The Role of Regulatory T Cells (Tregs) in Tumorigenesis: A Comprehensive Literature Review
Bibliographic record
Abstract
Introduction: Regulatory T cells (Tregs) are a subpopulation of CD4+ T lymphocytes that contribute to immune homeostasis by suppressing excessive immune activation. However, these immunosuppressive properties can lead to the suppression of anti-tumor immune responses. Depletion or blocking of Tregs through therapeutics has emerged as a possible method for enhancing anti-tumor immunity. However, the lack of selective targeting of Tregs in the tumor microenvironment is a significant limitation to the effectiveness of Treg therapies. Therefore, this investigation aims to review current literature on how Tregs suppress the antitumor immune response and how they can be targeted to promote anti-tumor immunity. Methods: This review examines recent literature on Tregs in the tumor microenvironment, focusing on both cell-contact dependent and independent mechanisms. Clinical trial studies were also included to assess therapeutic targeting of Tregs. The PubMed database was systematically searched for English articles from 2010 to present, supplemented by manual searches without date restrictions. Boolean expressions ensured comprehensive study retrieval. Results: The involvement of Tregs in the development of multiple cancer types is evident, and targeting these cells could potentially enhance the efficacy of antitumor immunity. In addition, we compiled a list of the novel approaches currently being used for Treg targeting in the context of cancer. Discussion: This review has identified the most promising targets for Treg-based therapies, opening avenues for accelerating the development of innovative cancer treatments. Conclusion: Our literature review offers insights into the complex interplay between the immune system and cancer. The understanding of this interaction is not just an endpoint but could potentially act as a steppingstone towards new scientific discoveries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".