ArticleJournal, genetic engineering & biotechnology2025
Identifying actionable genetic mutations and microsatellite instability in liquid biopsy of colorectal cancer.
Article in Journal, genetic engineering & biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionColorectal cancer (CRC) is a molecularly diverse disorder arising from gradual accumulation of genetic and epigenetic changes giving interest in characterization of genetic alterations and microsatellite instability (MSI) for identification of new personalized therapeutic targets.
objectivesEvaluating the incidence of somatic mutations in clinically relative signaling pathways involved in CRC tumorigenesis that include mainly Wnt and MAPK pathways together with MSI and gut microbiota.
methodsTwenty four CRC patients were enrolled in the study. Cancer hotspot V2 panel was tested using targeted NGS on MiSeqDx device in addition to MSI identification, also gut microbiota was detected using conventional techniques. Patients received FOLFOX regimen in addition to Xeloda especially in early stages.
resultsNon-synonymous genetic variants were detected inTP53, PIK3CA, KDR, KIT, APC, FGFR3 and MET. Twelve patients (50%) had MSI-LO, 25% had MSI-HI and 20.8% were MSS. Missense mutations in PIK3CA, TP53, and KDR were identified in 2, 3, and 2 patients with MSI-HI status, respectively. In MSI-LO, missense alterations in PIK3CA, TP53, KIT, and KDR were detected in (6, 11, 3, 2 cases, respectively). More than 50% of examined patients revealed mixed GIT flora, 18.8% of patients had E. Coli, 12.5% of patients showed Klebsiella spp. and only 6.3 % of patient revealed Proteus spp. H. pylori antigen was detected in 37.5%of patients and Blastocystis hominis cysts in only 4 patients.
conclusionCRC genetic mutational statuses as well as contributing environmental stress factors such as gut microbiota dysbiosis are prognostically crucial, associated with high risk potential of gene-environment interactions based on machine learning.
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