SPECIAL FOCUS y Proteomics in translational cancer research
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Biomarker discovery of nasopharyngeal carcinoma by proteomics Expert Review of Proteomics Downloaded from informahealthcare.com by Nyu Medical Center on 02/17/15 For personal use only.
Expert Rev. Proteomics 11(2), 215–225 (2014)
Liang Xiao1,2, Ta Xiao1, Zhi-Ming Wang2, William CS Cho3 and Zhi-Qiang Xiao*1 1 Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China 2 Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China 3 Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, P.R. China *Author for correspondence: Tel.: +86 731 8432 7239 Fax: +86 731 8432 7332 [email protected]
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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. KEYWORDS: biomarker • diagnosis • metastasis • nasopharyngeal carcinoma • prognosis • proteomics • recurrence • therapeutic response
Nasopharyngeal carcinoma (NPC) is a malignant tumor originated from nasopharyngeal epithelial cells. The cancer is an Epstein–Barr virus (EBV)-associated malignancy, with a remarkable racial and geographical distribution. The phenomenon indicates that the development of this cancer must be related to special genetic and environmental factors. It is highly prevalent in Southern China and Southern Asia, with an incidence of 30–50 per 100,000, which is about a 100-fold higher compared with other populations not at risk and poses a very serious health problem in these areas [1]. NPC is highly sensitive to radiotherapy (RT), which is the preferred treatment for it, but the outcome is related to the extent of the disease; therefore, early detection and diagnosis of NPC is crucial for better outcome in the patients [2]. Unfortunately, early-stage NPC is difficult to diagnose because of its deep location and vague symptoms. Moreover, radioresistance causes NPC recurrence and metastasis leads to RT failures. Therefore, discovery of biomarkers for early diagnosis, predicting metastasis, recurrence and therapeutic response of NPC is of great importance for guiding NPC treatment and improving patients’ prognosis. Thanks to the advancements in proteomics and bioinformatics 10.1586/14789450.2014.897613
in the recent 20 years, an emerging image that correlates NPC clinical parameters with protein biomarkers has been gained [3], which would improve early diagnosis and tailored therapeutic modalities of NPC. In this article, a brief overview of the proteomics approaches is given, and the latest discovery and advance of NPC biomarkers is reviewed. Proteomics approaches for identification of tumor markers
Various proteomics approaches have been employed to discover tumor biomarkers. The potential NPC markers discovered by proteomics are described in the following sections. Only a brief overview of the proteomics approaches for identifying tumor markers is given here since there have been numerous reviews introducing them in detail. Proteomics approaches can be classified into gel-based and gel-free methods or label-free and label-based methods. It is known that no proteomics technique could analyze the whole proteome in a single experiment; thus more than one complementary approaches in methodology would need to be employed to approach higher proteome coverage. A classical gel-based proteomics approach for the identification of the differentially
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Review
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expressed proteins (DEPs) in tumor samples and controls is 2D gel electrophoresis (2DE) coupled with mass spectrometry (MS) analysis. Since its development almost 40 years ago, 2DE is still one of the methods of choice for protein separation and quantification. In order to enhance the reproducibility and sensitivity of 2DE, 2D difference gel electrophoresis coupled with MS analysis is used to identify the DEPs in tumor samples and controls, which theoretically detects lowabundance proteins and decreases gel-to-gel difference [4]. One of the main advantages of gel-based proteomics approaches is that they give a visual representation of proteins and the DEPs within each sample. However, there are some disadvantages in the gel-based proteomics approaches, for example, the fact that they are generally low in the highthroughput and not suitable for identifying the highly acidic, basic or hydrophobic proteins, as well as the very large, very small or low-abundant proteins [5]. Due to the drawbacks of gel-based proteomics approaches, gel-free proteomics approaches have been developed for identification of the DPEs in tumor samples and controls. One of the most common gel-free proteomics methods is a highperformance liquid chromatography (LC) for separation, coupled with electrospray ionization-tandem MS for peptide identification and quantification, also known as ‘shotgun’ proteomics [6], which can identify and quantify proteins from a complex liquid protein mixture with the high-throughput. To accurately quantify the DEPs in tumor samples and controls, various quantitative proteomics approaches have been developed, which can be divided into label-based and labelfree proteomics [7]. Isobaric tag for relative and absolute quantification (iTRAQ) is a gel-free and label-based proteomics technique used to identify and quantify the DEPs from multiple samples in a single experiment, which is its main advantage [8]. Other gel-free and label-based proteomics approaches have also been developed, such as stable isotope labeling with amino acids in cell culture. In the stable isotope labeling with amino acids in cell culture experiment, isotopically labeled arginine and lysine (13C, 15N) are added to the culture medium to ensure that most of the peptides following tryptic digestion contain at least one labeled amino acid. Relative quantification is achieved by comparing the intensities of the isotope clusters of the labeled and unlabeled peptides in peptide ion mass spectra [8]. Label-free proteomics approaches include selected reaction monitoring (SRM) and multi-reaction monitoring (MRM) assays. SRM and MRM are the proteomics method of choice to quantify peptides specific for a given set of proteins, which monitor the generation of fragment ions upon collision-induced dissociation. The pairs of precursor and fragment m/z values are referred to as ‘transitions’. Each transition effectively constitutes an independent MS assay that allows one to identify and quantify a specific peptide and, by inference, the corresponding protein in complex matrices [9]. MRM/SRM is now emerging as an alternative to traditional immunoassays for validation of candidate protein biomarkers. 216
Biomarkers for early diagnosis of NPC
NPC is usually diagnosed at advanced clinical stages, resulting in poor outcomes. Therefore, identification of biomarkers for early diagnosis of NPC is important for improving prognosis of this disease. Since NPC is closely associated with EBV infection, EBV-related biomarkers for NPC have being focused. Serum antibodies against EBV proteins including viral capsid antigen IgA, early antigen IgA and nuclear antigen 1 (EBNA1) IgA [10,11], circulating EBV DNA [12,13], miRNAs [14,15] and hypermethylated DNA [16] were reported to have some values for early diagnosis and screening of NPC. But none of them is a stand-alone adequate serological biomarker for screening and diagnosing NPC due to either low sensitivity or specificity. The rapid development in proteomics, which detects the differences of protein patterns in body fluid and tissue specimens between patients and healthy people, presents a promising path for biomarker discovery. Currently, two biomarker-screening strategies have been developed using the proteomics method: screening specific disease proteins and constructing diagnosis patterns [17]. Some potential biomarkers have been identified that may be valuable in the early diagnosis of NPC (TABLE 1). Proteomic analysis of blood represents an appealing choice to researchers addressing the discovery of biomarkers because it can be quickly and easily obtained. The use of serum or plasma protein profiles and a classification tree algorithm were explored to distinguish NPC from noncancer, which is a promising diagnostic method with less trauma and complication. Ho et al. [18] demonstrated that serum protein obtained by SELDI-TOF-MS profiling could discriminate NPC from noncancer controls. The classification tree consisting of six characteristic serum protein peaks could correctly determine 83% of the NPC samples and 82% of the noncancer samples. The combination of the serum protein profiles and EBNA1 IgA test had a higher diagnostic sensitivity (99%) and specificity (96%). The data indicated that combining detection of serum protein profiles and EBNA1 IgA could further increase the accuracy of NPC screening. Similarly, Wei et al. [19] analyzed serum samples from patients with NPC and noncancer controls using SELDI-TOF-MS and found that four serum protein peaks (m/z 4097, 4180, 5912, and 8295) as a biomarker pattern discriminated NPC from noncancer with high sensitivity and specificity in both the training and test sets. Furthermore, the accuracy of two serum protein peaks (m/z 4581 and 7802) was 80% for predicting stage I and II NPC and 86% for predicting stage III and IV NPC. The results suggested that SELDI-TOFMS combined with a tree analysis model can provide an innovative clinical diagnostic platform to improve the detection of NPC. Tao et al. [20] compared the serum peptide profiles of NPC patients and healthy controls using matrix-assisted laser desorption/ionization-TOF-MS and used four serum peptides which were identified as FGA isoform 1 of fibrinogen a-chain precursor 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 Expert Rev. Proteomics 11(2), (2014)
Biomarker discovery of NPC by proteomics
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Table 1. Biomarkers for early diagnosis of nasopharyngeal carcinoma identified by proteomics. Biomarker
Specimen
Discovery technology
Ref.
