Dataset Information

Integrating SWATH-MS Proteomics and Transcriptome Analysis Identifies CHI3L1 as a Plasma Biomarker for Early Gastric Cancer.

ABSTRACT: Early diagnosis of gastric cancer (GC) provides patients opportunities for minimally invasive endoscopic resection. Here, we developed a new strategy integrated the state-of-the-art sequential windowed acquisition of all theoretical fragment ion (SWATH) mass spectra (MS) with multi-dataset joint analysis to screen for the stage-I GC plasma biomarker. In SWATH-MS assays, we identified 37 upregulated and 21 downregulated proteins in GC plasma. In the mRNA database analysis, 633 genes were identified as differentially expressed genes in at least 4 out of 5 datasets, but there were only 94 genes identified as upregulated. Only 1 gene, CHI3L1, was characterized as upregulated in both the dataset consensus list and the SWATH-MS list. Then, we detected the CHI3L1 level in the plasma of a large cohort consisting of 200 participants. The area under the ROC curve (AUC) of CHI3L1 in distinguishing GC from others was 0.788. Integrating the plasma CHI3L1 level with clinical factors further boosted the AUC to 0.887. In conclusion, we provide a novel strategy for biomarker screening, combining recent MS techniques with public database analysis, and identified plasma CHI3L1 as a potential biomarker for patients with endoscopically resectable GC.

SUBMITTER: Min L

PROVIDER: S-EPMC7186562 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Integrating SWATH-MS Proteomics and Transcriptome Analysis Identifies CHI3L1 as a Plasma Biomarker for Early Gastric Cancer.

Min Li L Zhu Shengtao S Wei Rui R Zhao Yu Y Liu Si S Li Peng P Zhang Shutian S

Molecular therapy oncolytics 20200407

Early diagnosis of gastric cancer (GC) provides patients opportunities for minimally invasive endoscopic resection. Here, we developed a new strategy integrated the state-of-the-art sequential windowed acquisition of all theoretical fragment ion (SWATH) mass spectra (MS) with multi-dataset joint analysis to screen for the stage-I GC plasma biomarker. In SWATH-MS assays, we identified 37 upregulated and 21 downregulated proteins in GC plasma. In the mRNA database analysis, 633 genes were identifi ...[more]

PMID: 32346614

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Project description:BackgroundSWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification.MethodsThe efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1.ResultsConventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally.ConclusionThis is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

Dataset Information

Integrating SWATH-MS Proteomics and Transcriptome Analysis Identifies CHI3L1 as a Plasma Biomarker for Early Gastric Cancer.

Publications

Integrating SWATH-MS Proteomics and Transcriptome Analysis Identifies CHI3L1 as a Plasma Biomarker for Early Gastric Cancer.

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