Teratoma: genes and variants
Teratoma is linked to 1 analyzed protein (SETBP1). 1 DNA variants are known to cause it; 7 more are uncertain, and 0 of those already look disease-causing on computable evidence.
Last updated 2026-09-30. Research information, not medical advice.
Genes linked to Teratoma
SETBP1: SET-binding protein
It regulates transcription and protein-phosphatase signaling in development and hematopoiesis. Specific gain-of-function variants cause Schinzel-Giedion syndrome, somatic hotspot variants occur in aggressive myeloid neoplasms, and loss-of-function variants can cause a distinct speech and developmental disorder.
1 disease-causing and 0 uncertain variants in SETBP1 are linked to Teratoma.
Weakly linked (only a few uncertain records): CREBBP, CHD2, ERBB4, KRAS, SETD2 and SUZ12.
Known disease-causing variants in Teratoma
| Variant | Position | Protein part | Clinical label |
|---|---|---|---|
| SETBP1 G872R | 872 | Disease-causing (★) |
Same protein, different disease
- Schinzel-Giedion syndrome is also caused by SETBP1 variants; they fall partly in the same places as the Teratoma variants (9 disease-causing).
Diseases related to Teratoma
- Acute myeloid leukemia, also linked to SETBP1
- Schinzel-Giedion syndrome, also linked to SETBP1
- Myelodysplastic syndrome, also linked to SETBP1
Frequently asked questions
Which genes are linked to Teratoma?
In CATVariant, Teratoma is linked to 1 analyzed protein: SETBP1 (SET-binding protein).
How many genetic variants are linked to Teratoma?
8 variants: 1 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 7 are of uncertain significance or have conflicting reports.
Which uncertain variants in Teratoma look disease-causing?
None of the uncertain variants currently reaches the likely-pathogenic range on computable evidence alone.
About this data
Variant–disease links come from ClinVar, Open Targets and UniProt, pooled from the latest public CATVariant analysis of each human protein. Evidence scores use the ACMG/AMP Bayesian points scale with computable criteria only (position among known disease variants, rarity in gnomAD, calibrated predictors, deep mutational scanning); there is no family or patient data, so they prioritise variants for expert review and never classify them.
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