Expression and activation of P38 MAP kinase in invasive ductal breast cancers: Correlation with expression of the estrogen receptor, HER2 and downstream signaling phosphorylated proteins
Bibliographic record
Abstract
MAP kinase signaling proteins have major implications in the molecular oncogenesis of breast cancers and have been extensively investigated as putative targets for therapy. This study reports the investigation of the expression of P38 MAPK and its phosphorylated form (p-P38 MAPK) in clinical specimens of invasive breast carcinomas and their correlation with estrogen receptor (ER) and HER2 expression, as well as MAPK and PI3 kinase-AKT pathway signaling phosphorylated proteins. Expression levels of P38 MAPK and p-P38 MAPK as well as p-AKT, p-GSK3β, p-S6 kinase, p-MEK1 and p-ERK1/2 were quantitatively assessed using multiplex bead immunoassay in frozen specimens from 45 invasive ductal breast cancers. Twenty-nine specimens were ER+, 15 were HER2+ and 10 were triple‑negative breast cancers (TNBCs). P38 MAPK was found to be expressed in all tumor specimens and was significantly (P=0.002) overexpressed in ER+ tumors. P38 MAPK expression was lower in TNBCs than in all of the other tumors. The median expression of p-P38 MAPK was also higher in ER+ tumors while lower in the TNBCs. HER2 status had no effect on P38 MAPK and p-P38 MAPK expression. No variation in the phosphorylation rate of P38 MAPK was observed in relation with ER, HER2 or TNBC status. Significantly higher (P=0.0048) expression of p-AKT was observed in HER2+ tumors. No significant difference in p-MEK1, p-GSK3β and p-S6K expression was found in any other comparisons based on ER and HER2 expression subtypes. Investigation of the expression of multiple phosphorylated signaling proteins can be used for personalized targeted therapy. In invasive breast cancer, the overexpression of P38 MAPK may serve as a biomarker for the evaluation of P38 MAPK inhibitors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".