m/z 6692, 6811, 6862, 7979, 9176, 10, 272
Serum
ProteinChip technology, SELDI-TOF-MS
[18]
m/z 4097, 4180, 5912, 8295
Serum
ProteinChip technology, SELDI-TOF-MS
[19]
m/z 3193.14, 1051.37, 4055.47, 1262.67
Serum
Sera were then separated by centrifugation, MALDI-TOF-MS, nano-LC/ESI–MS/MS
[20]
m/z 3159.83, 5187.65, 13738.6
Serum
ProteinChip technology, SELDI-TOF-MS
[21]
Cytokeratin 19, Erb3-binding protein, Rho GDP dissociation inhibitor-b
Serum
2DE, MS/MS
[26]
Heat shock 70 kDa protein, soluble intercellular adhesion molecule-1, serum amyloid A1 protein precursor
Serum
2DE, MALDI-TOF-MS
[32]
m/z 2803, 3953
Serum
ProteinChip array analysis, MS/MS
[51]
m/z 2019.691, 2223.114, 2244.074, 2467.500, 2491.888, 7977.352, 15931.654, 31988.322, 39347.955
Serum
ProteinChip technology, SELDI-TOF-MS
[55]
m/z 8605, 5320, 5355, 5380, 5336, 2791, 7154, 9366
Serum
ProteinChip technology, SELDI-TOF-MS
[56]
a-2 macroglobulin, complement factor B
Serum
2DE, MALDI-TOF-MS
[57]
m/z 808.99, 834.61, 3954.82, 8141.88
Serum
Peptides were extracted with magnetic beads, MALDI-TOF-MS
[58]
Transferrin, ZNF544 protein, transthyretin, FAD-synthetase, NM23-H1, 12-lipoxygenase, SAA1 protein precursor, cytochrome P450, soluble intercellular adhesion molecule-1, cathepsin G, lysine-specific histone demethylase 1
Serum
2DE, MALDI-TOF-MS
[59]
12 differential proteins including C3f
Plasma
Plasma protein fractionation, MALDI-TOF-MS
[22]
Chloride intracellular channel 1
Plasma
1-D SDS-PAGE, nano-HPLC-MS/MS, ESI-MS/MS
[23]
Kallikrein, thrombin-antithrombin III complex
Plasma
Cyanine dyes labeling, LC-MS/MS
[60]
Cathepsin D
Tissues
2DE, MS/MS
[35]
Stathmin, 14-3-3sigma, annexin I
Tissues
2DE, MS/MS
[36]
36 differential proteins including squamous cell carcinoma antigen-1
Tissues
2DE, MS/MS
[61]
Galectin-1
Tissues
2DE, MALDI-TOF-MS
[62]
13 differential proteins including Raf kinase inhibitor protein
Tissues
2DE, ESI-Q-TOF MS
[63]
Cytokeratin 18
Tissues
2DE, MS/MS
[46]
Cathepsin D, keratin 8, 14-3-3sigma, stathmin 1
Tissues
Proteins were extracted by a heat-induced antigen retrieval technique, iTRAQ-coupled 2D LC-MS/MS
[64]
60 differential proteins including gelsolin-like capping protein isoform 9
Tissues
2DE, MS/MS
[65]
20 differential proteins including L-plastin and S100A9
Tissues
2D-DIGE, MS/MS
[66]
Periostin
Tissues
2D-DIGE, MS/MS
[40]
11 differential proteins including galectin-1
Tissues
2DE, MALDI-TOF-MS
[67]
Cystatin A, cathepsin B, manganese superoxide dismutase, matrix metalloproteinase 2
Cell line
1-D SDS-PAGE, reverse-phase LC-MS/MS
[31]
14-3-3g
Cell line
2D-DIGE, MALDI-TOF-MS
[38]
2D-DIGE: 2D difference in gel electrophoresis; 2DE: 2D electrophoresis; ESI-Q-TOF-MS: Electrospray ionization quadrupole-TOF-mass spectrometry; iTRAQ: Isobaric tags for relative and absolute quantification; LC-MS/MS: Liquid chromatography tandem mass spectrometry; m/z: Mass-to-charge ratio; MALDI: Matrix-assisted laser desorption/ionization.
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Table 1. Biomarkers for early diagnosis of nasopharyngeal carcinoma identified by proteomics (cont.). Biomarker
Specimen
Discovery technology
Ref.
12 differential proteins including voltage-dependent anionselective channel protein 1, S100-A2, Hsc-70 interacting protein
Cell line
iTRAQ-coupled 2D LC-MS/MS
[68]
Fibronectin, Mac-2-binding protein, plasminogen activator inhibitor 1
Cell line
2DE, MALDI-TOF-MS
[69]
Annexin II, b (2)-tubulin
Cell line
2DE, MALDI-TOF-MS
[70]
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2D-DIGE: 2D difference in gel electrophoresis; 2DE: 2D electrophoresis; ESI-Q-TOF-MS: Electrospray ionization quadrupole-TOF-mass spectrometry; iTRAQ: Isobaric tags for relative and absolute quantification; LC-MS/MS: Liquid chromatography tandem mass spectrometry; m/z: Mass-to-charge ratio; MALDI: Matrix-assisted laser desorption/ionization.
validation set and 91.9 and 83.3% in the multicenter validation set. Their results indicated that the diagnostic model including four peptides was suitable for NPC, and FGA peptide fragments may serve as tumor-associated biomarkers for NPC. Huang et al. [21] used SELDI-TOF-MS for detection and analysis of serum proteins for distinguishing patients with NPC from control individuals, and selected three serum biomarkers ranging m/z 3–20 k to establish decision tree model. The ability of this model to detect NPC patients was evaluated and a sensitivity of 95.0%, a specificity of 83.33% and an accuracy rate of 90.63% were validated in blind testing set. They thought that this proteomic classification system may provide an innovative approach for the detection and diagnosis of NPC. Chang et al. [22] used matrix-assisted laser desorption/ ionization-TOF-MS analysis to screen potential NPC plasma biomarkers and found the sensitivities of 12 NPC biomarkers were various ranging from 36 to 83%, and the specificities were all over 90%. Combined use of these markers significantly increased diagnostic efficacy. Besides, Chang et al. [23] used secretomics to screen NPC biomarker and found and validated that chloride intracellular channel 1 was a potential NPC plasma biomarker, which successfully discriminated NPC from the healthy control group with a sensitivity of 63% and a specificity of 77%. The humoral immune response to cancer in humans has been well demonstrated by identification of autoantibodies to a number of tumor antigens. As the immune response to tumor antigens generates a remarkable biological amplification for weak signals of tumor antigens, autoantibodies can be detected in patients with early stage of cancer and serve as early diagnostic biomarkers for cancers. Several approaches, such as screening tumor-derived cDNA expression libraries and phage display libraries by immunoassay of serum autoantibodies against a range of tumors, have been used to screen anti-tumor antibodies. Proteomics provides major opportunities for screening and identification of tumor antigens and autoantibodies, which has an advantage over the recombinant DNA methods, such as not necessary to construct cDNA expression libraries, individual screening of a large number of patient sera and identification of intact proteins with potentially critical posttranslational modifications [24,25]. We [26] used a serum proteomic approach to identify tumor antigens and autoantibodies in NPC. The 218
results showed that three antigens (cytokeratin 19, Erb3-binding protein and Rho GDP dissociation inhibitor-b) elicited autoantibodies in more than 36.8% of NPC patients but not in healthy individuals, indicating those three antigens and their autoantibodies may have utility in the screening and early diagnosis of NPC. Exosomes, nanometer-sized microvesicles, contain biologically active proteins, lipids and RNAs and are secreted by tumor cells at high levels into biological fluids. Proteomic analysis of exosomes may find tumor biomarkers and form a noninvasive tool for diagnosis and monitoring of cancers. Recent quantitative proteomics study showed the specific effects of EBV on the B-cell exosomes proteome, indicating that these alterations of exosome proteome modulate the tumor microenvironment and may provide diagnostic biomarkers specific for EBV-associated NPC [27]. Biomarkers for predicting metastasis & recurrence of NPC
Compared with other head and neck squamous cell carcinomas, NPC exhibits higher metastatic and recurrent potentials. Furthermore, early-stage NPC is highly curable by RT or radiochemotherapy and has shown 5-year survival rates from 80 to 90%. However, moderate- and late-stage NPC is poorly controlled locally by RT or radiochemotherapy, and often develops distant metastasis despite local control with 5-year survival rate down to 33% [28]. Therefore, it is urgent to discover effective biomarkers for predicting metastasis and recurrence of NPC. qRT-PCR for detection of circulating EBV DNA level has been used to predict metastasis and recurrence of NPC in the southeast Asia and Western population, indicating that the monitoring of plasma levels may be a valuable tool in detecting disease recurrence and metastases in the epidemic and nonepidemic population [29,30]. Some potential biomarkers have been identified by proteomics that may be valuable in monitoring metastasis and recurrence of NPC (TABLE 2). Chang et al. [31] simultaneously analyzed the NPC cell secretome and tissue transcriptome using reverse-phase LC-tandem mass spectrometry to identify candidate genes/proteins that are highly up-regulated in NPC tissues and also secreted/released from NPC cells. They found that cystatin A could modulate the migration and invasion of NPC cells in vitro, and a higher pretreated serum level Expert Rev. Proteomics 11(2), (2014)
Biomarker discovery of NPC by proteomics
Review
Table 2. Biomarkers for predicting metastasis, recurrence and prognosis of nasopharyngeal carcinoma identified by proteomics. Biomarker
Specimen
Discovery technology
Ref.
Cystatin A
Cell line
1-D SDS-PAGE, reverse-phase LC-MS/MS
[31]
Serum amyloid A1 protein precursor, heat shock protein 70, soluble intercellular adhesion molecule-1
Serum
2DE, MALDI-TOF-MS
[32]
m/z 4155.34, 4194.87, 4210.78, 4249.56
Serum
Plasma protein fractionation, MALDI-TOF-MS
[33]
Raf kinase inhibitor protein
Tissues
2DE, MS/MS
[37]
14-3-3g
Cell line
2D-DIGE, MALDI-TOF-MS
[38]
12-lipoxygenase, cathepsin G, lysine-specific histone demethylase 1, heat shock protein 70, soluble intercellular adhesion molecule-1
Serum
2DE, MALDI-TOF-MS
[59]
Annexin II
Cell line
2DE, MALDI-TOF-MS
[70]
EBNA1
Cell line
2D-DIGE, LC-MS/MS
[71]
Heat shock protein 27
Cell line
2DE, MALDI-TOF-MS
[72]
14-3-3sigma
Cell line
Coimmunoprecipitation, ESI-Q-TOF-MS
[73]
16 differential proteins including peroxiredoxin 3
Cell line
2D-DIGE, MS/MS
[74]
C-Jun, histone H2AX, SEK1 and KIT
Tissues
ProteinChip profiling analysis
[75]
Serum amyloid A
Serum
ProteinChip profiling analysis, MS/MS
[34]
Annexin I
Cell line
2DE, MALDI-TOF-MS
[53]
Heat shock protein 70, soluble intercellular adhesion molecule-1
Serum
2DE, MALDI-TOF-MS
[32]
Cystatin A
Cell line
1-D SDS-PAGE, reverse-phase LC-MS/MS
[31]
14-3-3g
Cell line
2D-DIGE, MALDI-TOF-MS
[38]
Cathepsin D
Tissues
2DE, MS/MS
[35]
14-3-3sigma, annexin I
Tissues
2DE, MS/MS
[36]
Cytokeratin 18
Tissues
2DE, MS/MS
[46]
Periostin
Tissues
2D-DIGE, MS/MS
[40]
nm23-H1
Cell line
2DE, MS/MS
[47]
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Metastasis
Recurrence
Prognosis
Metastasis, recurrence and prognosis
2D-DIGE: 2D difference in gel electrophoresis; 2DE: 2D electrophoresis; ESI-Q-TOF-MS: Electrospray ionization quadrupole-TOF-mass spectrometry; LC-MS/MS: Liquid chromatography tandem mass spectrometry; m/z: Mass-to-charge ratio; MALDI: Matrix-assisted laser desorption/ionization.
