Validation of SCT Methylation as a Hallmark Biomarker for Lung Cancers
Bibliographic record
Abstract
INTRODUCTION: The human secretin gene (SCT) encodes secretin, a hormone with limited tissue distribution. Analysis of the 450k methylation array data in The Cancer Genome Atlas (TCGA) indicated that the SCT promoter region is differentially hypermethylated in lung cancer. Our purpose was to validate SCT methylation as a potential biomarker for lung cancer. METHODS: We analyzed data from TCGA and developed and applied SCT-specific bisulfite DNA sequencing and quantitative methylation-specific polymerase chain reaction assays. RESULTS: The analyses of TCGA 450K data for 801 samples showed that SCT hypermethylation has an area under the curve (AUC) value greater than 0.98 that can be used to distinguish lung adenocarcinomas or squamous cell carcinomas from nonmalignant lung tissue. Bisulfite sequencing of lung cancer cell lines and normal blood cells allowed us to confirm that SCT methylation is highly discriminative. By applying a quantitative methylation-specific polymerase chain reaction assay, we found that SCT hypermethylation is frequently detected in all major subtypes of malignant non-small cell lung cancer (AUC = 0.92, n = 108) and small cell lung cancer (AUC = 0.93, n = 40) but is less frequent in lung carcinoids (AUC = 0.54, n = 20). SCT hypermethylation appeared in samples of lung carcinoma in situ during multistage pathogenesis and increased in invasive samples. Further analyses of TCGA 450k data showed that SCT hypermethylation is highly discriminative in most other types of malignant tumors but less frequent in low-grade malignant tumors. The only normal tissue with a high level of methylation was the placenta. CONCLUSIONS: Our findings demonstrated that SCT methylation is a highly discriminative biomarker for lung and other malignant tumors, is less frequent in low-grade malignant tumors (including lung carcinoids), and appears at the carcinoma in situ 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 | 0.002 | 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 | 0.001 | 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".