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Journal articles on the topic 'Proteomics research'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Krieg, Rene C., Cloud P. Paweletz, Lance A. Liotta, and Emanuel F. Petricoin. "Clinical Proteomics for Cancer Biomarker Discovery and Therapeutic Targeting." Technology in Cancer Research & Treatment 1, no. 4 (August 2002): 263–72. http://dx.doi.org/10.1177/153303460200100407.

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As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the “software” of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the “wiring diagram” of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.

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2

Sadeesh, Nithin, Mauro Scaravilli, and Leena Latonen. "Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes." Cancers 13, no. 19 (September 27, 2021): 4829. http://dx.doi.org/10.3390/cancers13194829.

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Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.

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Geng, Ruihui, Zhaoshen Li, Shude Li, and Jun Gao. "Proteomics in Pancreatic Cancer Research." International Journal of Proteomics 2011 (August 14, 2011): 1–5. http://dx.doi.org/10.1155/2011/365350.

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Pancreatic cancer is a highly aggressive malignancy with a poor prognosis and deeply affects the life of people. Therefore, the earlier diagnosis and better treatments are urgently needed. In recent years, the proteomic technologies are well established and growing rapidly and have been widely applied in clinical applications, especially in pancreatic cancer research. In this paper, we attempt to discuss the development of current proteomic technologies and the application of proteomics to the field of pancreatic cancer research. This will explore the potential perspective in revealing pathogenesis, making the diagnosis earlier and treatment.

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Janech, Michael G., John R. Raymond, and John M. Arthur. "Proteomics in renal research." American Journal of Physiology-Renal Physiology 292, no. 2 (February 2007): F501—F512. http://dx.doi.org/10.1152/ajprenal.00298.2006.

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Proteomic technologies are used with increasing frequency in the renal community. In this review, we highlight the use in renal research of a number of available techniques including two-dimensional gel electrophoresis, liquid chromatography/mass spectrometry, surface-enhanced laser desorption/ionization, capillary electrophoresis/mass spectrometry, and antibody and tissue arrays. These techniques have been used to identify proteins or changes in proteins specific to regions of the kidney or associated with renal diseases or toxicity. They have also been used to examine protein expression changes and posttranslational modifications of proteins during signaling. A number of studies have used proteomic methodologies to look for diagnostic biomarkers in body fluids. The rapid rate of development of the technologies along with the combination of classic physiological and biochemical techniques with proteomics will enable new discoveries.

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Ji, Qing, Fangshi Zhu, Xuan Liu, Qi Li, and Shi-bing Su. "Recent Advance in Applications of Proteomics Technologies on Traditional Chinese Medicine Research." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/983139.

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Proteomics technology, a major component of system biology, has gained comprehensive attention in the area of medical diagnosis, drug development, and mechanism research. On the holistic and systemic theory, proteomics has a convergence with traditional Chinese medicine (TCM). In this review, we discussed the applications of proteomic technologies in diseases-TCM syndrome combination researches. We also introduced the proteomic studies on thein vivoandin vitroeffects and underlying mechanisms of TCM treatments using Chinese herbal medicine (CHM), Chinese herbal formula (CHF), and acupuncture. Furthermore, the combined studies of proteomics with other “-omics” technologies in TCM were also discussed. In summary, this report presents an overview of the recent advances in the application of proteomic technologies in TCM studies and sheds a light on the future global and further research on TCM.

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Vítámvás, P., K. Kosová, and I. T. Prášil. "Proteome analysis in plant stress research: a review." Czech Journal of Genetics and Plant Breeding 43, No. 1 (January 7, 2008): 1–6. http://dx.doi.org/10.17221/1903-cjgpb.

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Proteomic techniques that allow the identification and quantification of stress-related proteins, mapping of dynamics of their expression and posttranslational modifications represent an important approach in the research of plant stresses. In this review, we show an outline of proteomics methods and their applications in the research of plant resistance to various types of stresses.

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7

Mahajan, R., and P. Gupta. "Proteomics: taking over where genomics leaves off." Czech Journal of Genetics and Plant Breeding 46, No. 2 (June 29, 2010): 47–53. http://dx.doi.org/10.17221/34/2009-cjgpb.

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The proteomic studies are simultaneously developed in several directions and significantly influence our notions on the capabilities of biological sciences. The need for proteomics research is necessary as there are certain genes in a cell that encode proteins with specific functions. Using a variety of techniques, proteomics can be used to study how proteins interact within a system or how the protein expression changes in different parts of the body, in different stages of its life cycle and in different environmental conditions as every individual has one genome and many proteomes. Besides the qualitative and quantitative description of the expressed proteins, proteomics also deals with the analysis of mutual interactions of proteins. Thereby, candidate proteins can be identified which may be used as starting-points for diagnostic or even therapeutic approaches.

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8

Jain, K. K. "Oncoproteomics: State-of-the-Art." Technology in Cancer Research & Treatment 1, no. 4 (August 2002): 219–20. http://dx.doi.org/10.1177/153303460200100401.

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Proteomics is a promising approach in the identification of proteins and biochemical pathways involved in carcinogenesis. Proteomic technologies are now being incorporated in oncology in the post-genomic era. Cancer involves alterations in protein expression and provides a good model not only for detection of biomarkers but also their use in drug discovery. Proteomics has an impact on diagnostics as well as drug discovery. Genomics still remains an important approach but the value of proteomics lies in the fact that most of the diagnostics and drugs target proteins. The importance of application of proteomics in oncology is recognized by the publication of this special issue of TRCT.

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9

Li, Shi-Sheng. "Commentary — The Proteomics: A New Tool for Chinese Medicine Research." American Journal of Chinese Medicine 35, no. 06 (January 2007): 923–28. http://dx.doi.org/10.1142/s0192415x07005387.

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Proteomics technology is based on the vast analytical power for protein/peptide identification and quantification offered by modern mass spectrometry coupled with hyphenated separation techniques such as two-dimensional gel electrophoresis (2DE) and micro- or nano-scale multidimensional liquid chromatography. The rapid growth of proteomics field provides an array of new tools for the integration of traditional Chinese medicine (TCM) with modern technology and systems biology, and is potentially advancing the progress of modernization and internationalization of TCM. Cho, in this issue of the American Journal of Chinese Medicine, highlights the recent application of 2DE-based and bottom-up proteomics in Chinese medicine research, including the exploration of pharmacological mechanisms of the actions of TCM, the facilitation of herb authentication and identification, and the profiling of protein expression following acupuncture treatment in animal models. Recent development in proteomics has provided further refinement on the analysis of proteins posttranslational modifications as well as quantitative comparison of different proteomes, and enabled the study of proteomes of specific diseases or biological processes under clinically relevant conditions. It is conceivable that the application of technologies developed in proteomics, genomics and metabonomics in the clinical practice and basic research of Chinese medicine will eventually lead to the reconciliation and integration of TCM and contemporary medicine. Chinese medicine is fundamentally a highly personalized medicine; perhaps it is time to embrace the arrival of TCM OMICS era in Chinese medicine research.

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10

KOLCH, Walter, Harald MISCHAK, and Andrew R. PITT. "The molecular make-up of a tumour: proteomics in cancer research." Clinical Science 108, no. 5 (April 22, 2005): 369–83. http://dx.doi.org/10.1042/cs20050006.

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The enormous progress in proteomics, enabled by recent advances in MS (mass spectrometry), has brought protein analysis back into the limelight of cancer research, reviving old areas as well as opening new fields of study. In this review, we discuss the basic features of proteomic technologies, including the basics of MS, and we consider the main current applications and challenges of proteomics in cancer research, including (i) protein expression profiling of tumours, tumour fluids and tumour cells; (ii) protein microarrays; (iii) mapping of cancer signalling pathways; (iv) pharmacoproteomics; (v) biomarkers for diagnosis, staging and monitoring of the disease and therapeutic response; and (vi) the immune response to cancer. All these applications continue to benefit from further technological advances, such as the development of quantitative proteomics methods, high-resolution, high-speed and high-sensitivity MS, functional protein assays, and advanced bioinformatics for data handling and interpretation. A major challenge will be the integration of proteomics with genomics and metabolomics data and their functional interpretation in conjunction with clinical results and epidemiology.

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11

Wu, W., W. Hu, and J. J. Kavanagh. "Proteomics in cancer research." International Journal of Gynecologic Cancer 12, no. 5 (2002): 409–23. http://dx.doi.org/10.1136/ijgc-00009577-200209000-00001.

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With the human genome sequence now determined, the field of molecular medicine is moving beyond genomics to proteomics. In the field of cancer research, the key question is: how can oncologists best use techniques of proteomics in basic research and clinical application? In the postgenomic era, proteomics promises the discovery of biomarkers and tumor markers for early detection and diagnosis, novel protein-based drug targets for anticancer therapy, and new endpoints for the assessment of therapeutic efficacy and toxicity. This review paper will explore key themes in proteomics and their application in clinical cancer research.

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12

Jiang, Will, Jennifer C. Jones, Uma Shankavaram, Mary Sproull, Kevin Camphausen, and Andra V. Krauze. "Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications." Cancers 14, no. 9 (April 29, 2022): 2227. http://dx.doi.org/10.3390/cancers14092227.

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The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

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13

Pathade, Parag A., Vinod A. Bairagi, Yogesh S. Ahire, and Neela M. Bhatia. "Proteomics: Opportunities and Challenges." International Journal of Pharmaceutical Sciences and Nanotechnology 3, no. 4 (February 28, 2011): 1165–72. http://dx.doi.org/10.37285/ijpsn.2010.3.4.1.

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‘‘Proteomics’’, is the emerging technology leading to high-throughput identification and understanding of proteins. Proteomics is the protein equivalent of genomics and has captured the imagination of biomolecular scientists, worldwide. Because proteome reveals more accurately the dynamic state of a cell, tissue, or organism, much is expected from proteomics to indicate better disease markers for diagnosis and therapy monitoring. Proteomics is expected to play a major role in biomedical research, and it will have a significant impact on the development of diagnostics and therapeutics for cancer, heart ailments and infectious diseases, in future. Proteomics research leads to the identification of new protein markers for diagnostic purposes and novel molecular targets for drug discovery. Though the potential is great, many challenges and issues remain to be solved, such as gene expression, peptides, generation of low abundant proteins, analytical tools, drug target discovery and cost. A systematic and efficient analysis of vast genomic and proteomic data sets is a major challenge for researchers, today. Nevertheless, proteomics is the groundwork for constructing and extracting useful comprehension to biomedical research. This review article covers some opportunities and challenges offered by proteomics.

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14

Zhang, Hengwei, Robert Recker, Wai-Nang Paul Lee, and Gary Guishan Xiao. "Proteomics in bone research." Expert Review of Proteomics 7, no. 1 (February 2010): 103–11. http://dx.doi.org/10.1586/epr.09.90.

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15

Wu, W., W. Hu, and J. J. Kavanagh. "Proteomics in cancer research." International Journal of Gynecological Cancer 12, no. 5 (September 2002): 409–23. http://dx.doi.org/10.1046/j.1525-1438.2002.01200.x.

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16

Savage, Neil. "Proteomics: High-protein research." Nature 527, no. 7576 (November 2015): S6—S7. http://dx.doi.org/10.1038/527s6a.

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17

SUNDSTEN, T., and H. ORTSATER. "Proteomics in diabetes research." Molecular and Cellular Endocrinology 297, no. 1-2 (January 15, 2009): 93–103. http://dx.doi.org/10.1016/j.mce.2008.06.018.

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18

SCHONEICH, C. "Proteomics in gerontological research." Experimental Gerontology 38, no. 5 (May 2003): 473–81. http://dx.doi.org/10.1016/s0531-5565(03)00035-4.

