Key Factor Regulating Inflammatory Microenvironment, Metastasis, and Resistance in Breast Cancer: Interleukin‐1 Signaling
Bibliographic record
Abstract
Breast cancer is one of the top-ranked cancers for incidence and mortality worldwide. The biggest challenges in breast cancer treatment are metastasis and drug resistance, for which work on molecular evaluation, mechanism studies, and screening of therapeutic targets is ongoing. Factors that lead to inflammatory infiltration and immune system suppression in the tumor microenvironment are potential therapeutic targets. Interleukin-1 is known as a proinflammatory and immunostimulatory cytokine, which plays important roles in inflammatory diseases. Recent studies have shown that interleukin-1 cytokines drive the formation and maintenance of an inflammatory/immunosuppressive microenvironment through complex intercellular signal crosstalk and tight intracellular signal transduction, which were found to be potentially involved in the mechanism of metastasis and drug resistance of breast cancer. Some preclinical and clinical treatments or interventions to block the interleukin-1/interleukin-1 receptor system and its up- and downstream signaling cascades have also been proven effective. This study provides an overview of IL-1-mediated signal communication in breast cancer and discusses the potential of IL-1 as a therapeutic target especially for metastatic breast cancer and combination therapy and current problems, aiming at enlightening new ideas in the study of inflammatory cytokines and immune networks in the tumor microenvironment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".