Update on Radiotherapy Changes of Nasopharyngeal Carcinoma Tumor Microenvironment
Bibliographic record
Abstract
The utilization of radiotherapy (RT) serves as the principal approach for managing nasopharyngeal carcinoma (NPC). Consequently, it is imperative to investigate the correlation between the radiation microenvironment and radiation resistance in NPC. PubMed and China National Knowledge Infrastructure (CNKI) databases were accessed to perform a search utilizing the English keywords "nasopharyngeal cancer", "radiotherapy", and "microenvironment". The search time spanned from the establishment of the database until January 20, 2023. A total of 82 articles were included. The post-radiation tumor microenvironment (TME), or the radiation microenvironment, includes several components, such as the radiation-immune microenvironment and the radiation-hypoxic microenvironment. The radiation-immune microenvironment includes various factors like immune cells, signaling molecules, and extracellular matrix. RT can reshape the TME, leading to immune responses with both cytotoxic effects (T cells, B cells, natural killer (NK) cells) and immune escape mechanisms (regulatory T cells (Tregs), macrophages). RT enhances immune responses through DNA release, type I interferons, and immune cell recruitment. Radiation-hypoxic microenvironment affects metabolism and molecular changes. RT-induced hypoxia causes vascular changes, fibrosis, and vessel compression, leading to tissue hypoxia. Hypoxia activates hypoxia-inducible factor (HIF)-1α/2α, promoting angiogenesis and glycolysis in tumor cells. TME changes due to hypoxia also involve immune suppressive cells like myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs), and Tregs. The radiation microenvironment is involved in radiation resistance and holds a significant effect on the prognosis of patients with NPC. Exploring the radiation microenvironment provides new insights into RT and NPC research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| 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) | 0.005 | 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".