2026-04-17T05:14:18Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1135882025-11-20T13:50:19Zcom_2072_1057col_2072_478799col_2072_478916col_2072_478917col_2072_478921

00925njm 22002777a 4500 dc Diaz-Lagares, Angel author Méndez González, Jesús author Hervas, David author Saigí, Maria author Pajares, María José author Garcia, Diana author Crujeiras, Ana B. author Pio, Ruben author Montuenga, Luis M. author Zulueta, Javier J. author Nadal, Ernest author Rosell, Antoni, 1963- author Esteller, Manel author Sandoval, Juan author 2017-07-10T11:23:35Z 2017-07-10T11:23:35Z 2016-07-01 2017-07-10T11:23:35Z Purpose: lung cancer remains as the leading cause of cancer-related death worldwide, mainly due to late diagnosis. Cytology is the gold-standard method for lung cancer diagnosis in minimally invasive respiratory samples, despite its low sensitivity. We aimed to identify epigenetic biomarkers with clinical utility for cancer diagnosis in minimally/noninvasive specimens to improve accuracy of current technologies. Experimental design: the identification of novel epigenetic biomarkers in stage I lung tumors was accomplished using an integrative genome-wide restrictive analysis of two different large public databases. DNA methylation levels for the selected biomarkers were validated by pyrosequencing in paraffin-embedded tissues and minimally invasive and noninvasive respiratory samples in independent cohorts. Results: we identified nine cancer-specific hypermethylated genes in early-stage lung primary tumors. Four of these genes presented consistent CpG island hypermethylation compared with nonmalignant lung and were associated with transcriptional silencing. A diagnostic signature was built using multivariate logistic regression model based on the combination of four genes: BCAT1, CDO1, TRIM58, and ZNF177. Clinical diagnostic value was also validated in multiple independent cohorts and yielded a remarkable diagnostic accuracy in all cohorts tested. Calibrated and cross-validated epigenetic model predicts with high accuracy the probability to detect cancer in minimally and noninvasive samples. We demonstrated that this epigenetic signature achieved higher diagnostic efficacy in bronchial fluids as compared with conventional cytology for lung cancer diagnosis. Conclusions: minimally invasive epigenetic biomarkers have emerged as promising tools for cancer diagnosis. The herein obtained epigenetic model in combination with current diagnostic protocols may improve early diagnosis and outcome of lung cancer patients. Epigènesi Diagnòstic Marcadors bioquímics Metilació ADN Càncer de pulmó Epigenesis Diagnosis Biochemical markers Methylation DNA Lung cancer A novel epigenetic signature for early diagnosis in lung cancer