We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Development and Validation of an Esophageal Squamous Cell Carcinoma Detection Model by Large-Scale MicroRNA Profiling.
- Authors
Sudo, Kazuki; Kato, Ken; Matsuzaki, Juntaro; Boku, Narikazu; Abe, Seiichiro; Saito, Yutaka; Daiko, Hiroyuki; Takizawa, Satoko; Aoki, Yoshiaki; Sakamoto, Hiromi; Niida, Shumpei; Takeshita, Fumitaka; Fukuda, Takahiro; Ochiya, Takahiro
- Abstract
Key Points: Question: Can circulating microRNAs be used as biomarkers to detect esophageal squamous cell carcinoma? Findings: In this case-control study of 566 patients with esophageal squamous cell carcinoma and 4965 control patients without cancer, serum samples from patients with esophageal squamous cell carcinoma and control patients were used to establish a model to detect esophageal squamous cell carcinoma using 6 microRNAs in a training set. In the validation set, the model distinguished patients with cancer from control patients with high sensitivity and high specificity (0.96 and 0.98, respectively). Meaning: The 6-microRNA model is a promising noninvasive screening tool in esophageal squamous cell carcinoma. This case-control study establishes and assesses the validity of a model using serum microRNAs (miRNAs) to distinguish patients with esophageal squamous cell carcinoma (SCC) from patients without cancer. Importance: Patients with late-stage esophageal squamous cell carcinoma (ESCC) have a poor prognosis. Noninvasive screening tests using serum microRNAs (miRNAs) to accurately detect early-stage ESCC are needed to improve mortality. Objective: To establish a model using serum miRNAs to distinguish patients with ESCC from noncancer controls. Design, Setting, and Participants: In this case-control study, serum miRNA expression profiles of patients with ESCC (n = 566) and control patients without cancer (n = 4965) were retrospectively analyzed to establish a diagnostic model, which was tested in a training set and confirmed in a validation set. Patients histologically diagnosed as having ESCC who did not receive prior therapy or have a past or concurrent cancer other than ESCC were enrolled from the National Cancer Center Hospital in Tokyo, Japan. Between October 2010 and November 2015, control samples were collected from the National Cancer Center Biobank, the Biobank of the National Center for Geriatrics and Gerontology, and the general population undergoing routine health examination. Data analysis was performed between August 2015 and October 2018. Serum samples were randomly divided into discovery and validation sets. Main Outcomes and Measures: The expression of 2565 miRNAs was assessed in each sample. The discriminant model (named the EC index) was evaluated in the training set using Fisher linear discriminant analysis with a greedy algorithm. Receiver operating characteristic curve analysis evaluated the diagnostic ability of the model in the validation set. Results: In the training set, 283 patients with esophageal cancer (median age, 67 years [range, 37-90 years]; 83.4% male) were compared with 283 control patients (median age, 54 years [range, 22-100 years]; 43.1% male), and the EC index was constructed using 6 miRNAs (miR-8073, miR-6820-5p, miR-6794-5p, miR-3196, miR-744-5p, and miR-6799-5p). The area under the receiver operating characteristic curve was 1.00, with sensitivity of 1.00 and specificity of 0.98. The validation set included 283 patients (median age, 66 years [range, 42-87 years]; 83.0% male) and 4682 control patients (median age, 68 years [range, 20-98 years]; 44.7% male), and the area under the receiver operating characteristic curve for the EC index was 1.00, with sensitivity of 0.96 and specificity of 0.98. Conclusions and Relevance: What appears to be novel serum miRNA discriminant model was developed for the diagnosis of ESCC. A multicenter prospective study is ongoing to confirm the present observations.
- Subjects
JAPAN; ALGORITHMS; CHI-squared test; ESOPHAGEAL tumors; FISHER exact test; RESEARCH funding; SQUAMOUS cell carcinoma; LOGISTIC regression analysis; RETROSPECTIVE studies; CASE-control method; RECEIVER operating characteristic curves; DATA analysis software; GENE expression profiling; MICRORNA; STATISTICAL models; DESCRIPTIVE statistics; EARLY detection of cancer
- Publication
JAMA Network Open, 2019, Vol 2, Issue 5, pe194573
- ISSN
2574-3805
- Publication type
Article
- DOI
10.1001/jamanetworkopen.2019.4573