Comprehensive transcriptomic analysis of hepatocellular Carcinoma: Uncovering shared and unique molecular signatures across diverse etiologies
Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO),...
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| Published in | Biochemistry and biophysics reports Vol. 43; p. 102123 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.09.2025
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2405-5808 2405-5808 |
| DOI | 10.1016/j.bbrep.2025.102123 |
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| Abstract | Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g., CYP2C9, SLC22A1, RDH5) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g., ABCA8, GADD45B) and 221 HCV-specific DEGs (e.g., CDK1, CCNB1) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies.
•125 pan-etiology HCC genes (CYP2C9, SLC22A1) linked to retinol metabolism and solute transport.•14 HBV-specific and 221 HCV-specific DEGs reveal unique viral-driven HCC pathways.•PPI hubs (CDK1, CCNE1, TYMS) drive cell cycle and metabolic dysregulation.•Hub genes predict survival (CDK1 poor prognosis, CAT protective in HBV-HCC). |
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| AbstractList | Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g.,
CYP2C9
,
SLC22A1
,
RDH5
) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g.,
ABCA8
,
GADD45B
) and 221 HCV-specific DEGs (e.g.,
CDK1
,
CCNB1
) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies.
•
125 pan-etiology HCC genes (CYP2C9, SLC22A1) linked to retinol metabolism and solute transport.
•
14 HBV-specific and 221 HCV-specific DEGs reveal unique viral-driven HCC pathways.
•
PPI hubs (CDK1, CCNE1, TYMS) drive cell cycle and metabolic dysregulation.
•
Hub genes predict survival (CDK1 poor prognosis, CAT protective in HBV-HCC). Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g., , , ) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g., , ) and 221 HCV-specific DEGs (e.g., , ) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies. Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g., CYP2C9, SLC22A1, RDH5) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g., ABCA8, GADD45B) and 221 HCV-specific DEGs (e.g., CDK1, CCNB1) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies. •125 pan-etiology HCC genes (CYP2C9, SLC22A1) linked to retinol metabolism and solute transport.•14 HBV-specific and 221 HCV-specific DEGs reveal unique viral-driven HCC pathways.•PPI hubs (CDK1, CCNE1, TYMS) drive cell cycle and metabolic dysregulation.•Hub genes predict survival (CDK1 poor prognosis, CAT protective in HBV-HCC). Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g., CYP2C9, SLC22A1, RDH5) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g., ABCA8, GADD45B) and 221 HCV-specific DEGs (e.g., CDK1, CCNB1) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies. Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g., CYP2C9, SLC22A1, RDH5) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g., ABCA8, GADD45B) and 221 HCV-specific DEGs (e.g., CDK1, CCNB1) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies.Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g., CYP2C9, SLC22A1, RDH5) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g., ABCA8, GADD45B) and 221 HCV-specific DEGs (e.g., CDK1, CCNB1) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies. |
| ArticleNumber | 102123 |
| Author | Owrang, Mina Khorsand, Babak Naderi, Nazanin Hadisadegh, Seyedeh Negin Mohaghegh, Maedeh Houri, Hamidreza Aghaahmadi, Alireza Karimian, Seyedeh Sara |
| Author_xml | – sequence: 1 givenname: Babak surname: Khorsand fullname: Khorsand, Babak organization: Department of Neurology, University of California, Irvine, CA, USA – sequence: 2 givenname: Nazanin surname: Naderi fullname: Naderi, Nazanin organization: Department of Cell and Molecular Biology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran – sequence: 3 givenname: Seyedeh Sara surname: Karimian fullname: Karimian, Seyedeh Sara organization: Department of Oncology, University of Alberta, Canada – sequence: 4 givenname: Maedeh surname: Mohaghegh fullname: Mohaghegh, Maedeh organization: Department of Biological Sciences, Islamic Azad University, North Tehran Branch, Tehran, Iran – sequence: 5 givenname: Alireza surname: Aghaahmadi fullname: Aghaahmadi, Alireza organization: Department of Biology, Islamic Azad University of Central Tehran Branch, Tehran, Iran – sequence: 6 givenname: Seyedeh Negin surname: Hadisadegh fullname: Hadisadegh, Seyedeh Negin organization: Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA – sequence: 7 givenname: Mina surname: Owrang fullname: Owrang, Mina organization: Faculty of Medical Science, Sari Branch, Islamic Azad University, Sari, Iran – sequence: 8 givenname: Hamidreza surname: Houri fullname: Houri, Hamidreza email: hr.houri@sbmu.ac.ir organization: Celiac Disease and Gluten Related Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40678800$$D View this record in MEDLINE/PubMed |
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| Keywords | Hepatocellular carcinoma Differentially expressed genes Hepatitis B virus Liver cirrhosis |
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| Title | Comprehensive transcriptomic analysis of hepatocellular Carcinoma: Uncovering shared and unique molecular signatures across diverse etiologies |
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