Identification and Expression Analysis of CD73 Inhibitors in Cervical Cancer
Bibliographic record
Abstract
AIMS: The present study was conducted to examine the inhibitory effects of synthesized sulfonylhydrazones on the expression of CD73 (ecto-5'-NT). BACKGROUND: CD73 (ecto-5'-NT) represents the most significant class of ecto-nucleotidases, which are mainly responsible for the dephosphorylation of adenosine monophosphate to adenosine. Inhibition of CD73 played an important role in the treatment of cancer, autoimmune disorders, precancerous syndromes, and some other diseases associated with CD73 activity. OBJECTIVE: Keeping in view the significance of CD73 inhibitor in the treatment of cervical cancer, a series of sulfonylhydrazones (3a-3i) derivatives synthesized from 3-formylchromones were evaluated. METHODS: All sulfonylhydrazones (3a-3i) were evaluated for their inhibitory activity towards CD73 (ecto-5'-NT) by the malachite green assay and their cytotoxic effect was investigated on the HeLa cell line using MTT assay. Secondly, the most potent compound was selected for cell apoptosis, immunofluorescence staining, and cell cycle analysis. After that, CD73 mRNA and protein expression were analyzed by real-time PCR and Western blot. RESULTS: value of 30.20 ± 3.11 μM and 86.02 ± 7.11 μM, respectively. Furthermore, compound 3h was selected for cell apoptosis, immunofluorescence staining, and cell cycle analysis, which showed a promising apoptotic effect in HeLa cells. Additionally, compound 3h was further investigated for its effect on the expression of CD73 using qRT-PCR and western blot. CONCLUSION: Among all synthesized compounds (3a-3i), Compound 3h (E)-N'-((6-ethyl-4-oxo-4Hchromen- 3-yl) methylene)-4-methylbenzenesulfonohydrazide was identified as the most potent compound. Additional expression studies conducted on the HeLa cell line proved that this compound successfully decreased the expression level of CD73 and thus, inhibited the growth and proliferation of cancer cells.
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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".