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<p>Genodermatoses, a group of inherited skin disorders, are characterized by significant genetic heterogeneity and clinical variability, often posing diagnostic and therapeutic challenges. Advances in next-generation sequencing (NGS) technologies, such as whole exome sequencing (WES) and clinical exome sequencing (CES), have transformed the diagnostic landscape by enabling comprehensive genetic analysis. This study aimed to investigate the molecular spectrum and clinical relevance of genetic findings in 43 patients diagnosed with genodermatoses. Demographic, clinical, and molecular data were collected, and genetic testing was performed using the MGI-Seq platform. Variants were analyzed for pathogenicity, zygosity, and novelty. Neurofibromatosis Type 1 (27.9%) and Epidermolysis Bullosa (23.2%) were the most common diagnoses, followed by Ichthyosis (16.2%) and Oculocutaneous Albinism (13.9%). Less frequent conditions included Ectodermal Dysplasia (6.9%) and single cases of Palmoplantar Keratoderma, PTEN Hamartoma Syndrome, Rothmund-Thomson Syndrome, Xeroderma Pigmentosum, and Megaconial Congenital Muscular Dystrophy (each 2.3%). Molecular findings underscored the genetic complexity of genodermatoses, with 42 distinct variants identified across 19 genes. Of these, 13 variants (31%) were novel, expanding the known molecular spectrum. The novel variants were detected in genes including <i>NF1</i>,<i> COL7A1</i>,<i> ITGB4</i>,<i> COL17A1</i>,<i> NIPAL4</i>,<i> ALOX12B</i>,<i> KRT10</i>,<i> ST14</i>,<i> OCA2</i>, and <i>PTEN</i>, highlighting the diagnostic value of comprehensive genetic analysis. The mean age at diagnosis varied significantly among conditions, reflecting the diagnostic challenges and clinical variability of genodermatoses. This study emphasizes the critical role of WES and CES in diagnosing genodermatoses and understanding their molecular basis, which enhances diagnostic accuracy and supports personalized management strategies.</p>

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Molecular insights into genodermatoses: Genetic findings from 43 patients

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Arzu Deniz Sama,
  • Enise Avci Durmusalioglu,
  • Esra Isik,
  • Turkan Turkut Tan,
  • Mehmet Mert Topaloglu,
  • Yusuf Can Dogan,
  • Ayda Acar,
  • Nilay Duman,
  • Tahir Atik

摘要

Genodermatoses, a group of inherited skin disorders, are characterized by significant genetic heterogeneity and clinical variability, often posing diagnostic and therapeutic challenges. Advances in next-generation sequencing (NGS) technologies, such as whole exome sequencing (WES) and clinical exome sequencing (CES), have transformed the diagnostic landscape by enabling comprehensive genetic analysis. This study aimed to investigate the molecular spectrum and clinical relevance of genetic findings in 43 patients diagnosed with genodermatoses. Demographic, clinical, and molecular data were collected, and genetic testing was performed using the MGI-Seq platform. Variants were analyzed for pathogenicity, zygosity, and novelty. Neurofibromatosis Type 1 (27.9%) and Epidermolysis Bullosa (23.2%) were the most common diagnoses, followed by Ichthyosis (16.2%) and Oculocutaneous Albinism (13.9%). Less frequent conditions included Ectodermal Dysplasia (6.9%) and single cases of Palmoplantar Keratoderma, PTEN Hamartoma Syndrome, Rothmund-Thomson Syndrome, Xeroderma Pigmentosum, and Megaconial Congenital Muscular Dystrophy (each 2.3%). Molecular findings underscored the genetic complexity of genodermatoses, with 42 distinct variants identified across 19 genes. Of these, 13 variants (31%) were novel, expanding the known molecular spectrum. The novel variants were detected in genes including NF1, COL7A1, ITGB4, COL17A1, NIPAL4, ALOX12B, KRT10, ST14, OCA2, and PTEN, highlighting the diagnostic value of comprehensive genetic analysis. The mean age at diagnosis varied significantly among conditions, reflecting the diagnostic challenges and clinical variability of genodermatoses. This study emphasizes the critical role of WES and CES in diagnosing genodermatoses and understanding their molecular basis, which enhances diagnostic accuracy and supports personalized management strategies.