Identification of serum biomarkers for nasopharyngeal carcinoma by proteomic analysis

Autoantibody Signatures as a Biomarker Panel for the Detection of Nasopharyngeal Carcinoma

OBJECTIVE

The early symptoms of nasopharyngeal carcinoma (NPC) are not obvious, and it is difficult to make early diagnosis. A case-control study was conducted to identify potential biomarkers and established a diagnosis model for nasopharyngeal carcinoma.

METHODS

Plasma samples of 131 cases of NPC and 132 cases of healthy individuals were incubated with the Ray Biotech Human Lung Cancer IgG Autoantibody Detection Array G1, and signal values were used to develop a risk prediction model for NPC diagnosis.

RESULTS

Of the 30 autoantibodies, high expression of MAGE-A4, NY-ESO-1, HuD, Survivin, IMDH2, Ubiquilin-1, IMP1, PGP9.5, IMP3, C-Myc and low expression of Cyclin B1 were potential biomarkers for NPC diagnosis (p <0.05), among which Survivin, MAGE-A4 and IMP3 shows higher AUC of 0.674, 0.652 and 0.650 respectively, the specificity of them was 89.39% (95% CI: 82.85-94.08%), 90.15% (95% CI: 83.75-94.65%) and 88.64% (81.95-93.50%).The risk probability analysis for NPC diagnosis based on the panel of Cyclin B1, NY-ESO-1, Survivin, and IMP3 displayed the best diagnosis performance with an AUC of 0.779, p (Yi = 1) = 1/(1+EXP[8.316+1.672*CyclinB1-1.152*NY-ESO-1-2.052*Survivin-0.950*IMP3]), the specificity of that was 86.36% (95% CI: 79.31-91.71%).

CONCLUSIONS

Our findings demonstrated that the panel of Cyclin B1, NY-ESO-1, Survivin, and IMP3 has a good performance in the detection of NPC, and all 11 autoantibodies may also have a certain significance for the prognosis of NPC.

Deciphering nasopharyngeal carcinoma pathogenesis via proteomics

Introduction: Nasopharyngeal carcinoma (NPC) is a distinct head and neck squamous cell carcinoma in its etiological association of Epstein-Barr virus (EBV) infection, hidden anatomical location, remarkable racial and geographical distribution, and high incidence of locoregional recurrence or metastasis. Thanks to the advancements in proteomics in recent decades, more understanding of the disease etiology, carcinogenesis, and progression has been gained, potentially deciphering the molecular characteristics of the malignancy. Areas covered: In this review, we provide an overview of the proteomic aberrations that are likely involved or drive NPC development and progression, focusing on the contributions of major EBV-encoded factors, intercommunication with environment, protein features of high metastasis and therapy resistance, and protein-protein interactions that allow NPC cells to evade immune recognition and elimination. Finally, multistep carcinogenesis and subtypes of NPC from a proteomic perspective are inquired. Expert commentary: Proteomic studies have covered various aspects involved in NPC pathogenesis, yet much remains to be uncovered. Coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation are critical in ensuring the emergence of good science out of NPC proteomics. NPC proteogenomics and proteoform analysis are two promising fields to promote the application of the proteomic findings from bench to bedside.

Potential urine biomarkers for gestational hypertension and preeclampsia

Differential proteomic technology was used to identify urine proteomic profile of gestational hypertension and preeclampsia. Urine samples were collected from 10 patients with gestational hypertension, 10 patients with mild preeclampsia, 10 patients with severe preeclampsia and 10 normal pregnancies and analyzed by 2‑D difference gel electrophoresis, then matrix assisted laser desorption ionization mass spectrometry was used to identify differential proteins. Subsequently, ELISA was used to verify the content variation of the identified proteins in 200 urine samples. In total, 30 differential proteins were identified. For prostaglandin‑H2 D‑isomerase (L‑PGDS), perlecan and other 15 proteins, the contents in patients with gestational hypertension were higher than that of normal pregnancies, but lower in mild and severe preeclampsia. By contrast, serum albumin and α‑1‑antitrypsin was lower in samples from patients with gestational hypertension and higher in patients with mild and severe preeclampsia compared with normal pregnancies. ELISA verified that the urinary concentration of L‑PGDS and perlecan were significantly lower in patients with preeclampsia than in normal pregnancies (P<0.05). Urine proteomics is a useful tool to identify potential biomarkers to distinguish between different types of hypertensive disorders in pregnancy. L‑PGDS and perlecan could potentially be used as markers to reflect the state of renal function, and may participate in the genesis and development of renal injury during preeclampsia.

