Journal articles: 'Biomarker discovery research'

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Chen, Jiwen, and Naiyu Zheng. "Accelerating protein biomarker discovery and translation from proteomics research for clinical utility." Bioanalysis 12, no. 20 (October 2020): 1469–81. http://dx.doi.org/10.4155/bio-2020-0198.

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Discovery proteomics research has made significant progress in the past several years; however, the number of protein biomarkers deployed in clinical practice remains rather limited. There are several scientific and procedural gaps between discovery proteomics research and clinical implementation, which have contributed to poor biomarker validity and few clinical applications. The complexity and low throughput of proteomics approaches have added additional barriers for biomarker assay translation to clinical applications. Recently, targeted proteomics have become a powerful tool to bridge the biomarker discovery to clinical validation. In this perspective, we discuss the challenges and strategies in proteomics research from a clinical perspective, and propose several recommendations for discovery proteomics research to accelerate protein biomarker discovery and translation for future clinical applications.

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McNamara, Aoife E., and Lorraine Brennan. "Potential of food intake biomarkers in nutrition research." Proceedings of the Nutrition Society 79, no. 4 (July 2, 2020): 487–97. http://dx.doi.org/10.1017/s0029665120007053.

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The influence of dietary habits on health/disease is well-established. Accurate dietary assessment is essential to understand metabolic pathways/processes involved in this relationship. In recent years, biomarker discovery has become a major area of interest for improving dietary assessment. Well-established nutrient intake biomarkers exist; however, there is growing interest in identifying and using biomarkers for more accurate and objective measurements of food intake. Metabolomics has emerged as a key tool used for biomarker discovery, employing techniques such as NMR spectroscopy, or MS. To date, a number of putatively identified biomarkers were discovered for foods including meat, cruciferous vegetables and legumes. However, many of the results are associations only and lack the desired validation including dose–response studies. Food intake biomarkers can be employed to classify individuals into consumers/non-consumers of specific foods, or into dietary patterns. Food intake biomarkers can also play a role in correcting self-reported measurement error, thus improving dietary intake estimates. Quantification of food intake was previously performed for citrus (proline betaine), chicken (guanidoacetate) and grape (tartaric acid) intake. However, this area still requires more investigation and expansion to a range of foods. The present review will assess the current literature of identified specific food intake biomarkers, their validation and the variety of biomarker uses. Addressing the utility of biomarkers and highlighting gaps in this area is important to advance the field in the context of nutrition research.

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Cai, Yanning, and Qian Dong. "Metabonomics research accelerates discovery of medical biomarkers." E3S Web of Conferences 245 (2021): 03048. http://dx.doi.org/10.1051/e3sconf/202124503048.

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Biomarker refers to a characteristic that can be objectively detected and evaluated, and can be used as an indicator of normal biological process, pathological process or therapeutic intervention pharmacological response. As one of the key words of individualized medicine, the search and discovery of valuable biomarkers has become a research hotspot in the current medical field.

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Zhao, Xuemei, Vijay Modur, Leonidas N. Carayannopoulos, and Omar F. Laterza. "Biomarkers in Pharmaceutical Research." Clinical Chemistry 61, no. 11 (November 1, 2015): 1343–53. http://dx.doi.org/10.1373/clinchem.2014.231712.

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Abstract BACKGROUND Biomarkers are important tools in drug development and are used throughout pharmaceutical research. CONTENT This review focuses on molecular biomarkers in drug development. It contains sections on how biomarkers are used to assess target engagement, pharmacodynamics, safety, and proof-of-concept. It also covers the use of biomarkers as surrogate end points and patient selection/companion diagnostics and provides insights into clinical biomarker discovery and biomarker development/validation with regulatory implications. To survey biomarkers used in drug development—acknowledging that many pharmaceutical development biomarkers are not published—we performed a focused PubMed search employing “biomarker” and the names of the largest pharmaceutical companies as keywords and filtering on clinical trials and publications in the last 10 years. This yielded almost 500 entries, the majority of which included disease-related (approximately 60%) or prognostic/predictive (approximately 20%) biomarkers. A notable portion (approximately 8%) included HER2 (human epidermal growth factor receptor 2) testing, highlighting the utility of biomarkers for patient selection. The remaining publications included target engagement, safety, and drug metabolism biomarkers. Oncology, cardiovascular disease, and osteoporosis were the areas with the most citations, followed by diabetes and Alzheimer disease. SUMMARY Judicious biomarker use can improve pharmaceutical development efficiency by helping to select patients most appropriate for treatment using a given mechanism, optimize dose selection, and provide earlier confidence in accelerating or discontinuing compounds in clinical development. Optimal application of biomarker technology requires understanding of candidate drug pharmacology, detailed modeling of biomarker readouts relative to pharmacokinetics, rigorous validation and qualification of biomarker assays, and creative application of these elements to drug development problems.

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Dean, Brian. "Dissecting the Syndrome of Schizophrenia: Progress toward Clinically Useful Biomarkers." Schizophrenia Research and Treatment 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/614730.

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The search for clinically useful biomarkers has been one of the holy grails of schizophrenia research. This paper will outline the evolving notion of biomarkers and then outline outcomes from a variety of biomarkers discovery strategies. In particular, the impact of high-throughput screening technologies on biomarker discovery will be highlighted and how new or improved technologies may allow the discovery of either diagnostic biomarkers for schizophrenia or biomarkers that will be useful in determining appropriate treatments for people with the disorder. History tells those involved in biomarker research that the discovery and validation of useful biomarkers is a long process and current progress must always be viewed in that light. However, the approval of the first biomarker screen with some value in predicting responsiveness to antipsychotic drugs suggests that biomarkers can be identified and that these biomarkers that will be useful in diagnosing and treating people with schizophrenia.

