Objective: To investigate molecular and clinical characteristics of children with permanent congenital hypothyroidism (CH) in Yunnan, China.
Methods: The clinical data of 40 children with CH diagnosed and treated in the First People's Hospital of Yunnan Province during January 2016 and January 2019 were retrospectively analyzed. All children were followed up to 3 years old, and Gesell intelligent score was evaluated at age of 1, 2 and 3 years, respectively. Developmental status and prognosis were evaluated. Next-generation sequencing (NGS) was used to screen all exons and exon-intron boundary sequences of the 27 known CH associated genes, and the relationship between genotypes and clinical phenotypes was analyzed.
Results: Among the 40 children, the thyroid related pathogenic gene mutations were detected in 23 cases with a rate of 57.5%, and a total of 32 mutations of 8 genes were detected. Mutations in , and genes were the most common ones with mutation frequencies of 65.9%(29/44), 11.4%(5/44) and 9.1%(4/44), respectively. gene mutations were detected in 17 children with CH, and a total of 17 mutation types were detected. p.K530* was the most common mutation in gene, accounting for 20.7%(6/29). There was no significant difference in physical development and intelligence assessment between children with heterozygous mutation and compound heterozygous mutations. None of patients could terminate medication at 3 years of the follow-up and all of them were provisionally assessed as permanent CH. The physical and mental development assessment of children with other gene mutations were also in the normal range.
Conclusion: The detection rate of , and pathogenic mutations are high among children with permanent CH in Yunnan area, and no correlation is observed between gene mutation types and prognosis in children with CH.
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http://dx.doi.org/10.3724/zdxbyxb-2022-0199 | DOI Listing |
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Institute of Rare Diseases, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610000, Sichuan, China.
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BMC Bioinformatics
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Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, R3B 2E9, Canada.
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Department of Gastroenterology, The National Key Clinical Specialty, Clinical Research Center for Gut Microbiota and Digestive Diseases of Fujian Province, Key Laboratory for Intestinal Microbiome and Human Health of Xiamen, Zhongshan Hospital of Xiamen University, Xiamen, 361004, China.
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January 2025
University of São Paulo, ICMC, São Carlos, 13566-590, Brazil.
Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks.
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