Dataset Information

Integrative analysis to select cancer candidate biomarkers to targeted validation.

ABSTRACT: Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.

SUBMITTER: Kawahara R

PROVIDER: S-EPMC4791256 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrative analysis to select cancer candidate biomarkers to targeted validation.

Kawahara Rebeca R Meirelles Gabriela V GV Heberle Henry H Domingues Romênia R RR Granato Daniela C DC Yokoo Sami S Canevarolo Rafael R RR Winck Flavia V FV Ribeiro Ana Carolina P AC Brandão Thaís Bianca TB Filgueiras Paulo R PR Cruz Karen S P KS Barbuto José Alexandre JA Poppi Ronei J RJ Minghim Rosane R Telles Guilherme P GP Fonseca Felipe Paiva FP Fox Jay W JW Santos-Silva Alan R AR Coletta Ricardo D RD Sherman Nicholas E NE Paes Leme Adriana F AF

Oncotarget 20151201 41

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature select ...[more]

PMID: 26540631

Similar Datasets

Project description:The identification of new serum biomarkers with high sensitivity and specificity is an important priority in pancreatic cancer research. Through an extensive proteomics analysis of pancreatic cancer cell lines and pancreatic juice, we previously generated a list of candidate pancreatic cancer biomarkers. The present study details further validation of four of our previously identified candidates: regenerating islet-derived 1 beta (REG1B), syncollin (SYCN), anterior gradient homolog 2 protein (AGR2), and lysyl oxidase-like 2 (LOXL2).The candidate biomarkers were validated using enzyme-linked immunosorbent assays in two sample sets of serum/plasma comprising a total of 432 samples (Sample Set A: pancreatic ductal adenocarcinoma (PDAC, n?=?100), healthy (n?=?92); Sample Set B: PDAC (n?=?82), benign (n?=?41), disease-free (n?=?47), other cancers (n?=?70)). Biomarker performance in distinguishing PDAC from each control group was assessed individually in the two sample sets. Subsequently, multiparametric modeling was applied to assess the ability of all possible two and three marker panels in distinguishing PDAC from disease-free controls. The models were generated using sample set B, and then validated in Sample Set A.Individually, all markers were significantly elevated in PDAC compared to healthy controls in at least one sample set (p???0.01). SYCN, REG1B and AGR2 were also significantly elevated in PDAC compared to benign controls (p???0.01), and AGR2 was significantly elevated in PDAC compared to other cancers (p?<?0.01). CA19.9 was also assessed. Individually, CA19.9 showed the greatest area under the curve (AUC) in receiver operating characteristic (ROC) analysis when compared to the tested candidates; however when analyzed in combination, three panels (CA19.9?+?REG1B (AUC of 0.88), CA19.9?+?SYCN?+?REG1B (AUC of 0.87) and CA19.9?+?AGR2?+?REG1B (AUC of 0.87)) showed an AUC that was significantly greater (p?<?0.05) than that of CA19.9 alone (AUC of 0.82). In a comparison of early-stage (Stage I-II) PDAC to disease free controls, the combination of SYCN?+?REG1B?+?CA19.9 showed the greatest AUC in both sample sets, (AUC of 0.87 and 0.92 in Sets A and B, respectively).Additional serum biomarkers, particularly SYCN and REG1B, when combined with CA19.9, show promise as improved diagnostic indicators of pancreatic cancer, which therefore warrants further validation.

Dataset Information

Integrative analysis to select cancer candidate biomarkers to targeted validation.

Publications

Integrative analysis to select cancer candidate biomarkers to targeted validation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure