Novel Serum Protein Biomarkers for Precancerous Cervical Lesions and Cervical Cancer
Bibliographic record
Abstract
Cervical cancer is a health problem worldwide, although it is preventable and curable. Timely detection is crucial for eliminating this disease. Cytology is the official test for cervical cancer screening in most countries. Unfortunately, it has multiple barriers, e.g., its low sensitivity (47-55%) and its invasive nature. There is a need for alternative screening tests that can complement cytology’s limitations. Molecular biomarkers can fill this gap. The present study aimed to identify candidate cervical cancer biomarkers in human sera. We selected five human proteins from a previously reported secretome of cervical cancer cell lines as candidate biomarkers. We tested these proteins in a cohort of 212 Mexican women, divided into four clinical groups: control, low and high-grade squamous intraepithelial lesions, and cervical cancer. Immunodetection was done by Western blotting, ELISA, and/or surface plasmon resonance. Four of these five proteins were in higher abundance in sera of precancerous cervical lesions (GAPDH) or cervical cancer (EIF4A1, HNRNPA1, and FDPS) patients (p < 0.05). When tested individually, we found that these biomarkers were able to distinguish serum samples from healthy donors from those with cervical disease. Also, a lateral flow assay was developed for detecting FDPS in whole blood, paving the way for detecting these pathologies using rapid tests.
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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 | 0.001 |
| 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".