Abstract 339: Exploring the roles of GSDMD in mammary tumorigenesis
Bibliographic record
Abstract
Abstract Breast cancer (BC) is the most frequently diagnosed cancer worldwide, with an ever-increasing incidence rate. While screening and treatment methods have improved markedly over the last three decades, BC remains the leading cause of malignant death in women annually. Therefore, there is an urgent need to identify new biomarkers and therapeutic targets for BC. Recently, dysregulated gasdermin D (GSDMD), a central player of inflammation-induced cell death called pyroptosis, has been shown to be implicated in cancer. However, it is unclear whether GSDMD plays important roles in BC. In addition, the molecular mechanism underlying GSDMD-involved tumorigenesis remains unknown. Here, we discovered that GSDMD is significantly upregulated in BC. We have shown that overexpression or knockout of GSDMD increases or decreases BC cell proliferation, respectively. In addition, we have identified and validated several novel GSDMD-interacting proteins that play important roles in breast cancer. These present findings will not only shed light on the roles of GSDMD in BC tumorigenesis but also provide new preclinical data for future successful treatments of BC patients. Citation Format: Derek Yisen Zhang. Exploring the roles of GSDMD in mammary tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 339.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".