Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

INTRODUCTION

Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificial Intelligence (AI) and Machine Learning (ML) techniques and their applications in various fields have brought immense value in providing insights into advancement in support of cancer control.

METHODS

A systematic and thematic analysis was performed on the Scopus database to identify the top 100 cited articles in cancer research. Data were analyzed using RStudio and VOSviewer.Var1.6.6.

RESULTS

The top 100 articles in AI and ML in cancer received a 33 920 citation score with a range of 108 to 5758 times. Doi Kunio from the USA was the most cited author with total number of citations (TNC = 663). Out of 43 contributed countries, 30% of the top 100 cited articles originated from the USA, and 10% originated from China. Among the 57 peer-reviewed journals, the "Expert Systems with Application" published 8% of the total articles. The results were presented in highlight technological advancement through AI and ML via the widespread use of Artificial Neural Network (ANNs), Deep Learning or machine learning techniques, Mammography-based Model, Convolutional Neural Networks (SC-CNN), and text mining techniques in the prediction, diagnosis, and prevention of various types of cancers towards cancer control.

CONCLUSIONS

This bibliometric study provides detailed overview of the most cited empirical evidence in AI and ML adoption in cancer research that could efficiently help in designing future research. The innovations guarantee greater speed by using AI and ML in the detection and control of cancer to improve patient experience.

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

Colorectal carcinoma screening: Established methods and emerging technology

Colorectal carcinoma screening programs have shown success in lowering both the incidence and mortality rate of colorectal carcinoma at a population level, in part because this carcinoma is relatively slow growing and has an identifiable premalignant lesion. Still, many patients do not undergo the recommended screening for colorectal carcinoma, and of those who do, a subset may be over- or under-diagnosed by the currently available testing methods. The primary purpose of this article is to review the data regarding currently available colorectal cancer screening modalities, which include fecal occult blood testing, direct colonic visualization, and noninvasive imaging techniques. In addition, readers will be introduced to a variety of biomarkers that may serve as stand-alone or adjunct tests in the future. Finally, there is a brief discussion of the current epidemiologic considerations that public health officials must address as they create population screening guidelines. The data we provide as laboratory physicians and scientists are critical to the construction of appropriate recommendations that ultimately decrease the burden of disease from colorectal carcinoma.

A novel short-term high-lactose culture approach combined with a matrix-assisted laser desorption ionization-time of flight mass spectrometry assay for differentiating Escherichia coli and Shigella species using artificial neural networks

Background

Escherichia coli is currently unable to be reliably differentiated from Shigella species by routine matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis. In the present study, a reliable and rapid identification method was established for Escherichia coli and Shigella species based on a short-term high-lactose culture using MALDI-TOF MS and artificial neural networks (ANN).

Materials and methods

The Escherichia coli and Shigella species colonies, treated with (Condition 1)/without (Condition 2) a short-term culture with an in-house developed high-lactose fluid medium, were prepared for MALDI-TOF MS assays. The MS spectra were acquired in linear positive mode, with a mass range from 2000 to 12000 Da and were then compared to discover new biomarkers for identification. Finally, MS spectra data sets 1 and 2, extracted from the two conditions, were used for ANN training to investigate the benefit on bacterial classification produced by the new biomarkers.

Results

Twenty-seven characteristic MS peaks from the Escherichia coli and Shigella species were summarized. Seven unreported MS peaks, with m/z 2330.745, m/z 2341.299, m/z 2371.581, m/z 2401.038, m/z 3794.851, m/z 3824.839 and m/z 3852.548, were discovered in only the spectra from the E. coli strains after a short-term high-lactose culture and were identified as belonging to acid shock protein. The prediction accuracies of the ANN models, based on data set 1 and 2, were 97.71±0.16% and 74.39±0.34% (n = 5), with an extremely remarkable difference (p < 0.001), and the areas under the curve of the receiver operating characteristic curve were 0.72 and 0.99, respectively.

Conclusions

In summary, adding a short-term high-lactose culture approach before the analysis enabled a reliable and easy differentiation of Escherichia coli from the Shigella species using MALDI-TOF MS and ANN.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

Establishment of multiple diagnosis models for colorectal cancer with artificial neural networks

The current study aimed to develop multiple diagnosis models for colorectal cancer (CRC) based on data from The Cancer Genome Atlas database and analysis with artificial neural networks in order to enhance CRC diagnosis methods. A genetic algorithm and mean impact value were used to select genes to be used as numerical encoded parameters to reflect cancer metastasis or aggression. Back propagation and learning vector quantization neural networks were used to build four diagnosis models: Cancer/Normal, M0/M1, carcinoembryonic antigen (CEA) <5/≥5 and Clinical stage I-II/III-IV. The performance of each model was evaluated by predictive accuracy (ACC), the area under the receiver operating characteristic curve (AUC) and a 10-fold cross-validation test. The ACC and AUC of the Cancer/Normal, M0/M1, CEA and Clinical stage models were 100%, 1.000; 87.14%, 0.670; 100%, 1.000; and 100%, 1.000, respectively. The 10-fold cross-validation test of the ACC values and sensitivity for each test were 93.75-99.39%, 1.0000; 80.58-88.24%, 0.9286-1.0000; 67.21-92.31%, 0.7091-1.0000; and 59.13-68.85%, 0.6017-0.6585, respectively. The diagnosis models developed in the current study combined gene expression profiling data and artificial intelligence algorithms to create tools for improved diagnosis of CRC.

Selected reaction monitoring for colorectal cancer diagnosis using a set of five serum peptides identified by BLOTCHIP®-MS analysis

BACKGROUND

Colorectal cancer (CRC) is one of the most predominant types of cancer, and it is the fourth most common cause of cancer-related death and it is important to diagnose CRC in early stage to decrease the mortality by CRC. In our previous study, we identified a combination of five peptides as a biomarker candidate to diagnose CRC by BLOTCHIP^®-MS analysis using a set of healthy control subjects and CRC patients (stage II-IV). The aim of the present study was to validate the serum biomarker peptides reported in our previous study using a second cohort and to establish their potential usefulness in CRC diagnosis.

METHODS

A total of 56 patients with CRC (n = 14 each of stages I-IV), 60 healthy controls, and 60 patients with colonic adenoma were included in this study. The five peptides were extracted and analyzed by selected reaction monitoring using ProtoKey^® Colorectal Cancer Risk Test Kit (Protosera, Inc., Amagasaki, Japan).

RESULTS

The results clearly showed that the four CRC groups, stages I-IV, could be sufficiently discriminated from the control group and colonic polyp group. This five-peptide set could identify CRC at each stage compared to the control population in this validation cohort, including those with early-stage disease. The AUC values for each stage of CRC compared to the control population were 0.779, 0.946, 0.852, and 0.973 for stages I, II, III, and IV, respectively.

CONCLUSIONS

In this case-control validation study, we confirmed high diagnostic performance for CRC using five peptides that were identified in our previous study as serum biomarker candidates for the detection of CRC.

Identification of αB-Crystallin, a Biomarker of Renal Cell Carcinoma by SELDI-TOF MS

Spectrometric-based surface-enhanced laser desorption/ionization ProteinChip (SELDI-TOF) facilitates rapid and easy analysis of protein mixtures and is often exploited to define potential diagnostic markers from sera. However, SELDI-TOF is a relatively insensitive technique and unable to detect circulating proteins at low levels even if they are differentially expressed in cancer patients. Therefore, we applied this technology to study tissues from renal cell carcinomas (RCC) in comparison to healthy controls. We found that different biomarkers are identified from tissues than those previously identified in serum, and that serum markers are often not produced by the tumors themselves at detectable levels, reflecting the nonspecific nature of many circulating biomarkers. We detected and characterized αB-crystallin as an overexpressed protein in RCC tissues and showed differential expression by immunohistochemistry. We conclude that SELDI-TOF is more useful for the identification of biomarkers that are synthesized by diseased tissues than for the identification of serum biomarkers and identifies a separate set of markers. We suggest that SELDI-TOF should be used to screen human cancer tissues to identify potential tissue-specific proteins and simpler and more sensitive techniques can then be applied to determine their validity as biomarkers in biological fluids.

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.

Triacylglycerols in edible oils: Determination, characterization, quantitation, chemometric approach and evaluation of adulterations

Vegetable oils are a dietary source of lipids that constitute an essential component of a healthy diet. The commonly used vegetable oils differ significantly for their triacylglycerol (TAG) profile. TAGs represent the principal components of oils and may contain different fatty acids (FA) esterified with glycerol leading to several positional isomers. To differentiate individual TAGs species in edible oils, advanced analysis systems and innovative methods are therefore required. TAGs can be considered as good fingerprints for quality control and many studies have been performed to develop rapid and low cost analytical methods to determinate the authenticity, origin and eventually evidence frauds or adulterations. The present manuscript provides a general overview on the most common vegetable oils TAGs compositions and on the related analytical methodologies recently used. Finally, the chemometric applications developed to assess the authenticity, quality and botanical origin of various edible oils are discussed.

Serum peptidome profiling for the diagnosis of colorectal cancer: discovery and validation in two independent cohorts

Colorectal cancer (CRC) is one of the most common malignant neoplasms worldwide. Except for the existing fecal occult blood test, colonoscopy and sigmoidoscopy, no widely accepted in vitro diagnostic methods have been available. To identify potential peptide biomarkers for CRC, serum samples from a discovery cohort (100 CRC patients and 100 healthy controls) and an independent validation cohort (91 CRC patients and 91 healthy controls) were collected. Peptides were fractionated by weak cation exchange magnetic beads (MB-WCX) and analysed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Five peptides (peaks at m/z 1895.3, 2020.9, 2080.7, 2656.8 and 3238.5) were identified as candidate biomarkers for CRC. A diagnostic panel based on the five peptides can discriminate CRC patients from healthy controls, with an accuracy of 91.8%, sensitivity of 95.6%, and specificity of 87.9% in the validation cohort. Peptide peaks at m/z 1895.3, 2020.9 and 3238.5 were identified as the partial sequences of complement component 4 (C4), complement component 3 (C3) and fibrinogen α chain (FGA), respectively. This study potentiated peptidomic analysis as a promising in vitro diagnostic tool for diagnosis of CRC. The identified peptides suggest the involvement of the C3, C4 and FGA in CRC pathogenesis.

Surface immobilized antibody orientation determined using ToF-SIMS and multivariate analysis

Antibody orientation at solid phase interfaces plays a critical role in the sensitive detection of biomolecules during immunoassays. Correctly oriented antibodies with solution-facing antigen binding regions have improved antigen capture as compared to their randomly oriented counterparts. Direct characterization of oriented proteins with surface analysis methods still remains a challenge however surface sensitive techniques such as Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) provide information-rich data that can be used to probe antibody orientation. Diethylene glycol dimethyl ether plasma polymers (DGpp) functionalized with chromium (DGpp+Cr) have improved immunoassay performance that is indicative of preferential antibody orientation. Herein, ToF-SIMS data from proteolytic fragments of anti-EGFR antibody bound to DGpp and DGpp+Cr are used to construct artificial neural network (ANN) and principal component analysis (PCA) models indicative of correctly oriented systems. Whole antibody samples (IgG) test against each of the models indicated preferential antibody orientation on DGpp+Cr. Cross-reference between ANN and PCA models yield 20 mass fragments associated with F(ab')₂ region representing correct orientation, and 23 mass fragments associated with the Fc region representing incorrect orientation. Mass fragments were then compared to amino acid fragments and amino acid composition in F(ab')₂ and Fc regions. A ratio of the sum of the ToF-SIMS ion intensities from the F(ab')₂ fragments to the Fc fragments demonstrated a 50% increase in intensity for IgG on DGpp+Cr as compared to DGpp. The systematic data analysis methodology employed herein offers a new approach for the investigation of antibody orientation applicable to a range of substrates.

STATEMENT OF SIGNIFICANCE

Controlled orientation of antibodies at solid phases is critical for maximizing antigen detection in biosensors and immunoassays. Surface-sensitive techniques (such as ToF-SIMS), capable of direct characterization of surface immobilized and oriented antibodies, are under-utilized in current practice. Selection of a small number of mass fragments for analysis, typically pertaining to amino acids, is commonplace in literature, leaving the majority of the information-rich spectra unanalyzed. The novelty of this work is the utilization of a comprehensive, unbiased mass fragment list and the employment of principal component analysis (PCA) and artificial neural network (ANN) models in a unique methodology to prove antibody orientation. This methodology is of significant and broad interest to the scientific community as it is applicable to a range of substrates and allows for direct, label-free characterization of surface bound proteins.

Surface Adsorbed Antibody Characterization Using ToF-SIMS with Principal Component Analysis and Artificial Neural Networks

Artificial neural networks (ANNs) form a class of powerful multivariate analysis techniques, yet their routine use in the surface analysis community is limited. Principal component analysis (PCA) is more commonly employed to reduce the dimensionality of large data sets and highlight key characteristics. Herein, we discuss the strengths and weaknesses of PCA and ANNs as methods for investigation and interpretation of a complex multivariate sample set. Using time-of-flight secondary ion mass spectrometry (ToF-SIMS) we acquired spectra from an antibody and its proteolysis fragments with three primary-ion sources to obtain a panel of 72 spectra and a characteristic peak list of 775 fragment ions. We describe the use of ANNs as a means to interpret the ToF-SIMS spectral data, highlight the optimal neural network design and computational parameters, and discuss the technique limitations. Further, employing Bi3(+) as the primary-ion source, ANNs can accurately classify antibody fragments from the parent antibody based on ToF-SIMS spectra.

Establishment and characterization of a highly immunogenic human renal carcinoma cell line

Renal cell carcinoma (RCC) is the most common kidney cancer, and accounts for ~3% of all adult malignancies. RCC has proven refractory to conventional treatment modalities but appears to be the only histological form that shows any consistent response to immunotherapeutic approaches. The development of a clinically effective vaccine remains a major strategic target for devising active specific immunotherapy in RCC. We aimed to identify a highly immunogenic antigenic format for immunotherapeutic approaches, so as to boost immune responses in RCC patients. We established and cloned an immunogenic cell line, RCC85#21 named Elthem, which was derived from a non-aggressive and non-metastatic clear cell carcinoma. The cell line characterization was performed by genomics (real-time PCR, genome instability), proteomics (two dimensional electrophoresis, mass spectro-metry) and immunological analysis (mixed lymphocytes tumor cell cultures). Real-time PCR confirmed the RCC85#21 cell expression of tumor antigens and cytokine genes. No difference in microsatellite instability (MSI) in RCC85#21 cell line was found as compared to control, loss of heterozygosity was observed in the RCC85#21 clone, but not in the renal cancer cell lines from which it was generated. The image analysis of RCC85#21 by two-dimensional gels showed 700±26 spots and 119 spots were identified by mass spectrometry analysis. RCC85#21 promoted a significant RCC-specific T cells activation by exhibiting a cytotoxic phenotype after mixed lymphocyte and tumor cell cultures. CD8+ T cells isolated from RCC patients displayed an elevated reactivity against RCC85#21 and efficiently lysed the RCC85#21 clone. The RCC85#21 immunogenic cell line will be suitable for immune stimulation. The identification of novel tumor associated antigens will allow the evaluation of the immune response in vitro and, subsequently, in vivo paving the way for new immunotherapeutic strategies in the RCC setting.

Identification of biomarkers for radiation-induced acute intestinal symptoms (RIAISs) in cervical cancer patients by serum protein profiling

Radiation-induced acute intestinal symptoms (RIAISs) are the most frequent complication of radiotherapy that causes great pain and limits the treatment efficacy. The aim of this study was to identify serum biomarkers of RIAISs in cervical cancer patients by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Serum samples were collected from 66 cervical cancer patients prior to pelvic radiotherapy. In our study, RIAISs occurred in 11 patients. An additional 11 patients without RIAISs were selected as controls, whose age, stage, histological type and treatment methods were matched to RIAISs patients. The 22 sera were subsequently analyzed by SELDI-TOF MS, and the resulting protein profiles were evaluated to identify biomarkers using appropriate bioinformatics tools. Comparing the protein profiles of serum samples from the RIAIS group and the control group, it was found that 22 protein peaks were significantly different (P < 0.05), and six of these peaks with mass-to-charge (m/z) ratios of 7514.9, 4603.94, 6887.41, 2769.21, 3839.72 and 4215.7 were successfully identified. A decision tree model of biomarkers was constructed based on three biomarkers (m/z 1270.88, 1503.23 and 7514.90), which separated RIAIS-affected patients from the control group with an accuracy of 81%. This study suggests that serum proteomic analysis by SELDI-TOF MS can identify cervical cancer patients that are susceptible to RIAISs prior to pelvic radiotherapy.

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.

Current and future molecular diagnostics in colorectal cancer and colorectal adenoma

Colorectal cancer (CRC) is one of the most prevalent cancers in developed countries. On the other hand, CRC is also one of the most curable cancers if it is detected in early stages through regular colonoscopy or sigmoidoscopy. Since CRC develops slowly from precancerous lesions, early detection can reduce both the incidence and mortality of the disease. Fecal occult blood test is a widely used non-invasive screening tool for CRC. Although fecal occult blood test is simple and cost-effective in screening CRC, there is room for improvement in terms of the accuracy of the test. Genetic dysregulations have been found to play an important role in CRC development. With better understanding of the molecular basis of CRC, there is a growing expectation on the development of diagnostic tests based on more sensitive and specific molecular markers and those tests may provide a breakthrough to the limitations of current screening tests for CRC. In this review, the molecular basis of CRC development, the characteristics and applications of different non-invasive molecular biomarkers, as well as the technologies available for the detection were discussed. This review intended to provide a summary on the current and future molecular diagnostics in CRC and its pre-malignant state, colorectal adenoma.

Population proteomics: addressing protein diversity in humans

In the past several years, proteomics and its subdiscipline clinical proteomics have been engaged in the discovery of the next generation protein of biomarkers. As the effort and the intensive debate it has sparked continue, it is becoming apparent that a paradigm shift is needed in proteomics in order to truly comprehend the complexity of the human proteome and assess its subtle variations among individuals. This review introduces the concept of population proteomics as a future direction in proteomics research. Population proteomics is the study of protein diversity in human populations. High-throughput, top-down mass spectrometric approaches are employed to investigate, define and understand protein diversity and modulations across and within populations. Population proteomics is a discovery-oriented endeavor with a goal of establishing the incidence of protein structural variations and quantitative regulation of these modifications. Assessing human protein variations among and within populations is viewed as a paramount undertaking that can facilitate clinical proteomics' effort in discovery and validation of protein features that can be used as markers for early diagnosis of disease, monitoring of disease progression and assessment of therapy. This review outlines the growing need for analyzing individuals' proteomes and describes the approaches that are likely to be applied in such a population proteomics endeavor.

Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians

BACKGROUND

Multi-marker approaches for risk prediction in coronary artery disease (CAD) have been inconsistent due to biased selection of specific know biomarkers. We have assessed the global proteome of CAD-affected and unaffected subjects, and developed a pathway network model for elucidating the mechanism and risk prediction for CAD.

MATERIALS AND METHODS

A total of 252 samples (112 CAD-affected without family history and 140 true controls) were analyzed by Surface-Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) by using CM10 cationic chips and bioinformatics tools.

RESULTS

Out of 36 significant peaks in SELDI-TOF MS, nine peaks could do better discrimination of CAD subjects and controls (area under the curve (AUC) of 0.963) based on the Support Vector Machine (SVM) feature selection method. Of the nine peaks used in the model for discrimination of CAD-affected and unaffected, the m/z corresponding to 22,859 was identified as stress-related protein HSP27 and was shown to be highly associated with CAD (odds ratio of 3.47). The 36 biomarker peaks were identified and a network profile was constructed showing the functional association between different pathways in CAD.

CONCLUSION

Based on our data, proteome profiling with SELDI-TOF MS and SVM feature selection methods can be used for novel network biomarker discovery and risk stratification in CAD. The functional associations of the identified novel biomarkers suggest that they play an important role in the development of disease.

The proteomics big challenge for biomarkers and new drug-targets discovery

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.

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

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

Serum peptidome patterns of colorectal cancer based on magnetic bead separation and MALDI-TOF mass spectrometry analysis

Background. Colorectal cancer (CRC) is one of the most common cancers in the world, identification of biomarkers for early detection of CRC represents a relevant target. The present study aims to determine serum peptidome patterns for CRC diagnosis. Methods. The present work focused on serum proteomic analysis of 32 health volunteers and 38 CRC by ClinProt Kit combined with mass spectrometry. This approach allowed the construction of a peptide patterns able to differentiate the studied populations. An independent group of serum (including 33 health volunteers, 34 CRC, 16 colorectal adenoma, 36 esophageal carcinoma, and 31 gastric carcinoma samples) was used to verify the diagnostic and differential diagnostic capability of the peptidome patterns blindly. An immunoassay method was used to determine serum CEA of CRC and controls. Results. A quick classifier algorithm was used to construct the peptidome patterns for identification of CRC from controls. Two of the identified peaks at m/z 741 and 7772 were used to construct peptidome patterns, achieving an accuracy close to 100% (>CEA, P < 0.05). Furthermore, the peptidome patterns could differentiate validation group with high accuracy. Conclusions. These results suggest that the ClinProt Kit combined with mass spectrometry yields significantly higher accuracy for the diagnosis and differential diagnosis of CRC.

Improving colorectal cancer management: the potential of proteomics

Colorectal cancer (CRC) is the third most common cancer worldwide. Successful treatment is heavily dependent on tumor stage at the time of detection, but unfortunately CRC is often only detected in advanced stages. New biomarkers in the form of genes or proteins that can be used for diagnosis, prognostication, follow-up, and treatment selection and monitoring could be of great benefit for the management of CRC. Furthermore, proteins could prove valuable new targets for therapy. Therefore, clinical proteomics has gained a lot of scientific interest in this regard. To get an overall insight into the extent to which this research has contributed to a better management of CRC, we give a comprehensive overview of the results of proteomics research on CRC, focusing on expression proteomics, in other words, protein profiling studies. Furthermore, we evaluate the potential of the discriminating proteins identified in this research for clinical use as biomarkers for (early) diagnosis, prognosis and follow-up of CRC or as targets for new therapeutic regimens.

Candidate biomarker verification: Critical examination of a serum protein pattern for human colorectal cancer

PURPOSE

We critically examine a candidate serum protein pattern for human colorectal cancer (CRC) with respect to reproducibility, sample handling, and disease specificity.

EXPERIMENTAL DESIGN

Serum samples from CRC patients, patients with benign colon tumors and healthy individuals, were obtained at two collection sites and analyzed by SELDI-TOF MS on 8 days, over a period of 5 weeks. The spectra were subjected to multivariate analysis. Tissues from normal colon and CRC were analyzed by SELDI-TOF MS. Selected mass peaks were identified.

RESULTS

Using an elaborate experimental design we developed a multivariate classifier that correctly classified CRC and control serum measured on an independent day. The classifier did not discriminate between samples from CRC patients and patients with benign colon tumors, and, secondly, did not correctly classify serum from an independent collection site. All discriminatory mass peaks were identified as high abundant plasma proteins. Tissue profiling provided support of increased proteolytic activity in CRC tissue.

CONCLUSION AND CLINICAL RELEVANCE

Critical verification did not justify advancing the identified CRC serum protein pattern into clinical validation without improvement. We believe that proteomics biomarker research could benefit if the presented, or a similar, verification scheme was more commonly employed in explorative biomarker studies.

Proteomic analysis of primary colon cancer-associated fibroblasts using the SELDI-ProteinChip platform

OBJECTIVE

Cancer-associated fibroblasts (CAFs) are one of the hallmarks of the cancer microenvironment. Recent evidence has indicated that CAFs are more competent in enhancing cancer cell growth and migration than normal fibroblasts. However, the unique protein expression of CAFs has not been fully elucidated. This study aims to investigate the characterizations of colon CAFs by comparing the differential protein expression between CAFs and normal fibroblasts.

METHODS

Primary fibroblasts were isolated from surgical specimen of human colon cancer and matched normal colonic tissue. Purity of the cell population was verified through immunostain analysis. Total cell lysates and conditioned media from each group of cells were extracted, and protein expression analysis was conducted using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) ProteinChip platform.

RESULTS

Most primary cells showed typical fibroblast-like features after two weeks. Increased proportion of α-smooth muscle actin-positive myofibroblasts was detected within the CAFs in four of the six pairs of primary cells. Fibroblast activation protein was weakly expressed in most cells without differences. Using SELDI-TOF-MS ProteinChip platform, four protein peaks mass over charge ratio (m/z) 1142, 3011, 4035, and 4945 were detected in the total cell lysates, and two protein peaks m/z 1368 and 1389 were detected in the conditioned media. The potential candidate proteins found in the Swiss-Prot database include morphogenetic neuropeptides, FMRFamide-related peptides, insulin-like growth factor II, thymosin β-4-like protein 3, and tight junction-associated protein 1.

CONCLUSIONS

Using the SELDI-ProteinChip platform, differential protein expressions were identified in colon CAFs compared with normal colonic stromal fibroblasts. The complex proteomic alternations in colon CAFs may play important roles related to the colon cancer microenvironment.

Serum peptidome patterns for early screening of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers in the world. Early diagnosis is critical for guiding the therapeutic management of ESCC. The present study aims to determine serum peptidome patterns for diagnosing ESCC. To identify novel peptidome patterns for diagnosing ESCC, sera from 31 healthy volunteers and 32 ESCC patients were subjected to a comparative proteomic analysis using a ClinProt™ Kit combined with mass spectrometry (MS). This approach enables the determination of peptidome patterns that can differentiate between ESCC sera and sera from healthy volunteers. For further validation, the diagnostic and differential diagnostic capabilities of the peptidome patterns were verified blindly by using an independent group of sera, consisting of sera from 31 ESCC patients, 33 healthy volunteers, 38 colorectal patients, and 36 gastric cancer patients. A Quick Classifier Algorithm was used to construct the peptidome patterns for the identification of ESCC from the control samples. Five of the identified peaks at mass to charge ratios 759, 786, 1,866, 3,316, and 6,634 were used to construct the peptidome patterns with almost 100% accuracy. Furthermore, the peptidome patterns could also differentiate the validation group with high accuracy. These results suggest that the ClinProt™ Kit combined with MS achieves significantly high accuracy for ESCC diagnosis and differential diagnosis.

Serum peptidome patterns of breast cancer based on magnetic bead separation and mass spectrometry analysis

Background

Breast cancer is one of the most common cancers in the world, and the identification of biomarkers for the early detection of breast cancer is a relevant target. The present study aims to determine serum peptidome patterns for screening of breast cancer.

Methods

The present work focused on the serum proteomic analysis of 36 healthy volunteers and 37 breast cancer patients using a ClinProt Kit combined with mass spectrometry (MS). This approach allows the determination of peptidome patterns that are able to differentiate the studied populations. An independent group of sera (36 healthy volunteers and 37 breast cancer patients) was used to verify the diagnostic capabilities of the peptidome patterns blindly. An immunoassay method was used to determine the serum mucin 1 (CA15-3) of validation group samples.

Results

Support Vector Machine (SVM) Algorithm was used to construct the peptidome patterns for the identification of breast cancer from the healthy volunteers. Three of the identified peaks at m/z 698, 720 and 1866 were used to construct the peptidome patterns with 91.78% accuracy. Furthermore, the peptidome patterns could differentiate the validation group achieving a sensitivity of 91.89% (34/37) and a specitivity of 91.67% (33/36) (> CA 15–3, P < 0.05).

Conclusions

These results suggest that the ClinProt Kit combined with MS shows great potentiality for the diagnosis of breast cancer.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1501556838687844

Diagnostic significance of serum HMGB1 in colorectal carcinomas

High mobility group box 1 protein (HMGB1), a nuclear protein, can be translocated to the cytoplasm and secreted in colon cancer cells. However, the diagnostic significance of HMGB1 has not been evaluated in colorectal carcinomas. For this purpose, we have screened the expression and secretion of HMGB1 in 10 colon cancer cell lines and 1 control cell line and found that HMGB1 was detected in the culture medium. To evaluate the diagnostic value of HMGB1, we performed an enzyme-linked immunosorbent assay to measure HMGB1 levels and compared them to carcinoembryonic antigen (CEA) levels in the serum samples of 219 colorectal carcinoma patients and 75 healthy control subjects. We found that the serum HMGB1 level was increased by 1.5-fold in patients with colorectal carcinoma compared to those in healthy controls. When HMGB1 and CEA levels were compared, HMGB1 had similar efficacy as CEA regarding cancer detection (the sensitivity was 20.1% for HMGB1 vs. 25.6% for CEA, and the specificity was 96% for HMGB1 vs. 90.7% for CEA). Moreover, the diagnostic accuracy of HMGB1 for stage I cancer was significantly higher than that of CEA (sensitivity: 41.2% vs. 5.9%; specificity: 96% vs. 90.7). When we combined HMGB1 and CEA, the overall diagnostic sensitivity was higher than that of CEA alone (42% vs. 25.6%), and the diagnostic sensitivity for stage I was also elevated (47% vs. 5.9%). However, the prognosis of patients was not related with serum HMGB1 concentrations. Our findings indicate that serum HMGB1 levels are increased in a subset of colorectal carcinomas, suggesting their potential utility as a supportive diagnostic marker for colorectal carcinomas.

Serum proteomic analysis revealed diagnostic value of hemoglobin for nonalcoholic fatty liver disease

BACKGROUND & AIMS

Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases worldwide. The two linked studies presented herein aimed to identify and verify new biomarkers for NAFLD.

METHODS

First, 70 serum samples were analyzed using proteomics approaches to identify potential biomarkers for NAFLD. Second, a total of 6944 initial NAFLD-free subjects were followed up for 3 years to evaluate the predictive value of hemoglobin for NAFLD.

RESULTS

In the first study, 20 differentially expressed protein peaks (11 up-regulated and nine down-regulated) were observed in NAFLD patients upon comparison to the controls. With the aid of bioinformatic tools, we established a biomarker pattern for NAFLD with a sensitivity of 89% and a specificity of 83%. Further analysis suggested a protein peak to be hemoglobin subunit alpha. In the second study, prospective analysis showed that subjects with higher baseline hemoglobin levels were associated with higher incidence of NAFLD. Cox proportional hazards regression analyses showed that the age, gender, and body mass index adjusted hazard ratio (95% CI) for subjects with baseline hemoglobin level in quintile 2, 3, 4, and 5 vs. quintile 1 was 1.36 (1.02-1.81), 1.66 (1.23-2.25), 1.76 (1.28-2.41), and 1.83 (1.33-2.53), respectively.

CONCLUSIONS

Our study showed that serum hemoglobin may have significant predictive value for NAFLD.

Identification of Regional Lymph Node Involvement of Colorectal Cancer by Serum SELDI Proteomic Patterns

Background. To explore the application of serum proteomic patterns for the preoperative detection of regional lymph node involvement of colorectal cancer (CRC). Methods. Serum samples were applied to immobilized metal affinity capture ProteinChip to generate mass spectra by Surface-Enhanced Laser Desorption/ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). Proteomic spectra of serum samples from 70 node-positive CRC patients and 75 age- and gender-matched node-negative CRC patients were employed as a training set, and a classification tree was generated by using Biomarker Pattern Software package. The validity of the classification tree was then challenged with a blind test set including another 65 CRC patients. Results. The software identified an average of 46 mass peaks/spectrum and 5 of the identified peaks at m/z 3,104, 3,781, 5,867, 7,970, and 9,290 were used to construct the classification tree. The classification tree separated effectively node-positive CRC patients from node-negative CRC patients, achieving a sensitivity of 94.29% and a specificity of 100.00%. The blind test challenged the model independently with a sensitivity of 91.43% a specificity of 96.67%. Conclusions. The results indicate that SELDI-TOF-MS can correctly distinguish node-positive CRC patients from node-negative ones and show great potential for preoperative screening for regional lymph node involvement of CRC.

Screening for colorectal cancer: established and emerging modalities

It has been estimated that >95% of cases of colorectal cancer (CRC) would benefit from curative surgery if diagnosis was made at an early or premalignant polyp stage of disease. Over the past 10 years, most developed nation states have implemented mass population screening programs, which are typically targeted at the older (at-risk) age group (>50-60 years old). Conventional screening largely relies on periodic patient-centric investigation, particularly involving colonoscopy and flexible sigmoidoscopy, or else on the fecal occult blood test. These methods are compromised by either low cost-effectiveness or limited diagnostic accuracy. Advances in the development of diagnostic molecular markers for CRC have yielded an expanding list of potential new screening modalities based on investigations of patient stool (for colonocyte DNA mutations, epigenetic changes or microRNA expression) or blood specimens (for plasma DNA mutations, epigenetic changes, heteroplasmic mitochondrial DNA mutations, leukocyte transcriptome profile, plasma microRNA expression or protein and autoantibody expression). In this Review, we present a critical evaluation of the performance data and relative merits of these various new potential methods. None of these molecular diagnostic methods have yet been evaluated beyond the proof-of-principle and pilot-scale study stage and it could be some years before they replace existing methods for population screening in CRC.

Detection of renal allograft dysfunction with characteristic protein fingerprint by serum proteomic analysis

This study aimed to diagnose renal allograft dysfunction with specific biomarkers by serum proteomic analysis. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) and bioinformatics (support vector machine and leave-one cross validation) were used to analyze serum proteome. Enrolled patients included 38 biopsy-proved acute rejection (BPAR), 10 acute tubular necrosis (ATN), 24 subclinical rejection (SCR) and 29 stable control recipients verified by protocol biopsy. A characteristic protein profile can be detected in each renal allograft dysfunction group. BPAR patients were differentiated from stable patients with markers of 9710.1, 4971, 6675.5, 8563.8, 6709.2, 9319 and 4476.7 Da with high sensitivity and specificity. ATN can be clearly distinguished from BPAR and stable control. Subclinical rejection differentiated from stable control with markers of 9193.1, 2759.1, 8464.6 Da. The independent blind test yielded with high specificity and sensitivity for each group. Serum proteome analysis by SELDI-TOF MS combined with bioinformatics in renal allograft dysfunction is valuable and promising. Specific markers were detected in each group. Identification of these proteins may prove useful as diagnostic markers for allograft dysfunction and better to elucidate the mechanism of acute rejection.

Identification of biomarkers for colorectal cancer through proteomics-based approaches

The early detection of colorectal cancer is one of the great challenges in the battle against this disease. However, owing to its heterogeneous character, single markers are not likely to provide sufficient diagnostic power to be used in colorectal cancer population screens. This review provides an overview of recent studies aimed at the discovery of new diagnostic protein markers through proteomics-based approaches. It indicates that studies that start with the proteomic analysis of tumor tissue or tumor cell lines (near the source) have a high potential to yield novel and colorectal cancer-specific biomarkers. In the next step, the diagnostic accuracy of these candidate markers can be assessed by a targeted ELISA assay using serum from colorectal cancer patients and healthy controls. Instead, direct proteomic analysis of serum yields predominantly secondary markers composed of fragments of abundant serum proteins that may be associated with tumor-associated protease activity, and alternatively, immunoproteomic analysis of the serum antibody repertoire provides a valuable tool to identify the molecular imprint of colorectal cancer-associated antigens directly from patient serum samples. The latter approach also allows a relatively easy translation into targeted assays. Eventually, multimarker assays should be developed to reach a diagnostic accuracy that meets the stringent criteria for colorectal cancer screening at the population level.

Proteomic serum biomarkers and their potential application in cancer screening programs

Early diagnosis of cancer is of pivotal importance to reduce disease-related mortality. There is great need for non-invasive screening methods, yet current screening protocols have limited sensitivity and specificity. The use of serum biomarkers to discriminate cancer patients from healthy persons might be a tool to improve screening programs. Mass spectrometry based proteomics is widely applied as a technology for mapping and identifying peptides and proteins in body fluids. One commonly used approach in proteomics is peptide and protein profiling. Here, we present an overview of profiling methods that have the potential for implementation in a clinical setting and in national screening programs.

SELDI-TOF serum proteomics and colorectal cancer: a current overview

Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is a widely used technology platform for diagnostic biomarker discovery in tissue, plasma and serum. High-throughput and simplicity of experimental procedures have allowed this technology to become an important research tool for biomarker discovery during the last years. This review provides an overview of SELDI-TOF functionality, its advantages and drawbacks and gives a current literature overview of colorectal cancer based serum biomarker detection. Further improvements in instrumentation sensitivity and labelling chemistries will enable detection of novel, tissue-leakage biomarkers in serum. However, major emphasis should be given on subsequent identification of differentially observed protein peaks detected by SELDI-TOF. Clinical validation in large patient cohorts will then allow transferring novel biomarkers into clinical use.

Serum Proteome Pool Changes in Type 2 Diabetic Patients Treated with Anakinra

Introduction

High glucose concentrations induce the production of IL-1β in human pancreatic beta cells leading to impaired insulin secretion, decreased cell proliferation and apoptosis. Blockade of IL-1 signalling with the recombinant human IL-1 receptor antagonist anakinra reduces HbA1c in patients with type 2 diabetes. The aims of the present study were to identify: (1) candidate surrogates for improved glycemia in type 2 diabetic patients following treatment with anakinra, (2) proteins that change serum concentration because of anakinra treatment and (3) candidate biomarkers that may predict improved glycemia in type 2 diabetic subjects treated with anakinra.

Methods

Surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry was used to analyse serum from 67 type 2 diabetic patients who had received either placebo or anakinra for 13 weeks. Immunodepletion with magnetic protein G bead-coupled antibodies were used to identify three proteins and Western blotting confirmed the biomarker concentration pattern of four proteins.

Results

Twelve proteins, including transthyretin (TTR) and transferrin (Tf), were identified as candidate surrogates for improved glycemia. Six proteins, including retinol-binding protein 4 (RPB4) and a protein tentatively identified as modified apolipoprotein-A1 (apo-AI), increased expression as a consequence of anakinra treatment and four proteins were candidate biomarkers that may predict improved glycemia following anakinra treatment. Furthermore, we found increased RBP4 to be associated with improved beta cell secretory function and increased TTR, RBP4 and modified apo-AI (peak at 28,601 Da) to be associated with decreased inflammation.

Conclusions

Anakinra-induced changes in the serum proteome pool associated with a decreased cardiovascular disease risk, reduced inflammation and improved beta cell secretory function.

Application of support vector machine in cancer diagnosis

To investigate the clinical application of tumor marker detection combined with support vector machine (SVM) model in the diagnosis of cancer. Tumor marker detection results for colorectal cancer, gastric cancer and lung cancer were collected. With these tumor mark data sets, the SVM models for diagnosis with best kernel function were created, trained and validated by cross-validation. Grid search and cross-validation methods were used to optimize the parameters of SVM. Diagnostic classifiers such as combined diagnosis test, logistic regression and decision tree were validated. Sensitivity, specialty, Youden Index and accuracy were used to evaluate the classifiers. Leave-one-out was used as the algorithm test method. For colorectal cancer, the accuracy of 4 classifiers were 75.8, 76.6, 83.1, 96.0%, respectively; for gastric cancer, the accuracy of 4 classifiers were 45.7, 64.5, 63.7, 91.7%; for lung cancer, the results were 71.9, 68.6, 75.2, 97.5%. The accuracy of SVM classifier is especially high in 4 kinds of classifiers, which indicates the potential application of SVM diagnostic model with tumor marker in cancer detection.

Potential biomarkers for esophageal carcinoma detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

BACKGROUND

Currently, no satisfactory biomarkers are available to screen for esophageal squamous cell carcinoma (ESCC). The goal of this study was to find biomarkers and establish a serum protein fingerprint model for early diagnosis of ESCC using the ClinProt protocol of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).

METHODS

Serum samples were collected from 62 patients with ESCC, nine patients with esophageal adenocarcinoma (EA) and 38 healthy individuals. Proteomic spectra of mass to charge ratio (m/z) were generated following the application of plasma to weak cationic-exchanger magnetic beads (WCX-MB). The spectral data were analyzed using a support vector machine, and potential biomarkers were chosen for system training and used to construct diagnostic models.

RESULTS

Three differential patterns were established using MALDI-TOF MS. Pattern 1, consisting of 11 protein peaks, separated ESCC patients from the healthy individuals with a sensitivity of 90.0% and a specificity of 88.4%. Pattern 2, consisting of eight protein peaks, separated ESCC in stage I and stage II from stage III and stage IV with a sensitivity of 92.9% and a specificity of 82.3%. Pattern 3, consisting of seven protein peaks, separated ESCC from EA with a sensitivity of 91.3% and a specificity of 80.0%.

CONCLUSIONS

These results suggested that MALDI-TOF MS combined with MB separation yields significantly higher sensitivity and specificity for the detection of serum protein in patients with ESCC.

[Research progress in SELDI-TOF MS and its clinical applications]

Proteinchip profiling is a powerful and innovative proteomic technology for the discovery of biomarkers and the development of diagnostic/prognostic assays. On the basis of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), Ciphergen’s proteinchip system offers a single, unified, and high throughput platform for a multitude of proteomic research applications. Proteins are the major functional components of the cell. The study of proteomics helps to better understand the mechanism of a disease. Remarkable findings in disease biomarkers have shed light on the early diagnosis, monitoring, and prognosis of various diseases, especially for cancer. In this paper, the development and technology of SELDI-TOF MS are introduced. The research progress and encouraging research results in malignancies, infectious diseases, neurological diseases, and diabetes mellitus using SELDI-TOF MS are reviewed. This paper concludes by evaluating the pros and cons, and the future perspectives are also expounded.

Enhanced detection of early hepatocellular carcinoma by serum SELDI-TOF proteomic signature combined with alpha-fetoprotein marker

Background

Biomarkers for accurate diagnosis of early hepatocellular carcinoma (HCC) are limited in number and clinical validation. We applied SELDI-TOF-MS ProteinChip technology to identify serum profile for distinguishing HCC and liver cirrhosis (LC) and to compare the accuracy of SELDI-TOF-MS profile and alpha-fetoprotein (AFP) level in HCC diagnosis.

Patients and Methods

Serum samples were obtained from 120 HCC and 120 LC patients for biomarker discovery and validation studies. ProteinChip technology was employed for generating SELDI-TOF proteomic features and analyzing serum proteins/peptides.

Results

A diagnostic model was established by CART algorithm, which is based on 5 proteomic peaks with m/z values at 3324, 3994, 4665, 4795, and 5152. In the training set, the CART algorithm could differentiate HCC from LC subjects with a sensitivity and specificity of 98% and 95%, respectively. The results were assessed in blind validation using separate cohorts of 60 HCC and 60 LC patients, with an accuracy of 83% for HCC and 92% for LC patients. The diagnostic odd ratio (DOR) indicated that SELDI-TOF proteomic signature could achieve better diagnostic performance than serum AFP level at a cutoff of 20 ng/mL (AFP₂₀) (92.72 vs 9.11), particularly superior for early-stage HCC (87% vs 54%). Importantly, a combined use of both tests could enhance the detection of HCC (sensitivity, 95%; specificity, 98%; DOR, 931).

Conclusion

Serum SELDI-TOF proteomic signature, alone or in combination with AFP marker, promises to be a good tool for early diagnosis and/screening of HCC in at-risk population with liver cirrhosis.

Reduced levels of hydroxylated, polyunsaturated ultra long-chain fatty acids in the serum of colorectal cancer patients: implications for early screening and detection

BACKGROUND

There are currently no accurate serum markers for detecting early risk of colorectal cancer (CRC). We therefore developed a non-targeted metabolomics technology to analyse the serum of pre-treatment CRC patients in order to discover putative metabolic markers associated with CRC. Using tandem-mass spectrometry (MS/MS) high throughput MS technology we evaluated the utility of selected markers and this technology for discriminating between CRC and healthy subjects.

METHODS

Biomarker discovery was performed using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). Comprehensive metabolic profiles of CRC patients and controls from three independent populations from different continents (USA and Japan; total n = 222) were obtained and the best inter-study biomarkers determined. The structural characterization of these and related markers was performed using liquid chromatography (LC) MS/MS and nuclear magnetic resonance technologies. Clinical utility evaluations were performed using a targeted high-throughput triple-quadrupole multiple reaction monitoring (TQ-MRM) method for three biomarkers in two further independent populations from the USA and Japan (total n = 220).

RESULTS

Comprehensive metabolomic analyses revealed significantly reduced levels of 28-36 carbon-containing hydroxylated polyunsaturated ultra long-chain fatty-acids in all three independent cohorts of CRC patient samples relative to controls. Structure elucidation studies on the C28 molecules revealed two families harbouring specifically two or three hydroxyl substitutions and varying degrees of unsaturation. The TQ-MRM method successfully validated the FTICR-MS results in two further independent studies. In total, biomarkers in five independent populations across two continental regions were evaluated (three populations by FTICR-MS and two by TQ-MRM). The resultant receiver-operator characteristic curve AUCs ranged from 0.85 to 0.98 (average = 0.91 +/- 0.04).

CONCLUSIONS

A novel comprehensive metabolomics technology was used to identify a systemic metabolic dysregulation comprising previously unknown hydroxylated polyunsaturated ultra-long chain fatty acid metabolites in CRC patients. These metabolites are easily measurable in serum and a decrease in their concentration appears to be highly sensitive and specific for the presence of CRC, regardless of ethnic or geographic background. The measurement of these metabolites may represent an additional tool for the early detection and screening of CRC.

Diagnosis of C4d+ Renal Allograft Acute Humoral Rejection by Urine Protein Fingerprint Analysis

This study aimed to develop urine protein fingerprint models for the diagnosis of acute rejection (AR) and complement split product positive (C4d+) acute humoral rejection (AHR) following renal allograft transplantation. Urine samples from 101 renal transplant recipients were analysed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry combined with bioinformatics. The patients comprised 36 with stable allograft function (stable group), 10 with acute tubular necrosis (ATN) and 55 with AR (20 with C4d- acute cellular rejection [ACR] and 15 with C4d+ AHR). The ATN group was differentiated from the stable group with a sensitivity and specificity of 100% (pattern 1). The stable group was differentiated from the AR group with a specificity of 86.4% and a sensitivity of 85.4% (pattern 2). The C4d- ACR subgroup was differentiated from the C4d+ AHR subgroup with a specificity and sensitivity of 95% and 80%, respectively (pattern 3). It is concluded that urine protein fingerprint analysis can provide a noninvasive tool to diagnose AR and C4d+ AHR.

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.

Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective

Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.

An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++

Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make subsequent classifications unreliable. Current common practice for dealing with replicated data is to average each subject replicate sample set, reducing the dataset size and incurring loss of information. In this manuscript we compare three approaches to dealing with cluster-correlated data: unmodified Breiman's Random Forest (URF), forest grown using subject-level averages (SLA), and RF++ with subject-level bootstrapping (SLB). RF++, a novel Random Forest-based algorithm implemented in C++, handles cluster-correlated data through a modification of the original resampling algorithm and accommodates subject-level classification. Subject-level bootstrapping is an alternative sampling method that obviates the need to average or otherwise reduce each set of replicates to a single independent sample. Our experiments show nearly identical median classification and variable selection accuracy for SLB forests and URF forests when applied to both simulated and real datasets. However, the run-time estimated error rate was severely underestimated for URF forests. Predictably, SLA forests were found to be more severely affected by the reduction in sample size which led to poorer classification and variable selection accuracy. Perhaps most importantly our results suggest that it is reasonable to utilize URF for the analysis of cluster-correlated data. Two caveats should be noted: first, correct classification error rates must be obtained using a separate test dataset, and second, an additional post-processing step is required to obtain subject-level classifications. RF++ is shown to be an effective alternative for classifying both clustered and non-clustered data. Source code and stand-alone compiled versions of command-line and easy-to-use graphical user interface (GUI) versions of RF++ for Windows and Linux as well as a user manual (Supplementary File S2) are available for download at: http://sourceforge.org/projects/rfpp/ under the GNU public license.

Mining novel biomarkers for prognosis of gastric cancer with serum proteomics

Background

Although gastric caner (GC) remains the second cause of cancer-related death, useful biomarkers for prognosis are still unavailable. We present here the attempt of mining novel biomarkers for GC prognosis by using serum proteomics.

Methods

Sera from 43 GC patients and 41 controls with gastritis as Group 1 and 11 GC patients as Group 2 was successively detected by Surface Enhanced Laser Desorption/ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) with Q10 chip. Peaks were acquired by Ciphergen ProteinChip Software 3.2.0 and analyzed by Zhejiang University-ProteinChip Data Analysis System (ZJU-PDAS). CEA level were evaluated by chemiluminescence immunoassay.

Results

After median follow-up periods of 33 months, Group 1 with 4 GC patients lost was divided into 20 good-prognosis GC patients (overall survival more than 24 months) and 19 poor-prognosis GC patients (no more than 24 months). The established prognosis pattern consisted of 5 novel prognosis biomarkers with 84.2% sensitivity and 85.0% specificity, which were significantly higher than those of carcinoembryonic antigen (CEA) and TNM stage. We also tested prognosis pattern blindly in Group 2 with 66.7% sensitivity and 80.0% specificity. Moreover, we found that 4474-Da peak elevated significantly in GC and was associated with advanced stage (III+IV) and short survival (p < 0.03).

Conclusion

We have identified a number of novel biomarkers for prognosis prediction of GC by using SELDI-TOF-MS combined with sophisticated bioinformatics. Particularly, elevated expression of 4474-Da peak showed very promising to be developed into a novel biomarker associated with biologically aggressive features of GC.

Limitations in SELDI-TOF MS whole serum proteomic profiling with IMAC surface to specifically detect colorectal cancer

BACKGROUND

Surface enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) analysis on serum samples was reported to be able to detect colorectal cancer (CRC) from normal or control patients. We carried out a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify CRC.

METHODS

A retrospective cohort of 338 serum samples including 154 CRCs, 67 control cancers and 117 non-cancerous conditions was profiled using SELDI-TOF-MS.

RESULTS

No CRC "specific" classifier was found. However, a classifier consisting of two protein peaks separates cancer from non-cancerous conditions with high accuracy.

CONCLUSION

In this study, the SELDI-TOF-MS-based protein expression profiling approach did not perform to identify CRC. However, this technique is promising in distinguishing patients with cancer from a non-cancerous population; it may be useful for monitoring recurrence of CRC after treatment.