Author contributions: Al-Amrani S drafted the first draft and approved the final version; Al-Jabri Z, Al-Zaabi A, Alshekaili J, and Al-Khabori M critically edited the manuscript and approved the final version.
Corresponding author: Murtadha Al-Khabori, BSc, MD, MSc, Associate Professor, Staff Physician, Department of Hematology, Sultan Qaboos University, Al-Khod, Sultan Qaboos University Street, Muscat 123, Oman. mo.ude.uqs@irobahk
Received 2021 Feb 22; Revised 2021 May 4; Accepted 2021 Jul 15. Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Proteomics is the complete evaluation of the function and structure of proteins to understand an organism’s nature. Mass spectrometry is an essential tool that is used for profiling proteins in the cell. However, biomarker discovery remains the major challenge of proteomics because of their complexity and dynamicity. Therefore, combining the proteomics approach with genomics and bioinformatics will provide an understanding of the information of biological systems and their disease alteration. However, most studies have investigated a small part of the proteins in the blood. This review highlights the types of proteomics, the available proteomic techniques, and their applications in different research fields.
Keywords: Proteomics, Biomarker, Mass spectrometry, Two-dimensional electrophoresis, Drug discoveryCore Tip: Proteomics is the complete evaluation of the structure and function of proteins to understand an organism’s nature. Mass spectrometry is an essential tool that is used to profile proteins in the cell. However, biomarker discovery remains the major challenge of proteomics because of the complexity and dynamicity. This review highlights the types of proteomics, the available proteomic techniques, and their applications in different research fields.
Proteomics is a new type of ‘omics’ that has rapidly developed, especially in the therapeutics field. The word proteome was created by Marc Wilkins in 1995[1]. Proteomics is the study of the interactions, function, composition, and structures of proteins and their cellular activities[2]. Proteomics provides a better understanding of the structure and function of the organism than genomics. However, it is much more complicated than genomics because the protein expression is altered according to time and environmental conditions[3]. It is estimated that there are almost one million human proteins, many of which contain some modifications such as post-translational modifications (PTMs). However, it is also estimated that the human genome codes for about 26000-31000 proteins[4]. There are a variety of proteomics techniques including one-dimensional (1D) and two-dimensional (2D) gel electrophoresis (2-DE)[5], as well as gel-free high-throughput screening technologies such as multidimensional protein identification technology[6], stable isotope labeling with amino acids in cell culture[7], isotope-coded affinity tag, and isobaric tagging for relative and absolute quantitation[8]. Shotgun proteomics[9], 2D difference gel electrophoresis (2D-DIGE)[10], and protein microarrays[11] can be used in tissues, organelles, and cells. Large-scale western blot assays[12], multiple reaction monitoring assays[13], and label-free quantification of high mass resolution liquid chromatography (LC)-tandem mass spectrometry (MS) are commonly used for high-throughput processing. In the last decade, proteomics has been classified into protein expression mapping and protein interaction mapping[14]. The former method uses 2-DE combined with MS for quantitative proteome expression in cells, body fluids, or tissues. Protein expression mapping can provide an understanding of the PTMs of expressed proteins under different environmental conditions or disease states[14]. Protein-protein interaction mapping uses the yeast two-hybrid system coupled with MS to determine the interaction partners for each cell’s encoded proteins and the proteome-wide scale[15].
Proteomics is a multi-step technique in which every step should be very well controlled to avoid non-biological factors interfering with protein expression and interaction. Sample preparation is the most important step because it solubilizes all proteins in the sample and eliminates all interfering inhibitory compounds such as lipids. Adequate sample preparation is crucial to obtain reliable, accurate, and reproducible results[16]. PAGE is the most widely used method for protein separation and isolation[17]. High-performance LC (HPLC)[18], 1-DE, and 2-DE are the methods used to separate proteins[19]. Proteins are isolated using 1-DE based on their molecular mass. Protein solubility is rarely an issue since proteins are solubilized in sodium dodecyl sulfate (SDS).
Furthermore, 1-DE is easy to use, repeatable, and capable of resolving proteins with molecular masses ranging from 10 kDa to 300 kDa[17]. As 1-DE gel has minimal resolving power, it is most commonly used to characterize proteins after being purified. However, in more complex protein mixtures, such as a crude cell lysate, 2-DE may be used. In 2-DE, proteins are determined by their net charge and their molecular mass[17].
Proteomics can analyze the expression of a protein at different levels allowing the assessment of specific quantitative and qualitative cellular responses related to that protein[20]. Qualitative and quantitative proteomes are measured at post-transcriptional, transcriptomic, and genomic levels[21]. According to the conditions, qualitative proteomics utilizes microarrays, 2-DE, and 2D-LC to monitor protein mixture composition and protein expression changes[20]. In addition, it can provide information on the molecular mechanisms of diseases and compare two groups such as patients with healthy controls[20]. Quantitative proteomics can also provide deep insights into disease mechanisms, cellular functions[22], and biomarker discovery[23]. Several new strategies are used in quantitative proteomics, such as post-extraction or metabolic stable-isotope labeling alone or in combination with affinity labeling[24,25]. MS identifies compounds by sorting cations according to their mass-to-charge ratio[26].
The study of proteomics has many applications in different fields such as medicine, oncology, food microbiology, and agriculture. This review will shed light on proteomics, their techniques, some of its applications, and the challenges currently faced in this field.
Proteomics has three main types: expression proteomics, functional proteomics, and structural proteomics[27].
Expression proteomics is a novel approach that studies the quantitative and qualitative expression of proteins. It aims to specify the difference in protein expression between two conditions such as patients and controls[28]. In addition, it can identify disease-specific proteins and new proteins in signal transduction[17]. Expression proteomics experiments are usually used to study the patterns of protein expression in different cells. For example, a tumor tissue sample is compared to a normal tissue sample to identify differences in the levels of proteins[26]. Variations in protein expression, which are present or missing in tumor tissue compared to normal tissue, are detected using 2-DE and MS techniques[29].
Nuclear magnetic resonance spectroscopy and X-ray crystallography are used in structural proteomics to determine the three-dimensional structure and structural complexities of functional proteins. It specifies all protein interactions such as membranes, cell organelles, and ribosomes in the mixture[30]. The study of the nuclear pore complex is an example of structural proteomics[31].
This type of proteomics studies the protein functions and molecular mechanisms in the cell and determines the protein partner’s interactions. In particular, it investigates the interaction of an unknown protein with partners from a specific protein complex involved in a particular process. This may indicate the biological role of the protein[32]. In addition, the elucidation of protein-protein interactions in vivo can lead to comprehensive descriptions of cellular signaling pathways[33].
