Dataset Information

Label-free quantitative proteomics and N-terminal analysis of human metastatic lung cancer cells.

ABSTRACT: Proteomic analysis is helpful in identifying cancer-associated proteins that are differentially expressed and fragmented that can be annotated as dysregulated networks and pathways during metastasis. To examine meta-static process in lung cancer, we performed a proteomics study by label-free quantitative analysis and N-terminal analysis in 2 human non-small-cell lung cancer cell lines with disparate metastatic potentials-NCI--H1703 (primary cell, stage I) and NCI-H1755 (metastatic cell, stage IV). We identified 2130 proteins, 1355 of which were common to both cell lines. In the label-free quantitative analysis, we used the NSAF normalization method, resulting in 242 differential expressed proteins. For the N-terminal proteome analysis, 325 N-terminal peptides, including 45 novel fragments, were identified in the 2 cell lines. Based on two proteomic analysis, 11 quantitatively expressed proteins and 8 N-terminal peptides were enriched for the focal adhesion pathway. Most proteins from the quantitative analysis were upregulated in metastatic cancer cells, whereas novel fragment of CRKL was detected only in primary cancer cells. This study increases our understanding of the NSCLC metastasis proteome.

SUBMITTER: Min H

PROVIDER: S-EPMC4086339 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Label-free quantitative proteomics and N-terminal analysis of human metastatic lung cancer cells.

Min Hophil H Han Dohyun D Kim Yikwon Y Cho Jee Yeon JY Jin Jonghwa J Kim Youngsoo Y

Molecules and cells 20140508 6

Proteomic analysis is helpful in identifying cancer-associated proteins that are differentially expressed and fragmented that can be annotated as dysregulated networks and pathways during metastasis. To examine meta-static process in lung cancer, we performed a proteomics study by label-free quantitative analysis and N-terminal analysis in 2 human non-small-cell lung cancer cell lines with disparate metastatic potentials-NCI--H1703 (primary cell, stage I) and NCI-H1755 (metastatic cell, stage IV ...[more]

PMID: 24805778

Similar Datasets

Project description:Mutational activation of KRAS promotes various malignancies, including lung adenocarcinoma. Knowledge of the molecular targets mediating the downstream effects of activated KRAS is limited. Here, we provide the KRAS target proteins and N-glycoproteins using human bronchial epithelial cells with and without the expression of activated KRAS (KRAS(V12)). Using an OFFGEL peptide fractionation and hydrazide method combined with subsequent LTQ-Orbitrap analysis, we identified 5713 proteins and 608 N-glycosites on 317 proteins in human bronchial epithelial cells. Label-free quantitation of 3058 proteins (≥2 peptides; coefficient of variation (CV) ≤ 20%) and 297 N-glycoproteins (CV ≤ 20%) revealed the differential regulation of 23 proteins and 14 N-glycoproteins caused by activated KRAS, including 84% novel ones. An informatics-assisted IPA-Biomarker® filter analysis prioritized some of the differentially regulated proteins (ALDH3A1, CA2, CTSD, DST, EPHA2, and VIM) and N-glycoproteins (ALCAM, ITGA3, and TIMP-1) as cancer biomarkers. Further, integrated in silico analysis of microarray repository data of lung adenocarcinoma clinical samples and cell lines containing KRAS mutations showed positive mRNA fold changes (p < 0.05) for 61% of the KRAS-regulated proteins, including biomarker proteins, CA2 and CTSD. The most significant discovery of the integrated validation is the down-regulation of FABP5 and PDCD4. A few validated proteins, including tumor suppressor PDCD4, were further confirmed as KRAS targets by shRNA-based knockdown experiments. Finally, the studies on KRAS-regulated N-glycoproteins revealed structural alterations in the core N-glycans of SEMA4B in KRAS-activated human bronchial epithelial cells and functional role of N-glycosylation of TIMP-1 in the regulation of lung adenocarcinoma A549 cell invasion. Together, our study represents the largest proteome and N-glycoproteome data sets for HBECs, which we used to identify several novel potential targets of activated KRAS that may provide insights into KRAS-induced adenocarcinoma and have implications for both lung cancer therapy and diagnosis.

Project description:Matrix metalloproteinase-9 (MMP-9) is implicated in tumor metastasis as well as a variety of inflammatory and pathological processes. Although many substrates for MMP-9, including components of the extracellular matrix, soluble mediators such as chemokines, and cell surface molecules have been identified, we undertook a more comprehensive proteomics-based approach to identify new substrates to further understand how MMP-9 might contribute to tumor metastasis. Previous proteomics approaches to identify protease substrates have depended upon differential labeling of each sample. Instead we used a label-free quantitative proteomics approach based on ultraperformance LC-ESI-high/low collision energy MS. Conditioned medium from a human metastatic prostate cancer cell line, PC-3ML, in which MMP-9 had been down-regulated by RNA interference was compared with that from the parental cells. From more than 200 proteins identified, 69 showed significant alteration in levels after depletion of the protease (>+/-2-fold), suggesting that they might be candidate substrates. Levels of six of these (amyloid-beta precursor protein, collagen VI, leukemia inhibitory factor, neuropilin-1, prostate cancer cell-derived growth factor (PCDGF), and protease nexin-1 (PN-1)) were tested in the conditioned media by immunoblotting. There was a strong correlation between results by ultraperformance LC-ESI-high/low collision energy MS and by immunoblotting giving credence to the label-free approach. Further information about MMP-9 cleavage was obtained by comparison of the peptide coverage of collagen VI in the presence and absence of MMP-9 showing increased sensitivity of the C- and N-terminal globular regions over the helical regions. Susceptibility of PN-1 and leukemia inhibitory factor to MMP-9 degradation was confirmed by in vitro incubation of the recombinant proteins with recombinant MMP-9. The MMP-9 cleavage sites in PN-1 were sequenced. This study provides a new label-free method for degradomics cell-based screening leading to the identification of a series of proteins whose levels are affected by MMP-9, some of which are clearly direct substrates for MMP-9 and become candidates for involvement in metastasis.

Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Dataset Information

Label-free quantitative proteomics and N-terminal analysis of human metastatic lung cancer cells.

Publications

Label-free quantitative proteomics and N-terminal analysis of human metastatic lung cancer cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets