Circulating enolase 1 as a diagnostic biomarker for early-stage breast cancer
Bibliographic record
Abstract
Diagnosis of stage 1 breast cancer is challenging as small tumors are often left undetected by conventional imaging techniques. In addition, ~80% of detected breast masses are classified as benign, which means that a large proportion of diagnostic needle biopsies lead to unnecessary psychological stress and medical costs. We investigated circulating extracellular vesicles (EVs) as potential carriers of unique cancer-associated proteins capable of reporting on a breast cancer diagnosis. We isolated EVs from healthy (19), benign (19), and stage 1 breast cancer patient (86) plasma samples using size exclusion chromatography. Mass spectrometry identified 94 significantly changed proteins in the plasma EVs from breast cancer patients. Analysis of a subset of these proteins using a cohort of pre- and post-operative breast cancer patient plasma EVs identified enolase 1 as a promising biomarker. We further validated enolase 1 in a larger patient cohort by high-throughput ELISA of plasma. Enolase 1 was found to be significantly elevated in plasma from stage 1 breast cancer patients compared to healthy and benign individuals, and decreased in post-operative plasma upon tumor removal. Our findings suggest that an enolase 1 liquid blood biopsy could be used to support the detection of breast cancer at the earliest, most treatable, stage.
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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 | 0.001 |
| 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 | 0.001 | 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".