Enrichment of low-molecular-weight proteins from biofluids for biomarker discovery

The dramatic progress in mass spectrometry-based methods of protein identification has triggered a new quest for disease-associated biomarkers. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and its variant surface-enhanced laser desorption/ionization mass spectrometry, provide effective means to explore the less studied information slice of the human serum proteome -- low-molecular-weight proteins and peptides. These low-molecular-weight proteins and peptides are promising for the detection of important biomarkers. Due to the significant experimental problems imposed by high-abundance and high-molecular-weight proteins, it is important to effectively remove these species prior to mass spectrometry analysis of the low-molecular-weight serum and plasma proteomes. In this review, the advantages afforded by recently introduced methods for prefractionation of serum, as they pertain to the detection and identification of biomarkers, will be discussed.     

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)‐based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous “triangular strategies” aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a “rectangular” plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision‐making, and we discuss some first steps in this direction.     

Proteomics approaches to biomarker detection.

The development of mass spectrometry (MS) technologies has brought the ability to gather massive amounts of data characterising the proteomes of complex mixtures. A major focus in proteomics is to leverage this data-gathering capability to conduct comparative analyses of biofluids from healthy and disease-affected patients for the identification of highly specific biomarkers and/or the development of MS-based diagnostic platforms. Much effort has gone into optimising the biofluid proteome coverage that can be obtained using these technologies, leaving proteomics poised to make an important impact in disease diagnostics in the future.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery.

For most cancers, survival rates depend on the early detection of the disease. So far, no biomarkers exist to cope with this difficult task. New proteomic technologies have brought the hope of discovering novel early cancer-specific biomarkers in complex biological samples and/or of the setting up of new clinically relevant test systems. Novel mass spectrometry-(MS) based technologies in particular, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-ToF-MS), have shown promising results in the recent literature. Here, proteomic profiles of control and disease states are compared to find biomarkers for diagnosis. This paper aims to address the authors' own work and that of other groups in clinical cancer proteomics based on SELDI-ToF-MS. Shortcomings and hopes for the future are discussed.     

Advanced proteomic technologies for cancer biomarker discovery

Proteomic technologies have experienced major improvements in recent years. Such advances have facilitated the discovery of potential tumor markers with improved sensitivities and specificities for the diagnosis, prognosis and treatment monitoring of cancer patients. This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about and examples of their applications in cancer biomarker discovery will be presented in this review, so that readers can appreciate the immense progress in proteomic technologies from 1997 to 2008. Finally, a summary will be presented that discusses current hurdles faced by proteomic researchers, such as the wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems will be provided.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.     

Analysis of low-abundance, low-molecular-weight serum proteins using mass spectrometry.

To detect diseases early in the general population, new diagnostic approaches are needed that have adequate sensitivity and specificity. Recent studies have used mass spectrometry to identify a serum proteomic pattern for breast and ovarian cancer. Serum contains 60-80 mg/mL protein, but 57-71% of this is serum albumin, and 8-26% are gamma-globulins. These large proteins must be depleted before smaller, less-abundant proteins can be detected using mass spectrometry, but because serum albumin is known to act as a carrier for smaller proteins, removal of these molecules using columns or filtration may result in the loss of molecules of interest. The objective of this study was to develop a reproducible method to deplete serum samples of high-abundance proteins in order to analyze the less-abundant proteins present in serum. We used organic solvents to precipitate the large proteins out of solution. We also predicted that this would cause many smaller proteins to dissociate from their carrier molecules, allowing for detection of a larger number of peptides and small proteins. These treated samples were analyzed using capillary liquid chromatography coupled with electrospray ionization mass spectrometry. Analysis demonstrated reproducible results. Acetonitrile treatment clearly released many carrier-bound molecular species and was superior to ultrafiltration alone for serum proteomic analysis.     

Glycoengineered cell models for the characterization of cancer O-glycoproteome: an innovative strategy for biomarker discovery

Glycosylation is one of the most abundant forms of protein posttranslational modification. O-glycosylation is a major type of protein glycosylation, comprising different types and structures expressed in several physiologic and pathologic conditions. The understanding of protein attachment site and glycan structure is of the utmost importance for the clarification of the role glycosylation plays in normal cells and in pathological conditions. Neoplastic transformation frequently shows the expression of immature truncated O-glycans. These aberrantly expressed O-glycans have been shown to induce oncogenic properties and can be detected in premalignant lesions, meaning that they are an important source of biomarkers. This article addresses the recent application of genetically engineered cancer cell models to produce simplified homogenous O-glycans allowing the characterization of cancer cells O-glycoproteomes, using advanced mass spectrometry methods and the identification of potential cancer-specific O-glycosylation sites. This article will also discuss possible applications of these biomarkers in the cancer field.     

