23P Identification of HPSE as potential novel therapeutic target for lung adenocarcinoma patients
Bibliographic record
Abstract
Lung cancer is a leading cause of mortality globally, particularly the subtype called lung adenocarcinoma (LUAD). Current treatments have limited success, emphasizing the need for better therapies. Heparan sulfate proteoglycans (HSPGs) and heparanase (Hpse) play important roles in cancer progression, including LUAD, but their exact functions are not fully understood. Our study aims to explore Hpse's relevance in LUAD progression and its effects on tumor cells. We introduced shRNAs by lentiviral transfections against heparanase into a murine and a human cell line. We performed intravenous and orthotopic injections to assess the effects of our genetic perturbations on tumor growth, survival, and the tumor immune microenvironment. The study investigated the correlation between heparanase mRNA expression and overall survival in LUAD patients. Using the PRECOGG database and the Cancer Genome Atlas, we found that elevated HPSE expression correlated with poor overall survival in LUAD patients. Additionally, in vitro experiments demonstrated that heparanase promoted migration and invasion of lung cancer cells, suggesting its role in metastasis. Moreover, in vivo studies using mouse models showed that Hpse knockdown reduced tumor growth and metastasis and increased the survival of mice. Spectral flow cytometry analysis revealed many changes in the immune microenvironment between tumors with high and low Hpse expression, with significant alterations in myeloid and lymphoid cell populations. Notably, in Hpsel-low tumors, alveolar macrophages were more abundant. Surprisingly, these macrophages displayed an anti-tumorigenic phenotype characterized based on single-cell sequencing performed on leukocytes. To conclude, our findings support our clinical analysis revealing that heparanase should be investigated further as a therapeutic target for patients with LUAD. More effort should be put towards inhibitors and trials should focus on this patient population that is in dire need of novel treatment options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".