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Bioinformatic analysis of KIT juxtamembrane domain mutations in Syrian GIST patients: jigsaw puzzle completed.
Journal of the Egyptian National Cancer Institute 2023 August 15
BACKGROUND: The huge number of detected somatic KIT mutations highlights the necessity of in silico analyses that are almost absent in the relevant medical literature. The aim of this study is to report the mutation spectrum analysis of exon 11 encoding the juxtamembrane (JM) domain of the KIT gene in a group of Syrian GIST patients.
METHODS: Forty-eight formalin-fixed paraffin-embedded GIST tissue samples, collected between 2006 and 2016, were retrieved from the pathological archives and analyzed for KIT exon 11 mutations by DNA sequencing. Structural/functional impact of detected variants was predicted using several bioinformatic tools.
RESULTS: Twenty-one different variants have been detected in intron 10, exon 11, and intron 11 of the KIT gene, eight of which were novel changes. Mutations in exon 11 of the KIT gene were detected in 28 of 48 (58.3%) GIST patients and predicted to be pathogenic and cancer promoting. Specifically, age above 60 was very significantly associated with the negative selection of deletion mutations (p = .007), a phenomenon that points to deletion severity.
CONCLUSIONS: Six bioinformatic tools have proved efficient in predicting the impact of detected KIT variations in view of published structural, experimental, and clinical findings.
METHODS: Forty-eight formalin-fixed paraffin-embedded GIST tissue samples, collected between 2006 and 2016, were retrieved from the pathological archives and analyzed for KIT exon 11 mutations by DNA sequencing. Structural/functional impact of detected variants was predicted using several bioinformatic tools.
RESULTS: Twenty-one different variants have been detected in intron 10, exon 11, and intron 11 of the KIT gene, eight of which were novel changes. Mutations in exon 11 of the KIT gene were detected in 28 of 48 (58.3%) GIST patients and predicted to be pathogenic and cancer promoting. Specifically, age above 60 was very significantly associated with the negative selection of deletion mutations (p = .007), a phenomenon that points to deletion severity.
CONCLUSIONS: Six bioinformatic tools have proved efficient in predicting the impact of detected KIT variations in view of published structural, experimental, and clinical findings.
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