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Li, Rebecca, and Ida Sim. "How Clinical Trial Data Sharing Platforms Can Advance the Study of Biomarkers." Journal of Law, Medicine & Ethics 47, no. 3 (2019): 369–73. http://dx.doi.org/10.1177/1073110519876165.

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Although data sharing platforms host diverse data types the features of these platforms are well-suited to facilitating biomarker research. Given the current state of biomarker discovery, an innovative paradigm to accelerate biomarker discovery is to utilize platforms such as Vivli to leverage researchers' abilities to integrate certain classes of biomarkers.

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Zhang, Aihua, Hui Sun, Guangli Yan, Ping Wang, and Xijun Wang. "Metabolomics for Biomarker Discovery: Moving to the Clinic." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/354671.

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To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases.

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Njoku, Kelechi, Davide Chiasserini, Anthony D. Whetton, and Emma J. Crosbie. "Proteomic Biomarkers for the Detection of Endometrial Cancer." Cancers 11, no. 10 (October 16, 2019): 1572. http://dx.doi.org/10.3390/cancers11101572.

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Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.

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Liu, Qiang, Jianxin Guo, Jinghong Cui, Jing Wang, and Ping Yi. "Discover the Molecular Biomarker Associated with Cell Death and Extracellular Matrix Module in Ovarian Cancer." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/735689.

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High throughput technologies have provided many new research methods for ovarian cancer investigation. In tradition, in order to find the underlying functional mechanisms of the survival-associated genes, gene sets enrichment analysis (GSEA) is always regarded as the important choice. However, GSEA produces too many candidate genes and cannot discover the signaling transduction cascades. In this work, we have used a network-based strategy to optimize the discovery of biomarkers using multifactorial data, including patient expression, clinical survival, and protein-protein interaction (PPI) data. The biomarkers discovered by this strategy belong to the network-based biomarker, which is apt to reveal the underlying functional mechanisms of the biomarker. In this work, over 400 expression arrays in ovarian cancer have been analyzed: the results showed that cell death and extracellular module are the main themes related to ovarian cancer progression.

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Majkić-Singh, Nada. "What is a Biomarker? From its Discovery to Clinical Application." Journal of Medical Biochemistry 30, no. 3 (July 1, 2011): 186–92. http://dx.doi.org/10.2478/v10011-011-0029-z.

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What is a Biomarker? From its Discovery to Clinical ApplicationThe term biomarker in medicine most often stands for a protein measured in the circulation (blood) whose concentration indicates a normal or a pathological response of the organism, as well as a pharmacological response to the applied therapy. From a wider perspective, a biomarker is any indicator that is used as an index of the intensity of a disease or other physiological state in the organism. This means that biomarkers have a very important role in medical research and practice providing insight into the mechanism and course of a disease. Since a large number of biomarkers exist today that are used for different purposes, they have been classified into: 1) antecedent biomarkers, indicating risk of disease occurrence, 2) screening biomarkers, used to determine a subclinical form of disease, 3) diagnostic biomarkers, revealing an existing disease, 4) staging biomarkers, that define the stage and severity of a disease, and 5) prognostic biomarkers, that confirm the course of disease, including treatment response. Regardless of their role, their clinical significance depends on their sensitivity, specificity, predictive value, and also precision, reliability, reproducibility, and the possibility of easy and wide application. For a biomarker to become successful, it must undergo the process of validation, depending on the level of use. It is very important for every suggested biomarker, according to its purpose or its nature, to possess certain characteristics and to meet the strict requirements related to sensitivity, accuracy and precision, in order for the proper outcome to be produced in the estimation of the state for which it is intended. Finally, the development of guidelines for biomarker application is very important, based on well defined and properly conducted assessments of biomarker determination, providing the means by which research is translated into practice and allowing evidence based on facts to promote the clinical application of new biomarkers.

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Chancellor, Michael B., Sarah N. Bartolone, Andrew Veerecke, and Laura E. Lamb. "Crowdsourcing Disease Biomarker Discovery Research: The IP4IC Study." Journal of Urology 199, no. 5 (May 2018): 1344–50. http://dx.doi.org/10.1016/j.juro.2017.09.167.

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Lam, Maggie P. Y., Peipei Ping, and Elizabeth Murphy. "Proteomics Research in Cardiovascular Medicine and Biomarker Discovery." Journal of the American College of Cardiology 68, no. 25 (December 2016): 2819–30. http://dx.doi.org/10.1016/j.jacc.2016.10.031.

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Kim, Jayoung, Won Tae Kim, and Wun-Jae Kim. "Advances in urinary biomarker discovery in urological research." Investigative and Clinical Urology 61, Suppl 1 (2020): S8. http://dx.doi.org/10.4111/icu.2020.61.s1.s8.

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Vazquez, Joel H., and Mitchell R. McGill. "Redrawing the Map to Novel DILI Biomarkers in Circulation: Where Are We, Where Should We Go, and How Can We Get There?" Livers 1, no. 4 (December 1, 2021): 286–93. http://dx.doi.org/10.3390/livers1040022.

