miRNA expression profiling for identification of potential breast cancer biomarkers

To identify micro RNA (miRNA) biomarker candidates for early detection of breast cancer and detection of minimal residual breast cancer, we performed miRNA expression profiling in pooled RNA samples from breast tumors, and from bone marrow mononuclear cells, peripheral blood mononuclear cells and plasma from healthy controls. We found substantially higher levels of five miRNAs in the breast tumors compared to the normal samples. However, validation of these miRNA levels, and seven other candidates selected from the literature, in individual samples from healthy controls and patients with non-metastatic breast cancer did not suggest further examination of their biomarker potential.     

miRNA expression profiling for identification of potential breast cancer biomarkers

To identify micro RNA (miRNA) biomarker candidates for early detection of breast cancer and detection of minimal residual breast cancer, we performed miRNA expression profiling in pooled RNA samples from breast tumors, and from bone marrow mononuclear cells, peripheral blood mononuclear cells and plasma from healthy controls. We found substantially higher levels of five miRNAs in the breast tumors compared to the normal samples. However, validation of these miRNA levels, and seven other candidates selected from the literature, in individual samples from healthy controls and patients with non-metastatic breast cancer did not suggest further examination of their biomarker potential.     

Integrated proteomic profiling of cell line conditioned media and pancreatic juice for the identification of pancreatic cancer biomarkers

Pancreatic cancer is one of the leading causes of cancer-related deaths, for which serological biomarkers are urgently needed. Most discovery-phase studies focus on the use of one biological source for analysis. The present study details the combined mining of pancreatic cancer-related cell line conditioned media and pancreatic juice for identification of putative diagnostic leads. Using strong cation exchange chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer, we extensively characterized the proteomes of conditioned media from six pancreatic cancer cell lines (BxPc3, MIA-PaCa2, PANC1, CAPAN1, CFPAC1, and SU.86.86), the normal human pancreatic ductal epithelial cell line HPDE, and two pools of six pancreatic juice samples from ductal adenocarcinoma patients. All samples were analyzed in triplicate. Between 1261 and 2171 proteins were identified with two or more peptides in each of the cell lines, and an average of 521 proteins were identified in the pancreatic juice pools. In total, 3479 nonredundant proteins were identified with high confidence, of which ～ 40% were extracellular or cell membrane-bound based on Genome Ontology classifications. Three strategies were employed for identification of candidate biomarkers: (1) examination of differential protein expression between the cancer and normal cell lines using label-free protein quantification, (2) integrative analysis, focusing on the overlap of proteins among the multiple biological fluids, and (3) tissue specificity analysis through mining of publically available databases. Preliminary verification of anterior gradient homolog 2, syncollin, olfactomedin-4, polymeric immunoglobulin receptor, and collagen alpha-1(VI) chain in plasma samples from pancreatic cancer patients and healthy controls using ELISA, showed a significant increase (p < 0.01) of these proteins in plasma from pancreatic cancer patients. The combination of these five proteins showed an improved area under the receiver operating characteristic curve to CA19.9 alone. Further validation of these proteins is warranted, as is the investigation of the remaining group of candidates.     

Serum proteomic signature of human chagasic patients for the identification of novel potential protein biomarkers of disease

Chagas disease is initiated upon infection by Trypanosoma cruzi. Among the health consequences is a decline in heart function, and the pathophysiological mechanisms underlying this manifestation are not well understood. To explore the possible mechanisms, we employed IgY LC10 affinity chromatography in conjunction with ProteomeLab PF2D and two-dimensional gel electrophoresis to resolve the proteome signature of high and low abundance serum proteins in chagasic patients. MALDI-TOF MS/MS analysis yielded 80 and 14 differentially expressed proteins associated with cardiomyopathy of chagasic and other etiologies, respectively. The extent of oxidative stress-induced carbonyl modifications of the differentially expressed proteins (n = 26) was increased and coupled with a depression of antioxidant proteins. Functional annotation of the top networks developed by ingenuity pathway analysis of proteome database identified dysregulation of inflammation/acute phase response signaling and lipid metabolism relevant to production of prostaglandins and arachidonic acid in chagasic patients. Overlay of the major networks identified prothrombin and plasminogen at a nodal position with connectivity to proteome signature indicative of heart disease (i.e., thrombosis, angiogenesis, vasodilatation of blood vessels or the aorta, and increased permeability of blood vessel and endothelial tubes), and inflammatory responses (e.g., platelet aggregation, complement activation, and phagocyte activation and migration). The detection of cardiac proteins (myosin light chain 2 and myosin heavy chain 11) and increased levels of vinculin and plasminogen provided a comprehensive set of biomarkers of cardiac muscle injury and development of clinical Chagas disease in human patients. These results provide an impetus for biomarker validation in large cohorts of clinically characterized chagasic patients.     

Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy in the world with high morbidity and mortality rate. Identification of novel biomarkers in HCC remains impeded primarily because of the heterogeneity of the disease in clinical presentations as well as the pathophysiological variations derived from underlying conditions such as cirrhosis and steatohepatitis. The aim of this study is to search for potential metabolite biomarkers of human HCC using serum and urine metabolomics approach. Sera and urine samples were collected from patients with HCC (n = 82), benign liver tumor patients (n = 24), and healthy controls (n = 71). Metabolite profiling was performed by gas chromatography time-of-flight mass spectrometry and ultra performance liquid chromatography-quadrupole time of flight mass spectrometry in conjunction with univariate and multivariate statistical analyses. Forty three serum metabolites and 31 urinary metabolites were identified in HCC patients involving several key metabolic pathways such as bile acids, free fatty acids, glycolysis, urea cycle, and methionine metabolism. Differentially expressed metabolites in HCC subjects, such as bile acids, histidine, and inosine are of great statistical significance and high fold changes, which warrant further validation as potential biomarkers for HCC. However, alterations of several bile acids seem to be affected by the condition of liver cirrhosis and hepatitis. Quantitative measurement and comparison of seven bile acids among benign liver tumor patients with liver cirrhosis and hepatitis, HCC patients with liver cirrhosis and hepatitis, HCC patients without liver cirrhosis and hepatitis, and healthy controls revealed that the abnormal levels of glycochenodeoxycholic acid, glycocholic acid, taurocholic acid, and chenodeoxycholic acid are associated with liver cirrhosis and hepatitis. HCC patients with alpha fetoprotein values lower than 20 ng/ml was successfully differentiated from healthy controls with an accuracy of 100% using a panel of metabolite markers. Our work shows that metabolomic profiling approach is a promising screening tool for the diagnosis and stratification of HCC patients.     

Isolation and identification of potential urinary microparticle biomarkers of bladder cancer

Bladder cancer leads to approximately 13,000 deaths annually in the United States. Early disease is often treated with minimal morbidity and has good prognosis, while the opposite is true for advanced disease. Currently, no tools exist for early detection of this cancer. Microparticles are small, subcellular particles released by essentially all cells upon activation and are known to be produced constitutively by cancer cells. Since most bladder cancers originate in the urothelial cells lining the lumen of the organ, we hypothesize that these cells will release microparticles into the urine. The goal of this study was to identify potential biomarkers in the urinary microparticles of individuals with bladder cancer. Urine microparticles from five healthy individuals and four individuals with bladder cancer were isolated. Samples were delipidated by PAGE and trypsin-digested, peptides were extracted, and the proteome was examined by LC-MS/MS using a Thermo Finnigan LTQ and LTQ-FT ion trap mass spectrometer. Protein identification was determined by SEQUEST and relative quantitation was assessed by comparing spectral counts. Eight proteins were elevated in the microparticles from individuals with bladder cancer. They include five proteins associated with the epidermal growth factor receptor pathway, the alpha subunit of GsGTP binding protein, resistin, and retinoic acid-induced protein 3. Further studies will be needed to validate these potential biomarkers.     

