The loss of phenol sulfotransferase 1 in hepatocellular carcinogenesis

Biomarkers for the detection of early hepatocellular carcinoma (HCC) are urgently needed. To identify biomarkers of HCC, we performed a comparative proteomics analysis, based on 2‐DE of HCC tissues and surrounding non‐tumor tissues. Six xenobiotic enzymes were significantly down‐regulated in the HCC tissue. Among these, phenol sulfotransferase (SULT1A1) was confirmed by Western blot analysis in 105 HCC patients. SULT1A1 showed a significant decrease in 98.1% of the HCC tissues, with 88.6% sensitivity and 66.7% specificity for the detection of HCC. Immunohistochemistry for SULT1A1 was performed and compared with glypican‐3, which is a well‐known marker of HCC. The results showed down‐regulation of SULT1A1 and up‐regulation of glypican‐3 in 52.6 and 71.9% of the HCCs, and the use of both markers improved the sensitivity up to 78.9%. Moreover, SULT1A1 was useful in differentiating early HCC from benign dysplastic nodules. Clinically, the down‐regulation of SULT1A1 was closely associated with an advanced International Union Against Cancer stage and high levels of serum α‐fetoprotein. In conclusion, the results of this study demonstrate that the loss of SULT1A1 appears to be a characteristic molecular signature of HCC. SULT1A1 might be a useful biomarker for the detection of early HCC and help predict the clinical outcome of patients with HCC.     

Current advances in tumor proteomics and candidate biomarkers for hepatic cancer

Tumor proteomics apply proteomics techniques to tumor biological research, mainly by screening candidate biomarkers for early tumor diagnosis, prognosis and treatment. Hepatocellular carcinoma (HCC) is a type of malignant tumor with one of the highest death rates in the world. With the advent of the post-genomic age, tumor biological research developing the technology of proteomics has become a major focus of researchers. The discovery of novel candidate biomarkers is one of crucial problems for the early diagnosis of HCC. In general, there are three distinct types of candidate biomarkers for HCC based on different areas: biochemical biomarkers, antigenic biomarkers and epigenetic biomarkers. This review mainly discusses current advances in the problems and prospects of candidate biomarker for the early diagnosis of HCC, discovered by technologies of tumor proteomics.     

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.     

Proteomic analysis of HBV-associated HCC: Insights on mechanisms of disease onset and biomarker discovery

The development of hepatocellular carcinoma (HCC) can be considered as an end-stage outcome of chronic hepatitis B virus (HBV) infection. Early prognostic markers are needed to allow effective treatments and prevent HCC from developing. Proteomics analysis has been used to identify markers from clinical samples from HCC patients. This approach can be further improved by identifying early biomarkers before the onset of HCC. One way would be to use the cell-based HBV replication system, which is reflective of the early stage of virus infection and thus secreted proteins identified at this stage may have relevance in HCC prognosis. In this review, we focus the discussion on the current status of proteomics analysis of cellular proteins and HCC biomarker identification, with a special highlight on the potential of the cell-based HBV replication system for the identification of prognostic HCC biomarkers.     