Two methods can be used in proteomics: top-down and bottom-up workflows. The bottom-up method is sometimes called peptide-based proteomics. Here, the protein is digested by trypsin and separated by a specific column, followed by analysis of the peptides by MS[15]. The bottom-up approach can be classified into two groups according to the fractionation step. The first approach uses 2-DE to isolate the proteins from the gel. Then the proteins are digested into peptides that MS can identify. The second approach is called “shotgun” proteomics. Here, the digestion of protein occurs without fractionation, and LC is used to separate the peptides identified by MS[34]. In top-down proteomics, whole proteins or polypeptides are immediately assessed by MS. The molecular mass of proteins is sometimes calculated by using electrospray ionization (ESI) followed by matrix-assisted laser desorption/ionization (MALDI) MS[35]. Top-down proteomics can identify proteins with a molecular mass of > 200 kDa[36]. Both approaches have various advantages and limitations. In the bottom-up approach, there is low percentage coverage of the protein sequence, because the recovered sample includes small and inconsistent fractions of total peptides. This results in missing a large proportion of alternative splice variants and PTMs. However, in top-down proteomics, all characteristics of proteins are protected, and almost all existing modifications and correlations can also be recovered. Moreover, in top-down proteomics, the results of the exclusion of protein digestion with time are preserved[37]. The major challenge in top-down proteomics is the poor solubility of proteins compared to small peptides. Some proteins in the membrane have high solubility but need to be washed with SDS; however, SDS cannot be used in ESI[38]. Proteomics workflows involve sample preparation and analytical flow. The latter include separation of proteins, protein identification, and validation.
Proteomics experiments highly depend on the accuracy of sample preparation, in addition to a well-designed pre-analytical workflow. There is no standard technique for sample preparation in proteomics. Each method depends on the number of proteins in the sample, the sample’s complexity, and the study’s objectives. Extraction of proteins from the mixture is the most vital step in the preparation of samples. To maximize protein extraction and solubilization, the extraction should include organic solvents and detergents followed by a tissue disruption technique. The organic solvents and detergents can be removed by lyophilization[39]. In previous detergent-based methods, the extraction of 2,2,2-trifluoroethanol (TFE) macro-scale (> 100 µg) materials and nano-scale (30 µg)-based lysis have provided comparable protein detection rates[40].
Gel-based and chromatography-based approaches are used for the separation and isolation of proteins from the mixture.
The best technique for protein isolation and detection is PAGE[41]. For separation, 1-DE and 2-DE can be used. Furthermore, 2D-DIGE and SDS-PAGE are examples of 2D variations used in gel electrophoresis[42].
1-DE, can isolate proteins with a molecular weight of 10 kDa to 300 kDa. It uses SDS, a detergent that denatures secondary and non-disulfide-linked tertiary structures, and combines them with a negative charge proportional to their volume. This allows the calculation of molecular weights[43]. SDS-PAGE can be used to verify the purity of samples, test protein purification, and calculate molecular weights for unknown proteins[44].
2-DE differentiates proteins better than 1-DE due to the variation in molecular weight and isoelectric point of protein molecules[43]. It also has a better resolution than 1-DE because the protein is separated into two different dimensions. In 1-DE, the protein is separated based on net charge, but in 2-DE, protein separation is based on the molecular mass and isoelectric point. Thus, this method can detect different forms of proteins such as PTMs and phosphorylation. Some proteins that arise from different proteolysis processes and splicing of alternative mRNA can be resolved by 2-DE[45]. There are many applications of 2-DE, including protein expression profiling and cell map proteomics. Protein expression profiling can be used for comparing normal and diseased tissues. Mapping proteins in 2-DE can be used in cellular organelles[46], protein complexes[19], and microorganisms[47]. 2-DE can help catalog proteins, and the database can be created on the World Wide Web[48]. However, 2-DE cannot detect proteins at a low molecular weight and the limits of separation by isoelectric point and size[49].
Chromatography of affinity, size exclusion chromatography (SEC), and ion-exchange chromatography (IEC) techniques can be used to purify protein-based chromatography. In addition, western blotting and the enzyme-linked immunosorbent assay are used to identify selective proteins[50].
IEC is used to purify proteins according to their charges. This technique allows separating proteins according to their charge nature, which is not possible by other approaches. The charge accepted by the molecule of interest can be readily used by altering the pH of the buffer. The IEC technique is low cost and can persist in variable buffer conditions[30].
SEC can be used to separate different compounds according to their size (hydrodynamic volume) measured by how efficiently they enter the stationary phase’s pores. However, this technique is not as useful as other proteomics techniques[51]. Two basic versions of SEC are utilized: gel permeation chromatography (GPC) using organic solvents, which is used for polymer analysis; and gel filtration, which is performed using aqueous solvents.
Affinity chromatography is the process of protein separation according to its interaction with an immobilized ligand. In 2-DE and non-2-DE, affinity chromatography helps decrease the protein complexity[52]. There are three types of affinity chromatography: separation of protein before 2-DE, affinity chromatography of protein before MS, and affinity chromatography of peptides before MS.
LC is a powerful technique that can separate proteins from a complex mixture and can analyze large and fragile biomolecules. When combined with MS, it can be used for determining the peptides in the mixture[53]. LC can help researchers discover novel biomarkers and understand the mechanisms of carcinogenesis according to the modification of proteins. For example, some researchers use LC-MS/MS to rapidly monitor congenital adrenal hyperplasia from dried filter-paper blood samples[54].
The identification of proteins is a critical step in proteomics. MS can be used after the separation of the proteins by chromatography or electrophoresis[55]. Other techniques can also identify proteins such as Edman sequencing and protein microarray[17].
Edman sequencing has been used to detect the sequence of amino acids in peptides or proteins. This technique includes the reaction of chemicals, which remove and determine amino acid residues present at the N-terminus of the polypeptide chain. Thus, it plays a significant role in assessing biopharmaceutical quality and therapeutic proteins[17].
MS is the best analytical tool for rapidly facilitating the sequencing of proteins[56]. It can also be used to detect the molecular weight of proteins. In this technique, protein molecules are ionized, and their mass is calculated according to mass-to-charge ratios. The mass spectrometer has three main components: an analyzer, an ion source, and a detector. The methods used for ionization are ESI and MALDI[57]. In MALDI, a chemical matrix is mixed with the peptides, and spotted onto a metal multiwall microliter plate to make a crystal lattice. The matrix chemicals pass the energy to the samples after absorbing it. Then peptide ions are detected by a mass analyzer. MALDI creates mostly singly charged ions that help to determine the m/z value[58]. In ESI, the power is activated in the protein sample to create charged droplets that increase gaseous ion production, which then are analyzed with a mass analyzer[59]. The advantages of ESI are its high reproducibility and high elasticity to combine many categories of MS. Furthermore, ESI can be fixed to time-of-flight (TOF)-MS, quadruple, ion traps, and fourier transform ion cyclotron resonance. On the other hand, the disadvantages of ESI are that it cannot be applied for molecular imaging, it requires a large quantity of samples, and multiple peaks are produced due to the many charged ions that result in the complexity of MS/MS spectra[60].