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19

Lim, Megan S., and Kojo S. J. Elenitoba-Johnson. "Proteomics in pathology research." Laboratory Investigation 84, no. 10 (August 16, 2004): 1227–44. http://dx.doi.org/10.1038/labinvest.3700167.

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20

Fontana, Simona, Giacomo De Leo, Mirela Sedic, Sandra Kraljevic Pavelic, and Riccardo Alessandro. "Proteomics in antitumor research." Drug Discovery Today: Technologies 3, no. 4 (December 2006): 441–49. http://dx.doi.org/10.1016/j.ddtec.2006.11.002.

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21

Barceló-Batllori, Sílvia, and Ramon Gomis. "Proteomics in obesity research." PROTEOMICS - CLINICAL APPLICATIONS 3, no. 2 (February 2009): 263–78. http://dx.doi.org/10.1002/prca.200800178.

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22

Schulze, W. "Environmental proteomics – what proteins from soil and surface water can tell us: a perspective." Biogeosciences Discussions 1, no. 1 (July 22, 2004): 195–218. http://dx.doi.org/10.5194/bgd-1-195-2004.

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Abstract. Mass spectrometry based proteomics is widely used to study cellular processes in model organisms. However, it has not much been applied in environmental research because it was thought that free proteins would not be sufficiently stable in the environments. Based on recent observations that protein can readily be detected as a component of dissolve organic carbon, this article gives an overview about the possible use of proteomic methods in ecology and environmental sciences. At this stage, there are two areas of interest: (1) the identification of phylogenetic groups contributing to the DOC pool, and (2) identification of the origin of specific enzymes that are important for ecosystem processes. In this paper methods of mass spectrometry based proteomics were applied to identify proteins from DOC and water samples from different environments. It is demonstrated, that environmental proteomics is capable to distinguish the active set of organisms of different horizons of soils, and from various sources of surface water. Currently the limitation is given by the present knowledge of the genome of soil organisms. In addition, environmental proteomics allows to relate protein presence to biogeochemical processes, and to identify the source organisms for specific enzymes. Taking laccases as an example, it is shown that this enzyme is excreted into soils by a whole range of organisms from different phylogenetic groups. Further applications, such as in pollution reseach are conceivable. In summary, environmental proteomcis opens a new area of research between the fields of microbiology and biogeochemistry.

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23

Zhou, An. "Proteomics in stroke research: potentials of the nascent proteomics." Journal of Investigative Medicine 64, no. 8 (July 18, 2016): 1236–40. http://dx.doi.org/10.1136/jim-2016-000186.

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Among omics, the proteomics assumes a unique role in that it offers the effectors or actuators of a biological condition. This brief review attempts to summarize the development in a relatively new but important subdiscipline of proteomics, the so-called nascent proteomics, and its potential applications in stroke research. First, we will discuss a few examples of proteomics-led discoveries in stroke research, and challenges or unmet demands when using commonly practiced proteomics approaches. Then we will introduce nascent proteomics and its studying tools, followed by discussions on its potentials in stroke research.

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Bhawal, Ruchika, Ann L. Oberg, Sheng Zhang, and Manish Kohli. "Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer." Cancers 12, no. 9 (August 27, 2020): 2428. http://dx.doi.org/10.3390/cancers12092428.

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Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

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Thelen, Jay J., and Ján A. Miernyk. "The proteomic future: where mass spectrometry should be taking us." Biochemical Journal 444, no. 2 (May 11, 2012): 169–81. http://dx.doi.org/10.1042/bj20110363.

