Discovery of putative pancreatic cancer biomarkers using subcellular proteomics

Cytoplasmic Shotgun Proteomic Points to Key Proteins and Pathways in Temozolomide-Resistant Glioblastoma Multiforme

Temozolomide (TMZ) is the first line of chemotherapy to treat primary brain tumors of the type glioblastoma multiforme (GBM). TMZ resistance (TMZR) is one of the main barriers to successful treatment and is a principal factor in relapse, resulting in a poor median survival of 15 months. The present paper focuses on proteomic analyses of cytosolic fractions from TMZ-resistant (TMZR) LN-18 cells. The experimental workflow includes an easy, cost-effective, and reproducible method to isolate subcellular fraction of cytosolic (CYTO) proteins, mitochondria, and plasma membrane proteins for proteomic studies. For this study, enriched cytoplasmic fractions were analyzed in replicates by nanoflow liquid chromatography tandem high-resolution mass spectrometry (nLC-MS/MS), and proteins identified were quantified using a label-free approach (LFQ). Statistical analysis of control (CTRL) and temozolomide-resistant (TMZR) proteomes revealed proteins that appear to be differentially controlled in the cytoplasm. The functions of these proteins are discussed as well as their roles in other cancers and TMZ resistance in GBM. Key proteins are also described through biological processes related to gene ontology (GO), molecular functions, and cellular components. For protein-protein interactions (PPI), network and pathway involvement analyses have been performed, highlighting the roles of key proteins in the TMZ resistance phenotypes. This study provides a detailed insight into methods of subcellular fractionation for proteomic analysis of TMZ-resistant GBM cells and the potential to apply this approach to future large-scale studies. Several key proteins, protein-protein interactions (PPI), and pathways have been identified, underlying the TMZ resistance phenotype and highlighting the proteins' biological functions.

Comprehensive Analysis of Myoferlin in Human Pancreatic Cancer via Bioinformatics

Pancreatic cancer is the fourth leading cause of cancer-related death and urgently needs biomarkers for clinical diagnosis and prognosis. It has been reported that myoferlin (MYOF) is implicated in the regulation of proliferation, invasion, and migration of tumor cells in many cancers including pancreatic cancer. To confirm the prognostic value of MYOF in pancreatic cancer, a comprehensive cancer versus healthy people analysis was conducted using public data. MYOF mRNA expression levels were compared in many kinds of cancers including pancreatic cancer via the Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) databases. The results have shown that MYOF mRNA expression levels were upregulated in most types of cancers, especially in pancreatic cancer, compared with healthy people's tissues. Data from the Cancer Cell Line Encyclopedia (CCLE) and European Bioinformatics Institute (EMBL-EML) database also revealed that MYOF mRNA is highly expressed in most cancer cells, particularly in pancreatic cancer cell lines. Furthermore, the prognostic value of MYOF was evaluated using GEPIA and Long-term Outcome and Gene Expression Profiling Database of pan-cancers (LOGpc) database. Higher expression of MYOF was associated with poorer overall survival, especially in the lower stage and lower grade. Coexpressed genes, possible regulators, and the correlation between MYOF expressions were analyzed via the GEPIA and LinkedOmics database. Nineteen coexpressed genes were identified, and most of these genes were related to cancer. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the correlation between MYOF and immune response. Notably, we found that MYOF might have a potential novel immune regulatory role in tumor immunity. These results support that MYOF is a candidate prognostic biomarker for pancreatic cancer, which calls for further genomics research of pancreatic cancer and deeply functional studies on MYOF.

