Contribution of oncoproteomics to cancer biomarker discovery

Functions and Therapeutic Use of Heat Shock Proteins in Hepatocellular Carcinoma

Heat shock proteins are intracellular proteins expressed in prokaryotes and eukaryotes that help protect the cell from stress. They play an important role in regulating cell cycle and cell death, work as molecular chaperons during the folding of newly synthesized proteins, and also in the degradation of misfolded proteins. They are not only produced under stress conditions like acidosis, energy depletion, and oxidative stress but are also continuously synthesized as a result of their housekeeping functions. There are different heat shock protein families based on their molecular weight, like HSP70, HSP90, HSP60, HSP27, HSP40, etc. Heat shock proteins are involved in many cancers, particularly hepatocellular carcinoma, the main primary tumor of the liver in adults. Their deregulations in hepatocellular carcinoma are associated with metastasis, angiogenesis, cell invasion, and cell proliferation and upregulated heat shock proteins can be used as either diagnostic or prognostic markers. Targeting heat shock proteins is a relevant strategy for the treatment of patients with liver cancer. In this review, we provide insights into heat shock proteins and heat shock protein-like proteins (clusterin) in the progression of hepatocellular carcinoma and their use as therapeutic targets.

Proteomics and Its Current Application in Biomedical Area: Concise Review

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

Pectolinarigenin regulates the tumor-associated proteins in AGS-xenograft BALB/c nude mice

BACKGROUND

Pectolinarigenin (PEC) is a flavone extracted from Cirsium, and because it has anti-inflammatory properties, anti-cancer research is also being conducted. The objective of this work was to find out if PEC is involved in tumor control and which pathways it regulates in vivo and in vitro.

METHODS

AGS cell lines were xenografted into BALB/c nude mice to create tumors, and PEC was administered intraperitoneally to see if it was involved in tumor control. Once animal testing was completed, tumor proteins were isolated and identified using LC-MS analysis, and gene ontology of the found proteins was performed.

RESULTS

Body weight and hematological measurements on the xenograft mice model demonstrated that PEC was not harmful to non-cancerous cells. We found 582 proteins in tumor tissue linked to biological reactions such as carcinogenesis and cell death signaling. PEC regulated 6 out of 582 proteins in vivo and in vitro in the same way.

CONCLUSION

Our findings suggested that PEC therapy may inhibit tumor development in gastric cancer (GC), and proteomic research gives fundamental information about proteins that may have great promise as new therapeutic targets in GC.

Proteomics: Unraveling the Cross Talk Between Innate Immunity and Disease Pathophysiology, Diagnostics, and Treatment Options

Inflammation is crucial in diseases, and proteins play a key role in the interplay between innate immunity and pathology. This review explores how proteomics helps understanding this relationship, focusing on diagnosis and treatment. We explore the dynamic innate response and the significance of proteomic techniques in deciphering the complex network of proteins involved in prevalent diseases, including infections, cancer, autoimmune and neurodegenerative disorders. Proteomics identifies key proteins in host-pathogen interactions, shedding light on infection mechanisms and inflammation. These discoveries hold promise for diagnostic tools, therapies, and vaccines. In cancer research, proteomics reveals innate signatures associated with tumor development, immune evasion, and therapeutic response. Additionally, proteomic analysis has unveiled autoantigens and dysregulation of the innate immune system in autoimmunity, offering opportunities for early diagnosis, disease monitoring, and new therapeutic targets. Moreover, proteomic analysis has identified altered protein expression patterns in neurodegenerative diseases like Alzheimer's and Parkinson's, providing insights into potential therapeutic strategies. Proteomics of the innate immune system provides a comprehensive understanding of disease mechanisms, identifies biomarkers, and enables effective interventions in various diseases. Despite still in its early stages, this approach holds great promise to revolutionize innate immunity research and significantly improve patient outcomes across a wide range of diseases.

Biomarkers in Cancer Detection, Diagnosis, and Prognosis

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

The role and clinical relevance of long non-coding RNAs in glioma

Glioma represents a complex and heterogeneous disease, posing significant challenges to both clinicians and researchers. Despite notable advancements in glioma treatment, the overall survival rate for most glioma patients remains dishearteningly low. Hence, there is an urgent necessity to discover novel biomarkers and therapeutic targets specifically tailored for glioma. In recent years, long non-coding RNAs (lncRNAs) have emerged as pivotal regulators of gene expression and have garnered attention for their involvement in the development and progression of various cancers, including glioma. The dysregulation of lncRNAs plays a critical role in glioma pathogenesis and influences clinical outcomes. Consequently, there is growing interest in exploring the potential of lncRNAs as diagnostic and prognostic biomarkers, as well as therapeutic targets. By understanding the functions and dysregulation of lncRNAs in glioma, researchers aim to unlock new avenues for the development of innovative treatment strategies catered to glioma patients. The identification and thorough characterization of lncRNAs hold the promise of novel therapeutic approaches that could potentially improve patient outcomes and enhance the management of glioma, ultimately striving for better prospects and enhanced quality of life for those affected by this challenging disease. The primary objective of this paper is to comprehensively review the current state of knowledge regarding lncRNA biology and their intricate roles in glioma. It also delves into the potential of lncRNAs as valuable diagnostic and prognostic indicators and explores their feasibility as promising targets for therapeutic interventions.

