Retinoic acid increases expression of the calcium-binding protein S100P in human gastric cancer cells

Retinoids mediate a wide spectrum of antitumor activities through induction of growth arrest, differentiation or apoptosis. To determine whether the effects of retinoids are mediated by specific gene activation or repression, one-day treatments of SC-M1 CL23 gastric cancer cells with vehicle alone or all-TRANS retinoic acid (tRA) (10 microM) were compared using differential display analysis. A 432-bp cDNA fragment from the tRA-treated cells was differentially amplified and its sequence analysis indicated homology with the calcium-binding protein S100P. Levels of S100P mRNA were increased 3.5-fold in SC-M1 CL23 gastric cancer cells treated with 10 microM tRA for 1 day, and the regulation was time- and concentration-dependent. Treatment with tRA (10 microM) also increased S100P mRNA levels in tRA-sensitive HtTA cells but not in inherent RA-resistant TMC-1 cells. However, the tRA-mediated increase in S100P expression was maintained in SC-M1/R cells that were established long-term in tRA-containing medium and had acquired partial RA resistance to tRA-induced growth suppression. In conclusion, tRA increases S100P expression, and the regulation remains intact in cells which develop acquired RA resistance.     

Proteome profiling of human epithelial ovarian cancer cell line TOV-112D

A proteome profiling of the epithelial ovarian cancer cell line TOV-112D was initiated as a protein expression reference in the study of ovarian cancer. Two complementary proteomic approaches were used in order to maximise protein identification: two-dimensional gel electrophoresis (2DE) protein separation coupled to matrix assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and one-dimensional gel electrophoresis (1DE) coupled to liquid-chromatography tandem mass spectrometry (LC MS/MS). One hundred and seventy-two proteins have been identified among 288 spots selected on two-dimensional gels and a total of 579 proteins were identified with the 1DE LC MS/MS approach. This proteome profiling covers a wide range of protein expression and identifies several proteins known for their oncogenic properties. Bioinformatics tools were used to mine databases in order to determine whether the identified proteins have previously been implicated in pathways associated with carcinogenesis or cell proliferation. Indeed, several of the proteins have been reported to be specific ovarian cancer markers while others are common to many tumorigenic tissues or proliferating cells. The diversity of proteins found and their association with known oncogenic pathways validate this proteomic approach. The proteome 2D map of the TOV-112D cell line will provide a valuable resource in studies on differential protein expression of human ovarian carcinomas while the 1DE LC MS/MS approach gives a picture of the actual protein profile of the TOV-112D cell line. This work represents one of the most complete ovarian protein expression analysis reports to date and the first comparative study of gene expression profiling and proteomic patterns in ovarian cancer.     

Label-Free Quantitative Proteomics and N-terminal Analysis of Human Metastatic Lung Cancer Cells

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.     

Differential expression of calcium-related genes in gastric cancer cells transfected with cellular prion protein.

The prion protein (PrPC) has a primary role in the pathogenesis of transmissible spongiform encephalopathies, which causes prion disorders partially due to Ca2+ dysregulation. In our previous work, we found that overexpressed PrPC in gastric cancer was involved in apoptosis, cell proliferation, and metastasis of gastric cancer. To better understand how PrPC acts in gastric cancer, a human microarray was performed to select differentially regulated genes that correlate with the biological function of PrPC. The microarray data were analyzed and revealed 3798 genes whose expression increased at least 2-fold in gastric cancer cells transfected with PrPC. These genes encode proteins involved in several aspects of cell biology, among which, we specially detected molecules related to calcium, especially the S100 calcium-binding proteins, and found that PrPC upregulates S100A1, S100A6, S100B, and S100P but downregulates CacyBP in gastric cancer cells. We also found that intracellular Ca2+ levels in cells transfected with PrPC increased, whereas these levels decreased in knockdowns of these cells. Taken together, PrPC might increase intracellular Ca2+, partially through calcium-binding proteins, or PrPC might upregulate the expression of S100 proteins, partially through stimulating the intracellular calcium level in gastric cancer. Though the underlying mechanisms need further exploration, this study provides a new insight into the role of PrPC in gastric cancer and enriches our knowledge of prion protein.     

Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT)

Proteomic profiling has emerged as a useful tool for identifying tissue alterations in disease states including malignant transformation. The aim of this study was to reveal expression profiles associated with the highly motile/invasive ovarian cancer cell phenotype. Six ovarian cancer cell lines were subjected to proteomic characterization using multidimensional protein identification technology (MudPIT), and evaluated for their motile/invasive behavior, so that these parameters could be compared. Within whole cell extracts of the ovarian cancer cells, MudPIT identified proteins that mapped to 2245 unique genes. Western blot analysis for selected proteins confirmed the expression profiles revealed by MudPIT, demonstrating the fidelity of this high-throughput analysis. Unsupervised cluster analysis partitioned the cell lines in a manner that reflected their motile/invasive capacity. A comparison of protein expression profiles between cell lines of high (group 1) versus low (group 2) motile/invasive capacity revealed 300 proteins that were differentially expressed, of which 196 proteins were significantly upregulated in group 1. Protein network and KEGG pathway analysis indicated a functional interplay between proteins up-regulated in group 1 cells, with increased expression of several key members of the actin cytoskeleton, extracellular matrix (ECM) and focal adhesion pathways. These proteomic expression profiles can be utilized to distinguish highly motile, aggressive ovarian cancer cells from lesser invasive ones, and could prove to be essential in the development of more effective strategies that target pivotal cell signaling pathways used by cancer cells during local invasion and distant metastasis.     

Biomarkers of breast cancer apoptosis induced by chemotherapy and TRAIL

Treatment of breast cancer is complex and challenging due to the heterogeneity of the disease. To avoid significant toxicity and adverse side-effects of chemotherapy in patients who respond poorly, biomarkers predicting therapeutic response are essential. This study has utilized a proteomic approach integrating 2D-DIGE, LC-MS/MS, and bioinformatics to analyze the proteome of breast cancer (ZR-75-1 and MDA-MB-231) and breast epithelial (MCF-10A) cell lines induced to undergo apoptosis using a combination of doxorubicin and TRAIL administered in sequence (Dox-TRAIL). Apoptosis induction was confirmed using a caspase-3 activity assay. Comparative proteomic analysis between whole cell lysates of Dox-TRAIL and control samples revealed 56 differentially expressed spots (≥2-fold change and p < 0.05) common to at least two cell lines. Of these, 19 proteins were identified yielding 11 unique protein identities: CFL1, EIF5A, HNRNPK, KRT8, KRT18, LMNA, MYH9, NACA, RPLP0, RPLP2, and RAD23B. A subset of the identified proteins was validated by selected reaction monitoring (SRM) and Western blotting. Pathway analysis revealed that the differentially abundant proteins were associated with cell death, cellular organization, integrin-linked kinase signaling, and actin cytoskeleton signaling pathways. The 2D-DIGE analysis has yielded candidate biomarkers of response to treatment in breast cancer cell models. Their clinical utility will depend on validation using patient breast biopsies pre- and post-treatment with anticancer drugs.     

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

H ferritin gene silencing in a human metastatic melanoma cell line: a proteomic analysis

