Do hepatitis B virus and hepatitis C virus co‐infections increase hepatocellular carcinoma occurrence through synergistically modulating lipogenic gene expression?
Bibliographic record
Abstract
Hepatitis B virus (HBV) and hepatitis C virus (HCV) infections cause a wide range of liver diseases including hepatocellular carcinoma (HCC). Because of the similar modes of transmission, HBV HCV co-infections are found in approximately 7-20 million people globally. Compared with HBV or HCV mono-infections, co-infections are associated with more severe liver diseases and higher risk of HCC. Abnormal lipid biosynthesis and metabolism has been increasingly recognized as a cause for cancer. While HBV infection does not seem to significantly increase the risk of developing hepatic steatosis, steatosis is a prominent feature of chronic hepatitis C (CHC). In addition, steatosis in HBV or HCV mono-infections is a significant and independent risk factor for HCC. However, whether and how HBV HCV co-infections synergistically increase the risk of HCC development through modulating lipid metabolism is not well understood. Possible mechanisms by which steatosis causes HCC include: activation of sterol regulatory element-binding protein-mediated lipogenesis through the PI3K-Akt pathway, abnormal activation of peroxisome proliferator-activated receptors and endoplasmic reticulum stress. Here, we review the potential mechanisms by which HBV HCV co-infections may increase HCC risk through modulation of lipogenic gene expression. We begin with reviewing the impact of HBV and HCV on host lipogenic gene expression and carcinogenesis. We then discuss the potential mechanisms by which HBV and HCV can increase carcinogenesis through synergistically activating lipid biosynthesis and metabolism. We end by sharing our thoughts on future research directions in this emerging paradigm with an ultimate goal of developing effective therapeutics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".