Inflammation and Fibrosis in Patients with Non-Cirrhotic Hepatitis B Virus-Associated Hepatocellular Carcinoma: Impact on Prognosis after Hepatectomy and Mechanisms Involved
Bibliographic record
Abstract
Background: We investigated whether the degree of inflammation and fibrosis in para-carcinoma tissue can predict prognosis of patients with non-cirrhotic hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) after hepatectomy. We also explored the mechanisms through which inflammation and fibrosis might affect prognosis. Methods: Clinicopathological data were retrospectively analyzed from 293 patients with non-cirrhotic HBV-associated HCC who were treated at our institution by curative resection from 2012 to 2017. Based on the Scheuer score system, patients were classified into those showing mild or moderate-to-severe inflammation and fibrosis. Rates of overall and recurrence-free survival were compared between the groups using Kaplan–Meier curves, and survival predictors were identified using Cox regression. Using tumor and para-tumor tissues from independent samples of patients with non-cirrhotic HBV-associated HCC who were treated at our institution by curative resection from 2018 to 2019, we performed next-generation sequencing and time-of-flight cytometry (CyTOF) to examine the influence of inflammation and fibrosis on gene expression and immune cell infiltration. Results: In the analysis of the 293 patients, those with mild inflammation and fibrosis showed significantly better overall and recurrence-free survival than those with moderate-to-severe inflammation and fibrosis. Multivariate Cox regression confirmed that moderate-to-severe inflammation and fibrosis were independent risk factors for worse survival. RNA sequencing and CyTOF showed that more severe inflammation and fibrosis were associated with stronger invasion and migration by hepatocytes. In cancerous tissues, the biological processes of cell proliferation were upregulated, the signaling pathways promoting tumor growth were activated, the proportion of Th17 cells promoting tumor progression was increased, and CD8+ T cells expressed higher levels of PD-L1. In para-cancerous tissues, biological processes of immune response and cell chemotaxis were downregulated, and the proportion of tumor-killing immune cells was decreased. Conclusion: Worse inflammation and fibrosis in non-cirrhotic HBV-associated HCC is associated with worse prognosis, which may reflect more aggressive tumor behavior and an immunosuppressed, pro-metastatic tumor microenvironment.
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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 | 0.001 |
| 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) | 0.001 | 0.000 |
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".