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A newcomer to the -omics era, proteomics, is a broad instrument-intensive research area that has advanced rapidly since its inception less than 20 years ago. Although the ‘wet-bench’ aspects of proteomics have undergone a renaissance with the improvement in protein and peptide separation techniques, including various improvements in two-dimensional gel electrophoresis and gel-free or off-gel protein focusing, it has been the seminal advances in MS that have led to the ascension of this field. Recent improvements in sensitivity, mass accuracy and fragmentation have led to achievements previously only dreamed of, including whole-proteome identification, and quantification and extensive mapping of specific PTMs (post-translational modifications). With such capabilities at present, one might conclude that proteomics has already reached its zenith; however, ‘capability’ indicates that the envisioned goals have not yet been achieved. In the present review we focus on what we perceive as the areas requiring more attention to achieve the improvements in workflow and instrumentation that will bridge the gap between capability and achievement for at least most proteomes and PTMs. Additionally, it is essential that we extend our ability to understand protein structures, interactions and localizations. Towards these ends, we briefly focus on selected methods and research areas where we anticipate the next wave of proteomic advances.

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Plymoth, Amelie, and Pierre Hainaut. "Proteomics beyond proteomics: toward clinical applications." Current Opinion in Oncology 23, no. 1 (January 2011): 77–82. http://dx.doi.org/10.1097/cco.0b013e32834179c1.

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Brewis, Ian A. "Proteomics in reproductive research: The potential importance of proteomics to research in reproduction." Human Reproduction 14, no. 12 (December 1999): 2927–29. http://dx.doi.org/10.1093/humrep/14.12.2927.

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Kline, Rachel A., Lena Lößlein, Dominic Kurian, Judit Aguilar Martí, Samantha L. Eaton, Felipe A. Court, Thomas H. Gillingwater, and Thomas M. Wishart. "An Optimized Comparative Proteomic Approach as a Tool in Neurodegenerative Disease Research." Cells 11, no. 17 (August 26, 2022): 2653. http://dx.doi.org/10.3390/cells11172653.

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Recent advances in proteomic technologies now allow unparalleled assessment of the molecular composition of a wide range of sample types. However, the application of such technologies and techniques should not be undertaken lightly. Here, we describe why the design of a proteomics experiment itself is only the first step in yielding high-quality, translatable results. Indeed, the effectiveness and/or impact of the majority of contemporary proteomics screens are hindered not by commonly considered technical limitations such as low proteome coverage but rather by insufficient analyses. Proteomic experimentation requires a careful methodological selection to account for variables from sample collection, through to database searches for peptide identification to standardised post-mass spectrometry options directed analysis workflow, which should be adjusted for each study, from determining when and how to filter proteomic data to choosing holistic versus trend-wise analyses for biologically relevant patterns. Finally, we highlight and discuss the difficulties inherent in the modelling and study of the majority of progressive neurodegenerative conditions. We provide evidence (in the context of neurodegenerative research) for the benefit of undertaking a comparative approach through the application of the above considerations in the alignment of publicly available pre-existing data sets to identify potential novel regulators of neuronal stability.

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Wareth, Gamal, Mathias W. Pletz, Heinrich Neubauer, and Jayaseelan Murugaiyan. "Proteomics of Brucella: Technologies and Their Applications for Basic Research and Medical Microbiology." Microorganisms 8, no. 5 (May 20, 2020): 766. http://dx.doi.org/10.3390/microorganisms8050766.

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Brucellosis is a global zoonosis caused by Gram-negative, facultative intracellular bacteria of the genus Brucella (B.). Proteomics has been used to investigate a few B. melitensis and B. abortus strains, but data for other species and biovars are limited. Hence, a comprehensive analysis of proteomes will significantly contribute to understanding the enigmatic biology of brucellae. For direct identification and typing of Brucella, matrix-assisted laser desorption ionization—time of flight mass spectrometry (MALDI—TOF MS) has become a reliable tool for routine diagnosis due to its ease of handling, price and sensitivity highlighting the potential of proteome-based techniques. Proteome analysis will also help to overcome the historic but still notorious Brucella obstacles of infection medicine, the lack of safe and protective vaccines and sensitive serologic diagnostic tools by identifying the most efficient protein antigens. This perspective summarizes past and recent developments in Brucella proteomics with a focus on species identification and serodiagnosis. Future applications of proteomics in these fields are discussed.