Identification of CALM as the potential serum biomarker for predicting the recurrence of nasopharyngeal carcinoma using a mass spectrometry-based comparative proteomic approach

To date, there are no serum biomarkers available for the prediction of recurrent nasopharyngeal carcinoma (rNPC). The diagnosis of rNPC mostly depends on imaging and biopsy of diseased tissue; however, both of these methods work mostly if the target tumor is at an advanced stage. Therefore, the identificaqtion of recurrent biomarkers is urgently required. In the present study, we used tandem mass tag (TMT) labeling and high performance liquid chromatography (HPLC) fractionation followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify differentially expressed proteins. Serum was collected from 40 patients with NPC [recurrence (n=20) and no recurrence (n=20)]. Compared to non-recurrent NPC (nrNPC), we found 59 proteins to be significantly dysregulated in rNPC; most of these have been previously reported to play a role in carcinogenesis. The dysregulation of calmodulin (CALM) was confirmed in 74 new patients [recurrence (n=32) and no recurrence (n=42)] by ELISA. Moreover, we performed a preliminary pathway analysis which revealed that oxidative phosphorylation was altered in the patients with rNPC compared to those with nrNPC. Taken together, these data identify a potential diagnostic biomarker for rNPC and elucidate the potential molecular mechanisms that are dysregulated and contribute to the pathogenesis of rNPC.

A Review: Proteomics in Nasopharyngeal Carcinoma

Although radiotherapy is generally effective in the treatment of major nasopharyngeal carcinoma (NPC), this treatment still makes approximately 20% of patients radioresistant. Therefore, the identification of blood or biopsy biomarkers that can predict the treatment response to radioresistance and that can diagnosis early stages of NPC would be highly useful to improve this situation. Proteomics is widely used in NPC for searching biomarkers and comparing differentially expressed proteins. In this review, an overview of proteomics with different samples related to NPC and common proteomics methods was made. In conclusion, identical proteins are sorted as follows: Keratin is ranked the highest followed by such proteins as annexin, heat shock protein, 14-3-3σ, nm-23 protein, cathepsin, heterogeneous nuclear ribonucleoproteins, enolase, triosephosphate isomerase, stathmin, prohibitin, and vimentin. This ranking indicates that these proteins may be NPC-related proteins and have potential value for further studies.

Omics-based identification of biomarkers for nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a head and neck cancer that is highly found in distinct geographic areas, such as Southeast Asia. The management of NPC remains burdensome as the prognosis is poor due to the late presentation of the disease and the complex nature of NPC pathogenesis. Therefore, it is necessary to find effective molecular markers for early detection and therapeutic measure of NPC. In this paper, the discovery of molecular biomarker for NPC through the emerging omics technologies including genomics, miRNA-omics, transcriptomics, proteomics, and metabolomics will be extensively reviewed. These markers have been shown to play roles in various cellular pathways in NPC progression. The knowledge on their function will help us understand in more detail the complexity in tumor biology, leading to the better strategies for early detection, outcome prediction, detection of disease recurrence, and therapeutic approach.

Biomarker discovery of nasopharyngeal carcinoma by proteomics

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors in southern China and southern Asia, and poses one of the most serious public health problems in these areas. Early diagnosis, predicting metastasis, recurrence, prognosis and therapeutic response of NPC remain a challenge. Discovery of diagnostic and predictive biomarkers is an ideal way to achieve these objectives. Proteomics has great potential in identifying cancer biomarkers. Comparative proteomics has identified a large number of potential biomarkers associated with NPC, although the clinical performance of such biomarkers needs to be further validated. In this article, we review the latest discovery and progress of biomarkers for early diagnosis, predicting metastasis, recurrence, prognosis and therapeutic response of NPC, inform the readers of the current status of proteomics-based NPC biomarker findings and suggest avenues for future work.

Identification of patients with nasopharyngeal carcinoma by serum protein profiling using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

BACKGROUND

As diagnosis of nasopharyngeal carcinoma at an early disease stage is important, we attempted to distinguish between patients with nasopharyngeal carcinoma and noncancer controls by using serum protein profiles.

METHODS

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry and CM10 protein chip were used to detect the serum proteomic patterns of 65 patients with nasopharyngeal carcinoma before radiotherapy and 93 noncancer controls. Proteomic spectra of serum samples from 50 nasopharyngeal carcinoma patients and 60 noncancer controls were used as a training set. The validity of the classification tree was then challenged with a blind test set which included another 15 patients with nasopharyngeal carcinoma and 33 noncancer controls. Biomarker Wizard 3.01 and Biomarker Pattern 5.01 were used in combination to analyze the data and to develop diagnostic models.

RESULTS

21 protein peaks were significantly different between nasopharyngeal carcinoma and controls. 4 mass peaks (M4182, M5343, M5913 and M8702 mass/charge ratio) were chosen automatically to construct a classification tree. The classification tree correctly determined 93.8 % (45/48) of the test samples with 93.3 % (14/15) of the nasopharyngeal carcinoma samples and 93.9 % (31/33) of the noncancer samples. Using a combination of serum protein profiles and Epstein-Barr viral capsid antigen immunoglobulin A antibody tests, the diagnostic sensitivity and specificity were increased to 100 and 97 %, respectively.

