Associations with Single Rare Heterozygous NLRP7 Variants for Chinese Patients with Sporadic Gestational Trophoblastic Diseases
Bibliographic record
Abstract
Abstract Background Gestational trophoblastic diseases (GTDs) encompass a group of clinically heterogeneous tumors of trophoblastic cell origin. In some populations and studies, single NLRP7 heterozygous mutations and some non-synonymous variants (NSVs) have been shown to confer genetic susceptibility to sporadic GTDs. Our study aims to investigate whether single rare heterozygous NSVs in NLRP7 predispose patients to GTDs in Chinese. Methods We performed Sanger sequencing of all NLRP7 exons and exon-intron boundaries in a cohort of Chinese patients with non-recurrent sporadic GTDs and normal childbearing women. Using in-silico analysis and several prediction programs, we investigated the effects of identified NSVs on the function of the protein. Then we examine the function in inflammatory pathway to assessing the functional consequences of the identified rare NSVs in in-vitro studies.Results We found that the number of patients with rare single heterozygous NSVs that are predicted to have functional effects on the protein is significantly higher in the first cohort of 292 patients than in the 300 controls (p=0.028). Analysis of the validation cohort of 130 patients confirmed the significant higher number of patients with rare NSVs than controls (p=0.0014). We show that most of the rare variants that are identified only in the patients are predicted to have functional effects on the protein by various algorithms and in-vitro experiments. Conclusion Our results demonstrate that Chinese patients with GTDs have a higher burden of single heterozygous NLRP7 variants which were predicted to have effects on the protein than controls. We suggest that some of these single NSVs may contribute to the genetic susceptibility for GTDs in China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".