Expression of Myoglobin in Normal and Cancer Brain Tissues: Correlation With Hypoxia Markers
Bibliographic record
Abstract
BACKGROUND: Myoglobin (MB) is increasingly recognized as a key player in cancer growth and metastasis. Low oxygen tensions, commonly associated with highly aggressive and recurrent cancers, have been shown to regulate its expression in several cancers such as lung, neck, prostate and breast cancer. However, it is not yet known whether it contributes to the growth and spread of brain cancers especially Glioblastoma multiforme (GBM). METHODS: Here we investigate the expression of MB, and its correlation with the hypoxia markers carbonic anhydrase IX (CAIX) and lactate dehydrogenase A (LDHA), in human tissue microarrays of multiple organ tumors, brain tumors, and GBM tumors, and their respective cancer-adjacent normal tissues. Correlation between MB protein expression and tumor grade was also assessed. RESULTS: We show that MB protein is expressed in a wide variety of cancers, benign tumors, cancer-adjacent normal tissues, hyperplastic tissue samples and normal brain tissue, and low oxygen tensions modulate MB protein expression in different brain cancers, including GBM. Enhanced nuclear LDHA immune-reactivity in GBM was also observed. Finally, we report for the first time a positive correlation between MB expression and brain tumor grade. CONCLUSION: Our data suggest that hypoxia regulate MB expression in different brain cancers (including GBM) and that its expression is associated with a more aggressive phenotype as indicated by the positive correlation with the brain tumor grade. Additionally, a role for nuclear LDHA in promoting aggressive tumor phenotype is also suggested based on enhanced nuclear expression which was observed only in GBM.
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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 | 0.001 | 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".