Inhibition of autophagy improves resistance and enhances sensitivity of gastric cancer cells to cisplatin
Bibliographic record
Abstract
Autophagy plays critical roles in tumorigenesis, while the effects of autophagy on chemoresistance of cancer cells had great disparity. This study aims to explore the impacts of autophagy on the sensitivity and resistance of gastric cancer cells to cisplatin (DDP). We firstly demonstrated that there was stronger autophagy activity in gastric cancer SGC-7901 cells than that in DDP-resisting SGC-7901/DDP cells. Then, we discovered that inhibiting autophagy by chloroquine (CQ) significantly enhanced the proliferation-inhibiting and apoptosis-inducing effects of DDP to SGC-7901 and SGC-7901/DDP cells. Moreover, CQ could partially reverse the resistance of SGC-7901/DDP cells to DDP in a concentration-dependent manner. However, the autophagy inducer everolimus (RAD001) had no obvious effects on the sensitivity of gastric cells to DDP. Mechanistically, we demonstrated that CQ might enhance the sensitivity of SGC-7901cells and improve the resistance of SGC-7901/DDP cells to DDP through inhibiting the mTORC1 pathway, especially to SGC-7901/DDP cells. Additionally, we found interfering Beclin-1 using Beclin-1 shRNA also enhanced the proliferation-inhibiting and apoptosis-inducing effects of DDP on gastric cancer cells by inhibiting phosphorylation of Akt. Our study shows that inhibiting autophagy could improve the chemoresistance and enhanced sensitivity of gastric cancer cells to DDP and provide a rationale for the administration of cisplatin combined with CQ for treating patients with gastric cancer.
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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) | 0.001 | 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 | 0.001 |
| 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".