Dataset Information

An informatics-assisted label-free approach for personalized tissue membrane proteomics: case study on colorectal cancer.

ABSTRACT: We developed a multiplexed label-free quantification strategy, which integrates an efficient gel-assisted digestion protocol, high-performance liquid chromatography tandem MS analysis, and a bioinformatics alignment method to determine personalized proteomic profiles for membrane proteins in human tissues. This strategy provided accurate (6% error) and reproducible (34% relative S.D.) quantification of three independently purified membrane fractions from the same human colorectal cancer (CRC) tissue. Using CRC as a model, we constructed the personalized membrane protein atlas of paired tumor and adjacent normal tissues from 28 patients with different stages of CRC. Without fractionation, this strategy confidently quantified 856 proteins (?2 unique peptides) across different patients, including the first and robust detection (Mascot score: 22,074) of the well-documented CRC marker, carcinoembryonic antigen 5 by a discovery-type proteomics approach. Further validation of a panel of proteins, annexin A4, neutrophils defensin A1, and claudin 3, confirmed differential expression levels and high occurrences (48-70%) in 60 CRC patients. The most significant discovery is the overexpression of stomatin-like 2 (STOML2) for early diagnostic and prognostic potential. Increased expression of STOML2 was associated with decreased CRC-related survival; the mean survival period was 34.77 ± 2.03 months in patients with high STOML2 expression, whereas 53.67 ± 3.46 months was obtained for patients with low STOML2 expression. Further analysis by ELISA verified that plasma concentrations of STOML2 in early-stage CRC patients were elevated as compared with those of healthy individuals (p < 0.001), suggesting that STOML2 may be a noninvasive serological biomarker for early CRC diagnosis. The overall sensitivity of STOML2 for CRC detection was 71%, which increased to 87% when combined with CEA measurements. This study demonstrated a sensitive, label-free strategy for differential analysis of tissue membrane proteome, which may provide a roadmap for the subsequent identification of molecular target candidates of multiple cancer types.

SUBMITTER: Han CL

PROVIDER: S-EPMC3069341 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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An informatics-assisted label-free approach for personalized tissue membrane proteomics: case study on colorectal cancer.

Han Chia-Li CL Chen Jinn-Shiun JS Chan Err-Cheng EC Wu Chien-Peng CP Yu Kun-Hsing KH Chen Kuei-Tien KT Tsou Chih-Chiang CC Tsai Chia-Feng CF Chien Chih-Wei CW Kuo Yung-Bin YB Lin Pei-Yi PY Yu Jau-Song JS Hsueh Chuen C Chen Min-Chi MC Chan Chung-Chuan CC Chang Yu-Sun YS Chen Yu-Ju YJ

Molecular & cellular proteomics : MCP 20110105 4

We developed a multiplexed label-free quantification strategy, which integrates an efficient gel-assisted digestion protocol, high-performance liquid chromatography tandem MS analysis, and a bioinformatics alignment method to determine personalized proteomic profiles for membrane proteins in human tissues. This strategy provided accurate (6% error) and reproducible (34% relative S.D.) quantification of three independently purified membrane fractions from the same human colorectal cancer (CRC) ti ...[more]

PMID: 21209152

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Dataset Information

An informatics-assisted label-free approach for personalized tissue membrane proteomics: case study on colorectal cancer.

Publications

An informatics-assisted label-free approach for personalized tissue membrane proteomics: case study on colorectal cancer.

Similar Datasets

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