The Methylation Analysis of KLF11 and PCDH9 Genes in Patients with Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Background: Lung cancer is the leading cause of cancer-related deaths worldwide and the 5-year survival rate is still very poor due to the lack of effective tools for early detection. Epigenetics and especially studies on DNA methylation have given important information towards a better achievement of lung cancer pathogenesis in the recent decades. The inactivation of tumor suppressor genes via promoter hypermethylation is an obvious mechanism and is straightly related to carcinogenesis. In this study, we compared the methylation status of KLF11 and PCDH9 genes in non-small cell lung cancer and adjacent normal tissues.Methods: Genomic DNA was extracted from 30 tumor tissues, bisulfite treated and were analyzed in terms of promoter methylation status of KLF11 and PCDH9 genes by high resolution melting method. Statistical analysis was carried out by chi-square test.Results: No significant difference in methylation level at the PCDH9 promoter region in NSCLC tumors compared with non-tumor tissues was observed (P = 0.3132, chi-square test). In contrast, the difference in methylation levels between normal and tumor tissue samples for the promoter of the KLF11 gene was quite significant (P = 0.0001).Conclusions: Promoter methylation of KLF11 gene is an important mechanism in the development of NSCLC, therefore, it could be used as one of the potential therapeutic goals for molecular targeted therapy and epigenetic treatment. The role of the PCDH9 gene in the development of lung cancer is complex and requires more research and a larger statistical population.
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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 | 0.000 | 0.000 |
| 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".