Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry

Diagnostic Application of Serum Proteomic Patterns in Gastric Cancer Patients by ProteinChip Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one of the currently used techniques to identify biomarkers for cancers. This study was planned to establish a system to accurately distinguish gastric cancer patients by using SELDI-TOF-MS. A total of 100 serum samples obtained from 60 individuals with gastric cancer and 40 healthy individuals were screened. Protein expression profiles were expressed on CM10 ProteinChip arrays and analyzed. Peak intensities were analyzed with the Biomarker Wizard software to identify peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern software 5.0. Seventeen protein peaks showed significant differences between the two groups. The decision tree which gave the highest discrimination included four peaks at mass 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and specificity for classification of the decision tree were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested on a blinded test set, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). These results suggest that serum protein profiling by the SELDI system may distinguish gastric cancer patients from healthy controls with relatively high sensitivity and specificity.

Argininosuccinate synthetase 1 contributes to gastric cancer invasion and progression by modulating autophagy

Argininosuccinate synthetase 1 (ASS1) is a rate-limited enzyme in arginine biosynthesis. The oncogenic potential of ASS1 in terms of prognosis and cancer metastasis in arginine prototrophic gastric cancer (GC) remains unclear at present. We identify differentially expressed proteins in microdissected GC tumor cells relative to adjacent nontumor epithelia by isobaric mass tag for relative and absolute quantitation proteomics analysis. GC cells with stable expression or depletion of ASS1 were further analyzed to identify downstream molecules. We investigated their effects on chemoresistance and cell invasion in the presence or absence of arginine. ASS1 was highly expressed in GC and positively correlated with GC aggressiveness and poor outcome. Depletion of ASS1 led to inhibition of tumor growth and decreased cell invasion via induction of autophagy-lysosome machinery, resulting in degradation of active β-catenin, Snail, and Twist. Ectopic expression of ASS1 in GC cells reversed these effects and protected cancer cells from chemotherapy drug-induced apoptosis via activation of the AKT-mammalian target of rapamycin signaling pathway. ASS1 contributes to GC progression by enhancing aggressive potential resulting from active β-catenin, Snail, and Twist accumulation. Our results propose that ASS1 might contribute to GC metastasis and support its utility as a prognostic predictor of GC.-Tsai, C.-Y., Chi, H.-C., Chi, L.-M., Yang, H.-Y., Tsai, M.-M., Lee, K.-F., Huang, H.-W., Chou, L.-F., Cheng, A.-J., Yang, C.-W., Wang, C.-S., Lin, K.-H. Argininosuccinate synthetase 1 contributes to gastric cancer invasion and progression by modulating autophagy.

Plasma Protein Profiles Differ between Women Diagnosed with Cervical Intraepithelial Neoplasia (CIN) 1 and 3

Early detection of precancerous cells in the cervix and their clinical management is the main purpose of cervical cancer prevention and treatment programs. Cytological findings or testing for high risk (HR)-human papillomavirus (HPV) are inadequately sensitive for use in triage of women at high risk for cervical cancer. The current study is an exploratory study to identify candidate surface-enhanced laser desorption/ionization (SELDI) time of flight (TOF) mass spectrometry (MS) protein profiles in plasma that may distinguish cervical intraepithelial neoplasia (CIN 3) from CIN 1 among women infected with HR-HPV. We evaluated the SELDI-TOF-MS plasma protein profiles of HR-HPV positive 32 women with CIN 3 (cases) and 28 women with CIN1 (controls). Case-control status was kept blinded and triplicates of each sample and quality control plasma samples were randomized and after robotic sample preparations were run on WCX2 chips. After alignment of mass/charge (m-z values), an iterative method was used to develop a classifier on a training data set that had 28 cases and 22 controls. The classifier developed was used to classify the subjects in a test data set that has six cases and six controls. The classifier separated the cases from controls in the test set with 100% sensitivity and 100% specificity suggesting the possibility of using plasma SELDI protein profiles to identify women who are likely to have CIN 3 lesions.