Quantitative proteomic signature of liver cancer cells: tissue transglutaminase 2 could be a novel protein candidate of human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common diseases worldwide, with extremely poor prognosis due to failure in diagnosing it early. Alpha-fetoprotein (AFP) is the only available biomarker for HCC diagnosis; however, its use in the early detection of HCC is limited, especially because about one-third of patients afflicted with HCC have normal levels of serum AFP. Thus, identifying additional biomarkers that may be used in combination with AFP to improve early detection of HCC is greatly needed. A quantitative proteomic analysis approach using stable isotope labeling with amino acids in cell culture (SILAC) combined with LTQ-FT-MS/MS identification was used to explore differentially expressed protein profiles between normal (HL-7702) and cancer (HepG2 and SK-HEP-1) cells. A total of 116 proteins were recognized as potential markers that could distinguish between HCC and normal liver cells. Certain proteins, such as AFP, intercellular adhesion molecule-1 (ICAM-1), IQ motif containing GTPase activating protein 2 (IQGAP2), claudin-1 (CLDN1) and tissue transglutaminase 2 (TGM2), were validated both in multiple cell lines and in 61 specimens of clinical HCC cases. TGM2 was overexpressed in some of the AFP-deficient HCC cells (SK-HEP-1 and Bel-7402) and in about half of the tumor tissues with low levels of serum AFP (17/32, AFP-negative HCC). Trace amounts of TGM2 were found to be expressed in the samples with high serum AFP (26/29, AFP-positive HCC). Moreover, TGM2 expression in liver tissues showed an inverse correlation with the level of serum AFP in HCC patients. Notably, TGM2 existed in the supernatant of the AFP-deficient SK-HEP-1, SMMC-7721 and HLE cells, and it was found to be induced in AFP-producing cells (HepG2) by specific siRNA silence assay. Serum TGM2 levels of 109 HCC patients and 42 healthy controls were further measured by an established ELISA assay; the levels were significantly higher in HCC patients, and they correlated with the histological grade and tumor size. These data suggest that TGM2 may serve as a novel histological/serologic candidate involved in HCC, especially for the individuals with normal serum AFP. These novel findings may provide important clues to identify new biomarkers of HCC and indirectly improve early detection of the disease.     

Proteomic analysis of renal diseases: unraveling the pathophysiology and biomarker discovery

Current biomedical applications of proteomics have been conducted with four main objectives: to better understand the normal biology and physiology of cells, microorganisms, tissues and organs; to explore the pathogenic mechanisms and better understand the pathophysiology of medical diseases; to identify novel biomarkers for early disease detection, prediction and prognosis; and to define new therapeutic targets, drugs and vaccines. This review focuses predominantly on proteomic applications to unravel the pathophysiology and to define novel biomarkers for various renal diseases (i.e., glomerular diseases, tubulointerstitial diseases, renal vascular disorders and renal cancers). In addition, proteomic evaluations of renal transplantation and renal replacement therapy (for acute renal failure and end-stage renal disease) are summarized. Personal opinion, future perspectives and information resources for the field of renal and urinary proteomics are provided.     

Proteomic analysis of renal diseases: unraveling the pathophysiology and biomarker discovery

Current biomedical applications of proteomics have been conducted with four main objectives: to better understand the normal biology and physiology of cells, microorganisms, tissues and organs; to explore the pathogenic mechanisms and better understand the pathophysiology of medical diseases; to identify novel biomarkers for early disease detection, prediction and prognosis; and to define new therapeutic targets, drugs and vaccines. This review focuses predominantly on proteomic applications to unravel the pathophysiology and to define novel biomarkers for various renal diseases (i.e., glomerular diseases, tubulointerstitial diseases, renal vascular disorders and renal cancers). In addition, proteomic evaluations of renal transplantation and renal replacement therapy (for acute renal failure and end-stage renal disease) are summarized. Personal opinion, future perspectives and information resources for the field of renal and urinary proteomics are provided.     

Proteomic analysis of cancer tissues: shedding light on carcinogenesis and possible biomarkers

Lung, gastric, colorectal, pancreatic, and esophageal cancers, as well as hepatocellular carcinoma (HCC), were the six most common and highly fatal cancers for Japanese men in Japan in 2003, while for women uterine cervical cancer could also be added to this list. To identify diagnostic or therapeutic biomarkers for these cancers, investigators are nowadays performing proteomic analyses of cancer tissues and cells, and revealing a large number of molecules which are diagnostic, prognostic and informative of carcinogenesis. From reports of proteomic analyses of cancerous tissues and noncancerous tissues sampled from HCC, and pancreatic, esophageal, gastric, colorectal, lung and uterine cervical cancers, we classified the proteins into digestive enzymes, growth factors, cell adhesion molecules, calcium-binding proteins, proteases, protease inhibitors, transporter proteins, structural molecules, apoptosis inhibitor, molecular chaperone, as well as proteins related to cell growth, cell differentiation, cell transformation, tumor invasion, carcinogen metabolism, and others. The aim of this study was to understand carcinogenesis of major cancers from a proteomics perspective using samples from cancer patients, and to elucidate their tumor biomarkers.     