Identification of potential immunotherapy biomarkers for breast cancer by bioinformatics analysis

Breast cancer is a serious malignancy with a high incidence worldwide and a tendency to relapse. We used integrated bioinformatics analysis to identify potential biomarkers in breast carcinoma in the present study. Microarray data, 127breast tumor samples and 23 non-tumor samples, received from the Gene Expression Omnibus (GEO) dataset; 121 differentially expressed genes (DEGs) were selected. Functional analysis using DAVID revealed that these DEGs were highly gathered in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds (prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine) were identified using Connectivity Map. We used Cytoscape software and STRING dataset to structure a protein–protein interaction (PPI) network. The expression of CD24, MMP1, SDC1, and SPP1 was much higher in breast carcinoma tissue than in Para cancerous tissues analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and ONCOMINE. Overexpression ofCD24, MMP1, SDC1, and SPP1 indicated the poor prognosis in breast carcinoma patients analyzed by Kaplan–Meier (KM) Plotter. Immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor sections than in Para cancerous tissues. Hub genes expression at the protein level was correlated tothe breast cancer subtype and grade. Furthermore, immunity analysis showed that CD24, MMP1, SDC1, and SPP1 were potentially associated with five immune cell types infiltration (CD8+ T cells, CD4+ T cells, neutrophils, macrophages,and dendritic cells) by TIMER. Thus, this study indicates potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, further studies are required for verifying these results in vivo and vitro.     

Autoantibodies as potential biomarkers for nasopharyngeal carcinoma

Autoantibody signatures, as new biomarkers, may improve the early detection of nasopharyngeal carcinoma (NPC). We constructed a T7 phage cDNA library from mixed NPC tissues, and we isolated 31 tumor-associated proteins using biopan enrichment techniques with sera from NPC patients and from healthy population. DNA sequence analysis showed that among 31 phage-displayed proteins, 22 have sequence identity with known or putative tumor-associated proteins. The results of immunochemical reactivity of patients' sera with phage-expressed proteins showed enrichment in the number of immunogenic phage clones in the biopanning process and also confirmed that antibodies were present in the sera of patients but not in the sera of healthy donors. The autoantibody against phage-expressed protein MAGE, HSP70, Fibronectin, and CD44 measured by ELISA had greater predictive value than that against EBNA-1, respectively. The antibody levels against MAGE in sera positively correlated with the clinical stages of NPC, and the antibody levels against other three proteins partly correlated with the clinical stages of NPC. Our studies suggested that the autoantibodies against tumor-associated antigens in the sera of NPC patients could be used as a screening test for NPC. Studies of the corresponding proteins may have significances in tumor biology, novel drug development, and immunotherapy.     

Mining the malignant ascites proteome for pancreatic cancer biomarkers

Pancreatic cancer (PC) is one of the most lethal malignancies and disease‐specific biomarkers are desperately needed for better diagnosis, prognosis, monitoring treatment efficacy and for accelerating the development of novel targeted therapeutics. Being an advanced stage manifestation and a proximal fluid in contact with cancer tissues, the ascitic fluid presents itself as a promising rich source of biomarkers. Herein, we present a comprehensive proteomic analysis of pancreatic ascitic fluid. To fractionate the complex ascites proteome, we adopted a multi‐dimensional chromatographic approach that included size‐exclusion, ion‐exchange and lectin‐affinity chromatographic techniques. Our detailed proteomic analysis with high‐resolution Orbitrap^® mass spectrometer resulted in the identification of 816 proteins. We followed rigorous filtering criteria that consisted of PC‐specific information obtained from three publicly available databases (Oncomine, Protein Atlas and Unigene) to segregate 20 putative biomarker candidates for future validation. Since these proteins are of membranous and extra‐cellular origin, most are glycosylated, and many of them are over‐expressed in cancer tissues, the probability of these proteins entering the peripheral blood circulation is high. Their validation as serological PC biomarkers in the future is highly warranted.     

Protein profiling in human sera for identification of potential lung cancer biomarkers using antibody microarray

To identify potential biomarkers of lung cancer (LC), profiling of proteins in sera obtained from healthy and LC patients was determined using an antibody microarray. Based on our previous study on mRNA expression profiles between patients with LC and healthy persons, 19 proteins of interest were selected as targets for fabrication of an antibody microarray. Antibody to each protein and five nonspecific control antibodies were spotted onto a hydrogel‐coated glass slide and used for profiling of proteins in sera of LC patients in a two‐color fluorescence assay. Forty‐eight human sera samples were analyzed, and expression profiling of proteins were represented by the internally normalized ratio method. Six proteins were distinctly down‐regulated in sera of LC patients; this observation was validated by Wilcoxon test, false discovery rate, and Western blotting. Blind test of other 32 human sera using the antibody microarray followed by hierarchical clustering analysis revealed an approximate sensitivity of 88%, specificity of 80%, and an accuracy of 84%, respectively, in classifying the sera, which supports the potential of the six identified proteins as biomarkers for the prognosis of lung cancer.     

