MHBSt167 induced autophagy promote cell proliferation and EMT by activating the immune response in L02 cells
Bibliographic record
Abstract
Abstract Background Hepatitis B virus can induce hepatocellular carcinoma (HCC) by inducing a host immune response against infected hepatocytes. C-terminally truncated middle surface protein (MHBSt) has been reported to contribute to HCC through transcriptional activation in epidemiology studies, while the underlying mechanism of MHBSt-induced HCC is unknown. Methods In this study, a premature stop at codon 167 in MHBS (MHBSt167) was investigated into eukaryotic expression plasmid pcDNA3.1(-). MHBSt167 expressed plasmid was transfected into the L02 cell line, cell proliferation was analyzed by CCK-8 and high-content screening assays, the cell cycle was analyzed by flow cytometry, and epithelial-to-mesenchymal transition and autophagy were analyzed by immunoblotting and immunofluorescence. NF-κB activation and the MHBSt167-induced immune response were analyzed by immunoblotting and immunofluorescence. IFN-α, IFN-β and IL-1α expression were analyzed by qPCR. Autophagy inhibitors were used to analyze the relationship between the immune response and autophagy. Results The results showed that MHBSt167 promoted L02 cell proliferation, accelerated cell cycle progression from the S to G2 phase and promoted epithelial-to-mesenchymal transition through ER-stress, leading to autophagy and NF-κB activation and increased immune-related factor expression. The MHBSt167-induced acceleration of cell proliferation and the cell cycle was abolished by autophagy or NF-κB inhibitors. Conclusion In summary, MHBSt167 could promote cell proliferation, accelerate cell cycle progression, induce EMT and activate autophagy through ER-stress to induce the host immune response, supporting a potential role of MHBSt167 in contributing to carcinogenesis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".