Research on DNA methylation of human osteosarcoma cell MGMT and its relationship with cell resistance to alkylating agents
Bibliographic record
Abstract
The objective of this study was to explore the O(6)-methylguanine-DNA methyltransferase (MGMT) gene methylation status and its protein expression, as well as the effects of demethylating agent 5-Aza-2'-deoxycytidine (5-Aza-CdR) on MGMT gene expression and its resistance to alkylating agents, and to elucidate MGMT expression mechanism and significance in osteosarcoma. The human osteosarcoma cell lines Saos-2 and MG-63 were collected and treated with 5-Aza-CdR for 6 days. The cells not treated with 5-Aza-CdR were set as a negative control. The genomic DNA was extracted from the Saos-2 and MG-63 cells using methylation-specific PCR to detect the promoter CpG island methylation status of the MGMT gene. Cell sensitivity to alkylating agents before and after drug administration was detected by the MTT method. The variation in MGMT gene mRNA and protein was detected by reverse transcription PCR (RT-PCR) and Western blotting. The MGMT promoter gene of normal Saos-2 cells was methylated, with reduced MGMT mRNA and protein expression; the MGMT mRNA and protein expression of Saos-2 cells treated with 5-Aza-CdR was obviously enhanced, and its sensitivity to alkylating agents was reversed. Meanwhile, with promoter CpG island unmethylation of the MGMT gene, MGMT protein was expressed in the normal MG-63 cells and the MG-63 cells treated with 5-Aza-CdR, and both showed resistance to alkylating agents. The methylation status of the MGMT gene promoter in human osteosarcoma cells reflected the cells' ability to induce MGMT protein expression and can be used as a molecular marker to project the sensitivity of cancer tissues to alkylating agent drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".