Clinica Chimica Acta 431 (2014) 179–184

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Invited critical review

Serum proteomics for gastric cancer Wentao Liu, Qiumeng Yang, Bingya Liu, Zhenggang Zhu ⁎ Key Laboratory of Shanghai Gastric Neoplasms, Department of Surgery, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai Institute of Digestive Surgery, Shanghai 200025, PR China

a r t i c l e

i n f o

Article history: Received 8 July 2013 Received in revised form 28 January 2014 Accepted 5 February 2014 Available online 11 February 2014 Keywords: Gastric cancer Serum Biomarker Proteomics

a b s t r a c t According to the World Health Organization, 800,000 cancer-related deaths are caused by gastric cancer each year globally, hence making it the second leading cause of cancer-related deaths in the world. Gastric cancer is often either asymptomatic or causing only nonspecific symptoms in its early stages. By the time the symptoms occur, the cancer has usually reached an advanced stage, which is one of the main reasons for its relatively poor prognosis. Therefore, early diagnosis and early treatment are very crucial. The differential analysis of serum protein between cancer patients and healthy controls can be performed using proteomics techniques and can hence be adopted as tumor biomarkers for the early diagnosis of cancer. So far, several serum tumor biomarkers have been identified for gastric cancer. However due to their poor specificity and sensitivity, they have proven to be insufficient for the reliable diagnosis of gastric cancer. Thus, using modern advanced proteomics techniques to find some new and reliable serum tumor biomarkers for earlier and reliable diagnosis of gastric cancer is a must. Nowadays, proteomic-based techniques, such as SELDI and HCLP, are available to discover biomarkers in gastric cancer. Numerous novel serum tumor biomarkers such as SAA, plasminogen and C9c, have been discovered through serological proteomics strategies. This review mainly focuses on the serum proteomics techniques and their application in the research of gastric cancer. © 2014 Elsevier B.V. All rights reserved

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods used in the gastric cancer serum proteomics . . . . . . . . . . . . . . . 2.1. Techniques of protein extraction and isolation . . . . . . . . . . . . . . . 2.1.1. The 2-DE and 2D-DIGE techniques . . . . . . . . . . . . . . . . 2.1.2. The technique of surface enhanced laser desorption ionization (SELDI) 2.1.3. The technique of Liquid Chip based on magnetic bead enrichment . 2.2. Mass spectrometry and bioinformatics techniques . . . . . . . . . . . . . 3. Challenges and limitations in serum proteomics . . . . . . . . . . . . . . . . . 3.1. Depletion of high abundance proteins . . . . . . . . . . . . . . . . . . 3.2. Defects of the MS technique . . . . . . . . . . . . . . . . . . . . . . . 3.3. Experimental design and others . . . . . . . . . . . . . . . . . . . . . 4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction

⁎ Corresponding author at: Department of Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University, 197 Ruijin Road II, Shanghai 200025, PR China. Tel.: +86 21 64670644; fax: +86 21 64373909. E-mail address: [email protected] (Z. Zhu).

http://dx.doi.org/10.1016/j.cca.2014.02.001 0009-8981/© 2014 Elsevier B.V. All rights reserved

Adenocarcinoma of the stomach is one of the leading causes of cancer-related deaths worldwide and also happens to be the second most common cancer in males and the fourth most common cancer in females, with nearly one million of new cases diagnosed each year. The case–fatality ratio of gastric cancer is higher than the common malignancies like colon, breast and prostate cancers [1].

