Comparative Analysis of Annexin A1 (ANXA1) Protein Expression in Different Cancer Types
Bibliographic record
Abstract
Annexin A1 (ANXA1) is the first member of the Lipocortin family, a calcium‐dependent phospholipid‐binding protein with potent immunomodulatory activity. ANXA1 is related to cellular proliferation, apoptosis, progression, and metastasis in cancer, and its expression is variable depending on the tumor type. Our research will compare the expression of ANXA1 in various forms of cancer and establish if the protein is up‐or down‐regulated. Methods We analyzed 1904 immunohistochemistry datasets from The Human Protein Atlas (HPA). The samples represent 20 different forms of cancer from 455 patients. The expression of ANXA1 was compared to the same in tissue samples from 137 healthy subjects, which meant the tissues of origins of primary tumors. The levels of ANXA1 expression were quantified using a visual grading method based on staining intensity (I) and cell fraction stained (F). The Q score (combination of stained cell fraction and staining intensity) was obtained by the product of (F)*(I). Results 9 of 20 cancer types showed increased protein expression (up‐regulation) comparison to their healthy counterparts. Eight cancer types showed decreased protein expression (down‐regulation). Three types of cancer showed no significant differences. Conclusion ANXA1 expression may serve as an important diagnostic marker for such a variety of different cancer types.
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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 | 0.002 | 0.001 |
| 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".