Emerging roles of T helper 17 and regulatory T cells in lung cancer progression and metastasis
Bibliographic record
Abstract
Lung cancer is a leading cause of cancer-related deaths worldwide. Lung cancer risk factors, including smoking and exposure to environmental carcinogens, have been linked to chronic inflammation. An integral feature of inflammation is the activation, expansion and infiltration of diverse immune cell types, including CD4 + T cells. Within this T cell subset are immunosuppressive regulatory T (Treg) cells and pro-inflammatory T helper 17 (Th17) cells that act in a fine balance to regulate appropriate adaptive immune responses. In the context of lung cancer, evidence suggests that Tregs promote metastasis and metastatic tumor foci development. Additionally, Th17 cells have been shown to be an integral component of the inflammatory milieu in the tumor microenvironment, and potentially involved in promoting distinct lung tumor phenotypes. Studies have shown that the composition of Tregs and Th17 cells are altered in the tumor microenvironment, and that these two CD4 + T cell subsets play active roles in promoting lung cancer progression and metastasis. We review current knowledge on the influence of Treg and Th17 cells on lung cancer tumorigenesis, progression, metastasis and prognosis. Furthermore, we discuss the potential biological and clinical implications of the balance among Treg/Th17 cells in the context of the lung tumor microenvironment and highlight the potential prognostic function and relationship to metastasis in lung 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".