Exploring the Role of Sodium-Glucose Cotransporter as a New Target for Cancer Therapy
Bibliographic record
Abstract
PURPOSE: To evaluate the effects of SGLT2 inhibitors on the proliferation, tumorigenesis, migration, colony formation, apoptosis, selected gene expression pattern, and combination with known chemotherapeutic drugs in different human cancer cell lines. METHODS: The antiproliferative and combined effects of SGLT2 inhibitors were evaluated by MTT assay. Cell migration was assessed using wound-healing and colony formation assays. Apoptosis assay was conducted using annexin V-FITC/ propidium iodide staining. SGLT2 gene expression was determined using real-time PCR. RESULTS: Canagliflozin, dapagliflozin, and ipragliflozin significantly inhibited the growth of different cancer cell lines in a dose and time-dependent manner. IC50 values after 48 hours of treatment with canagliflozin, ipragliflozin, and dapagliflozin ranged from 41.97 µM to 69.49 µM, 63.67 µM to 255.80 µM, and 167.7 µM to 435.70 µM in the examined cancer cell lines, respectively. The combined treatment of SGLT2 with doxorubicin and raloxifene separately resulted in a synergistic effect in Caco-2 and A-549 cell lines. On the other hand, the combination of SGLT2 inhibitors with cisplatin resulted in an antagonistic effect in A-549, Du-145, and Panc-1 cell lines. Canagliflozin and ipragliflozin inhibited cell migration and colony formation ability at IC50 and Sub-IC50 in the examined cancer cell lines. Canagliflozin and ipragliflozin significantly induced apoptosis at IC50 and Double-IC50 in the Du-145 cell line compared to the control. Real-time PCR showed that the treatment with 0.1 IC50 and 0.2 IC50 of both canagliflozin and ipragliflozin resulted in diminished RNA expression of SGLT2, VEGF, and Bcl-2 genes in the Du-145 cell line. CONCLUSION: SGLT2 inhibitors have antiproliferation, anti-tumorigenesis, and anti-migration effects and may induce apoptosis in cancer cells. In addition, treatment with SGLT2 inhibitors resulted in the downregulation of selected genes in the Du-145 cell line.
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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 | 0.001 | 0.001 |
| 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".