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Circulating biomarkers of drug-induced liver injury (DILI) have been a focus of research in hepatology over the last decade, and several novel DILI biomarkers that hold promise for certain applications have been identified. For example, glutamate dehydrogenase holds promise as a specific biomarker of liver injury in patients with concomitant muscle damage. It may also be a specific indicator of mitochondrial damage. In addition, microRNA-122 is sensitive for early detection of liver injury in acetaminophen overdose patients. However, recent events in the field of DILI biomarker research have provided us with an opportunity to step back, consider how biomarker discovery has been done thus far, and determine how to move forward in a way that will optimize the discovery process. This is important because major challenges remain in the DILI field and related areas that could be overcome in part by new biomarkers. In this short review, we briefly describe recent progress in DILI biomarker discovery and development, identify current needs, and suggest a general approach to move forward.

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Wilson, Jennifer L., and Russ B. Altman. "Biomarkers: Delivering on the expectation of molecularly driven, quantitative health." Experimental Biology and Medicine 243, no. 3 (December 3, 2017): 313–22. http://dx.doi.org/10.1177/1535370217744775.

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Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers’ impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.

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Ioannidis, John P. A., and Patrick M. M. Bossuyt. "Waste, Leaks, and Failures in the Biomarker Pipeline." Clinical Chemistry 63, no. 5 (May 1, 2017): 963–72. http://dx.doi.org/10.1373/clinchem.2016.254649.

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Abstract BACKGROUND The large, expanding literature on biomarkers is characterized by almost ubiquitous significant results, with claims about the potential importance, but few of these discovered biomarkers are used in routine clinical care. CONTENT The pipeline of biomarker development includes several specific stages: discovery, validation, clinical translation, evaluation, implementation (and, in the case of nonutility, deimplementation). Each of these stages can be plagued by problems that cause failures of the overall pipeline. Some problems are nonspecific challenges for all biomedical investigation, while others are specific to the peculiarities of biomarker research. Discovery suffers from poor methods and incomplete and selective reporting. External independent validation is limited. Selection for clinical translation is often shaped by nonrational choices. Evaluation is sparse and the clinical utility of many biomarkers remains unknown. The regulatory environment for biomarkers remains weak and guidelines can reach biased or divergent recommendations. Removing inefficient or even harmful biomarkers that have been entrenched in clinical care can meet with major resistance. SUMMARY The current biomarker pipeline is too prone to failures. Consideration of clinical needs should become a starting point for the development of biomarkers. Improvements can include the use of more stringent methodology, better reporting, larger collaborative studies, careful external independent validation, preregistration, rigorous systematic reviews and umbrella reviews, pivotal randomized trials, and implementation and deimplementation studies. Incentives should be aligned toward delivering useful biomarkers.

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Schrader, Michael, and Hartmut Selle. "The Process Chain for Peptidomic Biomarker Discovery." Disease Markers 22, no. 1-2 (2006): 27–37. http://dx.doi.org/10.1155/2006/174849.

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Over the last few years the interest in diagnostic markers for specific diseases has increased continuously. It is expected that they not only improve a patient's medical treatment but also contribute to accelerating the process of drug development. This demand for new biomarkers is caused by a lack of specific and sensitive diagnosis in many diseases. Moreover, diseases usually occur in different types or stages which may need different diagnostic and therapeutic measures. Their differentiation has to be considered in clinical studies as well. Therefore, it is important to translate a macroscopic pathological or physiological finding into a microscopic view of molecular processes and vice versa, though it is a difficult and tedious task. Peptides play a central role in many physiological processes and are of importance in several areas of drug research. Exploration of endogenous peptides in biologically relevant sources may directly lead to new drug substances, serve as key information on a new target and can as well result in relevant biomarker candidates. A comprehensive analysis of peptides and small proteins of a biological system corresponding to the respective genomic information (peptidomics®methods) was a missing link in proteomics. A new peptidomic technology platform addressing peptides was recently presented, developed by adaptation of the striving proteomic technologies. Here, concepts of using peptidomics technologies for biomarker discovery are presented and illustrated with examples. It is discussed how the biological hypothesis and sample quality determine the result of the study. A detailed study design, appropriate choice and application of technology as well as thorough data interpretation can lead to significant results which have to be interpreted in the context of the underlying disease. The identified biomarker candidates will be characterised in validation studies before use. This approach for discovery of peptide biomarkes has potential for improving clinical studies.

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Zheng, Yingye. "Study Design Considerations for Cancer Biomarker Discoveries." Journal of Applied Laboratory Medicine 3, no. 2 (September 1, 2018): 282–89. http://dx.doi.org/10.1373/jalm.2017.025809.

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Abstract Background Biomarker discovery studies have generated an array of omic data; however, few novel biomarkers have reached clinical use. Guidelines for rigorous study designs are needed. Content Biases frequently occur during sample selection, outcome ascertainment, or unblinded sample handling and the assaying process. The principles of a prospective specimen collection and retrospective blinded evaluation design can be adapted to mitigate various sources of biases in discovery. We recommend establishing quality biospecimen repositories using matched 2-phase designs to minimize biases and maximize efficiency. We also highlight the importance of taking the clinical context into consideration in both sample selection and power calculation for discovery studies. Summary Biomarker discovery research should follow rigorous design principles in sample selection to avoid biases. Consideration of clinical application and the corresponding biomarker performance characteristics in study designs will lead to a more fruitful discovery study.

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Hu, Shen, Martha Arellano, Pinmanee Boontheung, Jianghua Wang, Hui Zhou, Jiang Jiang, David Elashoff, Roger Wei, Joseph A. Loo, and David T. Wong. "Salivary Proteomics for Oral Cancer Biomarker Discovery." Clinical Cancer Research 14, no. 19 (September 30, 2008): 6246–52. http://dx.doi.org/10.1158/1078-0432.ccr-07-5037.

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Zheng, Yaxin, Ye Xu, Bin Ye, Junyi Lei, Michael H. Weinstein, Michael P. O'Leary, Jerome P. Richie, Samuel C. Mok, and Brian C. S. Liu. "Prostate carcinoma tissue proteomics for biomarker discovery." Cancer 98, no. 12 (December 4, 2003): 2576–82. http://dx.doi.org/10.1002/cncr.11849.

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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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Torres, Rodrigo, and Robert L. Judson-Torres. "Research Techniques Made Simple: Feature Selection for Biomarker Discovery." Journal of Investigative Dermatology 139, no. 10 (October 2019): 2068–74. http://dx.doi.org/10.1016/j.jid.2019.07.682.

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Wagner, John A. "Overview of Biomarkers and Surrogate Endpoints in Drug Development." Disease Markers 18, no. 2 (2002): 41–46. http://dx.doi.org/10.1155/2002/929274.

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There are numerous factors that recommend the use of biomarkers in drug development including the ability to provide a rational basis for selection of lead compounds, as an aid in determining or refining mechanism of action or pathophysiology, and the ability to work towards qualification and use of a biomarker as a surrogate endpoint. Examples of biomarkers come from many different means of clinical and laboratory measurement. Total cholesterol is an example of a clinically useful biomarker that was successfully qualified for use as a surrogate endpoint. Biomarkers require validation in most circumstances. Validation of biomarker assays is a necessary component to delivery of high-quality research data necessary for effective use of biomarkers. Qualification is necessary for use of a biomarker as a surrogate endpoint. Putative biomarkers are typically identified because of a relationship to known or hypothetical steps in a pathophysiologic cascade. Biomarker discovery can also be effected by expression profiling experiment using a variety of array technologies and related methods. For example, expression profiling experiments enabled the discovery of adipocyte related complement protein of 30 kD (Acrp30 or adiponectin) as a biomarker forin vivoactivation of peroxisome proliferator-activated receptors (PPAR)γactivity.

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Huang, Ruo-Pan, Yingqing Mao, Ruochun Huang, Haw-Han Yen, and Yanni Sun. "Quantification of 500 human protein biomarkers using antibody arrays and its applications in immunology research (TECH2P.921)." Journal of Immunology 194, no. 1_Supplement (May 1, 2015): 206.31. http://dx.doi.org/10.4049/jimmunol.194.supp.206.31.

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Abstract Proteomic approach continues to be the trend for biomarker discovery through profiling as many proteins as possible in a biological sample. Though LC-MS technology can accurately detect medium-to-high abundance proteins (> ng/ml), many cellular modulators such as cytokines are present at pg/ml level. Immunoassays continue to be the most utilized tools for biomarker discovery and validation due to their convenience, sensitivity and specificity. Here we validated the sandwich detection of 500 most analyzed human protein biomarkers with a pair of antigen-specific antibodies. Subsequently, capture antibodies were fixed on glass slides with non-contact arrayer, and multiplex ELISA detection of these protein biomarkers was achieved through planar antibody array technology with laser fluorescence detection. To overcome the potential cross reactivity among different reagents, multiple non-overlapping arrays were developed based upon their cellular abundance, functionality, and cross reactivity. Most of the markers can achieve pg/ml detection - similar to single ELISA detection but with much wider dynamic range. These arrays were further validated with normal human sera, where over two thirds of these proteins are within the detection range. Separate array panels were developed for those proteins with high abundance in sera, where high sample dilutions are needed. In summary, our system provides a powerful, efficient, and low-cost tool for immunology research and biomarker discovery.

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Lu, Meixia, Jinxiang Zhang, and Lanjing Zhang. "Emerging Concepts and Methodologies in Cancer Biomarker Discovery." Critical Reviews™ in Oncogenesis 22, no. 5-6 (2017): 371–88. http://dx.doi.org/10.1615/critrevoncog.2017020626.

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Lu, Ming, Kym F. Faull, Julian P. Whitelegge, Jianbo He, Dejun Shen, Romaine E. Saxton, and Helena R. Chang. "Proteomics and Mass Spectrometry for Cancer Biomarker Discovery." Biomarker Insights 2 (January 2007): 117727190700200. http://dx.doi.org/10.1177/117727190700200005.

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Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management.

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Wikoff, William R., Samir Hanash, Brian DeFelice, Suzanne Miyamoto, Matt Barnett, Yang Zhao, Gary Goodman, et al. "Diacetylspermine Is a Novel Prediagnostic Serum Biomarker for Non–Small-Cell Lung Cancer and Has Additive Performance With Pro-Surfactant Protein B." Journal of Clinical Oncology 33, no. 33 (November 20, 2015): 3880–86. http://dx.doi.org/10.1200/jco.2015.61.7779.