CONCLUSIONS

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry could correctly distinguish nasopharyngeal carcinoma from noncancer individuals and showed great potential for the development of a screening test for the detection of nasopharyngeal carcinoma.

Proteomics: methodologies and applications to the study of human diseases

Proteomic approach has allowed large-scale studies of protein expression in different tissues and body fluids in discrete conditions and/or time points. Recent advances of methodologies in this field have opened new opportunities to obtain relevant information on normal and abnormal processes occurring in the human body. In the current report, the main proteomics techniques and their application to human disease study are reviewed.

Better cancer biomarker discovery through better study design

BACKGROUND

High-throughput laboratory technologies coupled with sophisticated bioinformatics algorithms have tremendous potential for discovering novel biomarkers, or profiles of biomarkers, that could serve as predictors of disease risk, response to treatment or prognosis. We discuss methodological issues in wedding high-throughput approaches for biomarker discovery with the case-control study designs typically used in biomarker discovery studies, especially focusing on nested case-control designs.

METHODS

We review principles for nested case-control study design in relation to biomarker discovery studies and describe how the efficiency of biomarker discovery can be effected by study design choices. We develop a simulated prostate cancer cohort data set and a series of biomarker discovery case-control studies nested within the cohort to illustrate how study design choices can influence biomarker discovery process.

RESULT

Common elements of nested case-control design, incidence density sampling and matching of controls to cases are not typically factored correctly into biomarker discovery analyses, inducing bias in the discovery process. We illustrate how incidence density sampling and matching of controls to cases reduce the apparent specificity of truly valid biomarkers 'discovered' in a nested case-control study. We also propose and demonstrate a new case-control matching protocol, we call 'antimatching', that improves the efficiency of biomarker discovery studies.

CONCLUSIONS

For a valid, but as yet undiscovered, biomarker(s) disjunctions between correctly designed epidemiologic studies and the practice of biomarker discovery reduce the likelihood that true biomarker(s) will be discovered and increases the false-positive discovery rate.

Identifying FGA peptides as nasopharyngeal carcinoma-associated biomarkers by magnetic beads

Early diagnosis and treatment is known to improve prognosis for nasopharyngeal carcinoma (NPC). The study determined the specific peptide profiles by comparing the serum differences between NPC patients and healthy controls, and provided the basis for the diagnostic model and identification of specific biomarkers of NPC. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) can be used to detect the molecular mass of peptides. Mass spectra of peptides were generated after extracting and purification of 40 NPC samples in the training set, 21 in the single center validation set and 99 in the multicenter validation set using weak cationic-exchanger magnetic beads. The spectra were analyzed statistically using FlexAnalysis™ and ClinProt™ bioinformatics software. The four most significant peaks were selected out to train a genetic algorithm model to diagnose NPC. The diagnostic sensitivity and specificity were 100% and 100% in the training set, 90.5% and 88.9% in the single center validation set, 91.9% and 83.3% in the multicenter validation set, and the false positive rate (FPR) and false negative rate (FNR) were obviously lower in the NPC group (FPR, 16.7%; FNR, 8.1%) than in the other cancer group (FPR, 39%; FNR, 61%), respectively. So, the diagnostic model including four peptides can be suitable for NPC but not for other cancers. FGA peptide fragments identified may serve as tumor-associated biomarkers for NPC.

MicroRNAs: potential diagnostic markers and therapeutic targets for EBV-associated nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a highly malignant cancer with local invasion and early distant metastasis. NPC is highly prevalent in the Southern China and South-eastern Asia. The genetic susceptibility, endemic environment factors, and Epstein-Barr virus (EBV) infection are believed to be the major etiologic factors of NPC. Once metastasis occurs, the prognosis is very poor. It is urgently needed to develop biomarkers for early clinical diagnosis/prognosis, and novel effective therapies for nasopharyngeal carcinoma. In this paper, we systematically reviewed the current progress of miRNA studies in NPC. It has been shown that both host encoded miRNAs and EBV encoded miRNAs play key roles in almost all the steps of epithelia cell carcinogenesis, including epithelial-mesenchymal to stem-like transition, cell growth, migration, invasion, and tumorigenesis. More importantly, some miRNAs could be secreted out and play a role in the microenvironments. The level of sera miRNAs is correlated with the copy numbers of host miRNAs in tumor biopsies. Promising results of gene therapy have been also achieved by lentiviral delivered miRNAs. Taken together, cell free miRNAs would be potential biomarkers of early clinical diagnosis/prognosis; while some miRNAs could be further developed into therapeutic agents in the future.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Early detection of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a unique disease with a clinical presentation, epidemiology, and histopathology differing from other squamous cell carcinomas of the head and neck. NPC is an Epstein-Barr virus-associated malignancy with a marked racial and geographic distribution. Specifically, it is highly prevalent in southern China, Southeast Asia, and the Middle East. To date, most NPC patients have been diagnosed in the advanced stage, but the treatment results for advanced NPC are not satisfactory. This paper provides a brief overview regarding NPC, with the focus on the early detection of initial and recurrent NPC lesions.

