Early diagnostic of lung cancer basedon methylation of mononuclear cell fraction:Method development
Bibliographic record
Abstract
Relevance: According to the International Agency for Research on Cancer (IARС), lung cancer (LC) today ranks first in cancer incidence worldwide [1]. In the Republic of Kazakhstan, about 3800 new cases of LC and more than 2000 deaths from LC are registered each year (one-year mortality exceeds 49.4%) [2]. This supports the relevance of early LC diagnostics. The study of DNA methylation in human peripheral blood mononuclear cells (PBMC) suggests its use as an early diagnostic and prognostic marker for LC before detecting a malignant neoplasm by visual diagnostic methods. The purpose of the study was to find specific diagnostic and prognostic markers by DNA methylation profiling of PBMC in patients with LC. Results: Methylation markers of blood mononuclear fraction were detected in CG islets associated with genes ICAM5, mir138, SYNE1, and KLK4 in 97% of plasma samples from patients with LC and were absent in healthy people. The usability of these markers to differentiate LC from 16 other cancers using NCBI GEO and TCGA methylation data was demonstrated with a specificity level of 0.96 and a sensitivity of 0.84. Conclusion: The specificity and sensitivity of the method of LC early diagnostics and prognosis based on the methylation of blood mononuclear cells (detection of methylation of CG islets associated with the ICAM5, mir138, SYNE1, and KLK4 genes in PBMC) are enough to use it in screening for LC.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".