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Purpose We have investigated the potential of metabolomics to discover blood-based biomarkers relevant to lung cancer screening and early detection. An untargeted metabolomics approach was applied to identify biomarker candidates using prediagnostic sera from the Beta-Carotene and Retinol Efficacy Trial (CARET) study. Patients and Methods A liquid chromatography/mass spectrometry hydrophilic interaction method designed to profile a wide range of metabolites was applied to prediagnostic serum samples from CARET participants (current or former heavy smokers), consisting of 100 patients who subsequently developed non–small-cell lung cancer (NSCLC) and 199 matched controls. A separate aliquot was used to quantify levels of pro-surfactant protein B (pro-SFTPB), a previously established protein biomarker for NSCLC. On the basis of the results from the discovery set, blinded validation of a metabolite, identified as N1,N12-diacetylspermine (DAS), and pro-SFTPB was performed using an independent set of CARET prediagnostic sera from 108 patients with NSCLC and 216 matched controls. Results Serum DAS was elevated by 1.9-fold, demonstrating significant specificity and sensitivity in the discovery set for samples collected up to 6 months before diagnosis of NSCLC. In addition, DAS significantly complemented performance of pro-SFTPB in both the discovery and validations sets, with a combined area under the curve in the validation set of 0.808 (P < .001 v pro-SFTPB). Conclusion DAS is a novel serum metabolite with significant performance in prediagnostic NSCLC and has additive performance with pro-SFTPB.

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Cho, William CS. "Contribution of oncoproteomics to cancer biomarker discovery." Molecular Cancer 6, no. 1 (2007): 25. http://dx.doi.org/10.1186/1476-4598-6-25.

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Wong, D. T., L. Zhang, J. Farrell, H. Zhou, D. Elashoff, K. Gao, and B. Paster. "Salivary biomarkers for pancreatic cancer detection." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): 4630. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.4630.

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4630 Pancreatic cancer is the 4th leading cause of cancer death. Lack of early detection technology for pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. We evaluated the performance and translational utilities of the salivary transcriptomic and microbial biomarkers for pancreatic cancer detection. Biomarker discovery strategies were used to profile transcriptome in saliva supernatant and microflora in saliva pellet. The Affymetrix Human Genome U133+2.0 array was used to discover altered gene expression in saliva supernatant. The Human Oral Microbe Identification Microarray (HOMIM) was used to investigate microflora shift in saliva pellet. Biomarkers selected from both studies were subjected to an independent clinical validation using a cohort of 30 pancreatic cancer, 30 chronic pancreatitis and 30 healthy matched-control saliva samples. Two panels of salivary biomarkers, including eleven mRNA biomarkers and two microbial biomarkers were discovered and validated for pancreatic cancer detection. The logistic regression model with the combination of three mRNA biomarkers (ACRV1, DMXL2 and DPM1) yielded a ROC-plot AUC value of 0.974 (95% CI, 0.896 to 0.997; P < 0.0001) with 93.3% sensitivity and 90% specificity in distinguishing pancreatic cancer patients from healthy subjects. The logistic regression model with the combination of two bacterial biomarkers (Neisseria elongata and Streptococcus mitis) yielded a ROC-plot AUC value of 0.895 (95% CI, 0.784 to 0.961; P < 0.0001) with 96.4% sensitivity and 82.1% specificity in distinguishing pancreatic cancer patients from healthy subjects. More importantly, the logistic regression model with the combination of four biomarkers (mRNA biomarkers, ACRV1, DMXL2 and DPM1; bacterial biomarker, S. mitis) could differentiate pancreatic cancer patients from all non-cancer subjects (chronic pancreatitis and healthy control), yielding a ROC-plot AUC value of 0.949 (95% CI, 0.877 to 0.985; P < 0.0001) with 92.9% sensitivity and 85.5% specificity. This is the first report demonstrating the value of multiplex salivary biomarkers for the non-invasive detection of a high impact systemic cancer. No significant financial relationships to disclose.

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Dua, R. Sascha, Clare M. Isacke, and Gerald P. H. Gui. "The Intraductal Approach to Breast Cancer Biomarker Discovery." Journal of Clinical Oncology 24, no. 7 (March 1, 2006): 1209–16. http://dx.doi.org/10.1200/jco.2005.04.1830.

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Established methods of breast cancer detection have well-described limitations, and new diagnostic techniques are evolving continually to improve diagnostic accuracy. The intraductal approach encompasses the modalities of nipple aspiration, ductal lavage, and duct endoscopy, and is a means of directly accessing the microenvironment of the breast and either sampling or visualizing this intraductal milieu. The aim of sampling this mammary microenvironment is to obtain samples from the physical surroundings of cells that are undergoing malignant transformation, thereby providing a new method of detection before the development of a clinically or radiologically discernible mass. A literature review was conducted to investigate the evolution of the intraductal approach and its particular application in the field of biomarker discovery, primarily using the intraductal technique of nipple aspiration, in combination with emerging protein profiling techniques.

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Qin, Li-Xuan, Qin Zhou, Faina Bogomolniy, Liliana Villafania, Narciso Olvera, Magali Cavatore, Jaya M. Satagopan, Colin B. Begg, and Douglas A. Levine. "Blocking and Randomization to Improve Molecular Biomarker Discovery." Clinical Cancer Research 20, no. 13 (May 1, 2014): 3371–78. http://dx.doi.org/10.1158/1078-0432.ccr-13-3155.

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Pusztai, L., and B. Leyland-Jones. "Promises and caveats of in silico biomarker discovery." British Journal of Cancer 99, no. 3 (July 29, 2008): 385–86. http://dx.doi.org/10.1038/sj.bjc.6604495.

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Kulasingam, Vathany, and Eleftherios P. Diamandis. "Tissue culture-based breast cancer biomarker discovery platform." International Journal of Cancer 123, no. 9 (November 1, 2008): 2007–12. http://dx.doi.org/10.1002/ijc.23844.

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Zhang, Xueli, Hong Zhang, Chuanwen Fan, Camilla Hildesjö, Bairong Shen, and Xiao-Feng Sun. "Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays." Cancers 14, no. 11 (May 27, 2022): 2664. http://dx.doi.org/10.3390/cancers14112664.