Role of proteomics in biomarker discovery and psychiatric disorders: current status,potentials, limitations and future challenges

Proteomic approaches have advanced clinical research towards more reliable, sensitive and specific biological diagnostic markers for diseases. Mood disorders are most difficult to diagnose and very much prevalent in society; hence, their proper diagnosis becomes essential. Despite tremendous research efforts to dissect the neurobiological basis of psychiatric disorders, the diagnosis and evaluation for such diseases is still poor. Biomarker discovery in psychiatry research has been accelerated by proteomic technologies, accepting the challenges in order to meet disease state-related investigations. Proteomics-based research for disease-specific protein signatures is expected to give a new direction in psychiatry research. Therefore, this may become a more powerful tool to predict the development, course and outcome of the disease towards personalized psychiatric ailments. The review discusses the role of proteomics in elucidating mechanisms of psychiatric disorders, current status, prospects, limitations and new possibilities towards a strong diagnostic tool in the clinical laboratory.     

Profiling proteoforms: promising follow-up of proteomics for biomarker discovery

Today, proteomics usually compares clinical samples by use of bottom-up profiling with high resolution mass spectrometry, where all protein products of a single gene are considered as an integral whole. At the same time, proteomics of proteoforms, which considers the variety of protein species, offers the potential to discover valuable biomarkers. Proteoforms are protein species that arise as a consequence of genetic polymorphisms, alternative splicing, post-translational modifications and other less-explored molecular events. The comprehensive observation of proteoforms has been an exclusive privilege of top-down proteomics. Here, we review the possibilities of a bottom-up approach to address the microheterogeneity of the human proteome. Special focus is given to shotgun proteomics and structure-based bioinformatics as a source of hypothetical proteoforms, which can potentially be verified by targeted mass spectrometry to determine the relevance of proteoforms to diseases.     

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer,COPD, and asthma

Introduction: Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges.

Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma.

Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.     

Mass spectrometry applied to the identification of Mycobacterium tuberculosis and biomarker discovery

An adequate and effective tuberculosis (TB) diagnosis system has been identified by the World Health Organization as a priority in the fight against this disease. Over the years, several methods have been developed to identify the bacillus, but bacterial culture remains one of the most affordable methods for most countries. For rapid and accurate identification, however, it is more feasible to implement molecular techniques, taking advantage of the availability of public databases containing protein sequences. Mass spectrometry (MS) has become an interesting technique for the identification of TB. Here, we review some of the most widely employed methods for identifying Mycobacterium tuberculosis and present an update on MS applied for the identification of mycobacterial species.     

血浆蛋白质组——人类蛋白质组计划的“探路者”

概述了血浆蛋白的研究现状、难点和策略.血浆是血液中无形的液体成分,是一种十分复杂和多样化的基质,包含数百万种蛋白质和小分子多肽、盐、类脂、氨基酸和糖等.血浆蛋白参与机体免疫、凝血-抗凝血、物质运输、营养和对生长信号调节等多种重要的生理功能.人体器官的病理变化可导致血浆蛋白在结构和数量上的改变,这种特征性的变化对疾病诊断和疗效监测具有十分重要的意义.然而,迄今为止人类对血浆蛋白的了解还十分有限,只有很少一部分血浆蛋白被用于常规的临床诊断.全面而系统地认识健康和疾病状态下血液循环中血浆蛋白的性质,会极大地加速对具有疾病诊断和治疗监测作用的血浆标志蛋白的研发.国际人类蛋白质组组织于2002年首先选择了血浆蛋白质组作为人类蛋白质组首期执行计划之一,其初期目标是：a.比较各种蛋白质组分析技术平台的优点和局限性;b.用这些技术平台分析人类血浆和血清的参考样本;c.建立人类血浆蛋白质组知识库.     

Evaluation of serum exosomal LncRNA‐based biomarker panel for diagnosis and recurrence prediction of bladder cancer

Exosomes are small membrane vesicles released by many cells. These vesicles can mediate cellular communications by transmitting active molecules including long non‐coding RNAs (lncRNAs). In this study, our aim was to identify a panel of lncRNAs in serum exosomes for the diagnosis and recurrence prediction of bladder cancer (BC). The expressions of 11 candidate lncRNAs in exosome were investigated in training set (n = 200) and an independent validation set (n = 320) via quantitative real‐time PCR. A three‐lncRNA panel (PCAT‐1, UBC1 and SNHG16) was finally identified by multivariate logistic regression model to provide high diagnostic accuracy for BC with an area under the receiver‐operating characteristic curve (AUC) of 0.857 and 0.826 in training set and validation set, respectively, which was significantly higher than that of urine cytology. The corresponding AUCs of this panel for patients with Ta, T1 and T2‐T4 were 0.760, 0.827 and 0.878, respectively. In addition, Kaplan‐Meier analysis showed that non‐muscle‐invasive BC (NMIBC) patients with high UBC1 expression had significantly lower recurrence‐free survival (P = 0.01). Multivariate Cox analysis demonstrated that UBC1 was independently associated with tumour recurrence of NMIBC (P = 0.018). Our study suggested that lncRNAs in serum exosomes may serve as considerable diagnostic and prognostic biomarkers of BC.     