Sequent, Mascot, Comet, and Tandem are instruments currently available for database searching[61]. However, most search devices do not produce matching data as they operate on differentiation algorithms and recording functions, creation integration, and data comparison from many studies and experiments. As a result, the identification of peptides by data search needs additional time[62]. High-quality data makes the data search more effective and less time consuming. Moreover, using accurate mass to measure ion fragments can shorten database explorations and produce more accurate results[63].
Bioinformatics analyses use novel proteomics algorithms to manage the large and varied data in the process of marker discovery[64]. Controlling this massive quantity of data and finding the association between other omics technologies (e.g., metabolomics and genomics) remain difficult. The analyses of proteomics data is challenging because of the parameters used in processing, quality valuation, and shortage of standards for data formats. The big challenge is how to analyze massive data and create real biological understanding[65]. Protein pathways are a collection of internal cell reactions that have a specific biological impact. For protein pathways, a variety of tools and databases are available[66]. The Kyoto Encyclopedia of Genes and Genomes, BioCarta, Pathway Knowledge Base Reactome and Ingenuity pathway databases have extensive information on metabolism, signaling, and interactions[67,68]. Unique databases for signal transduction pathways, such as GenMAPP or protein analysis through evolutionary relationships (PANTHER), have been created[69,70]. Furthermore, databases such as Netpath, which include cancer-related pathways, have been created to detect proteins unique to a specific cancer type[71]. Details about protein interactions in complexes can be found in databases including BioGRID, IntAct, MINT, and HRPD[72-74]. The STRING database links to various other databases for literature mining and is commonly used for protein interaction. Furthermore, using the STRING database, protein networks can be drawn based on the list of genes given and the available interactions[75,76].
Proteomics is a revolutionary technique that has been used in medicine, including drug and biomarker discovery. Proteomics can identify and monitor biomarkers by analyzing the proteins in the body fluids such as urine, serum, exhaled breath and spinal fluid. Proteomics can also facilitate drug development by providing a comprehensive map of protein interactions associated with disease pathways[77].
A biomarker is an assessable pointer of a normal or abnormal biological state in the body[78]. In clinical settings, cancer development and its response to therapy are measured by cancer biomarkers[79]. 2D-PAGE is used for the discovery of biomarkers. It can also compare the profiles of proteins in normal and diseased cells such as tumor tissues and body fluids[80]. Cancer biomarkers are divided into three classes, predictive, prognostic and diagnostic, based on their uses. Predictive biomarkers can predict the response to therapy. For instance, in breast cancer, the activation and the positivity of human epidermal growth factor receptor 2 can predict the response to trastuzumab[81]. In addition, in colorectal cancer, mutation of Kirsten rat sarcoma virus gene can predict resistance to treatment with epidermal growth factor receptor inhibitors (e.g., cetuximab)[82].
On the other hand, prognostic biomarkers can provide physicians with a prediction of the clinical outcomes. For example, the 21-gene repetition mark predicts breast cancer relapse and complete survival in node-negative, tamoxifen-treated breast cancer[83]. The third group of biomarkers is the diagnostic biomarker, which indicates if a patient has a specific disease condition. For example, in colorectal cancer, a stool DNA test is used as a diagnostic biomarker[84]. These biomarkers can be found in tissues, serum, blood, and urine. The body-fluid sampling for proteomics is thus less invasive and low cost. The discovery of biomarkers has progressed in many diseases such as acquired immune deficiency syndrome, cardiovascular diseases, diabetes, cancer, and renal diseases[85,86]. However, the highly complex mixtures of proteins and the high range of protein dynamics are examples of challenges in fluid sampling for proteomics. Each type of sample has a different usage according to the disease conditions. For instance, in kidney disease, the urine sample is used to assess urine proteins, reflecting changes in kidney functions[87]. In other human diseases, blood is also used for biomarker discovery. There are some challenges for using the plasma in biomarker discovery, such as protein dynamicity, the variation of the patient[87], and the low abundance of biomarkers in plasma. These challenges in biomarker discovery have yet to be addressed[88]. Most biomarker discovery studies are focused on cancer-related diseases due to their clinical importance. For instance, many biomarkers are associated with tumors that can be used to follow up with the patients[89].
Drug discovery is a complex process with many different stages including chemical, functional, and clinical proteomics-based approaches. The application of proteomics in drug discovery has been developed to include patients’ treatment and care[90]. 2-DE cannot be used in drug discovery because it fails to separate the membrane proteins that characterized about 50% of important drug targets[91]. Moreover, 2-DE cannot detect low-abundance proteins[90]. In drug discovery proteomics, understanding the function of proteins and their interactions in the mixture is very important. Also, the methods should be able to detect low-abundance proteins and their activity. Therefore, many technologies such as MS and protein-chip have been used to identify and separate phage proteins. In addition, other techniques such as activity-based assays and two-hybrid assays can be used for the same purpose[92]. Using 2D-PAGE-MALDI-TOF/TOF, Lavandula angustifolia was used as a drug to treat Alzheimer’s disease in rats[93].
The application of proteomics in cancer is called oncoproteomics. Oncoproteomics can be used to identify anticancer drugs and the personalization of cancer management[94]. Microarrays and laser capture microdissection (LCM) of the tumor tissue can classify proteins in cancer. Oncoproteomics applications are used in many tissues such as the colon, breast, rectum, prostate, and brain. In addition, proteomics can be used to diagnose cancer and discover novel therapies[95]. Many proteomics techniques can be used to detect biomarkers in cancer such as aptamer-based molecular probes, cancer immunomics, tissue microarrays, nano-proteomics (to isolate signatures of autoantibodies), and antibody microarrays[94].
Two approaches can be used in tumor proteomics, LCM and MS imaging (MSI)[96]. LCM can separate the target proteins from the areas within the tumor before analysis with MS. In addition, this approach can help to determine proteins that correlate with tumor progression in the early and late stages of the disease using the proteinChip SELDI system ® [97]. However, fewer studies use tumor tissues than serum due to the technical difficulties and low throughput using tumor tissues.
The second approach is using MSI. This direct tissue technique allows placing a small amount of MALDI matrix mixed directly with a fresh piece of the tumor[98]. This approach can help to map small molecules and proteins in a 3D view. This approach was to map eight normal lung tissues with 42 lung tumors[99]. Additionally, MSI can predict diagnosis, categorize lung cancer histology, and organize 85% of the nodal connections[96].
