Ectonucleotidase expression profile and activity in human cervical cancer cell lines
Bibliographic record
Abstract
Cervical cancer is the third most frequent cancer in women worldwide. Adenine nucleotide signaling is modulated by the ectonucleotidases that act in sequence, forming an enzymatic cascade. Considering the relationship between the purinergic signaling and cancer, we studied the E-NTPDases, ecto-5'-nucleotidase, and E-NPPs in human cervical cancer cell lines and keratinocytes. We evaluated the expression profiles of these enzymes using RT-PCR and quantitative real-time PCR analysis. The activities of these enzymes were examined using ATP, ADP, AMP, and p-nitrophenyl-5'-thymidine monophosphate (p-Nph-5'-TMP) as substrate, in a colorimetric assay. The extracellular adenine nucleotide hydrolysis was estimated by HPLC analysis. The hydrolysis of all substrates exhibited a linear pattern and these activities were cation-dependent. An interesting difference in the degradation rate was observed between cervical cancer cell lines SiHa, HeLa, and C33A and normal imortalized keratinocytes, HaCaT cells. The mRNA of ecto-5'-nucleotidase, E-NTPDases 5 and 6 were detectable in all cell lines, and the dominant gene expressed was the Entpd 5 enzyme, in SiHa cell line (HPV16 positive). In accordance with this result, a higher hydrolysis activity for UDP and GDP nucleotides was observed in the supernatant of the SiHa cells. Both normal and cancer cells presented activity and mRNAs of members of the NPP family. Considering that these enzymes exert an important catalytic activity, controlling purinergic nucleotide concentrations in tumors, the presence of ectonucleotidases in cervical cancer cells can be important to regulate the levels of extracellular adenine nucleotides, limiting their effects.
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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 | 0.001 |
| 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) | 0.002 | 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".