NLRP7 Enhances Choriocarcinoma Cell Survival and Camouflage in an Inflammasome Independent Pathway
Bibliographic record
Abstract
Background: Gestational choriocarcinoma (GC) is a highly malignant trophoblastic tumor that often develops from a complete hydatidiform mole (HM). NLRP7 is the major gene responsible for recurrent HM and is involved in the innate immune response, inflammation and apoptosis. NLRP7 can function in an inflammasome-dependent or -independent pathway. Recently, we have demonstrated that NLRP7 is highly expressed in GC tumor cells and contributes to their tumorigenesis. However, the underlying mechanisms are still unknown. Here, we investigated the mechanism by which NLRP7 controls these processes in malignant (JEG-3) and non-tumor (HTR8/SVneo) trophoblastic cells. Cell survival, dedifferentiation, camouflage, and aggressiveness were compared between normal JEG-3 cells or knockdown for NLRP7, JEG-3 Sh NLRP7. In addition, HTR8/SVneo cells overexpressing NLRP7 were used to determine the impact of NLRP7 overexpression on non-tumor cells. NLRP7 involvement in tumor cell growth and tolerance was further characterized in vivo using the metastatic mouse model of GC. Results: We demonstrate that NLRP7 (i) functions in an inflammasome-dependent and -independent manners in HTR8/SVneo and JEG-3 cells, respectively; (ii) differentially regulates the activity of NF-κB in tumor and non-tumor cells; (iii) increases malignant cell survival, dedifferentiation, and camouflage; and (iv) facilitates tumor cells colonization of the lungs in the preclinical model of GC. Conclusions: This study demonstrates for the first time the mechanism by which NLRP7, independently of its inflammasome machinery, contributes to GC growth and tumorigenesis. The clinical relevance of NLRP7 in this rare cancer highlights its potential therapeutic promise as a molecular target to treat resistant GC patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".