Hepatic CD4 T Cells Predict Hepatocellular Carcinoma Risk on Metabolic Dysfunction‐Associated Steatohepatitis Patients
Bibliographic record
Abstract
BACKGROUND & AIMS: Metabolic dysfunction-associated steatohepatitis (MASH) increasingly drives hepatocellular carcinoma (HCC) development. We characterized inflammatory infiltrates in liver biopsies from MASH patients who developed HCC versus controls to identify predictive immune signatures. METHOD: Formalin-fixed paraffin-embedded (FFPE) liver biopsies from MASH patients were categorized as pre-HCC MASH (n = 10) or control MASH (n = 13) by the ESCALON consortium. Standardized histological analysis and multiplexed immunohistochemistry were performed targeting CD4, CD8, PD1, PDL1, FoxP3, CXCR6, CD3, CD68, and CD20 using a PhenoImager Fusion scanner. Single-cell RNA-seq datasets characterized hepatic CD4 T cell heterogeneity. Clinical parameters measured included ALT, AST, GGT, alkaline phosphatase, platelets, and INR. RESULTS: Pre-HCC MASH showed inflammation extending from portal to periportal areas versus portal-only distribution in controls. Analysis of 291,908 cells revealed significantly higher CD4+ density (p = 0.0243) and CD4+PD1+ cells (p = 0.017) in pre-HCC patients, while CD8+ and regulatory T cell densities remained unchanged. Single-cell RNA-seq identified potential phenotypic shifts from Th1 cytotoxicity toward tissue-repair and Th17 CD4+ T cells in MASH livers. Combined immunological and clinical variables (sex, age, CD4+ T cell numbers, ALT, alkaline phosphatase and platelets) achieved excellent predictive performance (ROC-AUC = 0.944) for HCC development. CONCLUSIONS: Increase in liver CD4+ T cell infiltration characterizes MASH-to-HCC progression. These immune signatures combined with clinical parameters demonstrate remarkable predictive value for identifying high-risk MASH patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".