Elucidating the molecular basis of ATP-induced cell death in breast cancer: Construction of a robust prognostic model
Bibliographic record
Abstract
BACKGROUND: Breast cancer is a multifaceted and formidable disease with profound public health implications. Cell demise mechanisms play a pivotal role in breast cancer pathogenesis, with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic pertinence. AIM: To investigate the impact of ATP-induced cell death (AICD) on breast cancer, enhancing our understanding of its mechanism. METHODS: The foundational genes orchestrating AICD mechanisms were extracted from the literature, underpinning the establishment of a prognostic model. Simultaneously, a microRNA (miRNA) prognostic model was constructed that mirrored the gene-based prognostic model. Distinctions between high- and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized, with the aim of delineating common influence mechanisms, substantiated through enrichment analysis and immune infiltration assessment. RESULTS: The mRNA prognostic model in this study encompassed four specific mRNAs: P2X purinoceptor 4, pannexin 1, caspase 7, and cyclin 2. The miRNA prognostic model integrated four pivotal miRNAs: hsa-miR-615-3p, hsa-miR-519b-3p, hsa-miR-342-3p, and hsa-miR-324-3p. B cells, CD4+ T cells, CD8+ T cells, endothelial cells, and macrophages exhibited inverse correlations with risk scores across all breast cancer subtypes. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways, while miRNA risk scores significantly enriched 29 signaling pathways, with 16 pathways being jointly enriched. CONCLUSION: Of paramount significance, distinct mRNA and miRNA signature models were devised tailored to AICD, both potentially autonomous prognostic factors. This study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools, offering an unparalleled window for innovative interventions. Essentially, this paper reveals the hitherto enigmatic link between AICD and breast cancer, potentially leading to revolutionary progress in personalized oncology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".