of cystatin A was associated with a higher nodal stage and poorer prognosis of NPC patients. Liao et al. [32] performed serum proteome analysis to screen protein markers associated with lymph node metastasis in NPC and discovered and validated that heat shock protein 70 (HSP70), sICAM-1 and serum amyloid A (SAA) are potential metastasis-specific serum biomarkers of NPC, which may be of great underlying significance in clinical detection and management of NPC. It is known that NPC patients with liver metastasis (LM) have the worst prognosis among patients with NPC. To identify the biomarkers informahealthcare.com
associated with LM of NPC, Pan et al. [33] performed comparative serum proteomic analysis involving liver organ-specific metastasis-associated proteins of NPC and identified four protein mass peaks (m/z 4155.34, 4194.87, 4210.78 and 4249.56) as liver-specific, metastasis-associated protein peaks in NPC. Pattern based on the four potential serum biomarkers could discriminate LM NPC from non-LM NPC. Using protein chip profiling analysis, Cho et al. [34] identified SAA protein as a biomarker for diagnosis of NPC recurrence. We [35,36] performed comparative proteomic study of 219
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microdissected NPC and normal nasopharyngeal epithelial tissues to identify novel NPC biomarkers. The results showed that dysregulation of cathepsin D, stathmin, 14-3-3sigma and annexin I were related to metastasis and recurrence of NPC, and the four proteins are potential biomarkers for predicting metastasis and recurrence of NPC. Our [37] comparative proteomics of NPC also demonstrated that Raf kinase inhibitor protein may be a metastasis suppressor of NPC, and downregulation of Raf kinase inhibitor protein was associated with the increased invasive and metastatic capability of NPC. Wu et al. [38] performed comparative proteomic analyses of NPC and found that 14-3-3g up-regulation in primary NPC was significantly correlated with advanced clinical stage and metastasis. The tumor microenvironment including stromal cells is increasingly recognized as an important event in cancer proliferation, invasion and metastasis [39]. To screen stromaassociated proteins involved in pathogenesis of NPC, Li et al. [40] employed laser capture microdissection and quantitative proteomic analysis to compare differentially expressed stromal proteins between NPC and normal nasopharyngeal mucosa and found that over-expression of periostin was significantly associated with advanced clinical stage, regional lymph node metastasis and decreased overall survival in NPC.
the patients with lower serum cystatin A levels, the patients with higher serum cystatin A levels had higher risk for lower overall survival, suggesting that serum cystatin A might be an predictor for the prognosis of NPC patients. Using comparative proteomics, we found the dysregulation of cathepsin D [35], stathmin, 14-3-3sigma, annexin I [36], periostin [40], cytokeratin 18 [46] and nm23-H1 [47] were not only related to clinical stage, metastasis and recurrence of NPC, but also potential biomarkers for predicting patients’ prognosis. For an example, the levels of cathepsin D [35] were significantly correlated with clinical stage, recurrence and regional lymph node and distant metastasis in NPC. Survival curves showed that the NPC patients with the high levels of cathepsin D had a poor prognosis. Multivariate analysis confirmed that cathepsin D was an independent prognostic factor. Similarly, over-expression of periostin [40] was frequently observed in the stroma of NPC with lymph node metastases compared with the stroma of NPC without lymph node metastases. Statistical analysis showed the high levels of periostin were significantly correlated with advanced clinical stage, regional lymph node metastasis and lower overall survival in NPC. Besides, Wu et al. [38] identified that the expression of 14-3-3g protein was an independent prognostic factor for outcome of NPC and closely correlated with overall survival of the patients with NPC.
Biomarkers for predicting NPC prognosis
NPC originates from a hidden anatomical site and is more closely associated with advanced clinical stage at the time of diagnosis. Hence, the overall prognosis for NPC is poor with a 5-year survival rate of 50–60%. At present, tumor-node metastasis (TNM) stage and WHO histological type are still the main methods for predicting the prognosis of NPC. Numerous studies have showed that the prognosis could be significantly different in the NPC patients with the same TNM stage and WHO histological type, suggesting that the differences of other factors, such as intrinsic molecules and ethnicity, can affect the prognosis of NPC patients [41]. Therefore, it is urgent to discover biomarkers for predicting the prognosis of NPC patients. It has been reported that patients with NPC showed high levels of C-reactive protein and lactate dehydrogenase, which were associated with poor prognosis [42,43]. Jin et al. [44] built a prognostic score model incorporating circulating tumor markers for metastatic NPC. The results showed that performance status, age, hemoglobin, circulating lactate dehydrogenase, alkaline phosphatase and EBV DNA were independent prognostic factors, which indicates that clinical and laboratory characteristics can help to guide the prognostication of patients with metastatic NPC. It is worthwhile to note that pretherapy circulating EBV DNA load determined by qRT-PCR is an independent prognostic factor to International Union Against Cancer staging in NPC [45]. Some potential biomarkers have also been identified by proteomics that may be valuable in predicting the prognosis of NPC (TABLE 2). Chang et al. [31] found that the 3-year overall survival rates for patient subgroups stratified by serum cystatin A levels >8.9 ng/ml and
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Biomarker discovery of nasopharyngeal carcinoma by proteomics Expert Review of Proteomics Downloaded from informahealthcare.com by Nyu Medical Center on 02/17/15 For personal use only.