Reproducibility of SELDI Spectra Across Time and Laboratories

This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.

The reproducibility of mass spectrometry (MS) data collected using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) has been questioned. This investigation was designed to test the reproducibility of SELDI data collected over time by multiple users and instruments. Five laboratories prepared arrays once every week for six weeks. Spectra were collected on separate instruments in the individual laboratories. Additionally, all of the arrays produced each week were rescanned on a single instrument in one laboratory. Lab-to-lab and array-to-array variability in alignment parameters were larger than the variability attributable to running samples during different weeks. The coefficient of variance (CV) in spectrum intensity ranged from 25% at baseline, to 80% in the matrix noise region, to about 50% during the exponential drop from the maximum matrix noise. Before normalization, the median CV of the peak heights was 72% and reduced to about 20% after normalization. Additionally, for the spectra from a common instrument, the CV ranged from 5% at baseline, to 50% in the matrix noise region, to 20% during the drop from the maximum matrix noise. Normalization reduced the variability in peak heights to about 18%. With proper processing methods, SELDI instruments produce spectra containing large numbers of reproducibly located peaks, with consistent heights.

Supervised learning with decision tree-based methods in computational and systems biology

At the intersection between artificial intelligence and statistics, supervised learning allows algorithms to automatically build predictive models from just observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the review is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the review provides a survey of their applications in the context of computational and systems biology.

Cancer-specific MALDI-TOF profiles of blood serum and plasma: biological meaning and perspectives

MALDI-TOF mass-spectrometry has become a popular tool of cancer research during the last decade. High throughput and relative simplicity of this technology have made it attractive for biomarker discovery and validation across various platforms in blood serum/plasma. Many technical approaches have been developed for plasma/serum profiling including protein-chip based SELDI-TOF mass-spectrometry, purification of serum on magnetic beads, analysis of carrier-associated fraction and mass-spectrometric immunoassays. Extensive data about the identity of differential features detected on mass-spectra up to now makes it possible to draw conclusions about potency and perspectives of MALDI-TOF mass-spectrometry in this field. A great majority of identified differentially expressed proteins are either house-keeping or inflammatory proteins as well as their modifications or fragments. Discriminating ability of mass-spectra is likely to be based on differential modification and fragmentation patterns of abundant serum proteins reflecting activity of enzymes including proteases and their inhibitors.

Analytical considerations for mass spectrometry profiling in serum biomarker discovery

The potential of using mass spectrometry profiling as a diagnostic tool has been demonstrated for a wide variety of diseases. Various cancers and cancer-related diseases have been the focus of much of this work because of both the paucity of good diagnostic markers and the knowledge that early diagnosis is the most powerful weapon in treating cancer. The implementation of mass spectrometry as a routine diagnostic tool has proved to be difficult, however, primarily because of the stringent controls that are required for the method to be reproducible. The method is evolving as a powerful guide to the discovery of biomarkers that could, in turn, be used either individually or in an array or panel of tests for early disease detection. Using proteomic patterns to guide biomarker discovery and the possibility of deployment in the clinical laboratory environment on current instrumentation or in a hybrid technology has the possibility of being the early diagnosis tool that is needed.

SELDI-TOF MS profiling of serum for detection of nasopharyngeal carcinoma

Background

No satisfactory biomarkers are currently available to screen for nasopharyngeal carcinoma (NPC). We have developed and evaluated surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for detection and analysis of multiple proteins for distinguishing individuals with NPC from control individuals.

Methods

A preliminary learning set and a classification tree of spectra derived from 24 patients with NPC and a group of 24 noncancer controls were used to develop a proteomic model that discriminated cancer from noncancer effectively. Then, the validity of the classification tree was challenged with a blind test set, which included another 20 patients with NPC and 12 noncancer controls.

Results

A panel of 3 biomarkers ranging m/z 3–20 k was selected to establish Decision Tree model by BPS with sensitivity of 91.66% and specificity of 95.83%. The ability to detect NPC patients was evaluated, a sensitivity of 95.0% and specificity of 83.33% were validated in blind testing set.

Conclusion

This high-flux proteomic classification system will provide a highly accurate and innovative approach for the detection/diagnosis of NPC.