Evaluation of the Diagnostic and Predicative Values of 8-Iso-Prostaglandin F2α as a Biomarker of Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) is a commonly reported cancer that is widely prevalent among women. Its early detection improves patient survival and results in better outcomes. For diagnosis and follow-up care, tumor markers are one of the feasible investigations to be ordered. 8-Iso-prostaglandin F2α (8-iso-PGF2α) serves as a serum marker reflecting oxidative stress and subsequent damaging of DNA. In the present study, we aimed to evaluate both diagnostic and predictive values of 8-iso-PGF2α in BC patients. MATERIALS AND METHODS: Serum levels of 8-iso-PGF2α were assessed for 66 women with benign breast tumors and 65 women who had malignant BC. To compare the patients who had breast tumors with healthy individuals, 63 women free of breast diseases were selected as controls. RESULTS: The serum level of 8-iso-PGF2α in the BC patients (57.92 pg/mL) was significantly higher compared to those with benign tumors (18.89 pg/mL) (p < 0.001). In addition, individuals with no breast diseases had less 8-iso-PGF2α (4.02 pg/mL) compared to those who had developed a tumor (p < 0.001). Serum 8-iso-PGF2α was found to be positively correlated with both carcinoembryonic antigen (r = 0.74, p < 0.001) and cancer antigen 15-3 (r = 0.80, p < 0.001). Furthermore, serum 8-iso-PGF2α showed high diagnostic performance in BC (AUC = 0.999, sensitivity = 100%, specificity = 99.2% at a cutoff value of 36.18 pg/mL). CONCLUSIONS: Our study found that the high level of serum 8-iso-PGF2α helps to provide a non-invasive indicator to detect BC. Future work with a larger sample size and various phases of BC can confirm the current results which provide insights into the early detection of cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".