Proteomics was used to discover many leukemia biomarkers that could determine types of leukemia. Examples of these biomarkers include catalase, annexin 1, alpha-enolase, annexin A10, tropomyosin, tropomyosin 3, peroxiredoxin 2, and RhoGDI2. These biomarkers help to predict the diagnosis and outcome of the disease[100]. In addition, the proteomics approach can help developing new treatment pathways for leukemias using their proteomics profiles[101]. However, a major limitation of this approach is that important proteins controlling key cellular elements are present in low abundance and may not be readily detected.
Acute myeloid leukemia (AML) is an aggressive blood cancer. Patients reach complete remission after intensive chemotherapy given as induction and consolidation[102]. However, relapsed AML may acquire at least one specific mutation such as FLT3, RUNX1, or ASXL1. Mutations in signaling genes such as KIT, NRAS, PTPN11, and NPM1 are less frequent[103]. The use of proteomics in AML may guide the post-induction strategy of either chemotherapy or allogeneic stem cell transplantation. Moreover, proteomics can help discover new or modified therapy options for AML patients[104]. Since the 1980s, many studies have focused on finding biomarkers in AML. For example, Hanash et al[105] used 2-DE to identify the cell of origin in acute leukemia.
While the prognosis of AML patients has improved through the years, especially in younger patients, mortality remains the highest among all other cancers[106]. Proteomics can assist the development of personalized therapy in AML[104]. Kwak et al[107] used 2-DE and MS to identify eight differentially expressed proteins between 12 healthy people and 12 patients with AML. Proteasome 26S ATPase subunit, immunoglobulin heavy-chain variant, and haptoglobin-1 were upregulated, while five proteins (unknown protein, lipoprotein C-III, RBP4 gene product, SP-40 and α-2-HS-glycoprotein) were downregulated[107]. Another study identified seven other proteins. These proteins were annexin A10, alpha-enolase, tropomyosin 3, lipocortin 1 (annexin 1), peroxiredoxin 2, RhoGDI2, and catalase[108].
In a recent study, BCL11A expression was found to play a role in AML. The study included 292 AML patients. The study found a significant association between the laboratory variables and the levels of BCL11A. However, BCL11A was not associated with survival and complete remission[109].
Most studies in proteomics in AML were performed on peripheral blood cells and bone marrow samples at an early stage. However, one study compared AML at diagnosis, remission, and relapse. It concluded that the proteome expression at diagnosis and relapse is similar at a high protein concentration[79,110]. Another study that focused on AML (subtypes M1 and M2) compared patients with healthy individuals. Twenty-five proteins were characterized in the peripheral blood and bone marrow samples. The study found that 6-phosphogluconate dehydrogenase, Annexin III and L-plastin were only found in the M2 subtype. The annexin I and actin gamma 1 levels were found to correlate with drug resistance at relapse[111].
There are many challenges in proteomics. The major challenge is the broad change in protein expression with the environment and cell type[112]. In addition, there is no comparable proteomics method, unlike genomics, that uses polymerase chain reaction[113]. Moreover, protein activities are highly regulated post-transnationally, which adds difficulty is proteomics[114]. Finally, the type of samples and sample preparation techniques are other challenges in proteomics that can significantly change the quality of MS data. For example, the protein and phosphoprotein levels in breast cancer tumor samples were affected by the sample manipulation technique and bio-specimen type[115].
Proteomics is a fast, sensitive technology that provides high proteome coverage. Expression proteomics, functional proteomics, and structural proteomics are the three major types of proteomics. There are two different workflows in proteomics: top-down and bottom-up proteomics. In addition, there are increasing uses of proteomics in the majority of biological sciences. Finally, proteomics can assist in finding new biomarkers in different diseases and discover new therapies.
Conflict-of-interest statement: All authors declare no conflict of interest.
Manuscript source: Invited manuscript
Peer-review started: February 22, 2021
First decision: April 20, 2021
Article in press: July 15, 2021
Specialty type: Biochemistry and molecular biology
Country/Territory of origin: Oman
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P-Reviewer: Suriyaphol G S-Editor: Gao CC L-Editor: Filipodia P-Editor: Yuan YY
Safa Al-Amrani, Department of Microbiology and Immunology, Sultan Qaboos University, Muscat 123, Oman.
Zaaima Al-Jabri, Department of Microbiology and Immunology, Sultan Qaboos University, Muscat 123, Oman.
Adhari Al-Zaabi, Department of Human and Clinical Anatomy, Sultan Qaboos University, Muscat 123, Oman.
Jalila Alshekaili, Department of Microbiology and Immunology, Sultan Qaboos University Hospital, Muscat 123, Oman.
Murtadha Al-Khabori, Department of Hematology, Sultan Qaboos University, Muscat 123, Oman. mo.ude.uqs@irobahk.
1. Agrawal GK, Sarkar A, Righetti PG, Pedreschi R, Carpentier S, Wang T, Barkla BJ, Kohli A, Ndimba BK, Bykova NV, Rampitsch C, Zolla L, Rafudeen MS, Cramer R, Bindschedler LV, Tsakirpaloglou N, Ndimba RJ, Farrant JM, Renaut J, Job D, Kikuchi S, Rakwal R. A decade of plant proteomics and mass spectrometry: translation of technical advancements to food security and safety issues. Mass Spectrom Rev. 2013; 32 :335–365. [PubMed] [Google Scholar]
2. Wilkins MR, Sanchez JC, Gooley AA, Appel RD, Humphery-Smith I, Hochstrasser DF, Williams KL. Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol Genet Eng Rev. 1996; 13 :19–50. [PubMed] [Google Scholar]
