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Background <p>The infection caused by the Hepatitis B virus (HBV) poses a significant challenge to global health. However, identifying and prioritizing functional biomarkers associated with the disease will allow more focused efforts for pre-clinical investigation. This study aimed to employ advanced genomic networks and bioinformatics tools to prioritize biomarkers for HBV. </p> Results <p>Comprehensive curation from six well-known databases yields a set of 495 HBV-associated genes based on gene ontology enrichment, pathway enrichment and protein–protein interaction analyses. Further functional gene network analyses identified 10 hub genes (<i>IL-6</i>, <i>TNF</i>, <i>STAT3</i>, <i>CD4</i>, <i>IL-10</i>, <i>STAT1</i>, <i>IL-1B</i>, <i>AKT1</i>, <i>IL-2</i>, and <i>TLR4</i>), warranting further investigation. Among these genes, <i>IL-6</i>, <i>TNF</i>, and <i>STAT3</i> emerged as promising biomarkers for HBV through CytoHubba analysis.</p> Conclusions <p>Overall, this study offers a genomic network-centric approach to identify and prioritize potential HBV biomarkers to provide valuable insights into its pathogenesis.</p>

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Unveiling hepatitis B biomarkers through genomic network analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月21日

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  • Wirawan Adikusuma,
  • Firdayani Firdayani,
  • Lalu Muhammad Irham,
  • Ichtiarini Nurullita Santri,
  • Yohane Vincent Abero Phiri,
  • Dian Ayu Eka Pitaloka,
  • Rockie Chong,
  • Made Ary Sarasmita,
  • Rahmat Dani Satria

摘要

Background

The infection caused by the Hepatitis B virus (HBV) poses a significant challenge to global health. However, identifying and prioritizing functional biomarkers associated with the disease will allow more focused efforts for pre-clinical investigation. This study aimed to employ advanced genomic networks and bioinformatics tools to prioritize biomarkers for HBV.

Results

Comprehensive curation from six well-known databases yields a set of 495 HBV-associated genes based on gene ontology enrichment, pathway enrichment and protein–protein interaction analyses. Further functional gene network analyses identified 10 hub genes (IL-6, TNF, STAT3, CD4, IL-10, STAT1, IL-1B, AKT1, IL-2, and TLR4), warranting further investigation. Among these genes, IL-6, TNF, and STAT3 emerged as promising biomarkers for HBV through CytoHubba analysis.

Conclusions

Overall, this study offers a genomic network-centric approach to identify and prioritize potential HBV biomarkers to provide valuable insights into its pathogenesis.