Whole-Exome Sequencing of Nasopharyngeal Carcinoma Families Reveals Novel Variants Potentially Involved in Nasopharyngeal Carcinoma
Bibliographic record
Abstract
Genetic susceptibility is likely involved in nasopharyngeal carcinoma (NPC), a cancer caused by Epstein-Barr virus (EBV) infection. Understanding of genetic factors involved in NPC and how they contribute to EBV-induced carcinogenesis is limited. We conducted whole-exome capture/sequencing among 251 individuals from 97 multiplex families from Taiwan (205 affected, 21 obligate carriers, and 25 unaffected) using SeqCap EZ Human Exome Library v3.0 and Illumina HiSeq. Aligned sequences were filtered to identify likely-to-be-functional deleterious variants that co-segregated with disease. Ingenuity Pathway analysis was performed. Circulating magnesium levels were measured in 13 individuals in 2 families with NIPAL1 mutations and in 197 sporadic NPC cases and 237 controls. We identified variants in 12 genes likely involved in cancer pathogenesis, viral infection or immune responses to infection. These included genes postulated to be involved in magnesium transport (NIPAL1), EBV cell entry (ITGB6), modulation of EBV infection (BCL2L12, NEDD4L), telomere biology (CLPTM1L, BRD2, HNRNPU), modulation of cAMP signaling (RAPGEF3), DNA repair (PRKDC, MLH1), and Notch signaling (NOTCH1, DLL3). Pathway based analysis demonstrated enrichment for Notch signaling genes (p-value = 0.0006). Evaluation of individuals within NIPAL1 families suggested lower serum magnesium in NPC compared to unaffected members. A significant reduction in serum magnesium levels was observed among sporadic NPC cases compared to controls (7.1% NPC/1.7% controls below normal range; OR = 4.5; 95% CI = 1.4,14) and is consistent with findings demonstrating a role for magnesium channeling in T-cell responses to EBV. We identified novel genes associated with NPC that point to new areas of inquiry to better understand genetic factors that determine the fate of viral infections and/or otherwise predisposes to NPC.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".