Electrical Stimulation Generates Induced Tumor-Suppressing Cells, Offering a Potential Option for Combatting Breast Cancer and Bone Metastasis
Bibliographic record
Abstract
Treating advanced metastatic cancer, particularly with bone metastasis, remains a significant challenge. In previous studies, induced tumor-suppressing (iTS) cells were successfully generated through genetic, chemical, and mechanical interventions. This study investigates the potential of electrical stimulation to generate iTS cells. Using a custom electrical stimulator with platinum electrodes, mesenchymal stem cells (MSCs) and Jurkat T cells were stimulated under optimized conditions (50 mV/cm, 10-100 Hz, 1 h). Conditioned medium (CM) from electrically stimulated cells demonstrated tumor-suppressing capabilities, inhibiting tumor cell migration, 3D spheroid growth, and cancer tissue fragment viability. Additionally, the CM reduced osteoclast maturation while promoting osteoblast differentiation. Proteomic analysis revealed enrichment of tumor-suppressing proteins, including histone H4, in the CM. Functional studies identified Piezo1 as a key mediator, as its knockdown significantly impaired the tumor-suppressive effects. Mechanistically, the process was distinct from other methods, such as mechanical vibration, with SUN1 inhibition showing no effect on iTS cell generation by electrical stimulation. These findings demonstrate the efficacy of electrical stimulation in enhancing the antitumor capabilities of MSCs and T cells, offering a novel approach to cancer therapy. Further exploration of this strategy could provide valuable insights into developing new treatments for metastatic 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) | 0.000 | 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".