Proteogenomics approaches for studying cancer biology and their potential in the identification of acute myeloid leukemia biomarkers

Introduction: Mass spectrometry (MS)-based proteomics has become an indispensable tool for the characterization of the proteome and its post-translational modifications (PTM). In addition to standard protein sequence databases, proteogenomics strategies search the spectral data against the theoretical spectra obtained from customized protein sequence databases. Up to date, there are no published proteogenomics studies on acute myeloid leukemia (AML) samples.

Areas covered: Proteogenomics involves the understanding of genomic and proteomic data. The intersection of both datatypes requires advanced bioinformatics skills. A standard proteogenomics workflow that could be used for the study of AML samples is described. The generation of customized protein sequence databases as well as bioinformatics tools and pipelines commonly used in proteogenomics are discussed in detail.

Expert commentary: Drawing on evidence from recent cancer proteogenomics studies and taking into account the public availability of AML genomic data, the interpretation of present and future MS-based AML proteomic data using AML-specific protein sequence databases could discover new biological mechanisms and targets in AML. However, proteogenomics workflows including bioinformatics guidelines can be challenging for the wide AML research community. It is expected that further automation and simplification of the bioinformatics procedures might attract AML investigators to adopt the proteogenomics strategy.     

A multiplex platform for the identification of ovarian cancer biomarkers

Background

Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients.

Methods

We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek^® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients.

Results

Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek^® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population.

Conclusions

These data demonstrate that the Proseek^® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.

     

Serum proteomic profiling reveals potential biomarkers for cutaneous malignant melanoma

Cutaneous malignant melanoma (CMM) is the most serious type of skin cancer because of its tendency to metastasize. The prognosis and therapeutic management of patients are primarily based on clinical criteria (number of cancerous lymph nodes and/or the presence of distant metastases) and histopathological criteria (tumor depth, presence of ulceration and mitotic index). Although these factors are informative in advanced stages of the disease, they are less important in the early stages. In recent years, a number of attempts have been made to identify new serological prognostic biomarkers, especially for early forms of CMM. The recent development of proteomic techniques may offer new perspectives in this field. This article details the considerations of each of the proteomic techniques used today and describes the results of the most recent clinical studies conducted to identify new potential prognostic serum biomarkers for CMM. However, independent and large validation studies are needed before such markers can be used in everyday clinical practice.     

Serum exosome-derived biomarkers for the early detection of oral squamous cell carcinoma

Molecular and Cellular Biochemistry - Blood exosomes help regulate communication between tumour cells, moderating their behaviour. We sought to determine the protein content in serum exosomes...     

Cancer-associated carbohydrate antigens as potential biomarkers for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common human malignancies. Therefore, developing the early, high-sensitivity diagnostic biomarkers to prevent HCC is urgently needed. Serum a-fetoprotein (AFP), the clinical biomarker in current use, is elevated in only ~60% of patients with HCC; therefore, identification of additional biomarkers is expected to have a significant impact on public health. In this study, we used glycan microarray analysis to explore the potential diagnostic value of several cancer-associated carbohydrate antigens (CACAs) as biomarkers for HCC. We used glycan microarray analysis with 58 different glycan analogs for quantitative comparison of 593 human serum samples (293 HCC samples; 133 chronic hepatitis B virus (HBV) infection samples, 134 chronic hepatitis C virus (HCV) infection samples, and 33 healthy donor samples) to explore the diagnostic possibility of serum antibody changes as biomarkers for HCC. Serum concentrations of anti-disialosyl galactosyl globoside (DSGG), anti-fucosyl GM1 and anti-Gb2 were significantly higher in patients with HCC than in chronic HBV infection individuals not in chronic HCV infection patients. Overall, in our study population, the biomarker candidates DSGG, fucosyl GM1 and Gb2 of CACAs achieved better predictive sensitivity than AFP. We identified potential biomarkers suitable for early detection of HCC. Glycan microarray analysis provides a powerful tool for high-sensitivity and high-throughput detection of serum antibodies against CACAs, which may be valuable serum biomarkers for the early detection of persons at high risk for HCC.