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Background. The incidence of colorectal cancers has been constantly increasing. Although the mortality has slightly decreased, it is far from satisfaction. Precise early diagnosis for colorectal cancer has been a great challenge in order to improve patient survival. Patients and Methods. We started with searching for protein biomarkers based on our colorectal cancer biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and further predicted new biomarkers of protein–protein interaction (PPI) networks by machine learning (ML) methods. The best-selected biomarker was further verified by a receiver operating characteristic (ROC) test from microarray and RNA-seq data, biological network, and functional analysis, and immunohistochemistry in the tissue arrays from 198 specimens. Results. There were twelve proteins (MYO5A, CHGA, MAPK13, VDAC1, CCNA2, YWHAZ, CDK5, GNB3, CAMK2G, MAPK10, SDC2, and ADCY5) which were predicted by ML as colon cancer candidate diagnosis biomarkers. These predicted biomarkers showed close relationships with reported biomarkers of the PPI network and shared some pathways. An ROC test showed the CHGA protein with the best diagnostic accuracy (AUC = 0.9 in microarray data and 0.995 in RNA-seq data) among these candidate protein biomarkers. Furthermore, immunohistochemistry examination on our colon cancer tissue microarray samples further confirmed our bioinformatical prediction, indicating that CHGA may be used as a potential biomarker for early diagnosis of colon cancer patients. Conclusions. CHGA could be a potential candidate biomarker for diagnosing earlier colon cancer in the patients.

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Silva, Tharushi de, Joanne Voisey, Peter Hopkins, Simon Apte, Daniel Chambers, and Brendan O'Sullivan. "Markers of rejection of a lung allograft: state of the art." Biomarkers in Medicine 16, no. 6 (April 2022): 483–98. http://dx.doi.org/10.2217/bmm-2021-1013.

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Chronic lung allograft dysfunction (CLAD) affects approximately 50% of all lung transplant recipients by 5 post-operative years and is the leading cause of death in lung transplant recipients. Early CLAD diagnosis or ideally prediction of CLAD is essential to enable early intervention before significant lung injury occurs. New technologies have emerged to facilitate biomarker discovery, including epigenetic modification and single-cell RNA sequencing. This review examines new and existing technologies for biomarker discovery and the current state of research on biomarkers for identifying lung transplant rejection.

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Zhang, Guo-Fang, Sushabhan Sadhukhan, Gregory P. Tochtrop, and Henri Brunengraber. "Metabolomics, Pathway Regulation, and Pathway Discovery." Journal of Biological Chemistry 286, no. 27 (May 12, 2011): 23631–35. http://dx.doi.org/10.1074/jbc.r110.171405.

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Metabolomics is a data-based research strategy, the aims of which are to identify biomarker pictures of metabolic systems and metabolic perturbations and to formulate hypotheses to be tested. It involves the assay by mass spectrometry or NMR of many metabolites present in the biological system investigated. In this minireview, we outline studies in which metabolomics led to useful biomarkers of metabolic processes. We also illustrate how the discovery potential of metabolomics is enhanced by associating it with stable isotopic techniques.

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Muthana, Saddam M., and Jeffrey C. Gildersleeve. "Glycan microarrays: Powerful tools for biomarker discovery." Cancer Biomarkers 14, no. 1 (February 27, 2014): 29–41. http://dx.doi.org/10.3233/cbm-130383.

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Gill, Rosalynn D., Steve Williams, Rachel Ostroff, Ed Brody, Alex Stewart, Harvey Pass, William Rom, Joel L. Weissfeld, Jill Siegfried, and Mike Mehan. "Exposing the criminal record of every blood sample: Use of SOMAmer technology and sample mapping vectors to mitigate false biomarker discoveries in lung cancer." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): e21012-e21012. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e21012.

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e21012 Background: Biomarker discovery studies may fail to translate to the clinic because the study population does not match the intended clinical use or because hidden preanalytic variability in the discovery samples contaminates the apparent disease specific information in the biomarkers. This can arise from differences in blood sample processing between study sites or in samples collected differently at the same study site. Methods: To better understand the effect of different blood sample processing procedures, we evaluated protein measurement bias in a large multi-center lung cancer study using the >1000 protein SOMAscan™ assay. These analyses revealed that perturbations in serum collection and processing result in changes to families of proteins from known biological pathways. We subsequently developed protein biomarker signatures of cell lysis, platelet activation and complement activation and assembled these preanalytic signatures into quantitative multi-dimensional Sample Mapping Vector (SMV) scores. Results: The SMV score provides critical evaluation of the quality of every blood-based sample used in discovery and also enables the evaluation of candidate protein biomarkers for resistance to preanalytic variability. Despite uniform processing protocols for each clinic, the SMV analysis revealed unexpected case/control bias arising from collecting case and control serum from different clinics at the same academic centers, an effect that created false or bias-contaminated disease markers. We therefore used the SMV score to remove bias-susceptible analytes and to define a well-collected, unbiased training set. An improved classifier was developed, resistant to common artifacts in serum processing. Conclusions: . The performance of this classifier to detect lung cancer in a high-risk population is more likely to represent real-world diagnostic results. We believe this approach is generally applicable to clinical investigations in all fields of biomarker discovery and translational medicine.

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Borgia, J. A., C. Frankenberger, S. E. McCormack, K. A. Kaiser, L. Usha, J. Rotmensch, and J. S. Coon. "Biomarker discovery in endometrial carcinoma using novel proteomic methods." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 5034. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.5034.

