A Screening of Epigenetic Therapeutic Targets for Non-Small Cell Lung Cancer Reveals PADI4 and KDM6B as Promising Candidates
Bibliographic record
Abstract
Despite advances in diagnostic and therapeutic approaches for lung cancer, new therapies targeting metastasis by the specific regulation of cancer genes are needed. In this study, we screened a small library of epigenetic inhibitors in non-small-cell lung cancer (NSCLC) cell lines and evaluated 38 epigenetic targets for their potential role in metastatic NSCLC. The potential candidates were ranked by a streamlined approach using in silico and in vitro experiments based on publicly available databases and evaluated by real-time qPCR target gene expression, cell viability and invasion assays, and transcriptomic analysis. The survival rate of patients with lung adenocarcinoma is inversely correlated with the gene expression of eight epigenetic targets, and a systematic review of the literature confirmed that four of them have already been identified as targets for the treatment of NSCLC. Using nontoxic doses of the remaining inhibitors, KDM6B and PADI4 were identified as potential targets affecting the invasion and migration of metastatic lung cancer cell lines. Transcriptomic analysis of KDM6B and PADI4 treated cells showed altered expression of important genes related to the metastatic process. In conclusion, we showed that KDM6B and PADI4 are promising targets for inhibiting the metastasis of lung adenocarcinoma cancer cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".