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Le, Vu Anh, Cam Quyen Thi Phan, and Thuy Huong Nguyen. "Data mining in mass spectrometry-based proteomics studies." Science & Technology Development Journal - Engineering and Technology 2, no. 4 (March 24, 2020): 258–76. http://dx.doi.org/10.32508/stdjet.v2i4.483.

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The post-genomic era consists of experimental and computational efforts to meet the challenge of clarifying and understanding the function of genes and their products. Proteomic studies play a key role in this endeavour by complementing other functional genomics approaches, encompasses the large-scale analysis of complex mixtures, including the identification and quantification of proteins expressed under different conditions, the determination of their properties, modifications and functions. Understanding how biological processes are regulated at the protein level is crucial to understanding the molecular basis of diseases and often highlights the prevention, diagnosis and treatment of diseases. High-throughput technologies are widely used in proteomics to perform the analysis of thousands of proteins. Specifically, mass spectrometry (MS) is an analytical technique for characterizing biological samples and is increasingly used in protein studies because of its targeted, nontargeted, and high performance abilities. However, as large data sets are created, computational methods such as data mining techniques are required to analyze and interpret the relevant data. More specifically, the application of data mining techniques in large proteomic data sets can assist in many interpretations of data; it can reveal protein-protein interactions, improve protein identification, evaluate the experimental methods used and facilitate the diagnosis and biomarker discovery. With the rapid advances in mass spectrometry devices and experimental methodologies, MS-based proteomics has become a reliable and necessary tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed a great expansion of our knowledge of human diseases with the adoption of proteomic technologies based on MS, which leads to many interesting discoveries. Here, we review recent advances of data mining in MS-based proteomics in biomedical research. Recent research in many fields shows that proteomics goes beyond the simple classification of proteins in biological systems and finally reaches its initial potential – as an essential tool to aid related disciplines, notably biomedical research. From here, there is great potential for data mining in MS-based proteomics to move beyond basic research, into clinical research and diagnostics.

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Lopez, Elena, Luis Madero, Gustavo Melen, Manuel Ramirez, Dimitrios Roukos, and William Cho. "Clinical Proteomics in Cancer Research." Current Proteomics 10, no. 2 (August 1, 2013): 179–91. http://dx.doi.org/10.2174/15701646112099990001.

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Park, Yune-Jung, Min Kyung Chung, Daehee Hwang, and Wan-Uk Kim. "Proteomics in Rheumatoid Arthritis Research." Immune Network 15, no. 4 (2015): 177. http://dx.doi.org/10.4110/in.2015.15.4.177.

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ZHOU, Ye, Zheyi LIU, and Fangjun WANG. "Progress in structural proteomics research." Chinese Journal of Chromatography 37, no. 8 (2019): 788. http://dx.doi.org/10.3724/sp.j.1123.2019.04015.

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Takao, Toshifumi. "Mass Spectrometry and Proteomics Research." TRENDS IN THE SCIENCES 8, no. 2 (2003): 60–61. http://dx.doi.org/10.5363/tits.8.2_60.

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Cottingham, Katie. "NIAID Biodefense Proteomics Research Centers." Journal of Proteome Research 7, no. 9 (September 5, 2008): 3641. http://dx.doi.org/10.1021/pr8005809.

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Niederberger, Ellen, Gerd Geisslinger, David S. Warner, and Mark A. Warner. "Proteomics in Neuropathic Pain Research." Anesthesiology 108, no. 2 (February 1, 2008): 314–23. http://dx.doi.org/10.1097/01.anes.0000299838.13368.6e.

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Neuropathic pain is often caused by nerve injury or dysfunction in the peripheral and central nervous system and is frequently associated with allodynia and hyperalgesia. The underlying molecular mechanisms of neuropathic pain are largely unknown, and therefore, pharmacologic treatment is insufficient in many cases. To elucidate translational and posttranslational modifications in the nervous system that arise after nerve injury, a number of proteomic studies have been performed using different animal neuropathy models. The results of these proteomic approaches are summarized in this review to provide a better overview of proteins that are involved into the pathogenesis of nerve injury and neuropathic pain. This might allow a better understanding of the pathophysiologic signaling pathways in this impairment, facilitate the discovery of specific biomarkers, and thus promote the development of novel pain therapies.