Surface Plasmon Resonance (SPR) Sensor for Cancer Biomarker Detection

A biomarker is a physiological observable marker that acts as a stand-in and, in the best-case scenario, forecasts a clinically significant outcome. Diagnostic biomarkers are more convenient and cost-effective than directly measuring the ultimate clinical outcome. Cancer is among the most prominent global health problems and a major cause of morbidity and death globally. Therefore, cancer biomarker assays that are trustworthy, consistent, precise, and verified are desperately needed. Biomarker-based tumor detection holds a lot of promise for improving disease knowledge at the molecular scale and early detection and surveillance. In contrast to conventional approaches, surface plasmon resonance (SPR) allows for the quick and less invasive screening of a variety of circulating indicators, such as circulating tumor DNA (ctDNA), microRNA (miRNA), circulating tumor cells (CTCs), lipids, and proteins. With several advantages, the SPR technique is a particularly beneficial choice for the point-of-care identification of biomarkers. As a result, it enables the timely detection of tumor markers, which could be used to track cancer development and suppress the relapse of malignant tumors. This review emphasizes advancements in SPR biosensing technologies for cancer detection.

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

The issues and challenges with cancer biomarkers

A biomarker is a measurable indicator used to distinguish precisely/objectively either normal biological state/pathological condition/response to a specific therapeutic intervention. The use of novel molecular biomarkers within evidence-based medicine may improve the diagnosis/treatment of disease, improve health outcomes, and reduce the disease's socio-economic impact. Presently cancer biomarkers are the backbone of therapy, with greater efficacy and better survival rates. Cancer biomarkers are extensively used to treat cancer and monitor the disease's progress, drug response, relapses, and drug resistance. The highest percent of all biomarkers explored are in the domain of cancer. Extensive research using various methods/tissues is carried out for identifying biomarkers for early detection, which has been mostly unsuccessful. The quantitative/qualitative detection of various biomarkers in different tissues should ideally be done in accordance with qualification rules laid down by the Early Detection Research Network (EDRN), Program for the Assessment of Clinical Cancer Tests (PACCT), and National Academy of Clinical Biochemistry. Many biomarkers are presently under investigation, but lacunae lie in the biomarker's sensitivity and specificity. An ideal biomarker should be quantifiable, reliable, of considerable high/low expression, correlate with the outcome progression, cost-effective, and consistent across gender and ethnic groups. Further, we also highlight that these biomarkers' application remains questionable in childhood malignancies due to the lack of reference values in the pediatric population. The development of a cancer biomarker stands very challenging due to its complexity and sensitivity/resistance to the therapy. In past decades, the cross-talks between molecular pathways have been targeted to study the nature of cancer. To generate sensitive and specific biomarkers representing the pathogenesis of specific cancer, predicting the treatment responses and outcomes would necessitate inclusion of multiple biomarkers.

Analysis of differentially expressed proteins between HER2 positive and triple negative breast cancer and their prognostic significance

Both triple negative breast cancer (TNBA) and HER2-positive breast cancer lack expression of estrogen receptor alpha (ER) and progesterone receptor (PR), while human epidermal growth factor receptor 2 (HER2) in TNBC is also negative. This study aimed to identify the differentially expressed proteins (DEPs) between TNBC and HER2-positive breast cancer and to improve understanding of their role in the prognosis of breast cancer. By analyzing the breast cancer data set in The Cancer Proteome Atlas (TCPA) database, 15 DEPs between TNBC and HER2-positive breast cancer were identified. GO and pathway enrichment analysis were performed on DEPs, and the protein-protein interaction (PPI) network was constructed. The overall survival (OS) analysis of the breast cancer protein dataset in the Kaplan-Meier plotter showed that low expression of ACC1 suggested a higher OS of HER2-positive breast cancer (HR = 5.34, P < 0.05) and TNBC (HR = 2.88, P < 0.05). And TNBC patients with high TBA1B (HR = 0.16, P < 0.01) or low INPP4B (HR = 3.47, P < 0.05) expression have a better prognosis. Our research provides new insights into the prognostic indicators of TNBC and HER2-positive breast cancer, which could be further studied.

Recent advances in heat shock proteins in cancer diagnosis, prognosis, metabolism and treatment

Heat shock proteins (HSPs) are a group of proteins, also known as molecular chaperones, which participate in protein folding and maturation in response to stresses or high temperature. According to their molecular weights, mammalian HSPs are classified into HSP27, HSP40, HSP60, HSP70, HSP90, and large HSPs. Previous studies have revealed that HSPs play important roles in oncogenesis and malignant progression because they can modulate all six hallmark traits of cancer. Because of this, HSPs have been propelled into the spotlight as biomarkers for cancer diagnosis and prognosis, as well as an exciting anticancer drug target. However, the relationship between the expression level of HSPs and their activity and cancer diagnosis, prognosis, metabolism and treatment is not clear and has not been completely established. Herein, this review summarizes and discusses recent advances and perspectives in major HSPs as biomarkers for cancer diagnosis, as regulators for cancer metabolism or as therapeutic targets for cancer therapy, which may provide new directions to improve the accuracy of cancer diagnosis and develop more effective and safer anticancer therapeutics.

