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

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 (<i>A2M</i>), ankyrin-3 (<i>ANK3</i>), complement component 7 (<i>C7</i>), cadherin 6 (<i>CDH6</i>), cysteine-rich motor neuron protein 1 (<i>CRIM1</i>), dihydropyrimidinase-like 3 (<i>DPYSL3</i>), <i>F3</i>, gamma-aminobutyric acid (GABA) receptor subunit epsilon (<i>GABRE</i>), membrane metalloendopeptidase (<i>MME</i>), solute carrier family 38 member 1 (<i>SLC38A1</i>), tropomyosin alpha-1 chain (<i>TPM1</i>), von Willebrand factor (<i>VWF</i>), and zinc finger protein 83 (<i>ZNF83</i>)), and qRT-PCR confirmed these genes' expression patterns. Furthermore, these signature genes demonstrated strong correlations with multiple immune cell populations. In conclusion, the 13 genes (<i>A2M</i>, <i>ANK3</i>, <i>C7</i>, <i>CDH6</i>, <i>CRIM1</i>, <i>DPYSL3</i>, <i>F3</i>, <i>GABRE</i>, <i>MME</i>, <i>SLC38A1</i>, <i>TPM1</i>, <i>VWF</i>, and <i>ZNF83</i>) represent robust potential biomarkers for the diagnosis and treatment of LF. Among these genes, we first identified <i>Gabre</i> as related to LF and expressed in hepatocytes and cholangiocytes. The immune response mediated by these signature biomarkers plays a pivotal role in the pathogenesis and progression of LF through dynamic interactions between the biomarkers and immune-infiltrating cells.

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Journal
International Journal of Molecular Sciences
Published
2025-08-28
DOI
https://doi.org/10.3390/ijms26178387
Primary Topic
Liver Disease Diagnosis and Treatment
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article
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article

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

Yiling Elaine Chen, Zhijie Song, Yan Chen, Weilu Wang et al.
International Journal of Molecular Sciences
Liver Disease Diagnosis and Treatment
article

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

Yiling Elaine Chen, Zhijie Song, Yan Chen, Weilu Wang, Huibin Lian, Paul Kwong Hang Tam
article en

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 (<i>A2M</i>), ankyrin-3 (<i>ANK3</i>), complement component 7 (<i>C7</i>), cadherin 6 (<i>CDH6</i>), cysteine-rich motor neuron protein 1 (<i>CRIM1</i>), dihydropyrimidinase-like 3 (<i>DPYSL3</i>), <i>F3</i>, gamma-aminobutyric acid (GABA) receptor subunit epsilon (<i>GABRE</i>), membrane metalloendopeptidase (<i>MME</i>), solute carrier family 38 member 1 (<i>SLC38A1</i>), tropomyosin alpha-1 chain (<i>TPM1</i>), von Willebrand factor (<i>VWF</i>), and zinc finger protein 83 (<i>ZNF83</i>)), and qRT-PCR confirmed these genes' expression patterns. Furthermore, these signature genes demonstrated strong correlations with multiple immune cell populations. In conclusion, the 13 genes (<i>A2M</i>, <i>ANK3</i>, <i>C7</i>, <i>CDH6</i>, <i>CRIM1</i>, <i>DPYSL3</i>, <i>F3</i>, <i>GABRE</i>, <i>MME</i>, <i>SLC38A1</i>, <i>TPM1</i>, <i>VWF</i>, and <i>ZNF83</i>) represent robust potential biomarkers for the diagnosis and treatment of LF. Among these genes, we first identified <i>Gabre</i> as related to LF and expressed in hepatocytes and cholangiocytes. The immune response mediated by these signature biomarkers plays a pivotal role in the pathogenesis and progression of LF through dynamic interactions between the biomarkers and immune-infiltrating cells.

International Journal of Molecular SciencesVol. 26(17)
Macau University of Science and Technology (MO), University of Macau (MO)
Openalex Percentile: Top 69%
Liver Disease Diagnosis and Treatment
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