Expert Rev. Proteomics 11(2), 215–225 (2014)
Liang Xiao1,2, Ta Xiao1, Zhi-Ming Wang2, William CS Cho3 and Zhi-Qiang Xiao*1 1 Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China 2 Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China 3 Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, P.R. China *Author for correspondence: Tel.: +86 731 8432 7239 Fax: +86 731 8432 7332 [email protected]
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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. KEYWORDS: biomarker • diagnosis • metastasis • nasopharyngeal carcinoma • prognosis • proteomics • recurrence • therapeutic response
Nasopharyngeal carcinoma (NPC) is a malignant tumor originated from nasopharyngeal epithelial cells. The cancer is an Epstein–Barr virus (EBV)-associated malignancy, with a remarkable racial and geographical distribution. The phenomenon indicates that the development of this cancer must be related to special genetic and environmental factors. It is highly prevalent in Southern China and Southern Asia, with an incidence of 30–50 per 100,000, which is about a 100-fold higher compared with other populations not at risk and poses a very serious health problem in these areas [1]. NPC is highly sensitive to radiotherapy (RT), which is the preferred treatment for it, but the outcome is related to the extent of the disease; therefore, early detection and diagnosis of NPC is crucial for better outcome in the patients [2]. Unfortunately, early-stage NPC is difficult to diagnose because of its deep location and vague symptoms. Moreover, radioresistance causes NPC recurrence and metastasis leads to RT failures. Therefore, discovery of biomarkers for early diagnosis, predicting metastasis, recurrence and therapeutic response of NPC is of great importance for guiding NPC treatment and improving patients’ prognosis. Thanks to the advancements in proteomics and bioinformatics 10.1586/14789450.2014.897613
in the recent 20 years, an emerging image that correlates NPC clinical parameters with protein biomarkers has been gained [3], which would improve early diagnosis and tailored therapeutic modalities of NPC. In this article, a brief overview of the proteomics approaches is given, and the latest discovery and advance of NPC biomarkers is reviewed. Proteomics approaches for identification of tumor markers
Various proteomics approaches have been employed to discover tumor biomarkers. The potential NPC markers discovered by proteomics are described in the following sections. Only a brief overview of the proteomics approaches for identifying tumor markers is given here since there have been numerous reviews introducing them in detail. Proteomics approaches can be classified into gel-based and gel-free methods or label-free and label-based methods. It is known that no proteomics technique could analyze the whole proteome in a single experiment; thus more than one complementary approaches in methodology would need to be employed to approach higher proteome coverage. A classical gel-based proteomics approach for the identification of the differentially
2014 Informa UK Ltd
ISSN 1478-9450
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Review
Xiao, Xiao, Wang, Cho & Xiao
expressed proteins (DEPs) in tumor samples and controls is 2D gel electrophoresis (2DE) coupled with mass spectrometry (MS) analysis. Since its development almost 40 years ago, 2DE is still one of the methods of choice for protein separation and quantification. In order to enhance the reproducibility and sensitivity of 2DE, 2D difference gel electrophoresis coupled with MS analysis is used to identify the DEPs in tumor samples and controls, which theoretically detects lowabundance proteins and decreases gel-to-gel difference [4]. One of the main advantages of gel-based proteomics approaches is that they give a visual representation of proteins and the DEPs within each sample. However, there are some disadvantages in the gel-based proteomics approaches, for example, the fact that they are generally low in the highthroughput and not suitable for identifying the highly acidic, basic or hydrophobic proteins, as well as the very large, very small or low-abundant proteins [5]. Due to the drawbacks of gel-based proteomics approaches, gel-free proteomics approaches have been developed for identification of the DPEs in tumor samples and controls. One of the most common gel-free proteomics methods is a highperformance liquid chromatography (LC) for separation, coupled with electrospray ionization-tandem MS for peptide identification and quantification, also known as ‘shotgun’ proteomics [6], which can identify and quantify proteins from a complex liquid protein mixture with the high-throughput. To accurately quantify the DEPs in tumor samples and controls, various quantitative proteomics approaches have been developed, which can be divided into label-based and labelfree proteomics [7]. Isobaric tag for relative and absolute quantification (iTRAQ) is a gel-free and label-based proteomics technique used to identify and quantify the DEPs from multiple samples in a single experiment, which is its main advantage [8]. Other gel-free and label-based proteomics approaches have also been developed, such as stable isotope labeling with amino acids in cell culture. In the stable isotope labeling with amino acids in cell culture experiment, isotopically labeled arginine and lysine (13C, 15N) are added to the culture medium to ensure that most of the peptides following tryptic digestion contain at least one labeled amino acid. Relative quantification is achieved by comparing the intensities of the isotope clusters of the labeled and unlabeled peptides in peptide ion mass spectra [8]. Label-free proteomics approaches include selected reaction monitoring (SRM) and multi-reaction monitoring (MRM) assays. SRM and MRM are the proteomics method of choice to quantify peptides specific for a given set of proteins, which monitor the generation of fragment ions upon collision-induced dissociation. The pairs of precursor and fragment m/z values are referred to as ‘transitions’. Each transition effectively constitutes an independent MS assay that allows one to identify and quantify a specific peptide and, by inference, the corresponding protein in complex matrices [9]. MRM/SRM is now emerging as an alternative to traditional immunoassays for validation of candidate protein biomarkers. 216
Biomarkers for early diagnosis of NPC
NPC is usually diagnosed at advanced clinical stages, resulting in poor outcomes. Therefore, identification of biomarkers for early diagnosis of NPC is important for improving prognosis of this disease. Since NPC is closely associated with EBV infection, EBV-related biomarkers for NPC have being focused. Serum antibodies against EBV proteins including viral capsid antigen IgA, early antigen IgA and nuclear antigen 1 (EBNA1) IgA [10,11], circulating EBV DNA [12,13], miRNAs [14,15] and hypermethylated DNA [16] were reported to have some values for early diagnosis and screening of NPC. But none of them is a stand-alone adequate serological biomarker for screening and diagnosing NPC due to either low sensitivity or specificity. The rapid development in proteomics, which detects the differences of protein patterns in body fluid and tissue specimens between patients and healthy people, presents a promising path for biomarker discovery. Currently, two biomarker-screening strategies have been developed using the proteomics method: screening specific disease proteins and constructing diagnosis patterns [17]. Some potential biomarkers have been identified that may be valuable in the early diagnosis of NPC (TABLE 1). Proteomic analysis of blood represents an appealing choice to researchers addressing the discovery of biomarkers because it can be quickly and easily obtained. The use of serum or plasma protein profiles and a classification tree algorithm were explored to distinguish NPC from noncancer, which is a promising diagnostic method with less trauma and complication. Ho et al. [18] demonstrated that serum protein obtained by SELDI-TOF-MS profiling could discriminate NPC from noncancer controls. The classification tree consisting of six characteristic serum protein peaks could correctly determine 83% of the NPC samples and 82% of the noncancer samples. The combination of the serum protein profiles and EBNA1 IgA test had a higher diagnostic sensitivity (99%) and specificity (96%). The data indicated that combining detection of serum protein profiles and EBNA1 IgA could further increase the accuracy of NPC screening. Similarly, Wei et al. [19] analyzed serum samples from patients with NPC and noncancer controls using SELDI-TOF-MS and found that four serum protein peaks (m/z 4097, 4180, 5912, and 8295) as a biomarker pattern discriminated NPC from noncancer with high sensitivity and specificity in both the training and test sets. Furthermore, the accuracy of two serum protein peaks (m/z 4581 and 7802) was 80% for predicting stage I and II NPC and 86% for predicting stage III and IV NPC. The results suggested that SELDI-TOFMS combined with a tree analysis model can provide an innovative clinical diagnostic platform to improve the detection of NPC. Tao et al. [20] compared the serum peptide profiles of NPC patients and healthy controls using matrix-assisted laser desorption/ionization-TOF-MS and used four serum peptides which were identified as FGA isoform 1 of fibrinogen a-chain precursor 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 Expert Rev. Proteomics 11(2), (2014)
Biomarker discovery of NPC by proteomics
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Table 1. Biomarkers for early diagnosis of nasopharyngeal carcinoma identified by proteomics. Biomarker
Specimen
Discovery technology
Ref.
