Breast Cancer: Extracellular Matrix and Microbiome Interactions
Bibliographic record
Abstract
Breast cancer represents the most prevalent form of cancer and the leading cause of cancer-related mortality among females worldwide. It has been reported that several risk factors contribute to the appearance and progression of this disease. Despite the advancements in breast cancer treatment, a significant portion of patients with distant metastases still experiences no cure. The extracellular matrix represents a potential target for enhanced serum biomarkers in breast cancer. Furthermore, extracellular matrix degradation and epithelial-mesenchymal transition constitute the primary stages of local invasion during tumorigenesis. Additionally, the microbiome has a potential influence on diverse physiological processes. It is emerging that microbial dysbiosis is a significant element in the development and progression of various cancers, including breast cancer. Thus, a better understanding of extracellular matrix and microbiome interactions could provide novel alternatives to breast cancer treatment and management. In this review, we summarize the current evidence regarding the intricate relationship between breast cancer with the extracellular matrix and the microbiome. We discuss the arising associations and future perspectives in this field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".