Assessing Gene Expression and Methylation of KMT2D and IGF2 Genes in Patients with Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Background: Aberrant promoter methylation of CpG islands is an important mechanism for regulation of gene expression. Recent data suggest that epigenetic abnormalities may occur very early in lung carcinogenesis. Systemic methylation changes may be a diagnostic marker for tumor development or prognosis. In this study, the expression and methylation of KMT2D and IGF2 genes were investigated in the lung cancer tissue compared to the adjacent normal tissue.Methods: The status of methylation of KMT2D and IGF2 genes were investigated in 30 patients with NSCLC after genomic DNA extraction using bisulfite treatment and MS-HRM method and the expression of these genes were checked by Real-Time PCR method in same samples.Results: For KMT2D gene, the expression and methylation level increased in 46.6% and 6.67% (respectively) for tumor samples comparison with normal samples (P>0.05). Also, for IGF2 gene 50% tumor samples overexpressed and 50% tumor samples showed that reduced expression comparison with the normal samples (P>0.05). In addition, 96.66% of tumor tissues did not show any change in methylation level for IGF2 gene promoter (P>0.05).Conclusion: This study showed that expression and methylation level of KMT2D and IGF2 genes did not change in NSCLC tumor samples compared to normal samples. However, this study was designed as a pilot study, and further investigations are required to confirm our findings.
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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 | 0.000 |
| 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".