Epigenetic markers-based breast cancerearly detection method development
Bibliographic record
Abstract
Relevance: According to the International Agency for Research on Cancer (IARС), breast cancer ranks 1st-2nd among other cancers globally [1], including Kazakhstan [2]. In Kazakhstan, the annual growth in breast cancer incidence exceeds 26.6%. In 2018-2019, breast cancer was the 3rd most common cause of cancer death in Kazakhstan, accounting for 8.7-8.1 percent, respectively. Early detection of breast cancer remains an acute issue. In particular, early detection should be improved. Epigenetic studies of cancer patients confirm that epigenetic biomarkers could be used as early cancer diagnostic markers, including breast cancer. The study aimed to find specific diagnostic markers by methylation profiling of peripheral blood mononuclear cell (PBMC) DNA in breast cancer patients. Results: Plasma samples of the Kazakhstani population with breast cancer possessed mononuclear cell methylation markers in CpG islets associated with JAM3, C17orf64, MSC, and C7orf51 genes and the CpG islet associated with the intergenic region on chromosome 5, chr5: 77,208,034-77,329,434, which were missing in healthy individuals. These biomarkers allow differentiating breast cancer from other cancers with a specificity of 0.91 and a sensitivity of 0.94 compared to methylation data from open DNA methylation databases (for Illumina 450K): TCGA, GSE40279, GSE61496, GSE76269 и GSE66836. Conclusion: Early breast cancer detection method using peripheral blood mononuclear cell DNA methylation profile, namely in CpG islets associated with JAM3, C17orf64, MSC, and C7orf51 genes and the CpG islet associated with the intergenic region on chromosome 5, chr5: 77,208,034-77,329,434 is enough specific and sensitive to use it in breast cancer screening.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".