Comprehensive transcriptomic analysis in wild-type and ATM knockout lung cancer cells: Influence of cisplatin on oxidative stress-induced senescence
Bibliographic record
Abstract
Genetic mutations and impaired DNA repair mechanisms in cancer not only facilitate tumor progression but also reduce the effectiveness of chemotherapeutic agents, particularly cisplatin. Combination therapy has emerged as a promising strategy to overcome resistance. Comprehensive transcriptomic analyses, supported by integrated comparative bioinformatics and experimental approaches, are essential for identifying biomarkers and novel therapeutic targets underlying drug resistance. In this study, we performed overall survival and mutation analyses, examining 23 double-strand break repair proteins across more than 7500 tumors spanning 23 distinct cancer types. Our findings identify ATM (ataxia-telangiectasia mutated) as a key protein with the highest mutation frequency. Using CRISPR/Cas9, we investigated the effects of ATM mutations on signalling pathways that influence the cellular response to cisplatin. ATM knockout enhanced cisplatin cytotoxicity by activating alternative cell death pathways, including oxidative stress-induced senescence and necroptosis. Microarray analysis revealed a regulatory interplay between ATM and NRF2 in the activation of oxidative stress-induced senescence. Specifically, ATM knockout promoted senescence by increasing reactive oxygen species (ROS) accumulation and downregulating NRF2 expression. To enhance combination therapy, integrating genetic profiling with advanced tools such as CRISPR/Cas9 to target oxidative stress-induced senescence may provide innovative strategies to overcome drug resistance, thereby advancing personalized cancer treatment. These approaches lay the foundation for the development of personalized cancer therapies tailored to the unique mutational landscape of individual patients, offering promising prospects for improving treatment outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".