Ferritin, the major intracellular iron-storage protein, is made of 24 subunits of two types, H and L. Besides regulating intracellular iron homeostasis, it has been found that ferritin, in particular the H subunit (FHC), is involved in different biological events such as cell differentiation and pathologic states (i.e., neurodegeneration and cancer). This study is aimed at investigating the whole-cell proteome of FHC-expressing and sh-RNA-silenced human metastatic melanoma cells (MM07(m)) in the attempt to identify and classify the highest number of proteins directly or indirectly controlled by the FHC. We identified about 200 differentially expressed proteins and classified them in clusters on the basis of their functions, as proteins involved in metabolic processes, cell adhesion, migration, and proliferation processes. Some of them have captured our attention because of their involvement in metabolic pathways related to tumor progression and metastasis. In vitro assays confirmed that the FHC-silenced MM07(m) cells are characterized by a decreased growth activity, a reduced invasiveness, and a reduced cell adhesion capability. Moreover, nude mice (CD1 nu/nu), subcutaneously injected with FHC-silenced MM07(m) cells, showed a remarkable 4-fold reduction of their tumor growth capacity compared to those who received the FHC-unsilenced MM07(m) counterpart. In conclusion, these data indicate that gene silencing technology, coupled to proteomic analysis, is a powerful tool for a better understanding of H ferritin signaling pathways and lend support to the hypothesis that specific targeting of this gene might be an attractive and potentially effective strategy for the management of metastatic melanoma.     

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

     

Design of recombinant antibody microarrays for cell surface membrane proteomics

Generating proteomic maps of membrane proteins, common targets for therapeutic interventions and disease diagnostics, has turned out to be a major challenge. Antibody-based microarrays are among the novel rapidly evolving proteomic technologies that may enable global proteome analysis to be performed. Here, we have designed the first generation of a scaleable human recombinant scFv antibody microarray technology platform for cell surface membrane proteomics as well as glycomics targeting intact cells. The results showed that rapid and multiplexed profiling of the cell surface proteome (and glycome) could be performed in a highly specific and sensitive manner and that differential expression patterns due to external stimuli could be monitored.     

Proteins in Tumor-Derived Plasma Extracellular Vesicles Indicate Tumor Origin

Cancer-derived extracellular vesicles (EVs) promote tumorigenesis, premetastatic niche formation, and metastasis via their protein cargo. However, the proteins packaged by patient tumors into EVs cannot be determined in vivo because of the presence of EVs derived from other tissues. We therefore developed a cross-species proteomic method to quantify the human tumor-derived proteome of plasma EVs produced by patient-derived xenografts of four cancer types. Proteomic profiling revealed individualized packaging of novel protein cargo, and machine learning accurately classified the type of the underlying tumor.     

Differential proteomic analysis of noncardia gastric cancer from individuals of northern Brazil

Gastric cancer is the second leading cause of cancer-related death worldwide. The identification of new cancer biomarkers is necessary to reduce the mortality rates through the development of new screening assays and early diagnosis, as well as new target therapies. In this study, we performed a proteomic analysis of noncardia gastric neoplasias of individuals from Northern Brazil. The proteins were analyzed by two-dimensional electrophoresis and mass spectrometry. For the identification of differentially expressed proteins, we used statistical tests with bootstrapping resampling to control the type I error in the multiple comparison analyses. We identified 111 proteins involved in gastric carcinogenesis. The computational analysis revealed several proteins involved in the energy production processes and reinforced the Warburg effect in gastric cancer. ENO1 and HSPB1 expression were further evaluated. ENO1 was selected due to its role in aerobic glycolysis that may contribute to the Warburg effect. Although we observed two up-regulated spots of ENO1 in the proteomic analysis, the mean expression of ENO1 was reduced in gastric tumors by western blot. However, mean ENO1 expression seems to increase in more invasive tumors. This lack of correlation between proteomic and western blot analyses may be due to the presence of other ENO1 spots that present a slightly reduced expression, but with a high impact in the mean protein expression. In neoplasias, HSPB1 is induced by cellular stress to protect cells against apoptosis. In the present study, HSPB1 presented an elevated protein and mRNA expression in a subset of gastric cancer samples. However, no association was observed between HSPB1 expression and clinicopathological characteristics. Here, we identified several possible biomarkers of gastric cancer in individuals from Northern Brazil. These biomarkers may be useful for the assessment of prognosis and stratification for therapy if validated in larger clinical study sets.