Clinical and genetic heterogeneity of SATB2-associated syndrome

Marfan syndrome (OMIM #154700) is an autosomal dominantly inherited connective tissue disorder caused by mutations in the FBN1 gene and is marked by significant clinical variability, including cardiovascular manifestations. The causes of this variability remain inadequately studied, and relatively few genotype-phenotype correlations have been identified to date. In this study, we examined 66 children with Marfan syndrome to identify genotype-phenotype correlations. Biochemical, functional, and genetic research methods were employed, confirming a positive correlation between ectopia lentis and missense variants in the FBN1 gene. Additionally, in our cohort, patients with loss-of-function (LoF) mutations, compared to those with missense mutations, statistically showed larger aortic dimensions, earlier onset of foot deformities, marked dolichostenomelia, a higher frequency of elbow contractures, chest deformities, and aortic dilation. Patients with missense variants involving cysteine loss, as opposed to those without cysteine involvement, demonstrated an earlier onset of spinal deformity, higher natriuretic peptide (NT-proBNP) levels, larger aortic sizes, increased prevalence of chest deformities, particularly carinatum, ectopia lentis, and a greater propensity for osteoporosis. Patients with mutations in exons 11 to 20, compared to those with mutations in other exons, were less likely to exhibit pectus carinatum and aortic dilation and had less pronounced dolichostenomelia. Patients with mutations in exons 51 to 66, regardless of mutation type, were less likely to present with ectopia lentis. Thus, based on an analysis of genotype-phenotype correlations in a cohort of 66 children with Marfan syndrome, we identified several statistically significant correlations between phenotypic features of Marfan syndrome and mutation type and location within the FBN1 gene. This study confirmed that stable genotype-phenotype correlations are increasingly important for understanding clinical variability and for predicting disease severity.

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