Summary of study ST000010

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR000009. The data can be accessed directly via it's Project DOI: 10.21228/M80591 This work is supported by NIH grant, U2C- DK119886.


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Study IDST000010
Study TitleLung Cancer Cells 4
Study TypeMS analysis (Untargeted)
Study SummaryIn cancer cells, the process of epithelial–mesenchymal transition (EMT) confers migratory and invasive capacity, resistance to apoptosis, drug resistance, evasion of host immune surveillance and tumor stem cell traits. Cells undergoing EMT may represent tumor cells with metastatic potential. Characterizing the EMT secretome may identify biomarkers to monitor EMT in tumor progression and provide a prognostic signature to predict patient survival. Utilizing a transforming growth factor-β-induced cell culture model of EMT, we quantitatively profiled differentially secreted proteins, by GeLC-tandem mass spectrometry. Integrating with the corresponding transcriptome, we derived an EMT-associated secretory phenotype (EASP) comprising of proteins that were differentially upregulated both at protein and mRNA levels. Four independent primary tumor-derived gene expression data sets of lung cancers were used for survival analysis by the random survival forests (RSF) method. Analysis of 97-gene EASP expression in human lung adenocarcinoma tumors revealed strong positive correlations with lymph node metastasis, advanced tumor stage and histological grade. RSF analysis built on a training set (n = 442), including age, sex and stage as variables, stratified three independent lung cancer data sets into low-, medium- and high-risk groups with significant differences in overall survival. We further refined EASP to a 20 gene signature (rEASP) based on variable importance scores from RSF analysis. Similar to EASP, rEASP predicted survival of both adenocarcinoma and squamous carcinoma patients. More importantly, it predicted survival in the early-stage cancers. These results demonstrate that integrative analysis of the critical biological process of EMT provides mechanism-based and clinically relevant biomarkers with significant prognostic value. Research is published, core data not used but project description is relevant:
University of Michigan
LaboratoryKeshamouni Lab (MCTP)
Last NameKeshamouni
First NameVenkat
Submit Date2013-04-03
Num Groups13
Total Subjects39
Raw Data AvailableNo
Analysis Type DetailLC-MS
Release Date2013-05-03
Release Version1
Venkat Keshamouni Venkat Keshamouni application/zip

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Project ID:PR000009
Project DOI:doi: 10.21228/M80591
Project Title:Metabolomics of EMT
Project Type:MS analysis
Project Summary:Lung Cancer Cells 4
Institute:University of Michigan
Laboratory:Keshamouni Lab (MCTP)
Last Name:Keshamouni
First Name:Venkat