Absence of MMP2 Expression Correlates with Poor Clinical Outcomes in Rectal Cancer, and Is Distinct from MMP1-Related Outcomes in Colon Cancer
Bibliographic record
Abstract
PURPOSE: Treatments for colorectal cancer (CRC) are primarily disease stage based. However, heterogeneity in outcome within even a single stage highlights its limitations in predicting disease behavior. Recently, the role of gene expression as predictive and prognostic markers has been explored. Our objectives were to identify consistently differentially expressed genes through meta-analysis of high-throughput gene-expression studies, and evaluate their predictive and prognostic significance in colon (CC) and rectal (RC) cancers. EXPERIMENTAL DESIGN: Publications applying high-throughput gene- expression technologies to specific CRC stages were identified. A vote counting strategy was used to identify the most significant differentially expressed genes. Their predictive and prognostic values were independently assessed in a tissue microarray of 191 cases of stage II-IV CC/RC from two tertiary care centers. Their biological effects were also examined in vitro. RESULTS: MMP1 and MMP2 were identified as consistently underexpressed in liver metastasis compared with primary CRC. Shorter time to distant metastasis and overall survival occurred in stage III CC lacking MMP1 expression, and in stage III RC lacking MMP2. MMP1 levels in stage II and III CC were associated with increased likelihood of distant metastasis, whereas the risk of local recurrence in stage III RC could be stratified by MMP2. Promotion of cell invasion of CRC cell lines exposed to MMP1/2 inhibitors were confirmed in vitro. CONCLUSIONS: MMP1 and MMP2 may be useful biomarkers that can help stratify patients at higher risk of developing recurrence in colorectal cancer, and guide individualized treatment decisions to achieve better outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".