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J Pharm Biomed Anal ; 150: 176-182, 2018 Feb 20.
Artículo en Inglés | MEDLINE | ID: mdl-29245087

RESUMEN

In this study, we constructed a sandwich-type biosensor to identify six common types of mutations in exon 19 of the epidermal growth factor receptor (EGFR) gene, and tested them using tissue samples from patients with non-small cell lung carcinomas. Considering the characteristics that different locations of non-complementary in DNA probes resulting in different hybridization efficiency, we investigated the design of DNA capture probes with varying non-complementary sequence locations in an effort to optimize the selectivity of the biosensor. Our results revealed that non-complementary sequences located in the middle of a capture probe allow excellent hybridization specificity and achieve the strongest discrimination between mutations that differ by a single nucleotide. Based on this finding, we designed capture probes to identify six common types of EGFR mutations (del1-del6) successfully. Further, we proposed a grouped testing approach to reduce workload and rapidly identify mutation types. Subsequently, EGFR exon 19 hotspot deletion types in real samples were discriminated by this method. RT-PCR products from lung cancer patients were digested with λ-Exo and analyzed using electrochemical biosensors. The results of our grouped testing approach with optimized biosensors were consistent with that of direct sequencing, suggesting that our proposed protocol can be excellent candidate for genotyping of EGFR mutations in lung cancer patients.


Asunto(s)
Biomarcadores de Tumor/genética , Técnicas Biosensibles/instrumentación , Carcinoma de Pulmón de Células no Pequeñas/genética , Análisis Mutacional de ADN/instrumentación , Receptores ErbB/genética , Neoplasias Pulmonares/genética , Mutación , Carcinoma de Pulmón de Células no Pequeñas/patología , Técnicas Electroquímicas/instrumentación , Diseño de Equipo , Exones , Humanos , Neoplasias Pulmonares/patología , Reacción en Cadena de la Polimerasa , Valor Predictivo de las Pruebas , Reproducibilidad de los Resultados , Flujo de Trabajo , Carga de Trabajo
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