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37

Zerkowski, Hans-Reinhard, Thomas Grussenmeyer, Peter Matt, Martin Grapow, Stefan Engelhardt, and Ivan Lefkovits. "Proteomics Strategies in Cardiovascular Research." Journal of Proteome Research 3, no. 2 (April 2004): 200–208. http://dx.doi.org/10.1021/pr034079t.

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38

Cottingham, Katie. "Research Profile: Mouse serum proteomics." Journal of Proteome Research 4, no. 5 (October 2005): 1481. http://dx.doi.org/10.1021/pr050525w.

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Cottingham, Katie. "Research Profile: Proteomics and behavior." Journal of Proteome Research 5, no. 2 (February 2006): 229. http://dx.doi.org/10.1021/pr062693+.

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40

Cottingham, Katie. "Research Profile: Proteomics of starvation." Journal of Proteome Research 5, no. 9 (September 2006): 2075. http://dx.doi.org/10.1021/pr0627544.

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41

Dove, Alan. "Proteomics: Basic research climbs aboard." Nature Medicine 7, no. 9 (September 2001): 984. http://dx.doi.org/10.1038/nm0901-984a.

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42

Lawrie, Laura C., and Graeme I. Murray. "Proteomics in Urological Cancer Research." UroOncology 2, no. 4 (January 2002): 163–66. http://dx.doi.org/10.1080/1561095021000089382.

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43

Fink-Retter, A., D. Gschwantler-Kaulich, C. Singer, G. Hudelist, K. Pischinger, M. Manavi, and K. Czerwenka. "Proteomics in mammary cancer research." Journal of Clinical Oncology 26, no. 15_suppl (May 20, 2008): 22206. http://dx.doi.org/10.1200/jco.2008.26.15_suppl.22206.

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44

Gafni, A. "Proteomics in Aging-Related Research." Science of Aging Knowledge Environment 2004, no. 45 (November 10, 2004): pe41. http://dx.doi.org/10.1126/sageke.2004.45.pe41.

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45

Krause, Kerstin, Beate Jeßnitzer, and Dagmar Fuhrer. "Proteomics in Thyroid Tumor Research." Journal of Clinical Endocrinology & Metabolism 94, no. 8 (August 1, 2009): 2717–24. http://dx.doi.org/10.1210/jc.2009-0308.

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46

Fink-Retter, A., D. Gschwantler-Kaulich, G. Hudelist, C. F. Singer, K. Pischinger, M. Manavi, and K. Czerwenka. "Proteomics in mammary cancer research." European Journal of Cancer Supplements 6, no. 7 (April 2008): 79. http://dx.doi.org/10.1016/s1359-6349(08)70420-8.

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47

Wilm, Matthias. "Quantitative proteomics in biological research." PROTEOMICS 9, no. 20 (October 2009): 4590–605. http://dx.doi.org/10.1002/pmic.200900299.

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Cecconi, Daniela, Marta Palmieri, and Massimo Donadelli. "Proteomics in pancreatic cancer research." PROTEOMICS 11, no. 4 (January 13, 2011): 816–28. http://dx.doi.org/10.1002/pmic.201000401.

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49

Zhang, Aihua, Hui Sun, Ping Wang, and Xijun Wang. "Salivary proteomics in biomedical research." Clinica Chimica Acta 415 (January 2013): 261–65. http://dx.doi.org/10.1016/j.cca.2012.11.001.

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50

Pastwa, Elzbieta, Stella B. Somiari, Malgorzata Czyz, and Richard Idem Somiari. "Proteomics in human cancer research." PROTEOMICS – Clinical Applications 1, no. 1 (January 2007): 4–17. http://dx.doi.org/10.1002/prca.200600369.

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