MCM2, MCM4, and MCM6 in Breast Cancer: Clinical Utility in Diagnosis and Prognosis
Bibliographic record
Abstract
Breast cancer is a heterogeneous disease comprising the estrogen receptor (ER)-positive luminal subtype which is subdivided into luminal A and luminal B and ER-negative breast cancer which includes the triple-negative subtype. This study has four aims: 1) to examine whether Minichromosome Maintenance (MCM)2, MCM4, and MCM6 can be used as markers to differentiate between luminal A and luminal B subtypes; 2) to study whether MCM2, MCM4, and MCM6 are highly expressed in triple-negative breast cancer, as there is an urgent need to search for surrogate markers in this aggressive subtype, for drug development purposes; 3) to compare the prognostic values of these markers in predicting relapse-free survival; and 4) to compare the three approaches used for scoring the protein expression of these markers by immunohistochemistry (IHC). MCM2, MCM4, MCM6, and MKI67 mRNA expression was first studied using in silico analysis of available breast cancer datasets. We next used IHC to evaluate their protein expression on tissue microarrays using three scoring methods. MCM2, MCM4, and MCM6 can help in distinction between luminal A and luminal B whose therapeutic management and clinical outcomes are different. MCM2, MCM4, MCM6, and Ki-67 are highly expressed in breast cancer of high histological grades that comprise clinically aggressive tumors such as luminal B, HER2-positive, and triple-negative subtypes. Low transcript expression of these markers is associated with increased probability of relapse-free survival. A positive relationship exists among the three scoring methods of each of the four markers. An independent validation cohort is needed to confirm their clinical utility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".