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A plasma microRNA panel for early detection of colorectal cancer
Author(s) -
Wang Shuyang,
Xiang Jianbin,
Li Zhaoyong,
Lu Shaohua,
Hu Jie,
Gao Xue,
Yu Lei,
Wang Lei,
Wang Jiping,
Wu Ying,
Chen Zongyou,
Zhu Hongguang
Publication year - 2014
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.28136
Subject(s) - medicine , colorectal cancer , receiver operating characteristic , logistic regression , colonoscopy , oncology , microrna , area under the curve , cohort , cancer , stage (stratigraphy) , bioinformatics , biology , genetics , paleontology , gene
Colonoscopy remains the standard screening method for detecting colorectal cancer (CRC) at an early stage. However, many people avoid having a colonoscopy because of the fear for its potential complications. Our study aimed to identify plasma microRNAs for preliminarily screening CRC in general population, so that some unnecessary colonoscopies can be avoided. We investigated plasma microRNA expression in three independent cohorts including the discovery ( n = 80), training ( n = 112), and validation ( n = 49) phases recruited at two medical centers. Microarrays were used for screening 723 microRNAs in 80 plasma samples to identify candidate microRNAs. Quantitative reverse‐transcriptase PCR was performed on the 161 training and validation plasma samples to evaluate the candidate microRNAs discovered from microarrays. A logistic regression model was constructed based on the training cohort and then verified by using the validation dataset. Area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic accuracy. We identified a panel of miR‐409‐3p, miR‐7, and miR‐93 that yielded high diagnostic accuracy in discriminating CRC from healthy group (AUC: 0.866 and 0.897 for training and validation dataset, respectively). Moreover, the diagnostic performance of the microRNA panel persisted in nonmetastasis CRC stages (Dukes' A‐B, AUC: 0.809 and 0.892 for training and validation dataset, respectively) and in metastasis CRC stages (Dukes' C‐D, AUC: 0.917 and 0.865 for training and validation dataset, respectively). In conclusion, our study reveals a plasma microRNA panel that has potential clinical value in early CRC detection and would play a critical role on preliminarily screening CRC in general population.