Decoding competing endogenous RNA networks for cancer biomarker discovery

Crosstalk between competing endogenous RNAs (ceRNAs) is mediated by shared microRNAs (miRNAs) and plays important roles both in normal physiology and tumorigenesis; thus, it is attractive for systems-level decoding of gene regulation. As ceRNA networks link the function of miRNAs with that of transcripts sharing the same miRNA response elements (MREs), e.g. pseudogenes, competing mRNAs, long non-coding RNAs, and circular RNAs, the perturbation of crucial interactions in ceRNA networks may contribute to carcinogenesis by affecting the balance of cellular regulatory system. Therefore, discovering biomarkers that indicate cancer initiation, development, and/or therapeutic responses via reconstructing and analyzing ceRNA networks is of clinical significance. In this review, the regulatory function of ceRNAs in cancer and crucial determinants of ceRNA crosstalk are firstly discussed to gain a global understanding of ceRNA-mediated carcinogenesis. Then, computational and experimental approaches for ceRNA network reconstruction and ceRNA validation, respectively, are described from a systems biology perspective. We focus on strategies for biomarker identification based on analyzing ceRNA networks and highlight the translational applications of ceRNA biomarkers for cancer management. This article will shed light on the significance of miRNA-mediated ceRNA interactions and provide important clues for discovering ceRNA network-based biomarker in cancer biology, thereby accelerating the pace of precision medicine and healthcare for cancer patients.

Multiple Molecular Mechanisms to Overcome Multidrug Resistance in Cancer by Natural Secondary Metabolites

Plant secondary metabolites (SMs) common natural occurrences and the significantly lower toxicities of many SM have led to the approaching development and use of these compounds as effective pharmaceutical agents; especially in cancer therapy. A combination of two or three of plant secondary metabolites together or of one SM with specific anticancer drugs, may synergistically decrease the doses needed, widen the chemotherapeutic window, mediate more effective cell growth inhibition, and avoid the side effects of high drug concentrations. In mixtures they can exert additive or even synergistic activities. Many SM can effectively increase the sensitivity of cancer cells to chemotherapy. In phytotherapy, secondary metabolites (SM) of medicinal plants can interact with single or multiple targets. The multi-molecular mechanisms of plant secondary metabolites to overcome multidrug resistance (MDR) are highlighted in this review. These mechanisms include interaction with membrane proteins such as P-glycoprotein (P-gp/MDR1); an ATP-binding cassette (ABC) transporter, nucleic acids (DNA, RNA), and induction of apoptosis. P-gp plays an important role in the development of MDR in cancer cells and is involved in potential chemotherapy failure. Therefore, the ingestion of dietary supplements, food or beverages containing secondary metabolites e.g., polyphenols or terpenoids may alter the bioavailability, therapeutic efficacy and safety of the drugs that are P-gp substrates.

A Meta-Analysis of Proteomic Blood Markers of Colorectal Cancer

BACKGROUND

Early diagnosis will significantly improve the survival rate of colorectal cancer (CRC); however, the existing methods for CRC screening were either invasive or inefficient. There is an emergency need for novel markers in CRC's early diagnosis. Serum proteomics has gained great potential in discovering novel markers, providing markers that reflect the early stage of cancer and prognosis prediction of CRC. In this paper, the results of proteomics of CRC studies were summarized through a meta-analysis in order to obtain the diagnostic efficiency of novel markers.

METHODS

A systematic search on bibliographic databases was performed to collect the studies that explore blood-based markers for CRC applying proteomics. The detection and validation methods, as well as the specificity and sensitivity of the biomarkers in these studies, were evaluated. Newcastle- Ottawa Scale (NOS) case-control studies version was used for quality assessment of included studies.

RESULTS

Thirty-four studies were selected from 751 studies, in which markers detected by proteomics were summarized. In total, fifty-nine proteins were classified according to their biological function. The sensitivity, specificity, or AUC varied among these markers. Among them, Mammalian STE20-like protein kinase 1/ Serine threonine kinase 4 (MST1/STK4), S100 calcium-binding protein A9 (S100A9), and Tissue inhibitor of metalloproteinases 1 (TIMP1) were suitable for effect sizes merging, and their diagnostic efficiencies were recalculated after merging. MST1/STK4 obtained a sensitivity of 68% and a specificity of 78%. S100A9 achieved a sensitivity of 72%, a specificity of 83%, and an AUC of 0.88. TIMP1 obtained a sensitivity of 42%, a specificity of 88%, and an AUC of 0.71.

CONCLUSION

MST1/STK4, S100A9, and TIMP1 showed excellent performance for CRC detection. Several other markers also presented optimized diagnostic efficacy for CRC early detection, but further verification is still needed before they are suitable for clinical use. The discovering of more efficient markers will benefit CRC treatment.

Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey

Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.

Pathological identification of brain tumors based on the characteristics of molecular fragments generated by laser ablation combined with a spiking neural network

Quick and accurate diagnosis helps shorten intraoperative waiting time and make a correct plan for the brain tumor resection. The common cryostat section method costs more than 10 minutes and the diagnostic accuracy depends on the sliced and frozen process and the experience of the pathologist. We propose the use of molecular fragment spectra (MFS) in laser-induced breakdown spectroscopy (LIBS) to identify different brain tumors. Formation mechanisms of MFS detected from brain tumors could be generalized into 3 categories, for instance, combination, reorganization and break. Four kinds of brain tumors (glioma, meningioma, hemangiopericytoma, and craniopharyngioma) from different patients were used as investigated samples. The spiking neural network (SNN) classifier was proposed to combine with the MFS (MFS-SNN) for the identification of brain tumors. SNN performed better than conventional machine learning methods for the analysis of similar and limited MFS information. With the ratio data type, the identification accuracy achieved 88.62% in 2 seconds.

