A Tertiary Lymphoid Structure-Related Gene Signature Predicts Prognosis and Treatment Response in Hepatocellular Carcinoma
Bibliographic record
Abstract
Background: Hepatocellular carcinoma (HCC) carries a poor prognosis with limited treatment options. Tertiary lymphoid structures (TLS) impact tumor immunity, but their role in HCC requires clarification. This study aimed to develop and validate a TLS-related gene signature for predicting survival and therapeutic response in HCC, and to explore its mechanisms. Methods: We analyzed transcriptomic data from public databases (The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)) using LASSO-Cox regression to identify a six-gene TLS signature (CCL20, CD200, PLAC8, DNASE1L3, C7, and SKAP1). We validated this TLS score across multiple independent HCC cohorts, including patients receiving transarterial chemoembolization (TACE), programmed cell death protein-1 (PD-1)/ligand 1 (PD-L1) inhibitors, or lenvatinib. Through single-cell RNA sequencing (scRNA-seq), we characterized immune microenvironment differences between score groups. Results: The TLS score effectively stratified patients’ survival outcomes across all validation cohorts. Low TLS scores significantly correlated with improved overall survival, enhanced therapeutic response (especially to immune checkpoint inhibitors (ICIs)), and lower immune evasion potential. Mechanistically, scRNA-seq revealed distinct immune microenvironments: low-score tumors were enriched in cytotoxic and exhausted CD8+ T cells (Tex), favorable for immunotherapy, showing beneficial immune remodeling post-treatment (decreased Tex, increased effector memory T cells). High-score tumors featured dense regulatory T-cell (Treg) infiltration, contributing to immunosuppression. Conclusion: Our findings suggest a potential TLS-based biomarker for HCC prognosis and therapeutic response. This work offers preliminary insights into tumor immune microenvironment (TIME) heterogeneity, which may be modulated by the Treg/Tex balance, and proposes a possible tool for improving patient stratification.
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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 | 0.001 | 0.001 |
| 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".