuan Tian · Hui Xia · Xinbao Li · Ying Wang · Bihuan Hu · Yu Yang · Guiju Sun · Jing Sui

International journal of molecular sciences · 2025 doi:10.3390/ijms26062808

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Experiment

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Analysis

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title
Identification and Assessment of lncRNAs and mRNAs in PM2.5-Induced Hepatic Steatosis.
doi
10.3390/ijms26062808
pmid
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[0]
Peixuan Tian
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Hui Xia
[2]
Xinbao Li
[3]
Ying Wang
[4]
Bihuan Hu
[5]
Yu Yang
[6]
Guiju Sun
[7]
Jing Sui
journal
International journal of molecular sciences
year
2025
abstract
Research indicates that fine particulate matter (PM2.5) exposure is associated with the onset of non-alcoholic fatty liver disease (NAFLD), the most prevalent chronic liver disorder. However, the underlying pathogenesis mechanisms remain to be fully understood. Our study investigated the hub long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) associated with hepatic steatosis caused by PM2.5 exposure and their pathological mechanisms. The analysis of gene profiles in the GSE186900 dataset from the Gene Expression Omnibus (GEO) enabled the identification of 38 differentially expressed lncRNAs and 1945 mRNAs. To explore further, a co-expression network was established utilizing weighted gene co-expression network analysis (WGCNA). Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized for functional enrichment analysis. Our analysis identified specific modules, particularly the blue and turquoise modules, which showed a strong correlation with NAFLD. Through functional enrichment analysis, we identified several lncRNAs (including Gm15446, Tmem181b-ps, Adh6-ps1, Gm5848, Zfp141, Rmrp, and Rb1) which may be involved in modulating NAFLD, multiple metabolic pathways, inflammation, cell senescence, apoptosis, oxidative stress, and various signaling pathways. The hub lncRNAs identified in our study provide novel biomarkers and potential targets for the diagnosis and treatment of NAFLD.
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We conducted WGCNA on all genes using the “WGCNA” package in R software (Version 4.1.1).
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Cluster analysis of the GSE186900 dataset revealed 58 lncRNAs and 3945 mRNAs from 10 samples (Figure 1).
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