MMP-9 expression varies according to molecular subtypes of breast cancer
Bibliographic record
Abstract
BACKGROUND: In 2014, breast cancer remains a major cause of mortality worldwide mostly due to tumor relapse and metastasis. There is currently a great interest in identifying cancer biomarkers and signalling pathways mechanistically related to breast cancer progression. Matrix metalloproteinase-9 (MMP-9) is a member of matrix degrading enzymes involved in cancer development, invasion and metastasis. Our objective was to investigate MMP-9 expression in normal human breast tissue and to compare it to that of breast cancer of various histological grades and molecular subtypes. We also sought to correlate MMP-9 expression with the incidence of metastasis, survival rates and relapse in breast cancer patients. METHODS: MMP-9 was first studied using in silico analysis on available DNA microarray and RNA sequencing data of human breast cancer tissues and human breast cancer cell lines. We next ascertained MMP-9 expression in both normal breast tissue and in human breast carcinoma tissue microarrays. RESULTS: Significant increase in MMP-9 expression was found in breast cancer cells where compared to normal breast tissue. A positive correlation could also be established between elevated levels of MMP-9 and breast cancer of high histological grade. Furthermore, our results indicate that not only MMP-9 is differentially expressed between each molecular subset but also, more importantly MMP-9 overexpression revealed itself as a startling feature of triple-negative and HER2-positive breast cancers. Lastly, the clinical relevance of MMP-9 overexpression is strongly supported by its significant association with a higher incidence of metastasis and relapse. CONCLUSIONS: Differential expression of MMP-9 reflects the extent of cellular differentiation in breast cancer cells and is closely related to the most aggressive subtypes of breast cancer. Hence, MMP-9 is a promising prognostic biomarker of high-grade breast cancer. In our opinion, MMP-9 expression could help segregate subsets of aggressive breast cancer into clinically meaningful subtypes.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".