Investigation into drug resistance to cisplatin in cancer stem cell-enriched population in non-small cell lung cancer
Bibliographic record
Abstract
Abstract Objectives Understanding drug resistance in cancer is of importance in treatment. Cancer stem cells are main factor for drug resistance. Therefore, the possible gene/gene interactions/proteins were explored in our study using a cancer stem cell-enriched population (H1299/S) derived from a parental non-small cell lung cancer cell line (H1299/P). Methods Response to cisplatin, which is the main drug for the treatment of lung cancer, was evaluated with the Adenosine triphosphate (ATP) viability test. As a result of the gene expression analysis, while 14 genes were not evaluated, expression profiles were obtained for 37 genes out of 51 genes. By the drug-protein interaction analyses, Topoisomerase I (TOPI), Topoisomerase 2 alpha (TOP2A), Topoisomerase 2 beta (TOP2B), Cyclin-dependent kinases 4 (CDK4), Cyclin-dependent kinases 6 (CDK6), ATP binding cassette subfamily B member 1 (ABCB1), ATP binding cassette subfamily C member 1 (ABCC1), ATP binding cassette subfamily C member 3 (ABCC3), B-cell leukemia/lymphoma 2 (BCL2), Poly (ADP-ribose) polymerase 1 (PARP1), Breast cancer gene 1 ( BRCA1 ) and Cyclin-dependent kinase inhibitor 1A ( CDKN1A ) genes and protein products were statistically significantly found to be in association with drug resistance. Results In bioinformatics analyses, it was observed that 13 pathways were affected due to expression changes and 12 genes related to these pathways were determined to activate multidrug resistance mechanisms. Conclusions Platinum-based drugs, as well as a broad range of other agents including topoisomerase and PARP1 inhibitors, and anthracyclines, have been shown to potentially possess multiple drug resistance.
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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".