miR-149-3p reverses CD8 <sup>+</sup> T-cell exhaustion by reducing inhibitory receptors and promoting cytokine secretion in breast cancer cells
Bibliographic record
Abstract
Blockade of inhibitory receptors (IRs) is one of the most effective immunotherapeutic approaches to treat cancer. Dysfunction of miRNAs is a major cause of aberrant expression of IRs and contributes to the immune escape of cancer cells. How miRNAs regulate immune checkpoint proteins in breast cancer remains largely unknown. In this study, downregulation of miRNAs was observed in PD-1-overexpressing CD8 + T cells using miRNA array analysis of mouse breast cancer homografts. The data reveal that miR-149-3p was predicted to bind the 3'UTRs of mRNAs encoding T-cell inhibitor receptors PD-1, TIM-3, BTLA and Foxp1. Treatment of CD8 + T cells with an miR-149-3p mimic reduced apoptosis, attenuated changes in mRNA markers of T-cell exhaustion and downregulated mRNAs encoding PD-1, TIM-3, BTLA and Foxp1. On the other hand, T-cell proliferation and secretion of effector cytokines indicative of increased T-cell activation (IL-2, TNF-α, IFN-γ) were upregulated after miR-149-3p mimic treatment. Moreover, the treatment with a miR-149-3p mimic promoted the capacity of CD8 + T cells to kill targeted 4T1 mouse breast tumour cells. Collectively, these data show that miR-149-3p can reverse CD8 + T-cell exhaustion and reveal it to be a potential antitumour immunotherapeutic agent in breast 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 | 0.001 |
| 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".