m/z 6692, 6811, 6862, 7979, 9176, 10, 272
Serum
ProteinChip technology, SELDI-TOF-MS
[18]
m/z 4097, 4180, 5912, 8295
Serum
ProteinChip technology, SELDI-TOF-MS
[19]
m/z 3193.14, 1051.37, 4055.47, 1262.67
Serum
Sera were then separated by centrifugation, MALDI-TOF-MS, nano-LC/ESI–MS/MS
[20]
m/z 3159.83, 5187.65, 13738.6
Serum
ProteinChip technology, SELDI-TOF-MS
[21]
Cytokeratin 19, Erb3-binding protein, Rho GDP dissociation inhibitor-b
Serum
2DE, MS/MS
[26]
Heat shock 70 kDa protein, soluble intercellular adhesion molecule-1, serum amyloid A1 protein precursor
Serum
2DE, MALDI-TOF-MS
[32]
m/z 2803, 3953
Serum
ProteinChip array analysis, MS/MS
[51]
m/z 2019.691, 2223.114, 2244.074, 2467.500, 2491.888, 7977.352, 15931.654, 31988.322, 39347.955
Serum
ProteinChip technology, SELDI-TOF-MS
[55]
m/z 8605, 5320, 5355, 5380, 5336, 2791, 7154, 9366
Serum
ProteinChip technology, SELDI-TOF-MS
[56]
a-2 macroglobulin, complement factor B
Serum
2DE, MALDI-TOF-MS
[57]
m/z 808.99, 834.61, 3954.82, 8141.88
Serum
Peptides were extracted with magnetic beads, MALDI-TOF-MS
[58]
Transferrin, ZNF544 protein, transthyretin, FAD-synthetase, NM23-H1, 12-lipoxygenase, SAA1 protein precursor, cytochrome P450, soluble intercellular adhesion molecule-1, cathepsin G, lysine-specific histone demethylase 1
Serum
2DE, MALDI-TOF-MS
[59]
12 differential proteins including C3f
Plasma
Plasma protein fractionation, MALDI-TOF-MS
[22]
Chloride intracellular channel 1
Plasma
1-D SDS-PAGE, nano-HPLC-MS/MS, ESI-MS/MS
[23]
Kallikrein, thrombin-antithrombin III complex
Plasma
Cyanine dyes labeling, LC-MS/MS
[60]
Cathepsin D
Tissues
2DE, MS/MS
[35]
Stathmin, 14-3-3sigma, annexin I
Tissues
2DE, MS/MS
[36]
36 differential proteins including squamous cell carcinoma antigen-1
Tissues
2DE, MS/MS
[61]
Galectin-1
Tissues
2DE, MALDI-TOF-MS
[62]
13 differential proteins including Raf kinase inhibitor protein
Tissues
2DE, ESI-Q-TOF MS
[63]
Cytokeratin 18
Tissues
2DE, MS/MS
[46]
Cathepsin D, keratin 8, 14-3-3sigma, stathmin 1
Tissues
Proteins were extracted by a heat-induced antigen retrieval technique, iTRAQ-coupled 2D LC-MS/MS
[64]
60 differential proteins including gelsolin-like capping protein isoform 9
Tissues
2DE, MS/MS
[65]
20 differential proteins including L-plastin and S100A9
Tissues
2D-DIGE, MS/MS
[66]
Periostin
Tissues
2D-DIGE, MS/MS
[40]
11 differential proteins including galectin-1
Tissues
2DE, MALDI-TOF-MS
[67]
Cystatin A, cathepsin B, manganese superoxide dismutase, matrix metalloproteinase 2
Cell line
1-D SDS-PAGE, reverse-phase LC-MS/MS
[31]
14-3-3g
Cell line
2D-DIGE, MALDI-TOF-MS
[38]
2D-DIGE: 2D difference in gel electrophoresis; 2DE: 2D electrophoresis; ESI-Q-TOF-MS: Electrospray ionization quadrupole-TOF-mass spectrometry; iTRAQ: Isobaric tags for relative and absolute quantification; LC-MS/MS: Liquid chromatography tandem mass spectrometry; m/z: Mass-to-charge ratio; MALDI: Matrix-assisted laser desorption/ionization.
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Table 1. Biomarkers for early diagnosis of nasopharyngeal carcinoma identified by proteomics (cont.). Biomarker
Specimen
Discovery technology
Ref.