Secretory/releasing proteome-based identification of plasma biomarkers in HBV-associated hepatocellular carcinoma

For successful therapy, hepatocellular carcinoma (HCC) must be detected at an early stage. Herein, we used a proteomic approach to analyze the secretory/releasing proteome of HCC tissues to identify plasma biomarkers. Serum-free conditioned media (CM) were collected from primary cultures of cancerous tissues and surrounding noncancerous tissues. Proteomic analysis of the CM proteins permitted the identification of 1365 proteins. The enriched molecular functions and biological processes of the CM proteins, such as hydrolase activity and catabolic processes, were consistent with the liver being the most important metabolic organ. Moreover, 19% of the proteins were characterized as extracellular or membrane-bound. For validation, secretory proteins involved in transforming growth factor-β signaling pathways were validated in plasma samples. Alphafetoprotein (AFP), metalloproteinase (MMP)1, osteopontin (OPN), and pregnancy-specific beta-1-glycoprotein (PSG)9 were significantly increased in HCC patients. The overall performance of MMP1 and OPN in the diagnosis of HCC remained greater than that of AFP. In addition, this study represents the first report of MMP1 as a biomarker with a higher sensitivity and specificity than AFP. Thus, this study provides a valuable resource of the HCC secretome with the potential to investigate serological biomarkers. MMP1 and OPN could be used as novel biomarkers for the early detection of HCC and to improve the sensitivity of biomarkers compared with AFP.     

PTRF/cavin-1 and MIF proteins are identified as non-small cell lung cancer biomarkers by label-free proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the "omics" era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer.     

Four long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

The study aimed to identify the long noncoding RNAs (lncRNAs) biomarkers for occurrence and prognosis of patients with hepatocellular carcinoma (HCC), and simultaneously to investigate the potential role of lncRNAs in the oncogenesis of HCC. The lncRNAs expression data and the corresponding clinical information of HCC samples were extracted from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes and lncRNAs were identified and the correlation networks were constructed. In this study, we identified 212 differentially expressed lncRNAs and 7,577 differentially expressed genes between liver HCC tumor tissues and normal tissue samples. And then, combining with clinical information, a total of 11 lncRNAs and 162 genes as HCC biomarkers were identified by comprehensive bioinformatics analysis. Further, through coexpress network analysis, we confirmed four lncRNAs (lncRNA_ANKRD10.IT1, lncRNA_CTD.2583A14.8, lncRNA_RP11.404P21.3, and lncRNA_RP11.488L18.10), which can serve as prognostic biomarkers for HCC. The four lncRNAs identified in this study may serve as a potential therapy target for HCC.     

Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE

Novel cancer biomarkers are required to achieve early diagnosis and optimized therapy for individual patients. Cancer is a disease of the genome, and tumor tissues are a rich source of cancer biomarkers as they contain the functional translation of the genome, namely the proteome. Investigation of the tumor tissue proteome allows the identification of proteomic signatures corresponding to clinico-pathological parameters, and individual proteins in such signatures will be good biomarker candidates. Tumor tissues are also a rich source for plasma biomarkers, because proteins released from tumor tissues may be more cancer specific than those from non-tumor cells. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor satulation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells in tumor tissues. To develop clinical applications using the identified biomarkers, collaboration between research scientists, clinicians and diagnostic companies is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channeling the wealth of produced information towards concrete and specific clinical purposes is urgent.     

