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SILAC-based quantitative proteomic approach to identify potential biomarkers from the esophageal squamous cell carcinoma secretome.

ABSTRACT: The identification of secreted proteins that are differentially expressed between non-neoplastic and esophageal squamous cell carcinoma (ESCC) cells can provide potential biomarkers of ESCC. We used a SILAC-based quantitative proteomic approach to compare the secretome of ESCC cells with that of non-neoplastic esophageal squamous epithelial cells. Proteins were resolved by SDS-PAGE, and tandem mass spectrometry analysis (LC-MS/MS) of in-gel trypsin-digested peptides was carried out on a high-accuracy qTOF mass spectrometer. In total, we identified 441 proteins in the combined secretomes, including 120 proteins with > 2-fold upregulation in the ESCC secretome vs. that of non-neoplastic esophageal squamous epithelial cells. In this study, several potential protein biomarkers previously known to be increased in ESCC including matrix metalloproteinase 1, transferrin receptor, and transforming growth factor beta-induced 68 kDa were identified as overexpressed in the ESCC-derived secretome. In addition, we identified several novel proteins that have not been previously reported to be associated with ESCC. Among the novel candidate proteins identified, protein disulfide isomerase family a member 3 (PDIA3), GDP dissociation inhibitor 2 (GDI2), and lectin galactoside binding soluble 3 binding protein (LGALS3BP) were further validated by immunoblot analysis and immunohistochemical labeling using tissue microarrays. This tissue microarray analysis showed overexpression of protein disulfide isomerase family a member 3, GDP dissociation inhibitor 2, and lectin galactoside binding soluble 3 binding protein in 93%, 93% and 87% of 137 ESCC cases, respectively. Hence, we conclude that these potential biomarkers are excellent candidates for further evaluation to test their role and efficacy in the early detection of ESCC.

SUBMITTER: Kashyap MK

PROVIDER: S-EPMC3093916 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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SILAC-based quantitative proteomic approach to identify potential biomarkers from the esophageal squamous cell carcinoma secretome.

Kashyap Manoj Kumar MK Kashyap Manoj Kumar MK Harsha H C HC Renuse Santosh S Pawar Harsh H Sahasrabuddhe Nandini A NA Kim Min-Sik MS Marimuthu Arivusudar A Keerthikumar Shivakumar S Muthusamy Babylakshmi B Kandasamy Kumaran K Subbannayya Yashwanth Y Prasad Thottethodi Subrahmanya Keshava TS Mahmood Riaz R Chaerkady Raghothama R Meltzer Stephen J SJ Kumar Rekha V RV Rustgi Anil K AK Pandey Akhilesh A

Cancer biology & therapy 20101015 8

The identification of secreted proteins that are differentially expressed between non-neoplastic and esophageal squamous cell carcinoma (ESCC) cells can provide potential biomarkers of ESCC. We used a SILAC-based quantitative proteomic approach to compare the secretome of ESCC cells with that of non-neoplastic esophageal squamous epithelial cells. Proteins were resolved by SDS-PAGE, and tandem mass spectrometry analysis (LC-MS/MS) of in-gel trypsin-digested peptides was carried out on a high-acc ...[more]

PMID: 20686364

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Project description:BackgroundA plethora of prognostic biomarkers for esophageal squamous cell carcinoma (ESCC) that have hitherto been reported are challenged with low reproducibility due to high molecular heterogeneity of ESCC. The purpose of this study was to identify the optimal biomarkers for ESCC using machine learning algorithms.MethodsBiomarkers related to clinical survival, recurrence or therapeutic response of patients with ESCC were determined through literature database searching. Forty-eight biomarkers linked to recurrence or prognosis of ESCC were used to construct a molecular interaction network based on NetBox and then to identify the functional modules. Publicably available mRNA transcriptome data of ESCC downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets included GSE53625 and TCGA-ESCC. Five machine learning algorithms, including logical regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and XGBoost, were used to develop classifiers for prognostic classification for feature selection. The area under ROC curve (AUC) was used to evaluate the performance of the prognostic classifiers. The importances of identified molecules were ranked by their occurrence frequencies in the prognostic classifiers. Kaplan-Meier survival analysis and log-rank test were performed to determine the statistical significance of overall survival.ResultsA total of 48 clinically proven molecules associated with ESCC progression were used to construct a molecular interaction network with 3 functional modules comprising 17 component molecules. The 131,071 prognostic classifiers using these 17 molecules were built for each machine learning algorithm. Using the occurrence frequencies in the prognostic classifiers with AUCs greater than the mean value of all 131,071 AUCs to rank importances of these 17 molecules, stratifin encoded by SFN was identified as the optimal prognostic biomarker for ESCC, whose performance was further validated in another 2 independent cohorts.ConclusionThe occurrence frequencies across various feature selection approaches reflect the degree of clinical importance and stratifin is an optimal prognostic biomarker for ESCC.

Project description:BACKGROUND:MicroRNA (miR)s are promising diagnostic biomarkers of cancer. Recent next generation sequencer (NGS) studies have found that isoforms of micro RNA (isomiR) circulate in the bloodstream similarly to mature micro RNA (miR). We hypothesized that combination of circulating miR and isomiRs detected by NGS are potentially powerful cancer biomarker. The present study aimed to investigate their application in esophageal cancer. METHODS:Serum samples from patients with esophageal squamous cell carcinoma (ESCC) and age and sex matched healthy control (HC) individuals were investigated for the expression of miR/isomiRs using NGS. Candidate miR/isomiRs which met the criteria in the 1st group (ESCC = 18 and HC = 12) were validated in the 2nd group (ESCC = 30 and HC = 30). A diagnostic panel was generated using miR/isomiRs that were consistently confirmed in the 1st and 2nd groups. Accuracy of the panel was tested then in the 3rd group (ESCC = 18 and HC = 18). Their use was also investigated in 22 paired samples obtained pre- and post-treatment, and in patients with esophageal adenocarcinoma (EAD) and high-grade dysplasia (HGD). RESULTS:Twenty-four miR/isomiRs met the criteria for diagnostic biomarker in the 1st and 2nd group. A multiple regression model selected one mature miR (miR-30a-5p) and two isomiRs (isoform of miR-574-3p and miR-205-5p). The index calculated from the diagnostic panel was significantly higher in ESCC patients than in the HCs (13.3±8.9 vs. 3.1±1.3, p<0.001). The area under the receiver operating characteristics (ROC) curves of the panel index was 0.95. Sensitivity and specificity were 93.8%, and 81% in the 1st and 2nd groups, and 88.9% and 72.3% in the 3rd group, respectively. The panel index was significantly lower in patients with EAD (6.2±4.5) and HGD (4.2±1.7) than in those with ESCC and was significantly decreased at post-treatment compared with pre-treatment (6.2±5.6 vs 11.6±11.5, p = 0.03). CONCLUSION:Our diagnostic panel had high accuracy in the diagnosis of ESCC. MiR/isomiRs detected by NGS could serve as novel biomarkers of ESCC.

Dataset Information

SILAC-based quantitative proteomic approach to identify potential biomarkers from the esophageal squamous cell carcinoma secretome.

Publications

SILAC-based quantitative proteomic approach to identify potential biomarkers from the esophageal squamous cell carcinoma secretome.

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