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Due to the lack of specificity in the clinical manifestations, gastric cancer has usually been diagnosed at the middle-advanced stage, causing the 5-year survival rate to be between 10% and 28% [2]. Hence, early diagnosis and early treatment are very crucial. Under many physiological and pathological conditions, some important regulatory proteins secreted into the serum by the body undergo quantitative or qualitative changes. Clinically, the detection of the physiological indicators or parameters of the serum has been always an auxiliary for the diagnosis of cancer. Circulating serum proteins can be used as tumor biomarkers for the prevention, diagnosis and treatment of tumors. Therefore, finding reliable serum tumor biomarkers and using them to screen for tumors of the digestive system will prove to be a meaningful and practical attempt for the early intervention therapy of gastric cancer. Serum tumor biomarkers are the most commonly used screening method for malignant tumors, and have proven to have considerable value in the early diagnosis of liver and prostate cancers. However, the positive rate of currently used gastrointestinal tumor biomarkers, such as CEA, CA 19-9 and CA 72-4, is lower than 40% in gastric cancer patients [3] and lower than 20% in early gastric cancer patients, hereby making them insufficient for the diagnostic screening [4,5]. Moreover, each tumor biomarker has different expression levels at different stages of gastric cancer, for example the preoperative serum positive rate of AFP, CEA, CA19-9, and CA50 is only 5.9%, 16.1%, 32.6% and 29.7% respectively at the stage T4a of gastric cancer [6]. Hence, searching for novel serum protein biomarkers with higher specificity and sensitivity is crucial for the diagnostic screening of gastric cancer. In 1994, Wilkins and Williams from Macquarie University put forward the concept of the proteome and after that, proteomics and related techniques underwent rapid development, making it possible for the large-scale screening of tumor biomarkers [7]. Yet, the application of proteomics techniques for serum is still limited due to the complicated components of serum. Screening of serum tumor biomarkers by the serum proteomics techniques is still facing a lot of difficulties. With the development of proteomics techniques, mass spectrometry (MS) can accurately identify a specific protein by comparing the peptide sequence with the protein sequence database. By using proteomics techniques into the serology, it will be possible to discover new clinical serum biomarkers related to the tumors of the digestive system, hence leading to a high throughput screening of serum proteins. In addition to the development of the proteomics techniques, direct screening tests of malignant tumors by proteomics techniques have gradually been carried out. There are mainly two strategies in the recent studies of gastric cancer serum proteomics; one strategy is to establish a diagnostic model for gastric cancer with the specific serum peptide spectrum directly detected by mass spectrometry; the other strategy is to discover and validate new serum biomarkers for gastric cancer. This article will make a review on the serum proteomics techniques and their applications in the research of gastric cancer. 2. Methods used in the gastric cancer serum proteomics Usually, serum proteomic analysis mainly consists of three procedures: protein extraction and isolation, protein identification and protein verification. The strategy flowchart of screening serum tumor biomarkers by serum proteomics techniques is shown in Fig. 1. The protein extraction and separation can be accomplished by techniques such as twodimensional electrophoresis (2-DE), two-dimensional differential gel electrophoresis (2D-DIGE), and solid chip capture proteome or liquid nano magnetic beads capture target proteome. The protein detection however mainly depends on the mass-spectrometric technique while the protein verification can be achieved by conventional techniques such as enzyme-linked immuno-sorbent assay (ELISA) and Western blot where the expression of specific protein biomarkers can be detected. However, since there may be a large amount of relatively specific peptide fragments rather than some specific proteins present in the tumor serum,

Fig. 1. The flowchart of screening serum tumor biomarkers by serum proteomics techniques.

in recent years, many researchers have been trying to establish diagnostic models with the serum peptide groups rather than the specific serum proteins. Table 1 shows the applications of serum proteomics techniques in the gastric cancer studies over the past few years. 2.1. Techniques of protein extraction and isolation 2.1.1. The 2-DE and 2D-DIGE techniques As a classic proteomics technique, 2-DE is able to quantitatively compare the proteins and display the protein isoforms on the same gel, hence making it a commonly used technique in the proteomics research. In 2-DE, proteins are firstly separated according to their isoelectric points in the one-dimensional electrophoresis and then, they are transferred to the two-dimensional SDS-polyacrylamide gel to finally get separated based on their molecular weights. By using the 2-DE technique, many research groups have successfully identified tumor biomarkers from gastric cancer tissue [8], gastric cancer cell strain [9] and gastric fluids [10]. There are also some research groups using the 2-DE technique to directly separate the serum proteins to do some disease-related explorations: Li et al. [11] have separated 1138 protein spots from serum using 2-DE technique, and they have identified a total of 318 unique proteins. They showed that by deleting high abundance proteins, the efficiency of the 2-DE technique on serum can be improved. Serum amyloid A (SAA) and cathepsin B are found overexpressed in the serum of gastric cancer patients during analysis with the 2-DE technique [12,13]. By using the 2-DE technique, our group also found that as compared to normal serum, the complement factor I (CFI) precursor of gastric cancer serum is under-expressed and that the expression of CFI precursor declines with the increase in the pTNM stage of the gastric cancer patients [14]. The traditional 2-DE technique has made big progress with the development of proteomics techniques. Based on 2-DE, the technique of Fluorescence 2-Difference Gel Electrophoresis (2D-DIGE) is a new proteomics technique and offers better advantages in the comparative study: 2D-DIGE labels protein samples with different fluorescent dyes (Cy2, Cy3, and Cy5) and then the labeled protein lysates are equivalently mixed and run on a 2-DE gel. An N-hydroxysuccinimidyl ester reactive group of the dyes can covalently bind to the ε-amino of protein lysine residue, however hardly affecting the isoelectric point and molecular weight of the marked protein. Different proteins can be identified by comparing the signals of different fluorescence in the same spot. DIGE can display both protein samples from experimental group and control group in the same gel by labeling them with different fluorescence, making the experimental results more reliable. Moreover, it uses an internal standard as quality control, which not only allows reliable quantitative comparison of different proteins, but also ensures

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Table 1 Serum biomarkers discovered by using proteomics methods for gastric cancer. Serum markers 4474 Da peak and 5 different peaks [68] SAA and 32 peptides [69] C9 [70] ITIH3 [71] Six peaks [34] 2209 Da peptide [72] apoC-I and apoC-III [73] SLe(X) [19] Plasminogen, apolipoprotein A-IV, Kininogen-1 and seven other proteins [18] Tubulin beta chain, thymosin beta-4-like protein 3, and cytochrome b-c1 complex subunit 1 [74] Plasminogen and 25 glycoproteins [75]

Separation method

Detection method

Validation method

Sensitivity

Specificity

SELDI-TOF MS SELDI-TOF MS iTRAQ-LC–MS/MS iTRAQ-LC–MS/MS MALDI-TOF-MS MALDI-TOF/TOF MS MALDI-TOF

SELDI

66.7% 81% 90% 96% 100% 28% 98% No No 95%

80% 90% 74% 66% 75% 95% 71% No No 97.10%

No

No

WCX magnetic beads WCX magnetic beads C8 Reverse-phase 2D-DIGE 2D-DIGE WCX magnetic beads

MALDI-TOF/TOF MS & LTQ MALDI-TOF MS & LTQ

2DE & Western blot

MALDI-TOF/TOF MS

the accuracy of comparing multiple gels. Moreover, the statistical analysis of the information of protein spots in each gel makes the experimental results even more reliable [15]. Scholars have applied DIGE technique into the research work for different cancers such as prostate cancer [16] and pancreatic cancer [17]. By using DIGE technique, our group identified 13 differential serum proteins by comparing the serum proteomic profiles of gastric cancer patients and healthy volunteers. Immunohistochemistry (IHC) staining of Clusterin (CLU) protein showed that the expression of CLU in gastric cancer tissues was significantly higher than normal controls (P b 0.05) [18]. Bones J et al. [19] have screened out some candidate glycoprotein markers from gastric cancer patients when they combined the glycomics technique with 2D-DIGE. 2.1.2. The technique of surface enhanced laser desorption ionization (SELDI) The design of SELDI Protein Chip arrays is based on the chromatographic technique and mass spectrometry. It was originally defined by Hutchens and Yip [20]. The principle of SELDI is very simple: It gains an enhanced surface affinity capture capacity by using specific probe surfaces or arrays. Arrays with wide binding property and biochemical property constitute the core of SELDI. This kind of MS can directly separate protein samples and discover the serum markers, with the requisite amount of sample reduced to 0.5 μl. When compared to existing techniques such as LC–MS and 2-DE-MS, SELDI has the advantages of multifunction, high specificity, convenience, speed and economy. By making use of SELDI-TOF-MS to compare the serum protein profiles of the gastric cancer patients and the healthy controls, it will be possible to find new and more specific disease-related proteins. SELDI technique has been widely used in the research of tumor diagnosis for tumors such as ovarian cancer [21], breast cancer [22] and prostatic cancer [23]. In earlier studies, Ebert et al. [24] successfully identified gastric cancer markers with 100% sensitivity and 100% specificity by using SELDI/TOF-MS and Protein Chip technique coupling with a pattern-matching algorithm to analyze serum samples from gastric cancer patients and non-cancer patients. Further research work established a model for the serological diagnosis of gastric cancer through the SELDI technique [25–28]. In the recent years, a study has discovered differential peptides in serum of gastric cancer and identified SAA as a serum protein marker of gastric cancer through HPLC combined with MS technique [29]. Using SELDI and antibody microarrays combined with LC–MS/MS technique, Mohri et al. showed that the expression of macrophage migration inhibitory factor (MIF) and human neutrophil peptides 1–3 (HNPs 1–3) was increased in the serum and tissues of gastric cancer. Eventually, the result was also confirmed by ELISA [30]. 2.1.3. The technique of Liquid Chip based on magnetic bead enrichment In the first place, the Liquid Chip system uses different functional magnetic beads (hydrophobic magnetic beads, metal-chelated magnetic beads, anion and cation exchange magnetic beads, sugar magnetic beads) to separate protein samples; the functional magnetic beads