Top-down quantitative proteomics identified phosphorylation of cardiac troponin I as a candidate biomarker for chronic heart failure

The rapid increase in the prevalence of chronic heart failure (CHF) worldwide underscores an urgent need to identify biomarkers for the early detection of CHF. Post-translational modifications (PTMs) are associated with many critical signaling events during disease progression and thus offer a plethora of candidate biomarkers. We have employed a top-down quantitative proteomics methodology for comprehensive assessment of PTMs in whole proteins extracted from normal and diseased tissues. We systematically analyzed 36 clinical human heart tissue samples and identified phosphorylation of cardiac troponin I (cTnI) as a candidate biomarker for CHF. The relative percentages of the total phosphorylated cTnI forms over the entire cTnI populations (%P(total)) were 56.4 ± 3.5%, 36.9 ± 1.6%, 6.1 ± 2.4%, and 1.0 ± 0.6% for postmortem hearts with normal cardiac function (n = 7), early stage of mild hypertrophy (n = 5), severe hypertrophy/dilation (n = 4), and end-stage CHF (n = 6), respectively. In fresh transplant samples, the %P(total) of cTnI from nonfailing donor (n = 4), and end-stage failing hearts (n = 10) were 49.5 ± 5.9% and 18.8 ± 2.9%, respectively. Top-down MS with electron capture dissociation unequivocally localized the altered phosphorylation sites to Ser22/23 and determined the order of phosphorylation/dephosphorylation. This study represents the first clinical application of top-down MS-based quantitative proteomics for biomarker discovery from tissues, highlighting the potential of PTMs as disease biomarkers.     

Biomarkers in cancer screening, research and detection: present and future: a review

Biomarkers provide a powerful and dynamic approach to understanding the spectrum of malignancies with applications in observational and analytic epidemiology, randomized clinical trials, screening, diagnosis and prognosis. Defined as alterations in the constituents of tissues or body fluids, these markers offer a means for homogeneous classification of a disease and risk factor, and they can extend one's basic information about the underlying pathogenesis of disease. The goals in cancer research include finding biomarkers that can be used for the early detection of cancers, design individual therapies, and to identify underlying processes involved in the disease. Because so many myriad processes are involved in the diseased states, the goal is similar to 'finding a needle in a haystack'. However, the development of many -omic technologies, such as genomics and proteomics, has allowed us to monitor a large number of key cellular pathways simultaneously. This has enabled the identification of biomarkers and signalling molecules associated with cell growth, cell death and cellular metabolism. These are also facilitating in monitoring the functional disturbance, molecular and cellular damage, and damage response. This brief review describes the development of biomarkers in cancer research and detection with emphasis on different proteomic tools for the identification and discovery of new biomarkers, different clinical assays to detect various biomarkers in different specimens, role of biomarkers in cancer screening and last but not the least, the challenges in this direction of cancer research.     

Biomarker discovery using a comparative omics approach in a mouse model developing heterogeneous mammary cancer subtypes

Identification of biomarkers for early breast cancer detection in blood is a challenging task, since breast cancer is a heterogeneous disease with a wide range of tumor subtypes. This is envisioned to result in differences in serum protein levels. The p53(R270H/+) WAPCre mouse model is unique in that these mice spontaneously develop both ER- and ER+ tumors, in proportions comparable to humans. Therefore, these mice provide a well-suited model system to identify human relevant biomarkers for early breast cancer detection that are additionally specific for different tumor subtypes. Mammary gland tumors were obtained from p53(R270H/+) WAPCre mice and cellular origin, ER, and HER2 status were characterized. We compared gene expression profiles for tumors with different characteristics versus control tissue, and determined genes differentially expressed across tumor subtypes. By using literature data (Gene Ontology, UniProt, and Human Plasma Proteome), we further identified protein candidate biomarkers for blood-based detection of breast cancer. Functional overrepresentation analysis (using Gene Ontology, MSigDB, BioGPS, Cancer GeneSigDB, and proteomics literature data) showed enrichment for several processes relevant for human breast cancer. Finally, Human Protein Atlas data were used to obtain a prioritized list of 16 potential biomarkers that should facilitate further studies on blood-based breast cancer detection in humans.     

Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives

Cancer is a common disease that is a leading cause of death worldwide. Currently, early detection and novel therapeutic strategies are urgently needed for more effective management of cancer. Importantly, protein profiling using clinical proteomic strategies, with spectacular sensitivity and precision, offer excellent promise for the identification of potential biomarkers that would direct the development of targeted therapeutic anticancer drugs for precision medicine. In particular, clinical sample sources, including tumor tissues and body fluids (blood, feces, urine and saliva), have been widely investigated using modern high-throughput mass spectrometry-based proteomic approaches combined with bioinformatic analysis, to pursue the possibilities of precision medicine for targeted cancer therapy. Discussed in this review are the current advantages and limitations of clinical proteomics, the available strategies of clinical proteomics for the management of precision medicine, as well as the challenges and future perspectives of clinical proteomics-driven precision medicine for targeted cancer therapy.     

Mass spectrometry analysis of glycoprotein biomarkers in human blood of hepatocellular carcinoma

Introduction: Diagnosis of hepatocellular carcinoma (HCC) is important for improving the survival rate and selecting the optimum therapeutic option. However, some patients with HCC are not diagnosed until after symptoms appear, when the tumor is already advanced. Thus, biomarkers associated with HCC and novel diagnostic methods are required to improve the diagnosis of HCC. Mass spectrometry (MS) is one of the most widely used analytical tools in proteomic research. Furthermore, tandem MS (MS/MS) has been applied for the discovery and verification of protein biomarkers for clinical use.

Areas covered: We review candidate glycoprotein biomarkers, including their aberrant glycosylation discovered by MS-based proteomics techniques and their diagnostic strategies using human blood samples. Finally, we discuss the limitations and prospects of MS-based approaches for clinical applications.

Expert commentary: The development of biomarkers with high sensitivity and specificity is essential for optimizing the management of HCC. Various glycoprotein biomarkers of HCC have been identified using MS-based techniques. MS-based assays will continue to play an important role in clinical applications for discovery and verification of biomarkers. Furthermore, combination of multibiomarker, improvements in sample enrichment and the development of highly sensitive MS methods will facilitate more rapid adoption of MS for the diagnosis of HCC.     

18O Labeling for a Quantitative Proteomic Analysis of Glycoproteins in Hepatocellular Carcinoma

Introduction

Quantitative proteomics using tandem mass spectrometry is an attractive approach for identification of potential cancer biomarkers. Fractionation of complex tissue samples into subproteomes prior to mass spectrometric analyses increases the likelihood of identifying cancer-specific proteins that might be present in low abundance. In this regard, glycosylated proteins are an interesting class of proteins that are already established as biomarkers for several cancers.

Materials and Methods

In this study, we carried out proteomic profiling of tumor and adjacent non-cancer liver tissues from hepatocellular carcinoma (HCC) patients. Glycoprotein enrichment from liver samples using lectin affinity chromatography and subsequent ¹⁸O/¹⁶O labeling of peptides allowed us to obtain relative abundance levels of lectin-bound proteins. As a complementary approach, we also examined the relative expression of proteins in HCC without glycoprotein enrichment. Lectin affinity enrichment was found to be advantageous to quantitate several interesting proteins, which were not detected in the whole proteome screening approach. We identified and quantitated over 200 proteins from the lectin-based approach. Interesting among these were fetuin, cysteine-rich protein 1, serpin peptidase inhibitor, leucine-rich alpha-2-glycoprotein 1, melanoma cell adhesion molecule, and heparan sulfate proteoglycan-2. Using lectin affinity followed by PNGase F digestion coupled to ¹⁸O labeling, we identified 34 glycosylation sites with consensus sequence N-X-T/S. Western blotting and immunohistochemical staining were carried out for several proteins to confirm mass spectrometry results.

Conclusion

This study indicates that quantitative proteomic profiling of tumor tissue versus non-cancerous tissue is a promising approach for the identification of potential biomarkers for HCC.