The paradoxical role of cytokines and chemokines at the tumor microenvironment: a comprehensive review
Bibliographic record
Abstract
Tumor progression and eradication have long piqued the scientific community's interest. Recent discoveries about the role of chemokines and cytokines in these processes have fueled renewed interest in related research. These roles are frequently viewed as contentious due to their ability to both suppress and promote cancer progression. As a result, this review critically appraised existing literature to discuss the unique roles of cytokines and chemokines in the tumor microenvironment, as well as the existing challenges and future opportunities for exploiting these roles to develop novel and targeted treatments. While these modulatory molecules play an important role in tumor suppression via enhanced cancer-cell identification by cytotoxic effector cells and directly recruiting immunological effector cells and stromal cells in the TME, we observed that they also promote tumor proliferation. Many cytokines, including GM-CSF, IL-7, IL-12, IL-15, IL-18, and IL-21, have entered clinical trials for people with advanced cancer, while the FDA has approved interferon-alpha and IL-2. Nonetheless, low efficacy and dose-limiting toxicity limit these agents' full potential. Conversely, Chemokines have tremendous potential for increasing cancer immune-cell penetration of the tumor microenvironment and promoting beneficial immunological interactions. When chemokines are combined with cytokines, they activate lymphocytes, producing IL-2, CD80, and IL-12, all of which have a strong anticancer effect. This phenomenon opens the door to the development of effective anticancer combination therapies, such as therapies that can reverse cancer escape, and chemotaxis of immunosuppressive cells like Tregs, MDSCs, and TAMs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".