Kinesin family members KIF11 and KIF23 as potential therapeutic targets in malignant pleural mesothelioma
Bibliographic record
Abstract
Malignant pleural mesothelioma (MPM) is a rare and aggressive form of cancer commonly associated with asbestos exposure that stems from the thoracic mesothelium with high mortality rate. Currently, treatment options for MPM are limited, and new molecular targets for treatments are urgently needed. Using quantitative reverse transcription-polymerase chain reaction (RT-PCR) and an RNA interference-based screening, we screened two kinesin family members as potential therapeutic targets for MPM. Following in vitro investigation of the target silencing effects on MPM cells, a total of 53 MPMs were analyzed immunohistochemically with tissue microarray. KIF11 and KIF23 transcripts were found to be overexpressed in the majority of clinical MPM samples as well as human MPM cell lines as determined by quantitative RT-PCR. Gene knockdown in MPM cell lines identified growth inhibition following knockdown of KIF11 and KIF23. High expression of KIF11 (KIF11-H) and KIF23 (KIF23-H) were found in 43.4 and 50.9% of all the MPM cases, respectively. Patients who received curative resection with tumors displaying KIF23-H showed shorter overall survival (P=0.0194). These results provide that inhibition of KIF11 and KIF23 may hold promise for treatment of MPMs, raising the possibility that kinesin-based drug targets may be developed in the future.
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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".