12 differential proteins including voltage-dependent anionselective channel protein 1, S100-A2, Hsc-70 interacting protein
Cell line
iTRAQ-coupled 2D LC-MS/MS
[68]
Fibronectin, Mac-2-binding protein, plasminogen activator inhibitor 1
Cell line
2DE, MALDI-TOF-MS
[69]
Annexin II, b (2)-tubulin
Cell line
2DE, MALDI-TOF-MS
[70]
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2D-DIGE: 2D difference in gel electrophoresis; 2DE: 2D electrophoresis; ESI-Q-TOF-MS: Electrospray ionization quadrupole-TOF-mass spectrometry; iTRAQ: Isobaric tags for relative and absolute quantification; LC-MS/MS: Liquid chromatography tandem mass spectrometry; m/z: Mass-to-charge ratio; MALDI: Matrix-assisted laser desorption/ionization.
validation set and 91.9 and 83.3% in the multicenter validation set. Their results indicated that the diagnostic model including four peptides was suitable for NPC, and FGA peptide fragments may serve as tumor-associated biomarkers for NPC. Huang et al. [21] used SELDI-TOF-MS for detection and analysis of serum proteins for distinguishing patients with NPC from control individuals, and selected three serum biomarkers ranging m/z 3–20 k to establish decision tree model. The ability of this model to detect NPC patients was evaluated and a sensitivity of 95.0%, a specificity of 83.33% and an accuracy rate of 90.63% were validated in blind testing set. They thought that this proteomic classification system may provide an innovative approach for the detection and diagnosis of NPC. Chang et al. [22] used matrix-assisted laser desorption/ ionization-TOF-MS analysis to screen potential NPC plasma biomarkers and found the sensitivities of 12 NPC biomarkers were various ranging from 36 to 83%, and the specificities were all over 90%. Combined use of these markers significantly increased diagnostic efficacy. Besides, Chang et al. [23] used secretomics to screen NPC biomarker and found and validated that chloride intracellular channel 1 was a potential NPC plasma biomarker, which successfully discriminated NPC from the healthy control group with a sensitivity of 63% and a specificity of 77%. The humoral immune response to cancer in humans has been well demonstrated by identification of autoantibodies to a number of tumor antigens. As the immune response to tumor antigens generates a remarkable biological amplification for weak signals of tumor antigens, autoantibodies can be detected in patients with early stage of cancer and serve as early diagnostic biomarkers for cancers. Several approaches, such as screening tumor-derived cDNA expression libraries and phage display libraries by immunoassay of serum autoantibodies against a range of tumors, have been used to screen anti-tumor antibodies. Proteomics provides major opportunities for screening and identification of tumor antigens and autoantibodies, which has an advantage over the recombinant DNA methods, such as not necessary to construct cDNA expression libraries, individual screening of a large number of patient sera and identification of intact proteins with potentially critical posttranslational modifications [24,25]. We [26] used a serum proteomic approach to identify tumor antigens and autoantibodies in NPC. The 218
results showed that three antigens (cytokeratin 19, Erb3-binding protein and Rho GDP dissociation inhibitor-b) elicited autoantibodies in more than 36.8% of NPC patients but not in healthy individuals, indicating those three antigens and their autoantibodies may have utility in the screening and early diagnosis of NPC. Exosomes, nanometer-sized microvesicles, contain biologically active proteins, lipids and RNAs and are secreted by tumor cells at high levels into biological fluids. Proteomic analysis of exosomes may find tumor biomarkers and form a noninvasive tool for diagnosis and monitoring of cancers. Recent quantitative proteomics study showed the specific effects of EBV on the B-cell exosomes proteome, indicating that these alterations of exosome proteome modulate the tumor microenvironment and may provide diagnostic biomarkers specific for EBV-associated NPC [27]. Biomarkers for predicting metastasis & recurrence of NPC
Compared with other head and neck squamous cell carcinomas, NPC exhibits higher metastatic and recurrent potentials. Furthermore, early-stage NPC is highly curable by RT or radiochemotherapy and has shown 5-year survival rates from 80 to 90%. However, moderate- and late-stage NPC is poorly controlled locally by RT or radiochemotherapy, and often develops distant metastasis despite local control with 5-year survival rate down to 33% [28]. Therefore, it is urgent to discover effective biomarkers for predicting metastasis and recurrence of NPC. qRT-PCR for detection of circulating EBV DNA level has been used to predict metastasis and recurrence of NPC in the southeast Asia and Western population, indicating that the monitoring of plasma levels may be a valuable tool in detecting disease recurrence and metastases in the epidemic and nonepidemic population [29,30]. Some potential biomarkers have been identified by proteomics that may be valuable in monitoring metastasis and recurrence of NPC (TABLE 2). Chang et al. [31] simultaneously analyzed the NPC cell secretome and tissue transcriptome using reverse-phase LC-tandem mass spectrometry to identify candidate genes/proteins that are highly up-regulated in NPC tissues and also secreted/released from NPC cells. They found that cystatin A could modulate the migration and invasion of NPC cells in vitro, and a higher pretreated serum level Expert Rev. Proteomics 11(2), (2014)
Biomarker discovery of NPC by proteomics
Review
Table 2. Biomarkers for predicting metastasis, recurrence and prognosis of nasopharyngeal carcinoma identified by proteomics. Biomarker
Specimen
Discovery technology
Ref.
Cystatin A
Cell line
1-D SDS-PAGE, reverse-phase LC-MS/MS
[31]
Serum amyloid A1 protein precursor, heat shock protein 70, soluble intercellular adhesion molecule-1
Serum
2DE, MALDI-TOF-MS
[32]
m/z 4155.34, 4194.87, 4210.78, 4249.56
Serum
Plasma protein fractionation, MALDI-TOF-MS
[33]
Raf kinase inhibitor protein
Tissues
2DE, MS/MS
[37]
14-3-3g
Cell line
2D-DIGE, MALDI-TOF-MS
[38]
12-lipoxygenase, cathepsin G, lysine-specific histone demethylase 1, heat shock protein 70, soluble intercellular adhesion molecule-1
Serum
2DE, MALDI-TOF-MS
[59]
Annexin II
Cell line
2DE, MALDI-TOF-MS
[70]
EBNA1
Cell line
2D-DIGE, LC-MS/MS
[71]
Heat shock protein 27
Cell line
2DE, MALDI-TOF-MS
[72]
14-3-3sigma
Cell line
Coimmunoprecipitation, ESI-Q-TOF-MS
[73]
16 differential proteins including peroxiredoxin 3
Cell line
2D-DIGE, MS/MS
[74]
C-Jun, histone H2AX, SEK1 and KIT
Tissues
ProteinChip profiling analysis
[75]
Serum amyloid A
Serum
ProteinChip profiling analysis, MS/MS
[34]
Annexin I
Cell line
2DE, MALDI-TOF-MS
[53]
Heat shock protein 70, soluble intercellular adhesion molecule-1
Serum
2DE, MALDI-TOF-MS
[32]
Cystatin A
Cell line
1-D SDS-PAGE, reverse-phase LC-MS/MS
[31]
14-3-3g
Cell line
2D-DIGE, MALDI-TOF-MS
[38]
Cathepsin D
Tissues
2DE, MS/MS
[35]
14-3-3sigma, annexin I
Tissues
2DE, MS/MS
[36]
Cytokeratin 18
Tissues
2DE, MS/MS
[46]
Periostin
Tissues
2D-DIGE, MS/MS
[40]
nm23-H1
Cell line
2DE, MS/MS
[47]
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Metastasis
Recurrence
Prognosis
Metastasis, recurrence and prognosis
2D-DIGE: 2D difference in gel electrophoresis; 2DE: 2D electrophoresis; ESI-Q-TOF-MS: Electrospray ionization quadrupole-TOF-mass spectrometry; LC-MS/MS: Liquid chromatography tandem mass spectrometry; m/z: Mass-to-charge ratio; MALDI: Matrix-assisted laser desorption/ionization.