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5034 Background: A practical serum-based screening test for endometrial and ovarian carcinomas could greatly improve their early diagnosis, but developing such a test has proved elusive. Herein, we describe methods offering increased statistical power over conventional ‘batch’ analyses, via paired patient serum samples to identify biomarkers specific for uterine endometriod carcinomas (UEA). Methods: Paired serum samples (collected pre- and post- surgery) were prepared from patients undergoing UEA resection. A Ciphergen SELDI-TOF mass spectrometer was used to generate the patient serum proteomic profiles with data acquisition optimized to the 5,000–40,000 m/z (mass) range. Spectra quality was assessed using a specific function within the R-project statistical platform (v2.2.0), whereas data analyses were performed using Bioconductor, an extension of the R statistical platform; all in a blinded manner. Raw spectra were processed as follows: spectra pair normalization, baseline subtraction, and differential peak detection (with calibrated alignment). Aligned peaks were sorted into groups, based on tumor pathology and pre- vs. post-operative specimen type, and compared using a two tailed homoscedastic t-test in Microsoft Excel 2003. Results: Pre- and post-operative serum from 10 patients with UEA had their serum proteomic profiles evaluated for peaks lost after surgery. These values were then compared with a set of ‘normal’ samples (i.e. patients undergoing surgery for benign gynecologic disease). Even with our small sample size we identified 16 serum components that were significantly reduced or absent (p < 0.04) after tumor resection; 10 unique to UEA (p <0.04). From this group, species at m/z values of 4291.9, 4414.2, 5036.1, 9,615.0, and 25,508.8 seem most promising, based on their high level of significance (p < 0.02), for 10/10 patients. Conclusions: These results validate the power of our pre-and post-operative sample sets for the identification of new serum biomarker candidates for UEA. Work is in progress to increase the patient sample size, expand the study to patients with serous uterine and ovarian carcinomas, and identify the reported biomarker candidates via tandem mass spectrometry No significant financial relationships to disclose.

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Adam-Artigues, Anna, Iris Garrido-Cano, Juan Antonio Carbonell-Asins, Ana Lameirinhas, Soraya Simón, Belén Ortega-Morillo, María Teresa Martínez, et al. "Identification of a Two-MicroRNA Signature in Plasma as a Novel Biomarker for Very Early Diagnosis of Breast Cancer." Cancers 13, no. 11 (June 7, 2021): 2848. http://dx.doi.org/10.3390/cancers13112848.

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The early diagnosis of breast cancer is essential to improve patients’ survival rate. In this context, microRNAs have been described as potential diagnostic biomarkers for breast cancer. Particularly, circulating microRNAs have a strong value as non-invasive biomarkers. Herein, we assessed the potential of a microRNA signature based on miR-30b-5p and miR-99a-5p levels in plasma as a diagnostic biomarker for breast cancer. This two-microRNA signature was constructed by Principal Component Analysis and its prognostic value was assessed in a discovery cohort and blindly validated in a second cohort from an independent institution. ROC curve analysis and biomarker performance parameter evaluation demonstrated that our proposed signature presents a high value as a non-invasive biomarker for very early detection of breast cancer. In addition, pathway enrichment analysis identified three of the well-known pathways involved in cancer as targets of the two microRNAs.

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Cho, William CS. "Proteomics in translational cancer research: biomarker discovery for clinical applications." Expert Review of Proteomics 11, no. 2 (March 20, 2014): 131–33. http://dx.doi.org/10.1586/14789450.2014.899908.

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Cottingham, Katie. "Research Profile: A new pipeline for biomarker discovery and validation." Journal of Proteome Research 6, no. 10 (October 2007): 3875–76. http://dx.doi.org/10.1021/pr070790c.

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Liu, Yansheng, Ruth Hüttenhain, Ben Collins, and Ruedi Aebersold. "Mass spectrometric protein maps for biomarker discovery and clinical research." Expert Review of Molecular Diagnostics 13, no. 8 (November 2013): 811–25. http://dx.doi.org/10.1586/14737159.2013.845089.

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Xuan, Yue. "Advanced Mass Spectrometry Drives Translational Cancer Biomarker Research beyond Discovery." Genetic Engineering & Biotechnology News 39, no. 7 (July 2019): 49–51. http://dx.doi.org/10.1089/gen.39.07.14.

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Considine, Elizabeth C. "The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective." Metabolites 9, no. 7 (July 2, 2019): 126. http://dx.doi.org/10.3390/metabo9070126.

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Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

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Grund, Eric M., A. James Moser, Corinne L. DeCicco, Nischal M. Chand, Genesis L. Perez-Melara, Gregory M. Miller, Punit Shah, et al. "Abstract 5145: Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis." Cancer Research 82, no. 12_Supplement (June 15, 2022): 5145. http://dx.doi.org/10.1158/1538-7445.am2022-5145.