Emerging blood-based biomarkers for detection of gastric cancer

Early detection and efficient monitoring of tumor dynamics are prerequisites for reducing disease burden and mortality, and for improving the management of patients with gastric cancer (GC). Blood-based biomarker assays for the detection of early-stage GC could be of great relevance both for population-wide or risk group-based screening programs, while circulating biomarkers that reflect the genetic make-up and dynamics of the tumor would allow monitoring of treatment efficacy, predict recurrences and assess the genetic heterogeneity of the tumor. Recent research to identify blood-based biomarkers of GC has resulted in the identification of a wide variety of cancer-associated molecules, including various proteins, autoantibodies against tumor associated antigens, cell-free DNA fragments, mRNAs and various non-coding RNAs, circulating tumor cells and cancer-derived extracellular vesicles. Each type of these biomarkers provides different information on the disease status, has different advantages and disadvantages, and distinct clinical usefulness. In the current review, we summarize the recent developments in blood-based GC biomarker discovery, discuss the origin of various types of biomarkers and their clinical usefulness and the technological challenges in the development of biomarker assays for clinical use.

Proteomics in gastric cancer research: Benefits and challenges

Among various cancers, gastric cancer (GC) exhibits relatively high morbidity and mortality rate worldwide. The lack of effective methods in early detection and diagnosis, and immediate therapies makes treating such disease a challenge for both clinicians and oncologists. Proteomics has emerged as a promising technology platform for rationally identifying biomarkers and novel therapeutic targets for GC, as well as discovering underlying mechanisms of carcinogenesis. Its application has greatly benefited mechanistic studies of this disease. This review will demonstrate the applications of proteomic technology in GC research. The advantages and shortcomings of this technology, as reflected by current studies, will also be discussed to improve and expand its application in the field of cancer research.

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

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

Liquid chromatography-mass spectrometry based serum peptidomic approach for renal clear cell carcinoma diagnosis

Serum peptidomic approach was applied to investigate the peptidomic signature and discover the clinical biomarkers and biomarker patterns for RCC patients. The holistic orthogonal partial least-squares-discriminant analysis (OPLS-DA) based on qualified profile data successfully classified RCC patients from healthy controls, showing 100% sensitivity and specificity. Following critical criteria, several peptides presenting significant differences in serum level were picked out. The unsupervised hierarchical cluster analysis on those peptides was performed, showing 100% sensitivity and 93.3% specificity for RCC diagnosis regarding the present samples. Besides, receiver-operating characteristic (ROC) analysis was applied on single peptide biomarkers, with four peptides showing excellent predictive power. Among them, IYQLNSKLV and AGISMRSGDSPQD are reported for the first time for cancer detection.

Serum proteomics for gastric cancer

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.

A prognostic biomarker for gastric cancer with lymph node metastases

Gastric cancer is one of the leading causes of tumor-related deaths in China. The tumor, node, metastasis (TNM) classification system is useful for predicting clinical prognosis of patients with gastric cancer. However, determining the presence of lymph node involvement in the early stages of gastric cancer is difficult without biopsy. Therefore, it is necessary to identify novel serum biomarkers for TNM cancer staging and prognostic follow-up. In this study, we have reported fibrinopeptide-A (FPA) with alanine truncation at the N-terminal as a novel biomarker to differentiate gastric cancer with and without lymph node metastases. We analyzed 369 individual serum samples including gastric cancer patients without lymph node metastases (n = 33), gastric cancer patients with lymph node metastases (n = 157; confirmed by pathology), and age- and sex-matched healthy individuals (n = 179). The data showed that 85.4% of patients with lymph node metastases were positive for FPA with alanine truncation at the N-terminal (degAla-FPA, 1,465.63 Da), as determined by tandem mass spectrometry (MS). Using degAla-FPA as the biomarker, the sensitivity was 85.4% for gastric cancer patients with lymph node metastases, and the specificity was 100% for gastric cancer patients without lymph node metastases. The high sensitivity and specificity achieved with serum degAla-FPA levels indicated that MS technology could facilitate the discovery of a novel and quantitative prognostic biomarker for gastric cancer with lymph node involvement.

SELDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of a boosting decision tree model for diagnosis of pancreatic cancer

AIM

New technologies for the early detection of pancreatic cancer (PC) are urgently needed. The aim of the present study was to screen for the potential protein biomarkers in serum using proteomic fingerprint technology.

METHODS

Magnetic beads combined with surface-enhanced laser desorption/ionization (SELDI) TOF MS were used to profile and compare the protein spectra of serum samples from 85 patients with pancreatic cancer, 50 patients with acute-on-chronic pancreatitis and 98 healthy blood donors. Proteomic patterns associated with pancreatic cancer were identified with Biomarker Patterns Software.

