液体活检
结直肠癌
小RNA
医学
队列
置信区间
肿瘤科
内科学
实时聚合酶链反应
癌症
活检
生物信息学
基因
生物
遗传学
作者
Kota Nakamura,Goretti Hernández,Geeta Sharma,Yuma Wada,Jasjit K. Banwait,Natalia González,José Perea,Francesc Balaguer,Hiroyuki Takamaru,Yutaka Saito,Yuji Toiyama,Yasuhiro Kodera,C. Richard Boland,Luís Bujanda,Enrique Quintero,Ajay Goel
出处
期刊:Gastroenterology
[Elsevier]
日期:2022-11-01
卷期号:163 (5): 1242-1251.e2
被引量:14
标识
DOI:10.1053/j.gastro.2022.06.089
摘要
Early-onset colorectal cancer (EOCRC) is a distinct clinical and molecular entity with poor survival outcomes compared with late-onset CRC. Although the incidence of EOCRC is rising, current CRC screening strategies have several limitations in diagnostic performance for EOCRC. In view of this clinical challenge, novel and robust biomarkers for detection of EOCRC are necessary. The aim of this study was to develop a circulating micro RNA (miRNA) signature for the diagnosis of patients with EOCRC.A systematic discovery approach by analyzing a large, publicly available, noncoding RNA expression profiling dataset (GSE115513) was used. A panel of miRNAs was identified, which was subsequently validated in blood samples from patients with EOCRC in 2 independent cohorts (n = 149) compared with controls (n = 110) and pre/postoperative plasma specimens (n = 22) using quantitative reverse-transcription polymerase chain reaction assays.In the discovery phase, 4 miRNAs were found to be expressed in blood samples. A combination signature of these 4 miRNAs (miR-193a-5p, miR-210, miR-513a-5p, and miR-628-3p) yielded an area under the curve of 0.92 (95% confidence interval, 0.85-0.96) for identification of EOCRC in the training cohort. The miRNA panel performance was then confirmed in an independent validation cohort (area under the curve, 0.88; 95% confidence interval, 0.82-0.93). Moreover, the miRNA panel robustly identified patients with early-stage EOCRC (P < .001). The decreased expression of miRNAs in postsurgery plasma specimens indicated their tumor specificity.Our novel miRNA signature for the diagnosis of EOCRC has the potential to identify patients with EOCRC with high accuracy for clinical application in the noninvasive diagnosis of EOCRC.
科研通智能强力驱动
Strongly Powered by AbleSci AI