CpG dinucleotide methylation of the SPDEF gene as a blood-based epigenetic biomarker for prostate cancer diagnosis
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PCa) is the second most commonly diagnosed malignancy in men and is projected to cause approximately 35,250 deaths in 2024. The utility of total prostate-specific antigen (PSA) testing as a routine screening tool remains controversial due to limited specificity. Therefore, the identification of novel, noninvasive biomarkers is essential for improving early diagnosis. This study aimed to evaluate the methylation status of a specific CpG dinucleotide within the SPDEF (SAM-pointed domain-containing ETS transcription factor) gene promoter in blood leukocytes of PCa patients, using benign prostatic hyperplasia (BPH) samples as a control group. METHODS: Peripheral blood samples were collected from 360 men, including 180 diagnosed with PCa and 180 with BPH. A target CpG dinucleotide (cg11346722) in the SPDEF promoter was selected based on analysis of The Cancer Genome Atlas (TCGA) data. Methylation levels were assessed using methylation-sensitive restriction enzyme PCR (MSRE-PCR) and quantitative PCR (qPCR). Associations between methylation and clinical parameters-tumor stage (TS), histological grade, and total PSA levels-were analyzed. RESULTS: The mean methylation level at the SPDEF CpG site was significantly lower in PCa patients (hypomethylation: 92 ± 11.76%) compared to BPH controls (15.5 ± 15.12%) (p < 0.0001). Receiver operating characteristic (ROC) curve analysis demonstrated that SPDEF hypomethylation discriminated PCa from BPH with 98.3% sensitivity and 98.3% specificity at a < 55% methylation cutoff. A significant inverse correlation was observed between SPDEF methylation and both tumor stage (TS) and grade, whereas no correlation was found with total PSA levels. CONCLUSIONS: Hypomethylation of a specific CpG dinucleotide in the SPDEF promoter may serve as a promising noninvasive blood-based biomarker for the early detection and clinical stratification of prostate 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 | 0.001 |
| 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".