Angiogenesis Is Associated With Aggressive Biology That Counterbalances With Tumor Immunogenicity in Hepatocellular Carcinoma
Bibliographic record
Abstract
Background: Hepatocellular carcinoma (HCC) is an arterialized tumor; thus, anti-angiogenesis targeted therapy is in clinical practice. Herein, we hypothesized that HCC with high angiogenesis is biologically aggressive with worse survival. Methods: Angiogenesis score (AS) was derived from the Molecular Signatures Database (MSigDB) Hallmark Angiogenesis Gene Set, and median was used to divide high versus low groups. Transcriptome of HCC patients of The Cancer Genome Atlas (TCGA, n = 386) and GSE76427 (n = 115) cohorts were analyzed. Results: High AS correlated with angiogenesis-related gene expressions. Both microvascular and lymphatic endothelial cell infiltrations were higher in high angiogenesis HCC. Surprisingly, no survival difference was seen with varying levels of angiogenesis. High angiogenesis significantly enriched tumor aggravating signaling pathways: glycolysis, Notch, Hedgehog, KRAS, epithelial mesenchymal transition, and transforming growth factor-beta (TGF-β) in Gene Set Enrichment Analysis (GSEA), but also infiltrated less CD8+ T cells and T-helper 1 cells, and higher M1 macrophages and conventional dendritic cells (cDCs) with elevated cytolytic activity score in both cohorts. In agreement, immune response-related gene sets: inflammatory response, tumor necrosis factor-alpha (TNF-α) signaling, allograft rejection, interferon-alpha, and interferon-gamma were all enriched to high angiogenesis HCC. Programmed cell death protein 1 (PD1), programmed death ligand 1 (PD-L1), programmed death ligand 2 (PD-L2), and cytotoxic T lymphocyte-associated protein 4 (CTLA-4) were higher in high angiogenesis HCC in TCGA, but not in GSE76427 cohort. Conclusions: Angiogenesis quantified using transcriptome of HCC patients demonstrated that it is associated with aggressive biology but also with tumor immunogenicity and immune response that counterbalance and did not reflect in survival. Given high expression of immune checkpoint molecules, we cannot help but speculate that immunotherapy may be useful for high angiogenesis HCC patients.
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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 | 0.001 | 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".