Systems Immunology Analysis Reveals an Immunomodulatory Effect of Snail-p53 Binding on Neutrophil- and T Cell-Mediated Immunity in KRAS Mutant Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Immunomodulation and chronic inflammation are important mechanisms utilized by cancer cells to evade the immune defense and promote tumor progression. Therefore, various efforts were focused on the development of approaches to reprogram the immune response to increase the immune detection of cancer cells and enhance patient response to various types of therapy. A number of regulatory proteins were investigated and proposed as potential targets for immunomodulatory therapeutic approaches including p53 and Snail. In this study, we investigated the immunomodulatory effect of disrupting Snail-p53 binding induced by the oncogenic KRAS to suppress p53 signaling. We analyzed the transcriptomic profile mediated by Snail-p53 binding inhibitor GN25 in non-small cell lung cancer cells (A549) using Next generation whole RNA-sequencing. Notably, we observed a significant enrichment in transcripts involved in immune response pathways especially those contributing to neutrophil (IL8) and T-cell mediated immunity (BCL6, and CD81). Moreover, transcripts associated with NF-κB signaling were also enriched which may play an important role in the immunomodulatory effect of Snail-p53 binding. Further analysis revealed that the immune expression signature of GN25 overlaps with the signature of other therapeutic compounds known to exhibit immunomodulatory effects validating the immunomodulatory potential of targeting Snail-p53 binding. The effects of GN25 on the immune response pathways suggest that targeting Snail-p53 binding might be a potentially effective therapeutic strategy.
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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 | 0.000 | 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 | 0.000 |
| 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".