Interferon Gamma Receptor 2 Collaborates With Circular RNA/MicroRNA to Modulate Programmed Cell Death-Ligand 1 Levels in Nasopharyngeal Carcinoma
Bibliographic record
Abstract
Background: The effectiveness of immune checkpoint therapy highlights the need to understand abnormal programmed cell death protein-1 (PD-1) expression in nasopharyngeal carcinoma (NPC), especially when treatments fail, or resistance develops. Interferon gamma (IFN-γ) signaling is crucial for regulating programmed cell death-ligand 1 (PD-L1) expression. Our study focuses on interferon gamma receptor 2 (IFNGR2), an essential part of the IFN-γ pathway, and its impact on malignant traits in NPC. Methods: The expression levels of IFNGR2 and PD-L1 were accessed using quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR). To understand the cellular phenotypic effects, small interfering RNA (siRNA)/short hairpin RNA (shRNA) knockdown techniques were used to evaluate cell viability, clonogenic survival, migration and invasion, immunohistochemistry, and tumor formation assays. The relationship between IFNGR2 and microRNAs (miRNAs)/circular RNAs (circRNAs) will be verified using methods such as circRNA stability assay, rescue, and dual-luciferase reporter assay. Results: IFNGR2 was significantly overexpressed in NPC, and its expression positively correlated with PD-L1 levels. This overexpression contributed to increased cell proliferation, migration, invasion, clonogenicity, and tumor growth. Additionally, we identified an oncogenic circular RNA, circ_001377, and uncovered a novel mechanism by which upregulation of circ_001377 competitively bound to miR-498-3p. This interaction reduced miR-498-3p's ability to target IFNGR2. As a result, the diminished miR-498-3p led to increased IFNGR2 expression, which subsequently activated the IFN-γ signaling pathway and drove abnormal PD-L1 expression. Conclusions: IFNGR2 is an oncogenic factor in NPC. The circ_001377/miR-498-3p interaction drives IFNGR2 upregulation and PD-L1 overexpression, suggesting that targeting this axis could improve therapeutic outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".