Proteomic approaches to biomarker discovery in lung cancers by SELDI technology

The purpose of the present work is to identify protein profiles that could be used to discover specific biomarkers in serum and discriminate lung cancer. Thirty serum samples from patients with lung cancer (15 cases of primary brochogenic carcinoma, 9 cases of metastasis lung cancer and 6 cases of lung cancer after chemotherapy) and twelve from healthy individuals were analyzed by SELDI (Surfaced Enhanced Laser Desorption/Ionization) technology. Anion-exchange columns were used to fractionate the sera with 6 designated pH washing solutions. Two types of protein chip arrays, IMAC-Cu and WCX2, were employed. Protein chips were examined in PBSII ProteinChip Reader (Ciphergen Biosystems Inc.) and the resulting profiles between cancer and normal were analyzed with Biomarker Wizard System. In total, 15 potential lung cancer biomarkers, of which 6 were up-regulated and 9 were down-regulated, were discovered in the serum samples from patients with lung cancer. 5 of 15 these biomarkers were able to be detected on both WCX2 and IMAC-Cu protein chips. The sensitivities provided by the individual markers range from 44.8% to 93.1% and the specificities were 85.0%–94.4%. Our results suggest that serum is a capable resource for detection of lung cancer with specific biomarkers. Moreover, protein chip array system was shown to be a useful tool for identification, as well as detection of disease biomarkers in sera.     

Proteomic approaches to biomarker discovery in lung cancers by SELDI technology

Lung cancer is one of the most common tumors all over the world and one of those with higher mortality in clinic. For instance, 169500 new cases of lung cancer were estimated in the United States for 2001[1]. In recent years, both morbidity and mortality of lung cancer were reported gradually increasing in our country. Therefore, it has become an urgent task to search and discover specific biomarkers for lung cancer. In tumor genesis, certain cellular proteins must have changed their express…     

Studying multiple protein profiles over time to assess biomarker validity

Protein profile analysis is increasingly used for identification of disease biomarkers. The approaches vary from surface-enhanced laser desorption/ionization to protein arrays. Newer platforms are constantly being developed. Almost all are based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and are often coupled with sophisticated software tools. Protein profiling has been applied to a variety of samples including plasma, urine, cerebrospinal fluid, saliva and solid tissue. This article focuses on those instances where it is possible to obtain sequential samples from the same individual. In the authors use of a profile method, many protein changes with highly significant correlations to disease have been found. The main challenge lies in the validation of the marker to demonstrate its adequacy for use in the clinical setting. The latter requires a methodology that is robust and amenable to high-throughput. One problem is that interindividual variability among the healthy population can mask major changes that occur on an intraindividual basis. Often, a large change for an individual may remain within the range of healthy individuals. Thus, one strategy to optimize biomarker discovery is to examine serial samples from a given individual, where a disease biomarker is established by comparison with the individual’s own baseline sample. The focus of this review is to illustrate the principle and value of serial protein profiling using a rapid protein extraction method.     

蛋白质芯片在肿瘤血清标识物发现中的应用

蛋白质芯片是一种新型的高通量蛋白质组学技术,由于其具有高通量、微型化、可平行快速分析等优点,因此在肿瘤血清标识物发现研究方面具有广泛的应用前景。本文综述了蛋白质芯片的基本原理、类型及其在肿瘤血清标记物发现研究中的应用,将蛋白质芯片技术与传统的肿瘤标志物发现技术进行了比较,并对蛋白质芯片技术在肿瘤标识物发现研究上的进一步应用进行了展望。     

Mass spectrometry-based N-glycoproteomics for cancer biomarker discovery

Glycosylation is estimated to be found in over 50% of human proteins. Aberrant protein glycosylation and alteration of glycans are closely related to many diseases. More than half of the cancer biomarkers are glycosylated-proteins, and specific glycoforms of glycosylated-proteins may serve as biomarkers for either the early detection of disease or the evaluation of therapeutic efficacy for treatment of diseases. Glycoproteomics, therefore, becomes an emerging field that can make unique contributions to the discovery of biomarkers of cancers. The recent advances in mass spectrometry (MS)-based glycoproteomics, which can analyze thousands of glycosylated-proteins in a single experiment, have shown great promise for this purpose. Herein, we described the MS-based strategies that are available for glycoproteomics, and discussed the sensitivity and high throughput in both qualitative and quantitative manners. The discovery of glycosylated-proteins as biomarkers in some representative diseases by employing glycoproteomics was also summarized.