The Definition of Sarcomeric and Non-Sarcomeric Gene Mutations in Hypertrophic Cardiomyopathy Patients: A Multicenter Diagnostic Study Across Türkiye.

BACKGROUND: Hypertrophic cardiomyopathy is a common genetic heart disease and up to 40%-60% of patients have mutations in cardiac sarcomere protein genes. This genetic diagnosis study aimed to detect pathogenic or likely pathogenic sarcomeric and non-sarcomeric gene mutations and to confirm a final molecular diagnosis in patients diagnosed with hypertrophic cardiomyopathy. METHODS: A total of 392 patients with hypertrophic cardiomyopathy were included in this nationwide multicenter study conducted at 23 centers across Türkiye. All samples were analyzed with a 17-gene hypertrophic cardiomyopathy panel using next-generation sequencing technology. The gene panel includes ACTC1, DES, FLNC, GLA, LAMP2, MYBPC3, MYH7, MYL2, MYL3, PLN, PRKAG2, PTPN11, TNNC1, TNNI3, TNNT2, TPM1, and TTR genes. RESULTS: The next-generation sequencing panel identified positive genetic variants (variants of unknown significance, likely pathogenic or pathogenic) in 12 genes for 121 of 392 samples, including sarcomeric gene mutations in 30.4% (119/392) of samples tested, galactosidase alpha variants in 0.5% (2/392) of samples and TTR variant in 0.025% (1/392). The likely pathogenic or pathogenic variants identified in 69 (57.0%) of 121 positive samples yielded a confirmed molecular diagnosis. The diagnostic yield was 17.1% (15.8% for hypertrophic cardiomyopathy variants) for hypertrophic cardiomyopathy and hypertrophic cardiomyopathy phenocopies and 0.5% for Fabry disease. CONCLUSIONS: Our study showed that the distribution of genetic mutations, the prevalence of Fabry disease, and TTR amyloidosis in the Turkish population diagnosed with hypertrophic cardiomyopathy were similar to the other populations, but the percentage of sarcomeric gene mutations was slightly lower.

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins.

Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems' biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.