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Identification of SOX9-related prognostic DEGs and a prediction model for hepatitis C-induced early-stage fibrosis

  • 0Division of Liver Surgery, Department of General Surgery, West China Hospital, Si Chuan University, Chengdu, China.
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December 2, 2024

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December 2, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies nine SOX9-related genes for predicting liver fibrosis progression in Hepatitis C virus (HCV) patients. The new model improves prognosis prediction over existing methods, offering new strategies for liver fibrosis treatment.

Area Of Science

  • Hepatology
  • Molecular Biology
  • Bioinformatics

Background

  • Hepatitis C virus (HCV) infection causes liver inflammation, hepatic stellate cell (HSC) activation, and fibrosis.
  • SOX9 overexpression is linked to HSC activation, suggesting its role in liver fibrosis progression.

Purpose Of The Study

  • Identify genes associated with SOX9 in liver fibrosis.
  • Develop a prognostic model for predicting liver fibrosis progression and outcomes.
  • Discover potential therapeutic targets for liver fibrosis.

Main Methods

  • Analyzed the GSE15654 dataset of HCV-induced early-stage cirrhosis patients.
  • Identified prognostic genes using differential gene analysis, LASSO, and Cox regression.
  • Quantified immune cell infiltration using CIBERSORT analysis.
  • Validated the prognostic model using qRT-PCR and RNA sequencing.

Main Results

  • Elevated SOX9 expression correlates with poor outcomes in HCV-induced liver fibrosis.
  • Identified nine SOX9-related differentially expressed genes (DEGs) as prognostic factors.
  • Developed a model outperforming APRI and FIB4 scores in predicting patient prognosis.
  • SOX9 expression positively correlated with mast cell infiltration; ARHGEF5 and FRMD6 with M0 macrophage infiltration.

Conclusions

  • A prognostic model based on nine SOX9-related DEGs effectively predicts liver fibrosis progression and outcomes.
  • This study presents a novel strategy for improving liver fibrosis prediction and treatment.

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