Microtubule affinity‐regulating kinase 2 is associated with DNA damage response and cisplatin resistance in non‐small cell lung cancer
Bibliographic record
Abstract
Microtubule affinity-regulating kinases (MARKs) are involved in several cellular functions but few studies have correlated MARK kinase expression with cancer, and none have explored their role in lung cancer. In this study, we identified MARK2 as frequently disrupted by DNA hypomethylation and copy gain, resulting in concordant overexpression in independent lung tumor cohorts and we demonstrate a role for MARK2 in lung tumor biology. Manipulation of MARK2 in lung cell lines revealed its involvement in cell viability and anchorage-independent growth. Analyses of both manipulated cell lines and clinical tumor specimens identified a potential role for MARK2 in cell cycle activation and DNA repair. Associations between MARK2 and the E2F, Myc/Max and NF-κB pathways were identified by luciferase assays and in-depth assessment of the NF-κB pathway suggests a negative association between MARK2 expression and NF-κB due to activation of non-canonical NF-κB signaling. Finally, we show that high MARK2 expression levels correlate with resistance to cisplatin, a standard first line chemotherapy for lung cancer. Collectively, our work supports a role for MARK2 in promoting malignant phenotypes of lung cancer and potentially modulating response to the DNA damaging chemotherapeutic, cisplatin.
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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".