A Transcriptomics-Based Meta-Analysis Combined With Machine Learning Identifies a Secretory Biomarker Panel for Diagnosis of Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is generally incurable due to the late diagnosis and absence of markers that are concordant with expression in several sample sources (i.e., tissue, blood, plasma) and platforms (i.e., Microarray, sequencing). We optimized meta-analysis of 19 PDAC (tissue and blood) transcriptome studies from multiple platforms. The key biomarkers for PDAC diagnosis with secretory potential were identified and validated in different cohorts. Machine learning approach i.e., support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, GGH, PLBD1, HTATIP2, IL1R2, CTSA) that achieved ∼0.92 average sensitivity and ∼0.90 average specificity in distinguishing PDAC from healthy samples in five training sets using cross-validation. These markers were also validated in proteomics and single-cell transcriptomics studies suggesting their prognostic role in the diagnosis of PDAC. Our 9-gene classifier can not only clearly discriminate between better and poor survivors but can also precisely discriminate PDAC from chronic pancreatitis (AUC = 0.95), early stages of progression [Stage I and II (AUC = 0.82), IPMA and IPMN (AUC = 1), and IPMC (AUC = 0.81)]. The 9-gene marker outperformed the previously known markers in blood studies particularly (AUC = 0.84). The discrimination of PDAC from early precursor lesions in non-malignant tissue (AUC > 0.81) and peripheral blood (AUC > 0.80) may assist in an early diagnosis of PDAC in blood samples and thus will also facilitate risk stratification upon validation in clinical trials.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Is There a Trojan Horse to Aggressive Pancreatic Cancer Biology? A Review of the Trypsin-PAR2 Axis to Proliferation, Early Invasion, and Metastasis

Purpose: Pancreatic cancer is one of the most lethal of solid tumors and is associated with aggressive cancer biology. The purpose is to review the role of trypsin and effect on molecular and cellular processes potentially explaining the aggressive biology in pancreatic cancer.

Methods: A narrative literature review of studies investigating trypsin and its effect on protease systems in cancer, with special reference to pancreatic cancer biology.

Results: Proteases, such as trypsin, provides a significant advantage to developing tumors through the ability to remodel the extracellular matrix, promote cell invasion and migration, and facilitate angiogenesis. Trypsin is a digestive enzyme produced by the exocrine pancreas that is also related to mechanisms of proliferation, invasion and metastasis. Several of these mechanisms may be co-regulated or influenced by activation of proteinase-activated receptor 2 (PAR-2). The current role in pancreatic cancer is not clear but emerging data suggest several potential mechanisms. Trypsin may act as a Trojan horse in the pancreatic gland, facilitating several molecular pathways from the onset, which leads to rapid progression of the disease. Pancreatic cancer cell lines containing PAR-2 proliferate upon exposure to trypsin, whereas cancer cell lines not containing PAR-2 fail to proliferate upon trypsin expression. Several mechanisms of action include a proinflammatory environment, signals inducing proliferation and migration, and direct and indirect evidence for mechanisms promoting invasion and metastasis. Novel techniques (such as organoid models) and increased understanding of mechanisms (such as the microbiome) may yield improved understanding into the role of trypsin in pancreatic carcinogenesis.

Conclusion: Trypsin is naturally present in the pancreatic gland and may experience pathological activation intracellularly and in the neoplastic environment, which speeds up molecular mechanisms of proliferation, invasion, and metastasis. Further investigation of these processes will provide important insights into how pancreatic cancer evolves, and suggest new ways for treatment.

Ferlin Overview: From Membrane to Cancer Biology

In mammal myocytes, endothelial cells and inner ear cells, ferlins are proteins involved in membrane processes such as fusion, recycling, endo- and exocytosis. They harbour several C2 domains allowing their interaction with phospholipids. The expression of several Ferlin genes was described as altered in several tumoural tissues. Intriguingly, beyond a simple alteration, myoferlin, otoferlin and Fer1L4 expressions were negatively correlated with patient survival in some cancer types. Therefore, it can be assumed that membrane biology is of extreme importance for cell survival and signalling, making Ferlin proteins core machinery indispensable for cancer cell adaptation to hostile environments. The evidences suggest that myoferlin, when overexpressed, enhances cancer cell proliferation, migration and metabolism by affecting various aspects of membrane biology. Targeting myoferlin using pharmacological compounds, gene transfer technology, or interfering RNA is now considered as an emerging therapeutic strategy.