Proteomic profiling and identification of significant markers from high-grade osteosarcoma after cryotherapy and irradiation

Biological reconstruction of allografts and recycled autografts have been widely implemented in high-grade osteogenic sarcoma. For treating tumor-bearing autografts, extracorporeal irradiation (ECIR) and liquid nitrogen (LN) freezing techniques are being used worldwide as a gold standard treatment procedure. Both the methods aim to eradicate the tumor cells from the local recurrence and restore the limb function. Therefore, it is essential and crucial to find, and compare the alterations at molecular and physiological levels of the treated and untreated OGS recycled autografts to obtain valuable clinical information for better clinical practice. Thus, we aimed to investigate the significantly expressed altered proteins from ECIR-and cryotherapy/freezing- treated OGS (n = 12) were compared to untreated OGS (n = 12) samples using LC-ESI-MS/MS analysis, and the selected proteins from this protein panel were verified using immunoblot analysis. From our comparative proteomic analysis identified a total of 131 differentially expressed proteins (DEPs) from OGS. Among these, 91 proteins were up-regulated (2.5 to 3.5-folds), and 40 proteins were down-regulated (0.2 to 0.5 folds) (p < 0.01 and 0.05). The functional enrichment analysis revealed that the identified DEPs have belonged to more than 10 different protein categories include cytoskeletal, extracellular matrix, immune, enzyme modulators, and cell signaling molecules. Among these, we have confirmed two potential candidates’ expressions levels such as Fibronectin and Protein S100 A4 using western blot analysis. Our proteomic study revealed that LN-freezing and ECIR treatments are effectively eradicating tumor cells, and reducing the higher expressions of DEPs at molecular levels which may help in restoring the limb functions of OGS autografts effectively. To the best of our knowledge, this is the first proteomic study that compared proteomic profiles among freezing, ECIR treated with untreated OGS in recycled autografts. Moreover, the verified proteins could be used as prognostic or diagnostic markers that reveal valuable scientific information which may open various therapeutic avenues in clinical practice to improve patient outcomes.

Long noncoding RNA UBE2R2-AS1 promotes glioma cell apoptosis via targeting the miR-877-3p/TLR4 axis

Introduction: Brain glioma is the most common type of primary malignancy in the central nervous system (CNS), with high recurrence and mortality rate, especially glioblastoma (GBM). Recent evidence suggests a role for many long noncoding RNAs (lncRNAs) in the pathogenesis, proliferation, apoptosis, metastasis, and chemotherapeutic resistance of cancer cells. Although the functions of some lncRNAs in the occurrence and development of gliomas have been confirmed, detailed mechanisms of action are lacking. Furthermore, the biological roles of many other lncRNAs in glioma have not been reported at all.

Methods: In this study, we identified a novel lncRNA, UBE2R2-AS1, which was dramatically downregulated in glioma compared with normal tissue, by performing microarray detection of six pairs of glioma samples and adjacent normal tissues. In vitro experiments demonstrated that UBE2R2-AS1 regulated glioma cell proliferation, apoptosis, and migration.

Results: UBE2R2-AS1 acted as a competing endogenous RNA (ceRNA) to target Toll-like receptor 4 (TLR4) mRNA by binding to miR-877-3p. Furthermore, lncRNA UBE2R2-AS1 suppressed glioblastoma cell growth, migration, and invasion, as well as promoting cell apoptosis by targeting miR-877-3p/TLR4 directly.

Conclusion: This information regarding UBE2R2-AS1 and its glioma-related molecular mechanisms will aid the future identification of new lncRNA-directed diagnostics and drug-targeting therapies.

Pharmaceutical Advances and Proteomics Researches

Proteomics enables understanding the composition, structure, function and interactions of the entire protein complement of a cell, a tissue, or an organism under exactly defined conditions. Some factors such as stress or drug effects will change the protein pattern and cause the present or absence of a protein or gradual variation in abundances. The aim of this study is to explore relationship between proteomics application and drug discovery. "proteomics", "Application", and "pharmacology were the main keywords that were searched in PubMed (PubMed Central), Web of Science, and Google Scholar. The titles that were stablished by 2019, were studied and after study of the appreciated abstracts, the full texts of the 118 favor documents were extracted. Changes in the proteome provide a snapshot of the cell activities and physiological processes. Proteomics shows the observed protein changes to the causal effects and generate a complete three-dimensional map of the cell indicating their exact location. Proteomics is used in different biological fields and is applied in medicine, agriculture, food microbiology, industry, and pharmacy and drug discovery. Biomarker discovery, follow up of drug effect on the patients, and in vitro and in vivo proteomic investigation about the drug treated subjects implies close relationship between proteomics advances and application and drug discovery and development. This review overviews and summarizes the applications of proteomics especially in pharmacology and drug discovery.

Comparative Proteomic Profiling of Tumor-Associated Proteins in Human Gastric Cancer Cells Treated with Pectolinarigenin

Pectolinarigenin (PEC), a natural flavonoid that is present in citrus fruits, has been reported to exhibit antitumor effects in several cancers. Though the mechanism of PEC-induced cytotoxicity effects has been documented, the proteomic changes that are associated with the cellular response to this flavonoid are poorly understood in gastric cancer cells. In this study, a comparative proteomic analysis was performed to identify proteins associated with PEC-induced cell death in two human gastric cancer cell lines: AGS and MKN-28. Two-dimensional gel electrophoresis (2-DE) revealed a total of 29 and 56 protein spots with significant alteration were screened in AGS and MKN-28 cells respectively. In total, 13 (AGS) and 39 (MKN28) proteins were successfully identified by mass spectrometry from the differential spots and they are known to be involved in signal transduction, apoptosis, transcription and translation, cell structural organization, and metabolism, as is consistent with multiple effects of PEC on tumor cells. Notably, novel target proteins like Probable ATP-dependent RNA helicase DDX4 (DDX4) and E3 ubiquitin-protein ligase LRSAM1 (LRSAM1) along with the commonly differential expressed proteins on both the cell lines that are treated with PEC were confirmed by immunoblotting. The DDX4 accelerates cell cycle progression by abrogating the G2 checkpoint when overexpressed in cancer cells, while the aberrant expression of LRSAM1 may be involved in the cancer pathology. Thus, proteomic analysis provides vital information about target proteins that are important for PEC-induced cell death in gastric cancer cells.

EPSTEIN-BARR VIRUS AND NASOPHARYNGEAL CARCINOMA: VIRAL MARKERS FOR DIAGNOSTICS AND ASSESSMENT OF CLINICAL STATUS OF PATIENTS

The etiological role of the Epstein-Barr virus (EBV) in the development of an undifferentiated histological variant of nasopharyngeal carcinoma (uNPC) found for the first time in regions with a high incidence of this pathology, the Southern provinces of China and the countries of Southeast Asia, and later in the rest of the world, has served as a basis for the widespread use of EBV serological markers for the diagnosis of this form of tumor. In recent years, the use of a test based on the quantitative determination of the EBV DNA concentration in the blood plasma of uNPC patients for early detection and monitoring of the disease has become widespread in endemic regions. In non-endemic regions, such studies virtually have not been carried out, and moreover, the comparative evaluation of the significance of two viral markers, serological and EBV DNA load in the bloodstream of uNPC patients, for diagnostics and evaluation of the therapeutic effect was not investigated. The aim of this study was to compare the clinical value of two serological markers and plasma EBV DNA load in uNPC patients from non-endemic region (Russia). The obtained results indicate that IgA antibodies to the viral capsid antigen (IgA/VCA) and plasma EBV DNA concentration can be successfully used for the diagnosis of uNPC, while IgG/VCA antibodies have no practical significance as an uNPC marker. In addition, it was found that plasma EBV DNA load is more sensitive marker of uNPC than IgA/VCA titers because DNA copy numbers reflect more accurately the effect of the therapy and the clinical state of patients at the stages of remission or relapse. It was shown for the first time that in the non-endemic region the simultaneous evaluation of IgA/VCA antibody levels and the plasma EBV DNA loads are the most effective markers for the diagnostics of uNPC. However, we believe, that it is more practical to use IgA/VCA antibody levels for uNPC screening, and plasma EBV DNA copies - for monitoring of the disease.

Plectin-targeted liposomes enhance the therapeutic efficacy of a PARP inhibitor in the treatment of ovarian cancer

Advances in genomics and proteomics drive precision medicine by providing actionable genetic alterations and molecularly targeted therapies, respectively. While genomic analysis and medicinal chemistry have advanced patient stratification with treatments tailored to the genetic profile of a patient's tumor, proteomic targeting has the potential to enhance the therapeutic index of drugs like poly(ADP-ribose) polymerase (PARP) inhibitors. PARP inhibitors in breast and ovarian cancer patients with BRCA1/2 mutations have shown promise. About 10% of the patients who received Olaparib (PARP inhibitor) showed adverse side effects including neutropenia, thrombocytopenia and in some cases resulted in myelodysplastic syndrome, indicating that off-target effects were substantial in these patients. Through proteomic analysis, our lab previously identified plectin, a cytolinker protein that mislocalized onto the cell surface during malignant transformation of healthy ovarian tissue. This cancer specific phenotype allowed us to image pancreatic cancer successfully using plectin targeted peptide (PTP) conjugated to nanoparticles or displayed on capsid protein of adeno-associated virus (AAV) particles. Objective: The goal of this study was to integrate the available pharmacogenomics and proteomic data to develop effective anti-tumor therapies using a targeted drug delivery approach. Methods: Plectin expression and localization in human ovarian tumor specimens were analyzed followed by in vitro confirmation of cell surface plectin localization in healthy and ovarian cancer cell lines. PTP-conjugated liposomes were prepared and their specificity for plectin+ cells was determined in vitro and in vivo. A remote loading method was employed to encapsulate a PARP inhibitor (AZ7379) into liposomes. An ideal buffer exchange method and remote loading conditions were determined based on the amount of lipid and drug recovered at the end of a remote loading process. Finally, in vivo tumor growth studies were performed to determine the efficacy of PTP liposomes in preventing PARP activity in mice bearing OVCAR8 (high grade epithelial ovarian cancer (EOC)) tumors. Results: PTP liposomal AZ7379 delivery not only enhanced PARP inhibition but also resulted in decelerated tumor growth in mice bearing subcutaneous and intraperitoneal OVCAR8 tumors. In mice bearing subcutaneous or intraperitoneal tumors, treatment with PTP liposomes resulted in a 3- and 1.7-fold decrease in tumor volume, respectively, compared to systemic drug treatment. Conclusion: Targeted drug delivery assisted by genomic and proteomic data provides an adaptable model system that can be extended to effectively treat other cancers and diseases.

Can the Acute-Phase Reactant Proteins be Used as Cancer Biomarkers?

The association between the acute-phase reactant proteins (APRPs) and cancer has long been established. There have been numerous reports correlating altered levels of various APRPs with different types of cancers. However, researchers are often quick to dismiss the use of these APRPs as potential biomarkers for the diagnosis and monitoring of cancer because alterations in APRP concentrations are observed in a wide range of diseases. Recent progress in proteomics studies which profiled the serum proteins of cancer patients and those of normal individuals indicated that the altered APRP expressions were different for distinct types, subtypes, and even stages of cancer. Interestingly, these data are in agreement with those observed earlier using immunochemical and biochemical assays. In view of this compelling association of different patterns of APRPs with various types of cancers and in an apparent shift of paradigm, we present in this review some indications that APRP fingerprinting may be used as complementary cancer biomarkers.

YAP and 14-3-3γ are involved in HS-OA-induced growth inhibition of hepatocellular carcinoma cells: A novel mechanism for hydrogen sulfide releasing oleanolic acid

Hydrogen sulfide-releasing oleanolic acid (HS-OA) is an emerging novel class of compounds and consists of an oleanolic acid (OA) and a H₂S-releasing moiety. Although it exhibits improved anti-inflammatory activity, its potency in human cancers has not been understood yet. In this study, we examined the effects of HS-OA on the growth of liver cancer cell lines and the underlying mechanisms.

HS-OA inhibited the growth of all four cancer cell lines studied, with potencies of 10- to 30-fold greater than that of its counterpart (OA). HS-OA induced significant apoptosis and decreased viability, clonogenic activity and migration of Hep G₂ cells. Further studies showed that HS-OA resulted in the reduction of YAP expression and its downstream targets, CTGF and CYR 61, thus promoting cell apoptosis. In addition, HS-OA caused a decrease of 14-3-3γ expression, which led to Bad translocation to the mitochondria, ΔΨm loss, cytochrome c release, caspase activation and a recovery of 14-3-3γ reversed these effects induced by HS-OA.

These findings indicate that YAP and 14-3-3γ are involved in HS-OA's effects on liver cancer cells and identifying HS-OA as a potential new drug candidate for cancer therapy.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data

BACKGROUND

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model.

RESULTS

We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database.

CONCLUSION

In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Combining anaerobic bacterial oncolysis with vaccination that blocks interleukin-10 signaling may achieve better outcomes for late stage cancer management

Late stage solid tumors cause significant cancer mortality rates worldwide and effective therapy remains a big challenge. Cancer therapeutic vaccines elicit tumor specific T cells that kill tumor cells yet often fail to result in tumor destruction because of the limited T cell response and the local immune-suppressive environment. Blocking interleukin 10 (IL-10) signaling at the time of therapeutic vaccination elicits much stronger T cell responses than vaccination without IL-10 blocking. Anaerobic oncolytic bacteria target hypoxic regions of the late stage tumor tissues which not only stops tumor growth but also provides a pro-inflammatory environment that may increase the effectiveness of a therapeutic vaccine by recruiting more effector T cells to tumor site. In this review, we argue that combining both bacterial and vaccine therapies may improve the efficiency of late stage cancer management.

Targeting Heat Shock Proteins in Cancer: A Promising Therapeutic Approach

Heat shock proteins (HSPs) are a large family of chaperones that are involved in protein folding and maturation of a variety of "client" proteins protecting them from degradation, oxidative stress, hypoxia, and thermal stress. Hence, they are significant regulators of cellular proliferation, differentiation and strongly implicated in the molecular orchestration of cancer development and progression as many of their clients are well established oncoproteins in multiple tumor types. Interestingly, tumor cells are more HSP chaperonage-dependent than normal cells for proliferation and survival because the oncoproteins in cancer cells are often misfolded and require augmented chaperonage activity for correction. This led to the development of several inhibitors of HSP90 and other HSPs that have shown promise both preclinically and clinically in the treatment of cancer. In this article, we comprehensively review the roles of some of the important HSPs in cancer, and how targeting them could be efficacious, especially when traditional cancer therapies fail.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

Proteomics: A new perspective for cancer

In the past decades, several ground breaking discoveries in life science were made. The completion of sequencing the human genome certainly belongs to the key tasks successfully completed, representing a true milestone in the biomedicine. The accomplishment of the complete genome also brings along a new, even more challenging task for scientists: The characterization of the human proteome. Proteomics, the main tool for proteome research, is a relatively new and extremely dynamically evolving branch of science, focused on the evaluation of gene expression at proteome level. Due to the specific properties of proteins, current proteomics deals with different issues, such as protein identification, quantification, characterization of post-translational modification, structure and function elucidation, and description of possible interactions. This field incorporates technologies that can be applied to serum and tissue in order to extract important biological information in the form of biomarkers to aid clinicians and scientists in understanding the dynamic biology of their system of interest, such as a patient with cancer. The present review article provides a detail description of proteomics and its role in cancer research.

Proteomics in cancer research: Are we ready for clinical practice?

Although genomics has delivered major advances in cancer prognostics, treatment and diagnostics, it still only provides a static image of the situation. To study more dynamic molecular entities, proteomics has been introduced into the cancer research field more than a decade ago. Currently, however, the impact of clinical proteomics on patient management and clinical decision-making is low and the implementations of scientific results in the clinic appear to be scarce. The search for cancer-related biomarkers with proteomics however, has major potential to improve risk assessment, early detection, diagnosis, prognosis, treatment selection and monitoring. In this review, we provide an overview of the transition of oncoproteomics towards translational oncology. We describe which lessons are learned from currently approved protein biomarkers and previous proteomic studies, what the pitfalls and challenges are in clinical proteomics applications, and how proteomic research can be successfully translated into medical practice.

Cancer serum biomarkers based on aberrant post-translational modifications of glycoproteins: Clinical value and discovery strategies

Due to the increase in life expectancy in the last decades, as well as changes in lifestyle, cancer has become one of the most common diseases both in developed and developing countries. Early detection remains the most promising approach to improve long-term survival of cancer patients and this may be achieved by efficient screening of biomarkers in biological fluids. Great efforts have been made to identify specific alterations during oncogenesis. Changes at the cellular glycosylation profiles are among such alterations. The "glycosylation machinery" of cells is affected by malignant transformation due to the altered expression of glycogens, leading to changes in glycan biosynthesis and diversity. Alterations in the post-translational modifications of proteins that occur in cancer result in the expression of antigenically distinct glycoproteins. Therefore, these aberrant and cancer-specific glycoproteins and the autoantibodies that are produced in response to their presence constitute targets for cancer biomarkers' search. Different strategies have been implemented for the discovery of cancer glycobiomarkers and are herein reviewed, along with their potentialities and limitations. Practical issues related with serum analysis are also addressed, as well as the challenges that this area faces in the near future.

Targeted proteomics for biomarker discovery and validation of hepatocellular carcinoma in hepatitis C infected patients

Hepatocellular carcinoma (HCC)-related mortality is high because early detection modalities are hampered by inaccuracy, expense and inherent procedural risks. Thus there is an urgent need for minimally invasive, highly specific and sensitive biomarkers that enable early disease detection when therapeutic intervention remains practical. Successful therapeutic intervention is predicated on the ability to detect the cancer early. Similar unmet medical needs abound in most fields of medicine and require novel methodological approaches. Proteomic profiling of body fluids presents a sensitive diagnostic tool for early cancer detection. Here we describe such a strategy of comparative proteomics to identify potential serum-based biomarkers to distinguish high-risk chronic hepatitis C virus infected patients from HCC patients. In order to compensate for the extraordinary dynamic range in serum proteins, enrichment methods that compress the dynamic range without surrendering proteome complexity can help minimize the problems associated with many depletion methods. The enriched serum can be resolved using 2D-difference in-gel electrophoresis and the spots showing statistically significant changes selected for identification by liquid chromatography-tandem mass spectrometry. Subsequent quantitative verification and validation of these candidate biomarkers represent an obligatory and rate-limiting process that is greatly enabled by selected reaction monitoring (SRM). SRM is a tandem mass spectrometry method suitable for identification and quantitation of target peptides within complex mixtures independent on peptide-specific antibodies. Ultimately, multiplexed SRM and dynamic multiple reaction monitoring can be utilized for the simultaneous analysis of a biomarker panel derived from support vector machine learning approaches, which allows monitoring a specific disease state such as early HCC. Overall, this approach yields high probability biomarkers for clinical validation in large patient cohorts and represents a strategy extensible to many diseases.

Proteomics analysis of human brain glial cell proteome by 2D gel

INTRODUCTION

Proteomics is increasingly employed in both neurological and oncological research, and applied widely in every area of neuroscience research including brain cancer. Astrocytomas are the most common glioma and can occur in most parts of the brain and occasionally in the spinal cord. Patients with high-grade astrocytomas have a life expectancy of <1 year even after surgery, chemotherapy, and radiotherapy.

MATERIALS AND METHODS

We extracted proteins from tumors and normal brain tissues and then evaluated the protein purity by Bradford test and spectrophotometry method. In this study, we separated proteins by the two-dimensional (2DG) gel electrophoresis method, and the spots were analyzed and compared using statistical data.

RESULTS

On each analytical 2D gel, an average of 800 spots was observed. In this study, 164 spots exhibited up-regulation of expression level, whereas the remaining 179 spots decreased in astrocytoma tumor relative to normal tissue. RESULTS demonstrate that functional clustering and principal component analysis (PCA) has considerable merits in aiding the interpretation of proteomic data. Proteomics is a powerful tool in identifying multiple proteins that are altered following a neuropharmacological intervention in a disease of the central nervous system (CNS).

CONCLUSION

2-D gel and cluster analysis have important roles in the diagnostic management of astrocytoma patients, providing insight into tumor biology. The application of proteomics to CNS research has invariably been very successful in yielding large amounts of data.

Label-free quantitative mass spectrometry reveals a panel of differentially expressed proteins in colorectal cancer

To identify potential biomarkers involved in CRC, a shotgun proteomic method was applied to identify soluble proteins in three CRCs and matched normal mucosal tissues using high-performance liquid chromatography and mass spectrometry. Label-free protein profiling of three CRCs and matched normal mucosal tissues were then conducted to quantify and compare proteins. Results showed that 67 of the 784 identified proteins were linked to CRC (28 upregulated and 39 downregulated). Gene Ontology and DAVID databases were searched to identify the location and function of differential proteins that were related to the biological processes of binding, cell structure, signal transduction, cell adhesion, and so on. Among the differentially expressed proteins, tropomyosin-3 (TPM3), endoplasmic reticulum resident protein 29 (ERp29), 18 kDa cationic antimicrobial protein (CAMP), and heat shock 70 kDa protein 8 (HSPA8) were verified to be upregulated in CRC tissue and seven cell lines through western blot analysis. Furthermore, the upregulation of TPM3, ERp29, CAMP, and HSPA8 was validated in 69 CRCs byimmunohistochemistry (IHC) analysis. Combination of TPM3, ERp29, CAMP, and HSPA8 can identify CRC from matched normal mucosal achieving an accuracy of 73.2% using IHC score. These results suggest that TPM3, ERp29, CAMP, and HSPA8 are great potential IHC diagnostic biomarkers for CRC.