3. Holman JD, Dasari S, Tabb DL. Informatics of protein and posttranslational modification detection via shotgun proteomics. Methods Mol Biol. 2013; 1002 :167–179. [PMC free article] [PubMed] [Google Scholar]
4. Chandramouli K, Qian PY. Proteomics: challenges, techniques and possibilities to overcome biological sample complexity. Hum Genomics Proteomics. 2009; 2009 [PMC free article] [PubMed] [Google Scholar]
5. Vercauteren FG, Bergeron JJ, Vandesande F, Arckens L, Quirion R. Proteomic approaches in brain research and neuropharmacology. Eur J Pharmacol. 2004; 500 :385–398. [PubMed] [Google Scholar]
6. Florens L, Washburn MP. Proteomic analysis by multidimensional protein identification technology. Methods Mol Biol. 2006; 328 :159–175. [PubMed] [Google Scholar]
7. Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002; 1 :376–386. [PubMed] [Google Scholar]
8. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics. 2004; 3 :1154–1169. [PubMed] [Google Scholar]
9. Wolters DA, Washburn MP, Yates JR 3rd. An automated multidimensional protein identification technology for shotgun proteomics. Anal Chem. 2001; 73 :5683–5690. [PubMed] [Google Scholar]
10. Klose J, Nock C, Herrmann M, Stühler K, Marcus K, Blüggel M, Krause E, Schalkwyk LC, Rastan S, Brown SD, Büssow K, Himmelbauer H, Lehrach H. Genetic analysis of the mouse brain proteome. Nat Genet. 2002; 30 :385–393. [PubMed] [Google Scholar]
11. Cutler P. Protein arrays: the current state-of-the-art. Proteomics. 2003; 3 :3–18. [PubMed] [Google Scholar]
12. Schulz TC, Swistowska AM, Liu Y, Swistowski A, Palmarini G, Brimble SN, Sherrer E, Robins AJ, Rao MS, Zeng X. A large-scale proteomic analysis of human embryonic stem cells. BMC Genomics. 2007; 8 :478. [PMC free article] [PubMed] [Google Scholar]
13. Stahl-Zeng J, Lange V, Ossola R, Eckhardt K, Krek W, Aebersold R, Domon B. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics. 2007; 6 :1809–1817. [PubMed] [Google Scholar]
14. Yoithapprabhunath TR, Nirmal RM, Santhadevy A, Anusushanth A, Charanya D, Rojiluke , Sri Chinthu KK, Yamunadevi A. Role of proteomics in physiologic and pathologic conditions of dentistry: Overview. J Pharm Bioallied Sci. 2015; 7 :S344–S349. [PMC free article] [PubMed] [Google Scholar]
15. Pandey A, Mann M. Proteomics to study genes and genomes. Nature. 2000; 405 :837–846. [PubMed] [Google Scholar]
16. Rabilloud T, Lelong C. Two-dimensional gel electrophoresis in proteomics: a tutorial. J Proteomics. 2011; 74 :1829–1841. [PubMed] [Google Scholar]
17. Graves PR, Haystead TA. Molecular biologist's guide to proteomics. Microbiol Mol Biol Rev. 2002; 66 :39–63; table of contents. [PMC free article] [PubMed] [Google Scholar]
18. Verrills NM. Clinical proteomics: present and future prospects. Clin Biochem Rev. 2006; 27 :99–116. [PMC free article] [PubMed] [Google Scholar]
19. Rappsilber J, Siniossoglou S, Hurt EC, Mann M. A generic strategy to analyze the spatial organization of multi-protein complexes by cross-linking and mass spectrometry. Anal Chem. 2000; 72 :267–275. [PubMed] [Google Scholar]
20. Xiao GG, Recker RR, Deng HW. Recent advances in proteomics and cancer biomarker discovery. Clin Med Oncol. 2008; 2 :63–72. [PMC free article] [PubMed] [Google Scholar]
21. Low TY, van Heesch S, van den Toorn H, Giansanti P, Cristobal A, Toonen P, Schafer S, Hübner N, van Breukelen B, Mohammed S, Cuppen E, Heck AJ, Guryev V. Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis. Cell Rep. 2013; 5 :1469–1478. [PubMed] [Google Scholar]
22. Agnetti G, Kane LA, Guarnieri C, Caldarera CM, Van Eyk JE. Proteomic technologies in the study of kinases: novel tools for the investigation of PKC in the heart. Pharmacol Res. 2007; 55 :511–522. [PMC free article] [PubMed] [Google Scholar]
23. Grønborg M, Kristiansen TZ, Iwahori A, Chang R, Reddy R, Sato N, Molina H, Jensen ON, Hruban RH, Goggins MG, Maitra A, Pandey A. Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol Cell Proteomics. 2006; 5 :157–171. [PubMed] [Google Scholar]
24. Wang X, Chen CF, Baker PR, Chen PL, Kaiser P, Huang L. Mass spectrometric characterization of the affinity-purified human 26S proteasome complex. Biochemistry. 2007; 46 :3553–3565. [PubMed] [Google Scholar]
25. Jiang X, Coffino P, Li X. Development of a method for screening short-lived proteins using green fluorescent protein. Genome Biol. 2004; 5 :R81. [PMC free article] [PubMed] [Google Scholar]
26. Chandrasekhar K, Dileep A, Lebonah DE, Pramoda Kumari J. A short review on proteomics and its applications. Int Lett Natur Sci. 2014; 17 :77–84. [Google Scholar]
27. Hanash S. Disease proteomics. Nature. 2003; 422 :226–232. [PubMed] [Google Scholar]28. Banks RE, Dunn MJ, Hochstrasser DF, Sanchez JC, Blackstock W, Pappin DJ, Selby PJ. Proteomics: new perspectives, new biomedical opportunities. Lancet. 2000; 356 :1749–1756. [PubMed] [Google Scholar]
29. Hinsby AM, Olsen JV, Bennett KL, Mann M. Signaling initiated by overexpression of the fibroblast growth factor receptor-1 investigated by mass spectrometry. Mol Cell Proteomics. 2003; 2 :29–36. [PubMed] [Google Scholar]
30. Jungbauer A, Hahn R. Ion-exchange chromatography. In: Methods in enzymology. Elsevier, 2009: 349-371. [PubMed] [Google Scholar]
31. Rout MP, Aitchison JD, Suprapto A, Hjertaas K, Zhao Y, Chait BT. The yeast nuclear pore complex: composition, architecture, and transport mechanism. J Cell Biol. 2000; 148 :635–651. [PMC free article] [PubMed] [Google Scholar]
32. Gavin AC, Bösche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon AM, Cruciat CM, Remor M, Höfert C, Schelder M, Brajenovic M, Ruffner H, Merino A, Klein K, Hudak M, Dickson D, Rudi T, Gnau V, Bauch A, Bastuck S, Huhse B, Leutwein C, Heurtier MA, Copley RR, Edelmann A, Querfurth E, Rybin V, Drewes G, Raida M, Bouwmeester T, Bork P, Seraphin B, Kuster B, Neubauer G, Superti-Furga G. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002; 415 :141–147. [PubMed] [Google Scholar]
