Sperm protein 17 is a novel marker for predicting cisplatin response in esophageal squamous cancer cell lines
Bibliographic record
Abstract
Expression of sperm protein 17 (Sp17) mRNA has been reported in various malignancies. In an earlier study, we reported the upregulation of Sp17 transcripts in primary esophageal squamous cell carcinomas (ESCCs) using differential display and detected Sp17 transcripts in 86% of ESCCs by RT-PCR, whereas no transcripts were detected in the paired normal esophageal tissues. Herein we hypothesized that Sp17 might be used as a marker for detecting the response of anticancer therapies in ESCCs. Our results indicated that Sp17 protein levels in esophageal squamous cancer cell lines decreased in response to treatment with (i) the HSP90 activity inhibitor geldanamycin, (ii) the tyrosine kinase inhibitor erlotinib and (iii) cisplatin (chemotherapeutic agent commonly used in management of ESCC). In contrast, the Sp17 levels did not decrease in response to radiation therapy and treatment with the chemotherapeutic agent, gemcitabine. Further investigations showed that cisplatin induced decrease in Sp17 levels was due to transcriptional inhibition and cisplatin-resistant cell lines did not show this decrease in Sp17 levels in response to cisplatin treatment. In addition, we also carried our mass spectophotometric analysis to identify the binding partners of Sp17 to characterize its possible involvement in esophageal tumorigenesis and chemoresistance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".