Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches
Abstract Dysregulation of hepatic metabolism is a crucial factor in the development of fatty liver disease and significantly increases the risk of hepatocellular carcinoma (HCC). This study aims to identify the genes implicated in the prognosis of HCC among individuals suffering from metabolic fatty...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02638-5 |
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| author | Priyadharshini Annadurai Arnold Emerson Isaac |
| author_facet | Priyadharshini Annadurai Arnold Emerson Isaac |
| author_sort | Priyadharshini Annadurai |
| collection | DOAJ |
| description | Abstract Dysregulation of hepatic metabolism is a crucial factor in the development of fatty liver disease and significantly increases the risk of hepatocellular carcinoma (HCC). This study aims to identify the genes implicated in the prognosis of HCC among individuals suffering from metabolic fatty liver disease. We analysed protein–protein interaction (PPI) networks and constructed a weighted gene co-expression network analysis (WGCNA) using high-throughput gene expression profiling datasets. Our meta-analysis uncovered 442 differentially expressed genes (DEGs), comprising 30 upregulated and 412 downregulated genes. We constructed a PPI network from the DEGs and identified significant hub genes based on their degree centrality scores. Additionally, WGCNA highlighted impactful genes and tightly correlated modules, leading to the creation of a gene interaction network specific to metabolism-associated fatty liver disease (MAFLD). Pathway analysis revealed the candidate regulatory gene interleukin-7 receptor (IL7R), which is involved in cytokine-mediated signalling across both interaction networks. Pro-inflammatory cytokines interact with IL7R, activating the JAK/STAT pathway that influences gene expression throughout progression to HCC. IL7R activates STAT3, affecting the behaviour of activated hepatic stellate cells following initial liver damage. Furthermore, the expression of the IL7R gene was validated as a predictor of HCC malignancy through a logistic regression model, resulting in an accuracy of 92%. Findings suggest that IL7R could be the target gene associated with metabolism-linked HCC. It could significantly impact the management of metabolic-associated fatty liver disease (MAFLD) and may help enhance HCC diagnostics to improve patient outcomes. |
| format | Article |
| id | doaj-art-2fff43269ebc4398bdf200c3e5d586d7 |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-2fff43269ebc4398bdf200c3e5d586d72025-08-20T03:08:43ZengSpringerDiscover Oncology2730-60112025-05-0116111310.1007/s12672-025-02638-5Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approachesPriyadharshini Annadurai0Arnold Emerson Isaac1Bioinformatics Programming Laboratory, Department of Bioscience, School of Bio Science and Technology, Vellore Institute of Technology, KatpadiBioinformatics Programming Laboratory, Department of Bioscience, School of Bio Science and Technology, Vellore Institute of Technology, KatpadiAbstract Dysregulation of hepatic metabolism is a crucial factor in the development of fatty liver disease and significantly increases the risk of hepatocellular carcinoma (HCC). This study aims to identify the genes implicated in the prognosis of HCC among individuals suffering from metabolic fatty liver disease. We analysed protein–protein interaction (PPI) networks and constructed a weighted gene co-expression network analysis (WGCNA) using high-throughput gene expression profiling datasets. Our meta-analysis uncovered 442 differentially expressed genes (DEGs), comprising 30 upregulated and 412 downregulated genes. We constructed a PPI network from the DEGs and identified significant hub genes based on their degree centrality scores. Additionally, WGCNA highlighted impactful genes and tightly correlated modules, leading to the creation of a gene interaction network specific to metabolism-associated fatty liver disease (MAFLD). Pathway analysis revealed the candidate regulatory gene interleukin-7 receptor (IL7R), which is involved in cytokine-mediated signalling across both interaction networks. Pro-inflammatory cytokines interact with IL7R, activating the JAK/STAT pathway that influences gene expression throughout progression to HCC. IL7R activates STAT3, affecting the behaviour of activated hepatic stellate cells following initial liver damage. Furthermore, the expression of the IL7R gene was validated as a predictor of HCC malignancy through a logistic regression model, resulting in an accuracy of 92%. Findings suggest that IL7R could be the target gene associated with metabolism-linked HCC. It could significantly impact the management of metabolic-associated fatty liver disease (MAFLD) and may help enhance HCC diagnostics to improve patient outcomes.https://doi.org/10.1007/s12672-025-02638-5Metabolic-associated fatty liver diseaseHepatocellular carcinomaSteatosisProtein–protein interactionHepatic stellate cellsCytokine signaling |
| spellingShingle | Priyadharshini Annadurai Arnold Emerson Isaac Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches Discover Oncology Metabolic-associated fatty liver disease Hepatocellular carcinoma Steatosis Protein–protein interaction Hepatic stellate cells Cytokine signaling |
| title | Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches |
| title_full | Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches |
| title_fullStr | Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches |
| title_full_unstemmed | Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches |
| title_short | Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches |
| title_sort | unveiling the role of il7r in metabolism associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches |
| topic | Metabolic-associated fatty liver disease Hepatocellular carcinoma Steatosis Protein–protein interaction Hepatic stellate cells Cytokine signaling |
| url | https://doi.org/10.1007/s12672-025-02638-5 |
| work_keys_str_mv | AT priyadharshiniannadurai unveilingtheroleofil7rinmetabolismassociatedfattyliverdiseaseleadingtohepatocellularcarcinomathroughtranscriptomicandmachinelearningapproaches AT arnoldemersonisaac unveilingtheroleofil7rinmetabolismassociatedfattyliverdiseaseleadingtohepatocellularcarcinomathroughtranscriptomicandmachinelearningapproaches |