33. Monti M, Cozzolino M, Cozzolino F, Tedesco R, Pucci P. Functional proteomics: protein-protein interactions in vivo. Ital J Biochem. 2007; 56 :310–314. [PubMed] [Google Scholar]
34. Ortea I, O'Connor G, Maquet A. Review on proteomics for food authentication. J Proteomics. 2016; 147 :212–225. [PubMed] [Google Scholar]
35. Chait BT, Kent SB. Weighing naked proteins: practical, high-accuracy mass measurement of peptides and proteins. Science. 1992; 257 :1885–1894. [PubMed] [Google Scholar]
36. Han X, Jin M, Breuker K, McLafferty FW. Extending top-down mass spectrometry to proteins with masses greater than 200 kilodaltons. Science. 2006; 314 :109–112. [PubMed] [Google Scholar]
37. Chait BT. Chemistry. Mass spectrometry: bottom-up or top-down? Science. 2006; 314 :65–66. [PubMed] [Google Scholar]
38. Dalmasso E, Casenas D, Miller S. Top-down, Bottom-up: the merging of two high performance technologies. BioRadiations, 2009. [Google Scholar]
39. Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics. 2020; 17 :17. [PMC free article] [PubMed] [Google Scholar]
40. Wang H, Qian WJ, Mottaz HM, Clauss TR, Anderson DJ, Moore RJ, Camp DG 2nd, Khan AH, Sforza DM, Pallavicini M, Smith DJ, Smith RD. Development and evaluation of a micro- and nanoscale proteomic sample preparation method. J Proteome Res. 2005; 4 :2397–2403. [PMC free article] [PubMed] [Google Scholar]
41. Ramakrishnan S, Sulochana KN, Punitham R, Arunagiri K. Free alanine, aspartic acid, or glutamic acid reduce the glycation of human lens proteins. Glycoconj J. 1996; 13 :519–523. [PubMed] [Google Scholar]
42. Kostanski LK, Keller DM, Hamielec AE. Size-exclusion chromatography-a review of calibration methodologies. J Biochem Biophys Methods. 2004; 58 :159–186. [PubMed] [Google Scholar]
43. Chen ZT, Liang ZG, Zhu XD. A Review: Proteomics in Nasopharyngeal Carcinoma. Int J Mol Sci. 2015; 16 :15497–15530. [PMC free article] [PubMed] [Google Scholar]
44. Brunelle JL, Green R. One-dimensional SDS-polyacrylamide gel electrophoresis (1D SDS-PAGE) Methods Enzymol. 2014; 541 :151–159. [PubMed] [Google Scholar]
45. Lewis TS, Hunt JB, Aveline LD, Jonscher KR, Louie DF, Yeh JM, Nahreini TS, Resing KA, Ahn NG. Identification of novel MAP kinase pathway signaling targets by functional proteomics and mass spectrometry. Mol Cell. 2000; 6 :1343–1354. [PubMed] [Google Scholar]
46. Jung E, Heller M, Sanchez JC, Hochstrasser DF. Proteomics meets cell biology: the establishment of subcellular proteomes. Electrophoresis. 2000; 21 :3369–3377. [PubMed] [Google Scholar]
47. Benner P. A response by P. Benner to K. Cash, "Benner and expertise in nursing: a critique". Int J Nurs Stud. 1996; 33 :669–674. [PubMed] [Google Scholar]
48. Appel RD, Bairoch A, Hochstrasser DF. 2-D databases on the World Wide Web. In: Link AJ. 2-D Proteome Analysis Protocols. Springer, 1999: 383-391. [PubMed] [Google Scholar]
49. Moseley MA. Current trends in differential expression proteomics: isotopically coded tags. Trends Biotechnol. 2001; 19 :S10–S16. [PubMed] [Google Scholar]
50. Coskun O. Separation techniques: Chromatography. North Clin Istanb. 2016; 3 :156–160. [PMC free article] [PubMed] [Google Scholar]
51. Lecchi P, Gupte AR, Perez RE, Stockert LV, Abramson FP. Size-exclusion chromatography in multidimensional separation schemes for proteome analysis. J Biochem Biophys Methods. 2003; 56 :141–152. [PubMed] [Google Scholar]
52. Marouga R, David S, Hawkins E. The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal Bioanal Chem. 2005; 382 :669–678. [PubMed] [Google Scholar]
53. Chen G, Pramanik BN, Liu YH, Mirza UA. Applications of LC/MS in structure identifications of small molecules and proteins in drug discovery. J Mass Spectrom. 2007; 42 :279–287. [PubMed] [Google Scholar]
54. Lai CC, Tsai CH, Tsai FJ, Lee CC, Lin WD. Rapid monitoring assay of congenital adrenal hyperplasia with microbore high-performance liquid chromatography/electrospray ionization tandem mass spectrometry from dried blood spots. Rapid Commun Mass Spectrom. 2001; 15 :2145–2151. [PubMed] [Google Scholar]
55. Pastwa E, Somiari SB, Czyz M, Somiari RI. Proteomics in human cancer research. Proteomics Clin Appl. 2007; 1 :4–17. [PubMed] [Google Scholar]
56. Martin DB, Nelson PS. From genomics to proteomics: techniques and applications in cancer research. Trends Cell Biol. 2001; 11 :S60–S65. [PubMed] [Google Scholar]
57. Krishnankutty R, Bhat A, Azmi A, Souchelnytskyi S, Uddin S. An Overview of Proteomics Techniques and its Application as a Tool in Biomarker and Drug Discovery. J Proteomics Enzymol. 2016; 2 :2. [Google Scholar]
58. Smith L, Lind MJ, Welham KJ, Cawkwell L Cancer Biology Proteomics Group. Cancer proteomics and its application to discovery of therapy response markers in human cancer. Cancer. 2006; 107 :232–241. [PubMed] [Google Scholar]
59. Lahm HW, Langen H. Mass spectrometry: a tool for the identification of proteins separated by gels. Electrophoresis. 2000; 21 :2105–2114. [PubMed] [Google Scholar]
60. Chiou SH, Wu CY. Clinical proteomics: current status, challenges, and future perspectives. Kaohsiung J Med Sci. 2011; 27 :1–14. [PubMed] [Google Scholar]
61. Adkins JN, Varnum SM, Auberry KJ, Moore RJ, Angell NH, Smith RD, Springer DL, Pounds JG. Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry. Mol Cell Proteomics. 2002; 1 :947–955. [PubMed] [Google Scholar]
62. Carr S, Aebersold R, Baldwin M, Burlingame A, Clauser K, Nesvizhskii A Working Group on Publication Guidelines for Peptide and Protein Identification Data. The need for guidelines in publication of peptide and protein identification data: Working Group on Publication Guidelines for Peptide and Protein Identification Data. Mol Cell Proteomics. 2004; 3 :531–533. [PubMed] [Google Scholar]
63. Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003; 75 :4646–4658. [PubMed] [Google Scholar]
64. Vihinen M. Bioinformatics in proteomics. Biomol Eng. 2001; 18 :241–248. [PubMed] [Google Scholar]
65. Domon B, Aebersold R. Challenges and opportunities in proteomics data analysis. Mol Cell Proteomics. 2006; 5 :1921–1926. [PubMed] [Google Scholar]
66. Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH. Proteomics: Technologies and Their Applications. J Chromatogr Sci. 2017; 55 :182–196. [PubMed] [Google Scholar]
67. Croft D, O'Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B, Jupe S, Kalatskaya I, Mahajan S, May B, Ndegwa N, Schmidt E, Shamovsky V, Yung C, Birney E, Hermjakob H, D'Eustachio P, Stein L. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011; 39 :D691–D697. [PMC free article] [PubMed] [Google Scholar]
68. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012; 40 :D109–D114. [PMC free article] [PubMed] [Google Scholar]
69. Mi H, Guo N, Kejariwal A, Thomas PD. PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic Acids Res. 2007; 35 :D247–D252. [PMC free article] [PubMed] [Google Scholar]
70. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. PID: the Pathway Interaction Database. Nucleic Acids Res. 2009; 37 :D674–D679. [PMC free article] [PubMed] [Google Scholar]
71. Kandasamy K, Mohan SS, Raju R, Keerthikumar S, Kumar GS, Venugopal AK, Telikicherla D, Navarro JD, Mathivanan S, Pecquet C, Gollapudi SK, Tattikota SG, Mohan S, Padhukasahasram H, Subbannayya Y, Goel R, Jacob HK, Zhong J, Sekhar R, Nanjappa V, Balakrishnan L, Subbaiah R, Ramachandra YL, Rahiman BA, Prasad TS, Lin JX, Houtman JC, Desiderio S, Renauld JC, Constantinescu SN, Ohara O, Hirano T, Kubo M, Singh S, Khatri P, Draghici S, Bader GD, Sander C, Leonard WJ, Pandey A. NetPath: a public resource of curated signal transduction pathways. Genome Biol. 2010; 11 :R3. [PMC free article] [PubMed] [Google Scholar]
72. Schmidt A, Forne I, Imhof A. Bioinformatic analysis of proteomics data. BMC Syst Biol. 2014; 8 Suppl 2 :S3. [PMC free article] [PubMed] [Google Scholar]
73. Chatr-aryamontri A, Ceol A, Palazzi LM, Nardelli G, Schneider MV, Castagnoli L, Cesareni G. MINT: the Molecular INTeraction database. Nucleic Acids Res. 2007; 35 :D572–D574. [PMC free article] [PubMed] [Google Scholar]
74. Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M, Hinz U, Jandrasits C, Jimenez RC, Khadake J, Mahadevan U, Masson P, Pedruzzi I, Pfeiffenberger E, Porras P, Raghunath A, Roechert B, Orchard S, Hermjakob H. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 2012; 40 :D841–D846. [PMC free article] [PubMed] [Google Scholar]
75. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013; 41 :D808–D815. [PMC free article] [PubMed] [Google Scholar]
76. Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A. EnrichNet: network-based gene set enrichment analysis. Bioinformatics. 2012; 28 :i451–i457. [PMC free article] [PubMed] [Google Scholar]
77. He QY, Chiu JF. Proteomics in biomarker discovery and drug development. J Cell Biochem. 2003; 89 :868–886. [PubMed] [Google Scholar]
78. Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials; Board on Health Care Services; Board on Health Sciences Policy; Institute of Medicine, Micheel CM, Nass SJ, Omenn GS. Evolution of Translational Omics: Lessons Learned and the Path Forward. 2012. [PubMed] [Google Scholar]
79. Goossens N, Nakagawa S, Sun X, Hoshida Y. Cancer biomarker discovery and validation. Transl Cancer Res. 2015; 4 :256–269. [PMC free article] [PubMed] [Google Scholar]
80. Gam LH. Breast cancer and protein biomarkers. World J Exp Med. 2012; 2 :86–91. [PMC free article] [PubMed] [Google Scholar]
81. Piccart-Gebhart MJ, Procter M, Leyland-Jones B, Goldhirsch A, Untch M, Smith I, Gianni L, Baselga J, Bell R, Jackisch C, Cameron D, Dowsett M, Barrios CH, Steger G, Huang CS, Andersson M, Inbar M, Lichinitser M, Láng I, Nitz U, Iwata H, Thomssen C, Lohrisch C, Suter TM, Rüschoff J, Suto T, Greatorex V, Ward C, Straehle C, McFadden E, Dolci MS, Gelber RD Herceptin Adjuvant (HERA) Trial Study Team. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med. 2005; 353 :1659–1672. [PubMed] [Google Scholar]
82. Van Cutsem E, Köhne CH, Hitre E, Zaluski J, Chang Chien CR, Makhson A, D'Haens G, Pintér T, Lim R, Bodoky G, Roh JK, Folprecht G, Ruff P, Stroh C, Tejpar S, Schlichting M, Nippgen J, Rougier P. Cetuximab and chemotherapy as initial treatment for metastatic colorectal cancer. N Engl J Med. 2009; 360 :1408–1417. [PubMed] [Google Scholar]
83. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004; 351 :2817–2826. [PubMed] [Google Scholar]
84. Imperiale TF, Ransohoff DF, Itzkowitz SH, Levin TR, Lavin P, Lidgard GP, Ahlquist DA, Berger BM. Multitarget stool DNA testing for colorectal-cancer screening. N Engl J Med. 2014; 370 :1287–1297. [PubMed] [Google Scholar]
85. Safaei A, Rezaei-Tavirani M, Sobhi S, Akbari ME. Breast cancer biomarker discovery: Proteomics and genomics approaches. Iran J Cancer Prev. 2013; 6 :45–53. [Google Scholar]
86. Peyvandi H, Peyvandi AA, Safaei A, Zamanian Azodi M, Rezaei-Tavirani M. Introducing Potential Key Proteins and Pathways in Human Laryngeal Cancer: A System Biology Approach. Iran J Pharm Res. 2018; 17 :415–425. [PMC free article] [PubMed] [Google Scholar]
87. Wu J, Chen YD, Gu W. Urinary proteomics as a novel tool for biomarker discovery in kidney diseases. J Zhejiang Univ Sci B. 2010; 11 :227–237. [PMC free article] [PubMed] [Google Scholar]
88. Amiri-Dashatan N, Koushki M, Abbaszadeh HA, Rostami-Nejad M, Rezaei-Tavirani M. Proteomics Applications in Health: Biomarker and Drug Discovery and Food Industry. Iran J Pharm Res. 2018; 17 :1523–1536. [PMC free article] [PubMed] [Google Scholar]
89. Zamanian-Azodi M, Rezaei-Tavirani M, Mortazavian A, Vafaee R, Zali H, Soheili- Kashani M. Application of proteomics in cancer study. Am J Cancer Sci. 2013; 2 :116–134. [Google Scholar]