Human colon cancer cells highly express myoferlin to maintain a fit mitochondrial network and escape p53-driven apoptosis

Colon adenocarcinoma is the third most commonly diagnosed cancer and the second deadliest one. Metabolic reprogramming, described as an emerging hallmark of malignant cells, includes the predominant use of glycolysis to produce energy. Recent studies demonstrated that mitochondrial electron transport chain inhibitor reduced colon cancer tumour growth. Accumulating evidence show that myoferlin, a member of the ferlin family, is highly expressed in several cancer types, where it acts as a tumour promoter and participates in the metabolic rewiring towards oxidative metabolism. In this study, we showed that myoferlin expression in colon cancer lesions is associated with low patient survival and is higher than in non-tumoural adjacent tissue. Human colon cancer cells silenced for myoferlin exhibit a reduced oxidative phosphorylation activity associated with mitochondrial fission leading, ROS accumulation, decreased cell growth, and increased apoptosis. We observed the triggering of a DNA damage response culminating to a cell cycle arrest in wild-type p53 cells. The use of a p53 null cell line or a compound able to restore p53 activity (Prima-1) reverted the effects induced by myoferlin silencing, confirming the involvement of p53. The recent identification of a compound interacting with a myoferlin C2 domain and bearing anticancer potency identifies, together with our demonstration, this protein as a suitable new therapeutic target in colon cancer.

Label-Free Quantitative Proteomics Unravels Carboxypeptidases as the Novel Biomarker in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, with a high mortality rate and poor prognosis. However, little is known concerning the molecular mechanism of PDAC at the proteomics level. Here we report a proteomics analysis of PDAC tumor and adjacent tissues by shotgun proteomics followed by label-free quantification, and in total, 3031 and 3306 proteins were identified in three pairs of PDAC tumor and adjacent tissues, respectively; 40 of them were differentially expressed for at least three-fold in PDAC tumor tissues. Ontological and interaction network analysis highlighted the dysregulation of a set of four proteins in the carboxypeptidase family: carboxypeptidase A1 (CPA1), A2 (CPA2), B1 (CPB1), and chymotrypsin C (CTRC). Western blotting confirmed the downregulation of the carboxypeptidase network in PDAC. Immunohistochemistry of tissue microarray from 90 PDAC patients demonstrated that CPB1 was downregulated 7.07-fold (P < .0001, n = 81) in tumor comparing with the peritumor tissue. Further 208 pancreatic tissues from PDAC tumor, peritumor, and pancreatis confirmed the downregulation of CPB1 in the PDAC patients. In summary, our results displayed that the expression of carboxypeptidase is significantly downregulated in PDAC tumor tissues and may be novel biomarker in the patient with PDAC.

Chemical composition and the potential for proteomic transformation in cancer, hypoxia, and hyperosmotic stress

The changes of protein expression that are monitored in proteomic experiments are a type of biological transformation that also involves changes in chemical composition. Accompanying the myriad molecular-level interactions that underlie any proteomic transformation, there is an overall thermodynamic potential that is sensitive to microenvironmental conditions, including local oxidation and hydration potential. Here, up- and down-expressed proteins identified in 71 comparative proteomics studies were analyzed using the average oxidation state of carbon (Z_C) and water demand per residue (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O), calculated using elemental abundances and stoichiometric reactions to form proteins from basis species. Experimental lowering of oxygen availability (hypoxia) or water activity (hyperosmotic stress) generally results in decreased Z_C or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O of up-expressed compared to down-expressed proteins. This correspondence of chemical composition with experimental conditions provides evidence for attraction of the proteomes to a low-energy state. An opposite compositional change, toward higher average oxidation or hydration state, is found for proteomic transformations in colorectal and pancreatic cancer, and in two experiments for adipose-derived stem cells. Calculations of chemical affinity were used to estimate the thermodynamic potentials for proteomic transformations as a function of fugacity of O₂ and activity of H₂O, which serve as scales of oxidation and hydration potential. Diagrams summarizing the relative potential for formation of up- and down-expressed proteins have predicted equipotential lines that cluster around particular values of oxygen fugacity and water activity for similar datasets. The changes in chemical composition of proteomes are likely linked with reactions among other cellular molecules. A redox balance calculation indicates that an increase in the lipid to protein ratio in cancer cells by 20% over hypoxic cells would generate a large enough electron sink for oxidation of the cancer proteomes. The datasets and computer code used here are made available in a new R package, canprot.

Urinary Peptide Analysis Differentiates Pancreatic Cancer From Chronic Pancreatitis

OBJECTIVES

Differentiation of pancreatic cancer (PCA) from chronic pancreatitis (CP) is challenging. We searched for peptide markers in urine to develop a diagnostic peptide marker model.

METHODS

Capillary electrophoresis-mass spectrometry was used to search for peptides in urine of patients with PCA (n = 39) or CP (n = 41). Statistical different peptides were included in a peptide multimarker model. Peptide markers were sequence identified and validated by immunoassay and immunohistochemistry (IHC).

RESULTS

Applied to a validation cohort of 54 patients with PCA and 52 patients with CP, the peptide model correctly classified 47 patients with PCA and 44 patients with CP (area under the curve, 0.93; 87% sensitivity; 85% specificity). All 5 patients with PCA with concomitant CP were classified positive. Urine proteome analysis outperformed carbohydrate antigen 19-9 (area under the curve, 0.84) by a 15% increase in sensitivity at the same specificity. From 99 healthy subjects, only four were misclassified. Fetuin-A was the most prominent peptide marker source for PCA as verified by immunoassay and IHC. In silico protease mapping of the peptide markers' terminal sequences pointed to increased meprin-A activity in PCA, which in IHC was associated with neoangiogenesis.

CONCLUSIONS

Urinary proteome analysis differentiates PCA from CP and may serve as PCA screening tool.

Myoferlin plays a key role in VEGFA secretion and impacts tumor-associated angiogenesis in human pancreas cancer

Pancreatic ductal adenocarcinoma is one of the most deadly forms of cancers with no satisfactory treatment to date. Recent studies have identified myoferlin, a ferlin family member, in human pancreas adenocarcinoma where its expression was associated to a bad prognosis. However, the function of myoferlin in pancreas adenocarcinoma has not been reported. In other cell types, myoferlin is involved in several key plasma membrane processes such as fusion, repair, endocytosis and tyrosine kinase receptor activity. In this study, we showed that myoferlin silencing in BxPC-3 human pancreatic cancer cells resulted in the inhibition of cell proliferation in vitro and in a significant reduction of the tumor volume in chick chorioallantoic membrane assay. In addition to be smaller, the tumors formed by the myoferlin-silenced cells showed a marked absence of functional blood vessels. We further demonstrated that this effect was due, at least in part, to an inhibition of VEGFA secretion by BxPC-3 myoferlin-silenced cells. Using immunofluorescence and electron microscopy, we linked the decreased VEGFA secretion to an impairment of VEGFA exocytosis. The clinical relevance of our results was further strengthened by a significant correlation between myoferlin expression in a series of human pancreatic malignant lesions and their angiogenic status evaluated by the determination of the blood vessel density.

Biomarkers for early diagnosis of pancreatic cancer

Pancreatic ductal adenocarcinoma is an aggressive malignancy with a 5-year survival rate of approximately 5%. The lack of established strategies for early detection contributes to this poor prognosis. Although several novel candidate biomarkers have been proposed for earlier diagnosis, none have been adopted into routine clinical use. In this review, the authors examine the challenges associated with finding new pancreatic cancer diagnostic biomarkers and explore why translation of biomarker research for patient benefit has thus far failed. The authors also review recent progress and highlight advances in the understanding of the biology of pancreatic cancer that may lead to improvements in biomarker detection and implementation.

Biomarkers for pancreatic cancer: Recent achievements in proteomics and genomics through classical and multivariate statistical methods

Pancreatic cancer (PC) is one of the most aggressive and lethal neoplastic diseases. A valid alternative to the usual invasive diagnostic tools would certainly be the determination of biomarkers in peripheral fluids to provide less invasive tools for early diagnosis. Nowadays, biomarkers are generally investigated mainly in peripheral blood and tissues through high-throughput omics techniques comparing control vs pathological samples. The results can be evaluated by two main strategies: (1) classical methods in which the identification of significant biomarkers is accomplished by monovariate statistical tests where each biomarker is considered as independent from the others; and (2) multivariate methods, taking into consideration the correlations existing among the biomarkers themselves. This last approach is very powerful since it allows the identification of pools of biomarkers with diagnostic and prognostic performances which are superior to single markers in terms of sensitivity, specificity and robustness. Multivariate techniques are usually applied with variable selection procedures to provide a restricted set of biomarkers with the best predictive ability; however, standard selection methods are usually aimed at the identification of the smallest set of variables with the best predictive ability and exhaustivity is usually neglected. The exhaustive search for biomarkers is instead an important alternative to standard variable selection since it can provide information about the etiology of the pathology by producing a comprehensive set of markers. In this review, the most recent applications of the omics techniques (proteomics, genomics and metabolomics) to the identification of exploratory biomarkers for PC will be presented with particular regard to the statistical methods adopted for their identification. The basic theory related to classical and multivariate methods for identification of biomarkers is presented and then, the most recent applications in this field are discussed.

Monitoring pancreatic carcinogenesis by the molecular imaging of cathepsin E in vivo using confocal laser endomicroscopy

The monitoring of pancreatic ductal adenocarcinoma (PDAC) in high-risk populations is essential. Cathepsin E (CTSE) is specifically and highly expressed in PDAC and pancreatic intraepithelial neoplasias (PanINs), and its expression gradually increases along with disease progression. In this study, we first established an in situ 7,12-dimethyl-1,2-benzanthracene (DMBA)-induced rat model for PanINs and PDAC and then confirmed that tumorigenesis properties in this model were consistent with those of human PDAC in that CTSE expression gradually increased with tumor development using histology and immunohistochemistry. Then, using in vivo imaging of heterotopically implanted tumors generated from CTSE- overexpressing cells (PANC-1-CTSE) in nude mice and in vitro imaging of PanINs and PDAC in DMBA-induced rats, the specificity of the synthesized CTSE-activatable probe was verified. Quantitative determination identified that the fluorescence signal ratio of pancreatic tumor to normal pancreas gradually increased in association with progressive pathological grades, with the exception of no significant difference between PanIN-II and PanIN-III grades. Finally, we monitored pancreatic carcinogenesis in vivo using confocal laser endomicroscopy (CLE) in combination with the CTSE-activatable probe. A prospective double-blind control study was performed to evaluate the accuracy of this method in diagnosing PDAC and PanINs of all grades (>82.7%). This allowed us to establish effective diagnostic criteria for CLE in PDAC and PanINs to facilitate the monitoring of PDAC in high-risk populations.

Proteomic identification of potential prognostic biomarkers in resectable pancreatic ductal adenocarcinoma

Pancreatic cancer is a devastating disease with a mortality rate almost identical with its incidence. In this context, the investigation of the pancreatic cancer proteome has gained considerable attention because profiles of proteins may be able to identify disease states and progression more accurately. Therefore, our objective was to investigate the changes in the proteome of patients suffering from pancreatic ductal adenocarcinoma (PDAC) by a comprehensive quantitative approach. Comparative proteomic profiling by label-free LC-MS/MS analysis of nine matched pairs of tumor and nontumor pancreas samples was used to identify differences in protein levels characteristic for PDAC. In this analysis, 488 proteins were quantified by at least two peptides of which 99 proteins displayed altered levels in PDAC (p < 0.01, fold change >1.3). Screening of data revealed a number of molecules that had already been related to PDAC such as galectin-1 (LEG1), major vault protein, adenylyl cyclase-associated protein 1 (CAP1), but also a potential new prognostic biomarker prolargin (PRELP). The Kaplan-Meier survival analysis revealed a significant correlation of protein abundance of PRELP with postoperative survival of patients with PDAC. For selected proteins the findings were verified by targeted proteomics (SRM), validated by immunohistochemistry and Western blotting and their value as candidate biomarkers is discussed.

Genetic and molecular alterations in pancreatic cancer: implications for personalized medicine

Recent advances in human genomics and biotechnologies have profound impacts on medical research and clinical practice. Individual genomic information, including DNA sequences and gene expression profiles, can be used for prediction, prevention, diagnosis, and treatment for many complex diseases. Personalized medicine attempts to tailor medical care to individual patients by incorporating their genomic information. In a case of pancreatic cancer, the fourth leading cause of cancer death in the United States, alteration in many genes as well as molecular profiles in blood, pancreas tissue, and pancreas juice has recently been discovered to be closely associated with tumorigenesis or prognosis of the cancer. This review aims to summarize recent advances of important genes, proteins, and microRNAs that play a critical role in the pathogenesis of pancreatic cancer, and to provide implications for personalized medicine in pancreatic cancer.

Proteomics as a systems approach to pancreatitis

Proteomics is an approach to looking at the identity, amount, proteolysis, compartmentalization, and posttranslational modification of a large number of proteins simultaneously in a cell or tissue. Recently, proteomics has begun to be applied to the study of pancreatitis to ascertain mechanisms of disease and search for biomarkers of disease. Most mechanistic work has been carried out in animal models of acute pancreatitis. In 8 studies, 97 proteins have been reported to increase, 55 to decrease, and 23 to undergo proteolysis. Proteins showing increases are most often related to stress, inflammation, or the cytoskeleton, whereas decreases are seen in digestive enzymes and proteins related to metabolism. Many protein changes however, are not consistent between studies and only the most recent studies are rigorous and quantitative. By contrast, biomarker studies have focused on pancreatic juice and plasma of humans with disease and often are directed at distinguishing chronic pancreatitis from cancer. Chronic pancreatitis has also been investigated in tissue sections of histological samples. In this review, the results of studies to date are described as well as coverage of the methods used and special issues that must be considered. Areas are pointed out that are worthy of future study.

Subcellular fractionation for identification of biomarkers: serial detergent extraction by subcellular accessibility and solubility

Cellular localization of proteins is one of the most valuable sources of information regarding spatiotemporal biological events involved in human disease. This information is sometimes enhanced by carrying out protein isolation using a process known as subcellular fractionation. This involves the sequential extraction of proteins from specific compartments and/or organelles within the cell. Additionally, subcellular fractionation for biomarker discovery enables the in-depth analysis of biomolecules by reducing the complexity of the protein mixture. In this chapter, four custom fractionation approaches and one commercial kit are compared for their efficacy and compatibility with subsequent proteomic analysis.

Proteome-based biomarkers in pancreatic cancer

Pancreatic cancer, as a highly malignant cancer and the fourth cause of cancer-related death in world, is characterized by dismal prognosis, due to rapid disease progression, highly invasive tumour phenotype, and resistance to chemotherapy. Despite significant advances in treatment of the disease during the past decade, the survival rate is little improved. A contributory factor to the poor outcome is the lack of appropriate sensitive and specific biomarkers for early diagnosis. Furthermore, biomarkers for targeting, directing and assessing therapeutic intervention, as well as for detection of residual or recurrent cancer are also needed. Thus, the identification of adequate biomarkers in pancreatic cancer is of extreme importance. Recently, accompanying the development of proteomic technology and devices, more and more potential biomarkers have appeared and are being reported. In this review, we provide an overview of the role of proteome-based biomarkers in pancreatic cancer, including tissue, serum, juice, urine and cell lines. We also discuss the possible mechanism and prospects in the future. That information hopefully might be helpful for further research in the field.

Subcellular tissue proteomics of hepatocellular carcinoma for molecular signature discovery

Hepatocellular carcinoma (HCC) is one of the leading causes of mortality from solid organ malignancy worldwide. Because of the complexity of proteins within liver cells and tissues, the discovery of therapeutic targets of HCC has been difficult. To investigate strategies for decreasing the complexity of tissue samples for detecting meaningful protein mediators of HCC, we employed subcellular fractionation combined with 1D-gel electrophoresis and liquid chromatography-tandem mass spectrometry analysis. Moreover, we utilized a statistical method, namely, the Power Law Global Error Model (PLGEM), to distinguish differentially expressed proteins in a duplicate proteomic data set. Mass spectrometric analysis identified 3045 proteins in nontumor and HCC from cytosolic, membrane, nuclear, and cytoskeletal fractions. The final lists of highly differentiated proteins from the targeted fractions were searched for potentially translocated proteins in HCC from soluble compartments to the nuclear or cytoskeletal compartments. This analysis refined our targets of interest to include 21 potential targets of HCC from these fractions. Furthermore, we validated the potential molecular targets of HCC, MATR3, LETM1, ILF2, and IQGAP2 by Western blotting, immunohistochemisty, and immunofluorescent microscopy. Here we demonstrate an efficient strategy of subcellular tissue proteomics toward molecular target discovery of one of the most complicated human disease, HCC.

Human urine proteomics: building a list of human urine cancer biomarkers

In the last decade, several reports have focused on the identification and characterization of proteins present in urine. In an effort to build a list of proteins of interest as biomarkers, we reviewed the largest urine proteomes and built two updated lists of proteins of interest (available as supplementary tables). The first table includes a consensus list of 443 proteins found in urine by independent laboratories and reported on the top three largest urine proteomes currently published. This consensus list of proteins could serve as biomarkers to diagnose, monitor and manage a number of diseases. Here, we focus on a reduced list of 35 proteins with potential interest as cancer biomarkers in urine following two criteria: first, proteins previously detected in urine using bottom-up proteomic experiments, and second, those suggested as cancer protein biomarkers in human plasma. In an effort to standardize the information presented and its use in future studies, here we include the updated International Protein Index (v. 3.80) and primary Swiss-Prot accession numbers, official gene symbols and recommended full names. The main variables that influence urine proteomic experiments are also discussed.

Discovery of mouse spleen signaling responses to anthrax using label-free quantitative phosphoproteomics via mass spectrometry

Inhalational anthrax is caused by spores of the bacterium Bacillus anthracis (B. anthracis), and is an extremely dangerous disease that can kill unvaccinated victims within 2 weeks. Modern antibiotic-based therapy can increase the survival rate to ∼50%, but only if administered presymptomatically (within 24-48 h of exposure). To discover host signaling responses to presymptomatic anthrax, label-free quantitative phosphoproteomics via liquid chromatography coupled to mass spectrometry was used to compare spleens from uninfected and spore-challenged mice over a 72 h time-course. Spleen proteins were denatured using urea, reduced using dithiothreitol, alkylated using iodoacetamide, and digested into peptides using trypsin, and the resulting phosphopeptides were enriched using titanium dioxide solid-phase extraction and analyzed by nano-liquid chromatography-Linear Trap Quadrupole-Orbitrap-MS(/MS). The fragment ion spectra were processed using DeconMSn and searched using both Mascot and SEQUEST resulting in 252,626 confident identifications of 6248 phosphopeptides (corresponding to 5782 phosphorylation sites). The precursor ion spectra were deisotoped using Decon2LS and aligned using MultiAlign resulting in the confident quantitation of 3265 of the identified phosphopeptides. ANOVAs were used to produce a q-value ranked list of host signaling responses. Late-stage (48-72 h postchallenge) Sterne strain (lethal) infections resulted in global alterations to the spleen phosphoproteome. In contrast, ΔSterne strain (asymptomatic; missing the anthrax toxin) infections resulted in 188 (5.8%) significantly altered (q<0.05) phosphopeptides. Twenty-six highly tentative phosphorylation responses to early-stage (24 h postchallenge) anthrax were discovered (q<0.5), and ten of these originated from eight proteins that have known roles in the host immune response. These tentative early-anthrax host response signaling events within mouse spleens may translate into presymptomatic diagnostic biomarkers of human anthrax detectable within circulating immune cells, and could aid in the identification of pathogenic mechanisms and therapeutic targets.