Western blot Western blot CLIPROT Western blot ELISA

CLIPROT

offer a great flexibility to the fractionation of serum/plasma and other complicated biological samples. The total surface touching area between magnetic beads and sample is far larger than that in other similar techniques. Hence, the magnetic beads and sample are easily adequately mixed and the separating volume of sample from magnetic beads is much higher than in the SELDI technique. It has advantages such as easy automated processing and easy quantification for the further purification of biomarkers. Thus, the magnetic beads contribute to the high sensitivity and high reproducibility of Liquid Chip system. In the second place, the Liquid Chip system adopts the Anchor Chip technique, an ultra-sensitive and automated technique for preparing MALDI samples. The target samples are equipped with hydrophobic groups. During solvent evaporation, sample droplets shrink, resulting in higher concentration for analysis. Anchor Chip technique can increase the sensitivity of Liquid Chip system by 10 to 100 folds, which compared to the conventional chip, further contributes to the high sensitivity and high reproducibility of Liquid Chip system [31]. This kind of technique has been used to screen the serum proteins of many malignant tumors, such as pancreatic cancer [32] and colorectal cancer [33]. Our group applied it into the gastric cancer study [34]. We used Liquid Protein Chip time of flight mass spectrometry system to compare the serum proteomic profiles of gastric cancer patients and healthy controls. We selected out six most significant peaks to build a serum diagnostic model for gastric cancer and finally found that the sensitivity and specificity of the model were 75% and 100% respectively in the verification tests. In respect to gastric cancer, Ebert et al. [35] used C8 magnetic beads combined with MALDI to compare the serum peptide profiles of gastric cancer and healthy controls: they found a peptide fragment with an m/z value of 1465.64 Da was significantly up-regulated in the tumor serum and by means of ELISA, the differential peptide was finally identified as fibrinopeptide. 2.2. Mass spectrometry and bioinformatics techniques Electrospray ionization mass spectrometry (ESI-MS) and matrix assisted laser desorption mass spectrometry (MALDI-MS) are two ionization methods discovered in the late 1980s. The emergence of these two methods brought a revolutionary change to the traditional MS technique which was mainly used in the research of small molecules [36]. With the development of the MS technique, there have been some MS methods which could be used to identify proteins. These MS methods are mainly composed of peptide mass fingerprinting (PMF), tandem mass spectrometry (MS/MS) and liquid chromatography tandem mass spectrometry (LC–MS/MS). The advantage of ESI-MS is that it can be conveniently combined with various separation techniques, for example LC–MS/MS can be combined with the liquid chromatographytandem mass spectrometry to detect macromolecules [37]. In LC–MS/MS, complicated protein samples are first digested by proteases and then pre-separated by one-dimensional or multi-dimensional chromatography and finally, the peptide fragment mixtures are finally analyzed by MS/MS. LC–MS/MS is unbiased, covers for the shortage of 2-D PAGE-MS, equally separates high and low abundance

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proteins and finally, has a high level of automation. Based on multidimensional techniques, the shotgun technique is also a comprehensive and efficient method for identifying proteins; it uses MS/MS to analyze peptide fragments digested from the total proteins and by means of bioinformatics techniques, the specific proteins are finally identified from the protein mixtures. Recently, Joshua N. Adkins et al. [38] have successfully been able to identify 490 proteins from serum by using the chromatography–tandem mass spectrometry technique. The 490 identified proteins included many low abundance proteins such as human growth hormone, interleukin-12 (IL-12) and prostate-specific antigen. Through this technique, we can better understand the serum proteomic profile and hence, lay a good foundation for future research work and discovery of new disease-related serum protein markers. Yet, these techniques still have disadvantages: they cannot compare the quantitative difference of proteins and differentiate protein isoforms. LC–MS/MS can combine with many protein separation techniques to identify the differential serum proteins related to gastric cancer [39,40]. The mass spectrogram of MALDI is mainly generated by the single charge ions, so there is a one-to-one correspondence between ions in the spectrogram and the weight of peptide or protein [41]. Ions generated by MALDI are usually detected by a time-of-flight (TOF) detector [42]. Theoretically, if the TFO tube has a sufficient length, the mass number of a molecule that the TFO detector can detect is unlimited. Therefore, MALDI-TOF-MS is suited to analyze bio-macromolecules such as protein, peptide, nucleotide and polysaccharide [43]. MALDI coupled with MS/MS can be used to identify specific proteins from protein mixtures. Due to the specificity of peptide amino acid sequence, MALDI has a much higher specificity in identifying proteins; it can identify a specific protein only by analyzing a peptide. With further appropriate adjustments, it also can identify proteins with post-translational modifications. Therefore, it is the most commonly used method for identifying proteins [44]. All in all, the development of proteomics promotes the progress of MS. As far as new techniques are concerned, new separation techniques such as two-dimensional chromatograph (2D-LC) and liquid chromatogramcapillary electrophoresis are the main direction for development. Another strategy is analyzing the proteolytic digests directly from the whole proteome based on the MS technique [39,45]. The bioinformatics techniques and computer techniques have been widely used in sample processing and statistics. Valid data can be obtained through the MS technique and then the data is visualized, generalized and collected by the background processing software. Finally, a prediction model is established based on the protein expression profile data. The processing software adopts different mathematical algorithms; hence it can be used to set up different prediction models to predict gastric cancer. By comparison with the existing protein database, each specific protein can be identified. The commonly most used protein databases are IPI [46], UniProt and Swiss-Prot [47]. The IPI database is based on the integrity of several databases such as Swiss-Prot, TrEMBL, Ensembl [48] and RefSeq [49]. Due to its rich sequence information and high integrity, IPI is the most commonly used database in proteomics research. The UniProt database consists of Swiss-Prot database and TrEMBL database. At present, Swiss-Prot is the most accurate protein database with lowest redundancy. All protein sequence records in Swiss-Prot are done by manual annotations from experts. Coding sequences with mere annotation from EMBL-Bank, GenBank or DDBJ nucleic acid database [50] and some sequences without validation submitted by users are recorded in the TrEMBL database. Search engines such as Mascot, PepSea and PeptideSearch are however needed to search these databases. The most commonly used engine is Mascot. However, a new recently developed program called Paragon has been replacing Mascot since it overcomes the passive, probabilistic and estimated search mode of Mascot [51]. All in all, the large protein databases mentioned above contribute to the identification of specific proteins related to gastric cancer. Even with glycosylation or phosphorylation, the differential proteins can be

conveniently identified by comparing them with the sequences in the database. 3. Challenges and limitations in serum proteomics Although there are lots of studies and reports about serum proteomics every year, there are still many problems to be solved and improved in order to diminish the variations and biases in the test. When we use the differential serum peptide as a tumor biomarker, due to the existence of different variations and biases, there is a lack of high level of both specificity and sensitivity and the reproducibility of study is very poor. Making use of serum proteomics techniques to screen tumor serum protein markers is mainly facing the following difficulties: Firstly, various impurities should be removed before the study in order to eliminate interference with the test results; low abundance proteins in the serum are the most meaningful proteins and the presence of high abundance proteins in serum affects the detection of low abundance proteins. Secondly, since the proteomic components of different individuals vary a lot and that the amount of some serum proteins are varying with time and different physiological states, the experimental results stay unstable; hence, reasonable and reliable pretreatment of the serum is very important to the studies of serum proteomics. Different pretreatment methods may cause different effects. There are many factors affecting sample collection and pretreatment. A study [52] has showed that factors affecting the analysis result of serum proteins include sample storage time, storage temperature, frozen period, application of protease inhibitor and others. Thirdly, while considering other aspects, it is to be noted that the serum proteomics techniques such as SELDI have their own disadvantages: they usually have low reproducibility and fail to directly identify the proteins and this aspect has been puzzling a lot of scholars [53–56]. Besides, the process of expressing and purifying the target proteins or peptides, especially preparing antibodies, is too cumbersome to get as many kinds of purified proteins as possible from complicated samples [57]. Meanwhile, there is a need to avoid the cross-reaction between proteins or antibodies [58]. 3.1. Depletion of high abundance proteins Currently, the application of proteomics techniques in serology is just in its infancy. One preliminary research from global multicenter obtained some preliminary results by comparing multiple methods, techniques and conditions used in serum proteomics research [59]. However, the application of proteomics techniques in serum is still limited due to the complicated serum components. The affection of high abundance proteins on low abundance proteins is the main difficulty of the study. Cho SY et al. have showed that 51–71% of total serum proteins are serum albumin, while 8–26% is IgG [60]. Serum albumin and IgG abound in the serum will affect the displaying of some meaningful low abundance proteins on the 2-DE gel, making then undetectable for the proteomics techniques. Most of meaningful serum protein markers are low abundance proteins. Furthermore, the dynamic range of concentration of different serum proteins is too tremendous (at least up to 9 orders of magnitude in difference), making it hard to display all of them on the polyacrylamide gel since the latter's maximal range is only 4 orders of magnitude [61]. Therefore, the main purpose of pretreating serum sample is to eliminate the masking effect of high abundance and high molecular weight proteins on low abundance proteins. By now, there are a lot of commercial kits that can be used to remove high abundance proteins in serum and they can selectively remove one to twelve kinds of high abundance proteins [62–64]. However, due to the technical limitation, depletion of high abundance proteins may also remove some relatively important low abundance proteins such as some cytokines [62]. Yet, all of those kits cannot completely remove the high abundance proteins in serum. Hence, choosing an appropriate method to remove high abundance proteins is crucial to the study of serum proteomics. However, the loss of medium and low

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abundance proteins is inevitable in the process of removing high abundance proteins. 3.2. Defects of the MS technique There are still lots of problems with the use of the MS technique to detect the concentration of serum proteins by method of quantification. Subtle variations in the components of serum or plasma affect the signal intensity. The components of the specimen can also affect the effectiveness of protein ionization. In fact, even if a molecule is of the same concentration in two specimens, different components of the two specimens may interfere and result in different signal intensities of the same molecule in the two specimens [65]. The FDA has yet not approved the application of serum proteomics techniques such as SELDI in the clinical diagnosis of tumor. However, even though the application of clinical proteomics in serology has gained more attention, the reproducibility of serum proteomics still needs to be improved [66]. Many researchers tried to repeat the pattern of serum diagnosis but all of them failed. The components of serum protein vary a lot and they are influenced by many factors such as menstrual cycle, age and inflammation. Currently, SELDI cannot detect serum components of concentrations less than 1 μg/μl, which may be 1000 times higher than the concentration of some circulating tumor markers, for example the concentration of α-fetoprotein is 150 pmol/l. Therefore, the serum proteins identified by MS usually are high abundance proteins, which might not be the targeted important tumor markers. Furthermore, the common serum tumor markers such as CEA and PSA have not yet been discovered by the serum proteomics methods [67]. Hence, using serum proteomics techniques to find a reliable gastrointestinal tumor biomarker which can be used in clinical medicine still needs some time. 3.3. Experimental design and others Chance and bias seriously threaten the reliability of the results of the serum proteomics study. Random, control and repeat are three principles used to avoid the influence of the element of chance on experimental results. Bias is unavoidable in the serum proteomics study. Bias may exist at all stages of the experiment and thus affects the experimental results [55,56], for example, at stages such as specimen collection and storage, MS operation and statistical analysis of data usually produce bias. Unfortunately bias may not be eliminated by repeating the experiment. But to some extent, the influence of bias on experiments can be eliminated by scientific experimental design [55]. Therefore, a scientific, reasonable and cautious design for the experiment is very important in the study of serum proteomics. 4. Conclusions A reliable serum tumor biomarker may play an important role in the prevention, diagnosis, prognosis and treatment of gastric cancer. Researchers usually use different proteomics techniques to discover novel serum tumor biomarkers. Each of the proteomics technique has its own advantages and disadvantages. During the last decade, several novel serum biomarkers for gastric cancer have been identified by different proteomics techniques. Currently, the most widely used serum biomarkers in clinical medicine are CEA, CA 19-9 and CA 72-4. However, their positive rates in gastric cancer patients are lower than 40%, causing them to be insufficient for the reliable diagnosis of gastric cancer. A common problem for all the current serum biomarkers is that they usually have very low positive rates in large samples. Hence, large-scale studies with new and reliable proteomics techniques are encouraged in order to identify novel and unbiased serum biomarkers for gastric cancer. Finally, the function and structure of a novel serum biomarker will be elucidated by the bioinformatics techniques, paving the road for the development of different vaccines and drugs.

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Acknowledgments This work was supported by two grants from the National Natural Science Foundation of China (No. 30872477 and No. 30901729). Some technological developments were supported by the Shanghai Leading Academic Discipline Project (No. S30204) and the National HighTech Program (No. 2006AA02A402 and No. 2006AA02A301).

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