90. Simpson RJ, Dorow DS. Cancer proteomics: from signaling networks to tumor markers. Trends Biotechnol. 2001; 19 :S40–S48. [PubMed] [Google Scholar]
91. Drews J. Drug discovery: a historical perspective. Science. 2000; 287 :1960–1964. [PubMed] [Google Scholar]
92. Burbaum J, Tobal GM. Proteomics in drug discovery. Curr Opin Chem Biol. 2002; 6 :427–433. [PubMed] [Google Scholar]
93. Zali H, Zamanian-Azodi M, Rezaei Tavirani M, Akbar-Zadeh Baghban A. Protein Drug Targets of Lavandula angustifolia on treatment of Rat Alzheimer's Disease. Iran J Pharm Res. 2015; 14 :291–302. [PMC free article] [PubMed] [Google Scholar]
94. Jain KK. Innovations, challenges and future prospects of oncoproteomics. Mol Oncol. 2008; 2 :153–160. [PMC free article] [PubMed] [Google Scholar]
95. Jain KK. Recent advances in clinical oncoproteomics. J BUON. 2007; 12 Suppl 1 :S31–S38. [PubMed] [Google Scholar]
96. Wouters BG. Proteomics: methodologies and applications in oncology. Semin Radiat Oncol. 2008; 18 :115–125. [PubMed] [Google Scholar]
97. Simone NL, Paweletz CP, Charboneau L, Petricoin EF 3rd, Liotta LA. Laser capture microdissection: beyond functional genomics to proteomics. Mol Diagn. 2000; 5 :301–307. [PubMed] [Google Scholar]
98. Stoeckli M, Chaurand P, Hallahan DE, Caprioli RM. Imaging mass spectrometry: a new technology for the analysis of protein expression in mammalian tissues. Nat Med. 2001; 7 :493–496. [PubMed] [Google Scholar]
99. Yanagisawa K, Shyr Y, Xu BJ, Massion PP, Larsen PH, White BC, Roberts JR, Edgerton M, Gonzalez A, Nadaf S, Moore JH, Caprioli RM, Carbone DP. Proteomic patterns of tumour subsets in non-small-cell lung cancer. Lancet. 2003; 362 :433–439. [PubMed] [Google Scholar]
100. López-Pedrera C, Villalba JM, Siendones E, Barbarroja N, Gómez-Díaz C, Rodríguez-Ariza A, Buendía P, Torres A, Velasco F. Proteomic analysis of acute myeloid leukemia: Identification of potential early biomarkers and therapeutic targets. Proteomics. 2006; 6 Suppl 1 :S293–S299. [PubMed] [Google Scholar]
101. Gjertsen BT, Sjøholt G. Proteomic Strategies of Therapeutic Individualization and Target Discovery in Acute Myeloid Leukemia. In: Daoud SS. Cancer Proteomics. Springer, 2008: 161-187. [Google Scholar]
102. Aasebø E, Berven FS, Hovland R, Døskeland SO, Bruserud Ø, Selheim F, Hernandez-Valladares M. The Progression of Acute Myeloid Leukemia from First Diagnosis to Chemoresistant Relapse: A Comparison of Proteomic and Phosphoproteomic Profiles. Cancers (Basel) 2020; 12 [PMC free article] [PubMed] [Google Scholar]
103. Cocciardi S, Dolnik A, Kapp-Schwoerer S, Rücker FG, Lux S, Blätte TJ, Skambraks S, Krönke J, Heidel FH, Schnöder TM, Corbacioglu A, Gaidzik VI, Paschka P, Teleanu V, Göhring G, Thol F, Heuser M, Ganser A, Weber D, Sträng E, Kestler HA, Döhner H, Bullinger L, Döhner K. Clonal evolution patterns in acute myeloid leukemia with NPM1 mutation. Nat Commun. 2019; 10 :2031. [PMC free article] [PubMed] [Google Scholar]
104. Hu CW, Qutub AA. Proteomics in Acute Myeloid Leukemia. IntechOpen. 2017 [Google Scholar]105. Hanash SM, Baier LJ. Two-dimensional gel electrophoresis of cellular proteins reveals myeloid origin of blasts in two children with otherwise undifferentiated leukemia. Cancer. 1986; 57 :1539–1543. [PubMed] [Google Scholar]
106. Aasebø E, Forthun RB, Berven F, Selheim F, Hernandez-Valladares M. Global Cell Proteome Profiling, Phospho-signaling and Quantitative Proteomics for Identification of New Biomarkers in Acute Myeloid Leukemia Patients. Curr Pharm Biotechnol. 2016; 17 :52–70. [PMC free article] [PubMed] [Google Scholar]
107. Kwak JY, Ma TZ, Yoo MJ, Choi BH, Kim HG, Kim SR, Yim CY, Kwak YG. The comparative analysis of serum proteomes for the discovery of biomarkers for acute myeloid leukemia. Exp Hematol. 2004; 32 :836–842. [PubMed] [Google Scholar]
108. Cui JW, Wang J, He K, Jin BF, Wang HX, Li W, Kang LH, Hu MR, Li HY, Yu M, Shen BF, Wang GJ, Zhang XM. Proteomic analysis of human acute leukemia cells: insight into their classification. Clin Cancer Res. 2004; 10 :6887–6896. [PubMed] [Google Scholar]
109. Tao H, Ma X, Su G, Yin J, Xie X, Hu C, Chen Z, Tan D, Xu Z, Zheng Y, Liu H, He C, Mao ZJ, Yin H, Wang Z, Chang W, Gale RP, Wu D, Yin B. BCL11A expression in acute myeloid leukemia. Leuk Res. 2016; 41 :71–75. [PubMed] [Google Scholar]
110. Pemmaraju N, Kantarjian H, Kadia T, Cortes J, Borthakur G, Newberry K, Garcia-Manero G, Ravandi F, Jabbour E, Dellasala S, Pierce S, Verstovsek S. A phase I/II study of the Janus kinase (JAK) 1 and 2 inhibitor ruxolitinib in patients with relapsed or refractory acute myeloid leukemia. Clin Lymphoma Myeloma Leuk. 2015; 15 :171–176. [PMC free article] [PubMed] [Google Scholar]
111. Luczak M, Kaźmierczak M, Handschuh L, Lewandowski K, Komarnicki M, Figlerowicz M. Comparative proteome analysis of acute myeloid leukemia with and without maturation. J Proteomics. 2012; 75 :5734–5748. [PubMed] [Google Scholar]
112. Bichsel VE, Liotta LA, Petricoin EF 3rd. Cancer proteomics: from biomarker discovery to signal pathway profiling. Cancer J. 2001; 7 :69–78. [PubMed] [Google Scholar]
113. Blackstock WP, Weir MP. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 1999; 17 :121–127. [PubMed] [Google Scholar]
114. Srinivas PR, Srivastava S, Hanash S, Wright GL Jr. Proteomics in early detection of cancer. Clin Chem. 2001; 47 :1901–1911. [PubMed] [Google Scholar]
115. Meric-Bernstam F, Akcakanat A, Chen H, Sahin A, Tarco E, Carkaci S, Adrada BE, Singh G, Do KA, Garces ZM, Mittendorf E, Babiera G, Bedrosian I, Hwang R, Krishnamurthy S, Symmans WF, Gonzalez-Angulo AM, Mills GB. Influence of biospecimen variables on proteomic biomarkers in breast cancer. Clin Cancer Res. 2014; 20 :3870–3883. [PMC free article] [PubMed] [Google Scholar]
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