of cystatin A was associated with a higher nodal stage and poorer prognosis of NPC patients. Liao et al. [32] performed serum proteome analysis to screen protein markers associated with lymph node metastasis in NPC and discovered and validated that heat shock protein 70 (HSP70), sICAM-1 and serum amyloid A (SAA) are potential metastasis-specific serum biomarkers of NPC, which may be of great underlying significance in clinical detection and management of NPC. It is known that NPC patients with liver metastasis (LM) have the worst prognosis among patients with NPC. To identify the biomarkers informahealthcare.com
associated with LM of NPC, Pan et al. [33] performed comparative serum proteomic analysis involving liver organ-specific metastasis-associated proteins of NPC and identified four protein mass peaks (m/z 4155.34, 4194.87, 4210.78 and 4249.56) as liver-specific, metastasis-associated protein peaks in NPC. Pattern based on the four potential serum biomarkers could discriminate LM NPC from non-LM NPC. Using protein chip profiling analysis, Cho et al. [34] identified SAA protein as a biomarker for diagnosis of NPC recurrence. We [35,36] performed comparative proteomic study of 219
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Xiao, Xiao, Wang, Cho & Xiao
microdissected NPC and normal nasopharyngeal epithelial tissues to identify novel NPC biomarkers. The results showed that dysregulation of cathepsin D, stathmin, 14-3-3sigma and annexin I were related to metastasis and recurrence of NPC, and the four proteins are potential biomarkers for predicting metastasis and recurrence of NPC. Our [37] comparative proteomics of NPC also demonstrated that Raf kinase inhibitor protein may be a metastasis suppressor of NPC, and downregulation of Raf kinase inhibitor protein was associated with the increased invasive and metastatic capability of NPC. Wu et al. [38] performed comparative proteomic analyses of NPC and found that 14-3-3g up-regulation in primary NPC was significantly correlated with advanced clinical stage and metastasis. The tumor microenvironment including stromal cells is increasingly recognized as an important event in cancer proliferation, invasion and metastasis [39]. To screen stromaassociated proteins involved in pathogenesis of NPC, Li et al. [40] employed laser capture microdissection and quantitative proteomic analysis to compare differentially expressed stromal proteins between NPC and normal nasopharyngeal mucosa and found that over-expression of periostin was significantly associated with advanced clinical stage, regional lymph node metastasis and decreased overall survival in NPC.
the patients with lower serum cystatin A levels, the patients with higher serum cystatin A levels had higher risk for lower overall survival, suggesting that serum cystatin A might be an predictor for the prognosis of NPC patients. Using comparative proteomics, we found the dysregulation of cathepsin D [35], stathmin, 14-3-3sigma, annexin I [36], periostin [40], cytokeratin 18 [46] and nm23-H1 [47] were not only related to clinical stage, metastasis and recurrence of NPC, but also potential biomarkers for predicting patients’ prognosis. For an example, the levels of cathepsin D [35] were significantly correlated with clinical stage, recurrence and regional lymph node and distant metastasis in NPC. Survival curves showed that the NPC patients with the high levels of cathepsin D had a poor prognosis. Multivariate analysis confirmed that cathepsin D was an independent prognostic factor. Similarly, over-expression of periostin [40] was frequently observed in the stroma of NPC with lymph node metastases compared with the stroma of NPC without lymph node metastases. Statistical analysis showed the high levels of periostin were significantly correlated with advanced clinical stage, regional lymph node metastasis and lower overall survival in NPC. Besides, Wu et al. [38] identified that the expression of 14-3-3g protein was an independent prognostic factor for outcome of NPC and closely correlated with overall survival of the patients with NPC.
Biomarkers for predicting NPC prognosis
NPC originates from a hidden anatomical site and is more closely associated with advanced clinical stage at the time of diagnosis. Hence, the overall prognosis for NPC is poor with a 5-year survival rate of 50–60%. At present, tumor-node metastasis (TNM) stage and WHO histological type are still the main methods for predicting the prognosis of NPC. Numerous studies have showed that the prognosis could be significantly different in the NPC patients with the same TNM stage and WHO histological type, suggesting that the differences of other factors, such as intrinsic molecules and ethnicity, can affect the prognosis of NPC patients [41]. Therefore, it is urgent to discover biomarkers for predicting the prognosis of NPC patients. It has been reported that patients with NPC showed high levels of C-reactive protein and lactate dehydrogenase, which were associated with poor prognosis [42,43]. Jin et al. [44] built a prognostic score model incorporating circulating tumor markers for metastatic NPC. The results showed that performance status, age, hemoglobin, circulating lactate dehydrogenase, alkaline phosphatase and EBV DNA were independent prognostic factors, which indicates that clinical and laboratory characteristics can help to guide the prognostication of patients with metastatic NPC. It is worthwhile to note that pretherapy circulating EBV DNA load determined by qRT-PCR is an independent prognostic factor to International Union Against Cancer staging in NPC [45]. Some potential biomarkers have also been identified by proteomics that may be valuable in predicting the prognosis of NPC (TABLE 2). Chang et al. [31] found that the 3-year overall survival rates for patient subgroups stratified by serum cystatin A levels >8.9 ng/ml and
