Immunogenic cell death signature predicts survival and reveals the role of VEGFA + Mast cells in lung adenocarcinoma
Bibliographic record
Abstract
Lung cancer is prevalent worldwide and is a major cause of cancer-related mortality. Despite being the primary model for immunotherapy research, the response rates of lung cancer patients to immunotherapy are unsatisfactory. Furthermore, research on immunogenic cell death (ICD) in lung cancer is limited, which limits the development of strategies that combine ICD-related therapies with immunotherapy. In this study, we compiled and summarized 69 genes associated with ICD and developed an IRS. Across seven independent datasets, the IRS was identified as an independent prognostic factor. IRS was positively associated with multiple tumor proliferation pathways and negatively associated with immune-related pathways. Additionally, IRS negatively correlated with the infiltration of various immune cells, supporting its association with survival outcomes. Based on the correlation between IRS and immune activity, we validated the ability of IRS to predict immunotherapy efficacy across seven immunotherapy datasets and demonstrated that patients who respond to immunotherapy tend to have a lower IRS. Moreover, utilizing single-cell RNA sequencing, we revealed the role of mast cells in the TME with the highest IRS. Through interactions with various receptors on macrophages, endothelial cells, and tumor cells, mast cells promote tumor progression, providing a comprehensive explanation for poor prognosis and lack of response to immunotherapy in patients with high IRS. Our study offers new guidance for combination therapies in lung adenocarcinoma patients and elucidated the mechanism by which mast cells contribute to cancer development within the TME.
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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 | 0.001 | 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 | 0.001 | 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".