RESULTS

A total of 37 differential m/z peaks were identified that were related to PC (P<0.01). A tree model of biomarkers was constructed with the software based on the three biomarkers (7762 Da, 8560 Da, 11654 Da), this showing excellent separation between pancreatic cancer and non-cancer., with a sensitivity of 93.3% and a specificity of 95.6%. Blind test data showed a sensitivity of 88% and a specificity of 91.4%.

CONCLUSIONS

The results suggested that serum biomarkers for pancreatic cancer can be detected using SELDI-TOF-MS combined with magnetic beads. Application of combined biomarkers may provide a powerful and reliable diagnostic method for pancreatic cancer with a high sensitivity and specificity.

Discovery of diagnostic serum biomarkers of gastric cancer using proteomics

Gastric cancer has significant morbidity and mortality worldwide and locally. Good prognosis relies on an early diagnosis. However, this remains a challenge due to the lack of specific and sensitive serum biomarkers for early detection. Hence, there is a constant search for these biomarkers for screening purposes. Proteomic profiling enables a new approach to the discovery of biomarkers in disease. This review presents recent attempts in search of gastric cancer serum biomarker using proteomics. Different methodologies and different types of samples were employed by different groups of researchers. Major difficulties were encountered in the discovery processes, including interference from abundant proteins and continuous changing serum proteomes from different individuals.

Identification of serum amyloid A in the serum of gastric cancer patients by protein expression profiling

The purpose of this study was to screen serum samples from gastric carcinoma patients and to determine whether serum amyloid A protein (SAA) served as a biomarker. SELDI technology was used to screen for changes in SAA levels in the serum samples. A mass cluster with a mass/charge (m/z) value between 11.1 and 11.9 kDa was identified in the serum samples from gastric carcinoma patients which was much higher than that of the control group. Furthermore, the increase in this m/z peak correlated with the severity of the cancer. High-performance liquid chromatography (HPLC) analysis confirmed that the peak was SAA1. In conclusion, this increase in SAA may be used as a potential biomarker for gastric cancer.

Diagnosis of gallbladder cancer using matrix-assisted laser desorption/ionization time-of-flight profiling

Proteomic fingerprint technology combining magnetic beads with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used to profile and compare the serum proteins from 45 patients with gallbladder cancer and 50 healthy blood donors. The proteomic patterns were identified; the tree model of biomarkers was constructed and evaluated using the Biomarker Patterns Software. The model tree was constructed based on the 3 biomarkers (5913 Da, 6181 Da and 13,752 Da), which generated excellent separation between the gallbladder cancer and control groups. The sensitivity was 86.7% and the specificity was 93.3%. The blind test data showed a sensitivity of 80% and a specificity of 90%. Taken together, our studies suggested that biomarkers for gallbladder cancer could be discovered in serum by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with the use of magnetic beads. The pattern of combined markers would provide a powerful and reliable diagnostic method for gallbladder cancer with high sensitivity and specificity.

New serum biomarkers for detection of endometriosis using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

This study used proteomic fingerprint technology, combining nano-sized magnetic beads with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), to screen for potential protein biomarkers for the diagnosis of endometriosis. Serum proteins from 126 patients with endometriosis and 120 healthy controls were profiled and compared. Biomarker pattern software identified 46 discriminating mass-to-charge m/z ratio peaks that were related to endometriosis. The model constructed by the software, based on three of these peaks (m/z 5988.7, 7185.3 and 8929.8), generated excellent separation between the endometriosis and control groups. The sensitivity was 91.4% and the specificity 95.0%. Blind testing on a second series of serum samples from patients with endometriosis and healthy controls indicated a sensitivity of 89.3% and a specificity of 90.0%. Biomarkers for endometriosis can be discovered in serum by MALDI-TOF-MS in combination with nano-sized magnetic beads. The pattern of combined markers provides a powerful and reliable diagnostic method for endometriosis, with high sensitivity and specificity.

Serum biomarker panels for the diagnosis of gastric adenocarcinoma

Background:

Currently, serum biomarkers, which are sufficiently sensitive and specific for early detection and risk classification of gastric adenocarcinoma do not exist. Therefore, this study identified a panel of serum biomarkers for the diagnosis of gastric adenocarcinoma.

Methods:

A 29-plex array platform with 29 biomarkers, consisting of 11 proteins discovered through proteomics and 18 previously known to be cancer-associated, was constructed. A test/training set consisting of 120 gastric adenocarcinoma and 120 control samples were examined. After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations. These algorithms were independently validated using a set of 95 gastric adenocarcinoma and 51 control samples.

Results:

Epidermal growth factor receptor (EGFR), pro-apolipoprotein A1 (proApoA1), apolipoprotein A1, transthyretin (TTR), regulated upon activation, normally T-expressed and presumably secreted (RANTES), D-dimer, vitronectin (VN), interleukin-6, α-2 macroglobulin, C-reactive protein and plasminogen activator inhibitor-1 were selected as classifiers in the two algorithms. These algorithms differentiated between the majority of gastric adenocarcinoma and control serum samples in the training/test set with high accuracy (>88%). These algorithms also accurately classified in the validation set (>85%).

Conclusion:

Two panels of combinatorial biomarkers, including EGFR, TTR, RANTES, and VN, are developed, which are less invasive method for the diagnosis of gastric adenocarcinoma. They could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy.

Serum peptidome profiling in patients with gastric cancer

To identify discriminating protein patterns in serum samples between gastric cancer patients (early and advanced stages) and healthy controls. We used magnetic bead-based separation followed by matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) to identify patients with gastric cancer. In total, serum samples from 62 gastric cancer patients (32 in the training set and 30 in the test set; 19 of which had early-stage tumors and 43 of which had advanced-stage tumors) and 64 healthy controls (32 in the training set and 32 in the test set) were analyzed. The mass spectra, analyzed using ClinProTools software, distinguished between cancer patients and healthy individuals based on three different algorithm models. In the training set, patients with gastric cancer could be identified with a mean sensitivity of 94.7% and a mean specificity of 99%. Similar results were obtained with the test set, showing 79.3% sensitivity and 86.5% specificity. Our study demonstrates the high sensitivity and specificity of screening serum protein patterns using MALDI-TOF MS for the identification of patients with gastric cancer.

Identification of serum biomarkers for ovarian cancer using MALDI-TOF-MS combined with magnetic beads

BACKGROUND

The objective of this study was to search for potential protein biomarkers in serum for diagnosis of ovarian cancer, by use of proteomic fingerprint techniques.

METHOD

MALDI-TOF-MS was combined with magnetic beads to profile and compare serum protein spectra from 40 ovarian cancer patients and from 60 healthy controls.

RESULTS

The tree analysis model of potential cancer biomarkers was constructed with Biomarker Patterns software on the basis of three identified biomarkers (5486, 6440, and 13720 Da), resulting in excellent discrimination between the ovarian cancer and non-cancer in our tests. The sensitivity was 90% and the specificity was 86.7%. In a blind test the sensitivity was 88% and the specificity was 83.3%.

CONCLUSION

The results suggested that biomarkers for ovarian cancer diagnosis in serum could be identified by MALDI-TOF-MS combined with the use of magnetic beads. The use of combined biomarkers would further enable powerful and reliable diagnosis of ovarian cancer with high sensitivity and specificity.

Upregulation of plasma C9 protein in gastric cancer patients

Gastric cancer is one of the leading causes of cancer-related deaths worldwide. Current biomarkers used in the clinic do not have sufficient sensitivity for gastric cancer detection. To discover new and better biomarkers, protein profiling on plasma samples from 25 normal, 15 early-stage and 21 late-stage cancer was performed using an iTRAQ-LC-MS/MS approach. The level of C9 protein was found to be significantly higher in gastric cancer compared with normal subjects. Immunoblotting data revealed a congruent trend with iTRAQ results. The discriminatory power of C9 between normal and cancer states was not due to inter-patient variations and was independent from gastritis and Helicobacter pylori status of the patients. C9 overexpression could also be detected in a panel of gastric cancer cell lines and their conditioned media compared with normal cells, implying that higher C9 levels in plasma of cancer patients could be attributed to the presence of gastric tumor. A subsequent blind test study on a total of 119 plasma samples showed that the sensitivity of C9 could be as high as 90% at a specificity of 74%. Hence, C9 is a potentially useful biomarker for gastric cancer detection.

Five serum proteins identified using SELDI-TOF-MS as potential biomarkers of gastric cancer

OBJECTIVE

The aims of this study were to detect serum proteomic patterns in gastric cancer serum samples using Surface-enhanced Laser Desorption/ionization-Time-of-flight-Mass Spectrometry ProteinChip array technology, to screen biomarker candidates, to build diagnostic models and to evaluate their clinical significance.

METHODS

Serum samples from patients with gastric cancer and normal healthy control subjects (n = 125) were analysed using surface-enhanced laser desorption/ionization technology. The spectra were generated on weak cation exchange (WCX2) chips, and protein peak clustering and classification analyses were established using Ciphergen Biomarker Wizard and Biomarker Pattern software, respectively. The diagnostic models were developed and validated by discriminant analysis. In addition, the results of the surface-enhanced laser desorption/ionization model were compared with the biomarkers carcinoembryonic antigen and carbohydrate antigen 199 in a subset of samples using a microparticle enzyme immunoassay.

RESULTS

Five protein peaks at 2046, 3179, 1817, 1725 and 1929 m/z were automatically chosen as components of the best biomarker pattern for diagnosis of gastric cancer. In addition, we identified a single protein peak at 4665 m/z, which could distinguish between stage I/II and stage III/IV gastric cancer with a specificity and sensitivity of 91.6% (11/12) and 95.4% (21/22), respectively. When this biomarker was validated in the second set of samples, the specificity and sensitivity were 91.7% (11/12) and 86.3% (19/22), respectively.

CONCLUSIONS

The present results suggest that serum surface-enhanced laser desorption/ionization protein profiling can distinguish patients with gastric cancer, and in particular stage I/II patients, from normal subjects with a relatively high sensitivity and specificity. Surface-enhanced Laser Desorption/ionization-Time-of-flight-Mass Spectrometry is a potential new diagnostic tool for the screening of gastric cancer.

Influence of sample storage duration on serum protein profiles assessed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS)

BACKGROUND

Issues have been raised concerning the robustness and validity of alleged serum markers discovered by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). Pre-analytical variables have been shown to exert a profound effect on protein profiles, irrespective of true biological variation. However, little is known about the possible effects of sample storage duration on protein profiles. We, therefore, aimed to investigate the effects of extended storage duration on the serum protein profile.

METHODS

Archival sera from 140 breast cancer patients, stored at -30 degrees C for 1-11 years, were profiled by SELDI-TOF MS using immobilised metal affinity capture (IMAC) arrays, a condition applied in the majority of breast cancer biomarker discovery studies.

RESULTS

Fourteen peak clusters, structurally identified as C3a des-arginine anaphylatoxin and multiple fragments of albumin and fibrinogen, were found to be significantly associated with sample storage duration by five distinct patterns. These proteins have been described previously as potential cancer markers, rendering them specific to both disease and sample handling issues.

CONCLUSIONS

To prevent experimental variation being interpreted erroneously as disease associated variation, assessment of potential confounding pre-analytical parameters is a pre-requisite in biomarker discovery and validation studies. In addition, with respect to the different (non-)linear patterns observed in the current study, simply performing linear corrections to account for sample storage duration will not necessarily suffice.

The current state of proteomics in GI oncology

Proteomics refers to the study of the entire set of proteins in a given cell or tissue. With the extensive development of protein separation, mass spectrometry, and bioinformatics technologies, clinical proteomics has shown its potential as a powerful approach for biomarker discovery, particularly in the area of oncology. More than 130 exploratory studies have defined candidate markers in serum, gastrointestinal (GI) fluids, or cancer tissue. In this article, we introduce the commonly adopted proteomic technologies and describe results of a comprehensive review of studies that have applied these technologies to GI oncology, with a particular emphasis on developments in the last 3 years. We discuss reasons why the more than 130 studies to date have had little discernible clinical impact, and we outline steps that may allow proteomics to realize its promise for early detection of disease, monitoring of disease recurrence, and identification of targets for individualized therapy.

Deep proteome profiling of sera from never-smoked lung cancer patients

Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.

Technology insight: the application of proteomics in gastrointestinal disease

Analysis of the human genome has increased our knowledge of the genes that are associated with disease. At the same time, however, it has become clear that having complete DNA sequences alone is not sufficient to elucidate the biological functions of the proteins that they encode. For this reason, proteomics-the analysis of proteins-has become increasingly attractive, because the proteome reflects both the intrinsic genetic programming of a cell and the impact of its immediate environment. The principal goals of clinical proteomics are to identify biomarkers for the early diagnosis of disease and potential targets for therapeutic intervention. Other goals include the identification of biomarkers for the early detection of disease recurrence (relapse) and how they might be combined with diagnostic imaging techniques to improve the sensitivity for detecting disease. This Review describes conventional proteomic technologies, their strengths and limitations, and demonstrates their application to clinical practice, with specific reference to their use in the gastroenterology field.