Inherited Polymorphisms in Hyaluronan Synthase 1 Predict Risk of Systemic B-Cell Malignancies but Not of Breast Cancer
Bibliographic record
Abstract
Genetic variations in the hyaluronan synthase 1 gene (HAS1) influence HAS1 aberrant splicing. HAS1 is aberrantly spliced in malignant cells from multiple myeloma (MM) and Waldenstrom macroglobulinemia (WM), but not in their counterparts from healthy donors. The presence of aberrant HAS1 splice variants predicts for poor survival in multiple myeloma (MM). We evaluated the influence of inherited HAS1 single nucleotide polymorphisms (SNP) on the risk of having a systemic B cell malignancy in 1414 individuals compromising 832 patients and 582 healthy controls, including familial analysis of an Icelandic kindred. We sequenced HAS1 gene segments from 181 patients with MM, 98 with monoclonal gammopathy of undetermined significance (MGUS), 72 with Waldenstrom macroglobulinemia (WM), 169 with chronic lymphocytic leukemia (CLL), as well as 34 members of a monoclonal gammopathy-prone Icelandic family, 212 age-matched healthy donors and a case-control cohort of 295 breast cancer patients with 353 healthy controls. Three linked single nucleotide polymorphisms (SNP) in HAS1 intron3 are significantly associated with B-cell malignancies (range p = 0.007 to p = 10(-5)), but not MGUS or breast cancer, and predict risk in a 34 member Icelandic family (p = 0.005, Odds Ratio = 5.8 (OR)), a relatively homogeneous cohort. In contrast, exon3 SNPs were not significantly different among the study groups. Pooled analyses showed a strong association between the linked HAS1 intron3 SNPs and B-cell malignancies (OR = 1.78), but not for sporadic MGUS or for breast cancer (OR<1.0). The minor allele genotypes of HAS1 SNPs are significantly more frequent in MM, WM, CLL and in affected members of a monoclonal gammopathy-prone family than they are in breast cancer, sporadic MGUS or healthy donors. These inherited changes may increase the risk for systemic B-cell malignancies but not for solid tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".