Dataset Information

Serum microRNA signatures and metabolomics have high diagnostic value in gastric cancer.

ABSTRACT:

Background

Many novel diagnostic biomarkers have been developed for gastric cancer (GC) recently. We chose two methods with high diagnostic value, the detection of serum microRNAs and metabolomics based on gas chromatography/mass spectrometry (GC/MS), and aimed to establish appropriate models.

Methods

We reviewed the diagnostic accuracies of all microRNAs identified by previous diagnostic tests. Then appropriate microRNAs and their combinations were validated the diagnostic value. We included 80 patients with GC and 82 healthy controls (HCs) and detected the expression of the microRNAs. GC/MS analysis was conducted, and we used three multivariate statistical analyses to establish diagnostic models. The concentrations of carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) were detected for comparison with the novel models.

Results

Sixty-seven published studies and 70 microRNAs were finally included in the systematic review. MiR-18a, miR-19a, miR-21, miR-92a, miR-199a and miR-421 were chosen to further validate their diagnostic efficiencies. Five of those microRNAs in GC patients had significantly different expression. The combination of miR-19a and miR-92a had the highest area under the curve (AUC) at 0.850 with a sensitivity of 91.3% and a specificity of 61.0%. The GC/MS analysis performed an excellent diagnostic value and the AUC reached 1.0.

Conclusion

There is a good potential for microRNAs and GC/MS analysis as new diagnostic methods in view of their high diagnostic value compared with traditional biomarkers.

SUBMITTER: Liu HN

PROVIDER: S-EPMC5899358 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Serum microRNA signatures and metabolomics have high diagnostic value in gastric cancer.

Liu Hai-Ning HN Wu Hao H Tseng Yu-Jen YJ Chen Yan-Jie YJ Zhang Dan-Ying DY Zhu Lin L Dong Ling L Shen Xi-Zhong XZ Liu Tao-Tao TT

BMC cancer 20180413 1

<h4>Background</h4>Many novel diagnostic biomarkers have been developed for gastric cancer (GC) recently. We chose two methods with high diagnostic value, the detection of serum microRNAs and metabolomics based on gas chromatography/mass spectrometry (GC/MS), and aimed to establish appropriate models.<h4>Methods</h4>We reviewed the diagnostic accuracies of all microRNAs identified by previous diagnostic tests. Then appropriate microRNAs and their combinations were validated the diagnostic value. ...[more]

PMID: 29653559

Similar Datasets

Project description:BackgroundTo improve the quality of life of colorectal cancer patients, it is important to establish new screening methods for early diagnosis of colorectal cancer.Methodology/principal findingsWe performed serum metabolome analysis using gas-chromatography/mass-spectrometry (GC/MS). First, the accuracy of our GC/MS-based serum metabolomic analytical method was evaluated by calculating the RSD% values of serum levels of various metabolites. Second, the intra-day (morning, daytime, and night) and inter-day (among 3 days) variances of serum metabolite levels were examined. Then, serum metabolite levels were compared between colorectal cancer patients (N?=?60; N?=?12 for each stage from 0 to 4) and age- and sex-matched healthy volunteers (N?=?60) as a training set. The metabolites whose levels displayed significant changes were subjected to multiple logistic regression analysis using the stepwise variable selection method, and a colorectal cancer prediction model was established. The prediction model was composed of 2-hydroxybutyrate, aspartic acid, kynurenine, and cystamine, and its AUC, sensitivity, specificity, and accuracy were 0.9097, 85.0%, 85.0%, and 85.0%, respectively, according to the training set data. In contrast, the sensitivity, specificity, and accuracy of CEA were 35.0%, 96.7%, and 65.8%, respectively, and those of CA19-9 were 16.7%, 100%, and 58.3%, respectively. The validity of the prediction model was confirmed using colorectal cancer patients (N?=?59) and healthy volunteers (N?=?63) as a validation set. At the validation set, the sensitivity, specificity, and accuracy of the prediction model were 83.1%, 81.0%, and 82.0%, respectively, and these values were almost the same as those obtained with the training set. In addition, the model displayed high sensitivity for detecting stage 0-2 colorectal cancer (82.8%).Conclusions/significanceOur prediction model established via GC/MS-based serum metabolomic analysis is valuable for early detection of colorectal cancer and has the potential to become a novel screening test for colorectal cancer.

Dataset Information

Serum microRNA signatures and metabolomics have high diagnostic value in gastric cancer.

Background

Methods

Results

Conclusion

Publications

Serum microRNA signatures and metabolomics have high diagnostic value in gastric cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets