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Abstract This study aimed to analyze the molecular characteristics of oral epithelial dysplasia (OED), highlighting the pathways and variants of genes that are frequently mutated in oral squamous cell carcinoma (OSCC) and other cancers. Ten archival OED cases were retrieved for retrospective clinicopathological analysis and exome sequencing. Comparative genomic analysis was performed between high-grade dysplasia (HGD) and low-grade dysplasia (LGD), focusing on 57 well-known cancer genes, of which 10 were previously described as the most mutated in OSCC. HGD cases had significantly more variants; however, a similar mutational landscape to OSCC was observed in both groups. CASP8+FAT1/HRAS, TP53, and miscellaneous molecular signatures were also present. FAT1 is the gene that is most affected by pathogenic variants. Hierarchical divisive clustering showed division between the two groups: "HGD-like cluster" with 4HGD and 2LGD and "LGD-like cluster" with 4 LGD. MLL4 pathogenic variants were exclusively in the "LGD-like cluster". TP53 was affected in one case of HGD; however, its pathway was usually altered. We describe new insights into the genetic basis of epithelial malignant transformation by genomic analysis, highlighting those associated with FAT1 and TP53. Some LGDs presented a similar mutational landscape to HGD after cluster analysis. Perhaps molecular alterations have not yet been reflected in histomorphology. The relative risk of malignant transformation in this molecular subgroup should be addressed in future studies.
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OBJECTIVES: Next-generation sequencing (NGS) data in the identification of disease-causing genes provides a promising opportunity in the diagnosis of disease. Beyond the previous efforts for NGS data alignment, variant detection, and visualization, developing a comprehensive annotation system supported by multiple layers of disease phenotype-related databases is essential for deciphering the human genome. To satisfy the impending need to decipher the human genome, it is essential to develop a comprehensive annotation system supported by multiple layers of disease phenotype-related databases. METHODS: AnsNGS (Annotation system of sequence variations for next-generation sequencing data) is a tool for contextualizing variants related to diseases and examining their functional consequences. The AnsNGS integrates a variety of annotation databases to attain multiple levels of annotation. RESULTS: The AnsNGS assigns biological functions to variants, and provides gene (or disease)-centric queries for finding disease-causing variants. The AnsNGS also connects those genes harbouring variants and the corresponding expression probes for downstream analysis using expression microarrays. Here, we demonstrate its ability to identify disease-related variants in the human genome. CONCLUSIONS: The AnsNGS can give a key insight into which of these variants is already known to be involved in a disease-related phenotype or located in or near a known regulatory site. The AnsNGS is available free of charge to academic users and can be obtained from http://snubi.org/software/AnsNGS/.