Synthesis of chromonylthiazolidines and their cytotoxicity to human cancer cell lines

Nine new chromonylthiazolidine derivatives were successfully semi-synthesized from paeonol. All of the compounds, including starting materials, the intermediate compound and products, were evaluated for their cytotoxic effects toward eight human cancer cell lines. The synthesized chromonylthiazolidines displayed weak cytotoxic effects against the tested cancer cell lines, but selective cytotoxic effects were observed. Compounds 3a and 3b showed the most selective cytotoxic effects against human epidermoid carcinoma (IC50 44.1 ± 3.6 μg/mL) and breast cancer (IC50 32.8 ± 1.4 μg/mL) cell lines, respectively. The results suggest that chromoylthiazolidines are potential low-cost, and selective anticancer agents.

Controlling the fluorescence of benzofuran-modified uracil residues in oligonucleotides by triple-helix formation

We developed fluorescent turn-on probes containing a fluorescent nucleoside, 5-(benzofuran-2-yl)deoxyuridine (dU(BF)) or 5-(3-methylbenzofuran-2-yl)deoxyuridine (dU(MBF)), for the detection of single-stranded DNA or RNA by utilizing DNA triplex formation. Fluorescence measurements revealed that the probe containing dU(MBF) achieved superior fluorescence enhancement than that containing dU(BF). NMR and fluorescence analyses indicated that the fluorescence intensity increased upon triplex formation partly as a consequence of a conformational change at the bond between the 3-methylbenzofuran and uracil rings. In addition, it is suggested that the microenvironment around the 3-methylbenzofuran ring contributed to the fluorescence enhancement. Further, we developed a method for detecting RNA by rolling circular amplification in combination with triplex-induced fluorescence enhancement of the oligonucleotide probe containing dU(MBF).

[Search for potential gastric cancer biomarkers using low molecular weight blood plasma proteome profiling by mass spectrometry]

Gastric cancer, one of the most widespread malignant tumors, still lacks reliable serum/plasma biomarkers of its early detection. In this study we have developed, unified, and tested a new methodology for search of gastric cancer biomarkers based on profiling of low molecular weight proteome (LMWP) (1-17 kDa). This approach included three main components: sample pre-fractionation, matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS), data analysis by a bioinformatics software package. Applicability and perspectives of the developed approach for detection of potential gastric cancer markers during LMWP analysis have been demonstrated using 69 plasma samples from patients with gastric cancer (stages I-IV) and 238 control samples. The study revealed peptides/polypeptides, which may be potentially used for detection of this pathology.

Comparative serum proteomic analysis of serum diagnosis proteins of colorectal cancer based on magnetic bead separation and maldi-tof mass spectrometry

BACKGROUND

At present, the diagnosis of colorectal cancer (CRC) requires a colorectal biopsy which is an invasive procedure. We undertook this pilot study to develop an alternative method and potential new biomarkers for diagnosis, and validated a set of well-integrated tools called ClinProt to investigate the serum peptidome in CRC patients.

METHODS

Fasting blood samples from 67 patients diagnosed with CRC by histological diagnosis, 55 patients diagnosed with colorectal adenoma by biopsy, and 65 healthy volunteers were collected. Division was into a model construction group and an external validation group randomly. The present work focused on serum proteomic analysis of model construction group by ClinProt Kit combined with mass spectrometry. This approach allowed construction of a peptide pattern able to differentiate the studied populations. An external validation group was used to verify the diagnostic capability of the peptidome pattern blindly. An immunoassay method was used to determine serum CEA of CRC and controls.

RESULTS

The results showed 59 differential peptide peaks in CRC, colorectal adenoma and health volunteers. A genetic algorithm was used to set up the classification models. Four of the identified peaks at m/z 797, 810, 4078 and 5343 were used to construct peptidome patterns, achieving an accuracy of 100% (> CEA, P < 0. 05). Furthermore, the peptidome patterns could differentiate the validation group with high accuracy close to 100%.

CONCLUSIONS

Our results showed that proteomic analysis of serum with MALDI-TOF MS is a fast and reproducible approach, which may provide a novel approach to screening for CRC.

Identification of candidate biomarkers for the early detection of nasopharyngeal carcinoma by quantitative proteomic analysis

Nasopharyngeal carcinoma (NPC) is a major head and neck cancer with high occurrence in Southeast Asia and southern China. To identify novel biomarkers for the early detection of NPC patients, 2D-DIGE combined with MALDI-TOF-MS analysis was performed to identify differentially expressed proteins in the carcinogenesis and progression of NPC using LCM-purified normal nasopharyngeal epithelial tissues and various stages of NPC biopsies. As a result, 26 differentially expressed proteins were identified, of which two proteins with sharp expressional changes in the carcinogenic process, ENO1 and CYPA, were validated by western blot analysis and identified as critical seed proteins in the functional network. Immunohistochemistry assay was further performed to detect the expression of the two proteins with a tissue microarray that included various stages of NPC tissues. The ability of these proteins to detect NPC early was evaluated via a receiver operating characteristic analysis. The results indicated that the combination of the two proteins could perfectly discriminate NNET and AH from stage I of NPC with high sensitivity and specificity, which is more effective than using either of the two proteins individually. In summary, the combination of ENO1 and CYPA can serve as potential molecular markers for the early detection of NPC.

BIOLOGICAL SIGNIFICANCE

NPC is a lethal malignancy that is most prevalent in Southeast Asia, and early detection and treatment are essential for the survival and good prognosis of NPC patients. In the present work, we identified 26 differentially expressed proteins in NNET, AH and different stages of NPC tissues by using 2D-DIGE combined with MALDI-TOF/TOF analysis. Of these proteins, the down-regulation of ENO1 and over-expression of CYPA were confirmed with a high-throughput tissue microarray that included various stages of NPC tissues via an IHC assay, and the results indicated that the combination of ENO1 and CYPA can serve as a potential molecular marker for the early detection of NPC.