作者
Marina Bagnoli, Silvana Canevari, Daniela Califano, Simona Losito, Massimo Di Maio, Francesco Raspagliesi, Maria Luisa Carcangiu, Giuseppe Toffoli, Erika Cecchin, Roberto Sorio, Vincenzo Canzonieri, Daniela Russo, Giosué Scognamiglio, Gennaro Chiappetta, Gustavo Baldassarre, Domenica Lorusso, Giovanni Scambia, Gian Franco Zannoni, Antonella Savarese, Mariantonia Carosi, Paolo Scollo, Enrico Breda, Viviana Murgia, Francesco Perrone, Sandro Pignata, Loris De Cecco, Delia Mezzanzanica
发表日期
2016/8/1
期刊
The lancet oncology
卷号
17
期号
8
页码范围
1137-1146
出版商
Elsevier
简介
Background
Risk of relapse or progression remains high in the treatment of most patients with epithelial ovarian cancer, and development of a molecular predictor could be a valuable tool for stratification of patients by risk. We aimed to develop a microRNA (miRNA)-based molecular classifier that can predict risk of progression or relapse in patients with epithelial ovarian cancer.
Methods
We analysed miRNA expression profiles in three cohorts of samples collected at diagnosis. We used 179 samples from a Multicenter Italian Trial in Ovarian cancer trial (cohort OC179) to develop the model and 263 samples from two cancer centres (cohort OC263) and 452 samples from The Cancer Genome Atlas epithelial ovarian cancer series (cohort OC452) to validate the model. The primary clinical endpoint was progression-free survival, and we adapted a semi-supervised prediction method to the miRNA expression profile of …
学术搜索中的文章
M Bagnoli, S Canevari, D Califano, S Losito, M Di Maio… - The lancet oncology, 2016