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Abstract Delayed diagnosis and rapid progression are major drivers of poor survival outcomes for pancreatic ductal adenocarcinoma (PDAC). PDAC is expected to be the second leading cause of cancer death and has a dismal 5 year survival rate of 10%. There is an urgent unmet need to detect the disease at an early stage and stratify patients into more effective treatment regimens within clinically meaningful timeframes. To accomplish this, robust quality controlled OMIC molecular profiling platforms and analytic solutions need to be deployed into precision medicine protocols to discover actionable biomarkers. Project Survival® is a multicenter (n=6), prospective biomarker study (NCT 02781012) of PDAC and relevant controls combining high-fidelity longitudinal phenotypic characterization, multi-omic profiling (proteomics, signaling lipidomics, structural lipidomics, and metabolomics), and agnostic Bayesian artificial intelligence network inference (bAIcis®) to discover biomarkers with diagnostic and therapeutic utility. This study utilizes a systems medicine approach for translational biomarker discovery by performing analysis of matched subject sera, plasma, buffy coat, saliva, urine, and tumor/adjacent normal tissues and integrating them with the respective full clinical annotation using the BERG Interrogative Biology® platform. Multiple longitudinal time points were taken over the course of the six-year timeline enabling dynamic modeling. Utilizing the Project Survival® molecular and clinical data, we have analyzed and integrated baseline samples from 121 at risk patients and 279 patients with PDAC. Samples were randomized and analyzed over the course of recruitment allowing for agnostic discovery and integration to determine diagnostic utility. Discovery analysis identified 123 potential molecular markers, of which, four demonstrated a combined AUC of 0.85, PPV 0.83, NPV 0.72, and OR 13.1. In parallel, 4 non-canonical clinical measurements were assessed for diagnostic utility providing an AUC 0.79, PPV 0.84, NPV 0.72 and OR 13.2. Combining molecular and clinical features demonstrated an AUC of 0.9, PPV 0.9, NPV 0.77, OR 29.2, and p-value 1.4 E-40. Molecular markers revealed no treatment associated expression effects. Taken together, these marker panels demonstrate diagnostic utility to detect PDAC and will be further validated using robust bioanalysis methods as well as in an independent cohort of samples to provide enhanced insight into their positioning in the diagnostic landscape for PDAC. Citation Format: Eric M. Grund, A. James Moser, Corinne L. DeCicco, Nischal M. Chand, Genesis L. Perez-Melara, Gregory M. Miller, Punit Shah, Valarie Bussberg, Vladimir Tolstikov, Rangaprasad Sarangarajan, Elder Granger, Niven Narian, Michael A. Kiebish. Project Survival®: Discovery of a molecular-clinical phenome biomarker panel to detect pancreatic ductal adenocarcinoma among at risk populations using high-fidelity longitudinal phenotypic and multi-omic analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5145.

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Palstrøm, Nicolai Bjødstrup, Rune Matthiesen, Lars Melholt Rasmussen, and Hans Christian Beck. "Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research." Biomedicines 10, no. 1 (January 12, 2022): 162. http://dx.doi.org/10.3390/biomedicines10010162.

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The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

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Schiess, Ralph, Bernd Wollscheid, and Ruedi Aebersold. "Targeted proteomic strategy for clinical biomarker discovery." Molecular Oncology 3, no. 1 (December 11, 2008): 33–44. http://dx.doi.org/10.1016/j.molonc.2008.12.001.

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Wood, Steven L., and Janet E. Brown. "Personal Medicine and Bone Metastases: Biomarkers, Micro-RNAs and Bone Metastases." Cancers 12, no. 8 (July 29, 2020): 2109. http://dx.doi.org/10.3390/cancers12082109.

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Bone metastasis is a major cause of morbidity within solid tumours of the breast, prostate, lung and kidney. Metastasis to the skeleton is associated with a wide range of complications including bone fractures, spinal cord compression, hypercalcaemia and increased bone pain. Improved treatments for bone metastasis, such as the use of anti-bone resorptive bisphosphonate agents, within post-menopausal women have improved disease-free survival; however, these treatments are not without side effects. There is thus a need for biomarkers, which will predict the risk of developing the spread to bone within these cancers. The application of molecular profiling techniques, together with animal model systems and engineered cell-lines has enabled the identification of a series of potential bone-metastasis biomarker molecules predictive of bone metastasis risk. Some of these biomarker candidates have been validated within patient-derived samples providing a step towards clinical utility. Recent developments in multiplex biomarker quantification now enable the simultaneous measurement of up to 96 micro-RNA/protein molecules in a spatially defined manner with single-cell resolution, thus enabling the characterisation of the key molecules active at the sites of pre-metastatic niche formation as well as tumour-stroma signalling. These technologies have considerable potential to inform biomarker discovery. Additionally, a potential future extension of these discoveries could also be the identification of novel drug targets within cancer spread to bone. This chapter summarises recent findings in biomarker discovery within the key bone metastatic cancers (breast, prostate, lung and renal cell carcinoma). Tissue-based and circulating blood-based biomarkers are discussed from the fields of genomics, epigenetic regulation (micro-RNAs) and protein/cell-signalling together with a discussion of the potential future development of these markers towards clinical development.

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Voss, Joachim, Young Ah Goo, Kevin Cain, Nancy Woods, Monica Jarrett, Lynne Smith, Robert Shulman, and Margaret Heitkemper. "Searching for the Noninvasive Biomarker Holy Grail: Are Urine Proteomics the Answer?" Biological Research For Nursing 13, no. 3 (May 17, 2011): 235–42. http://dx.doi.org/10.1177/1099800411402056.

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Recently, biobehavioral nursing scientists have focused their attention on the search for biomarkers or biological signatures to identify patients at risk for various health problems and poor disease outcomes. In response to the national impetus for biomarker discovery, the measurement of biological fluids and tissues has become increasingly sophisticated. Urine proteomics, in particular, may hold great promise for biobehavioral focused nursing scientists for examination of symptom-and syndrome-related research questions. Urine proteins are easily accessible secreted proteins that provide direct and indirect windows into bodily functions. Advances in proteomics and biomarker discovery provide new opportunities to conduct research studies with banked and fresh urine to benefit diagnosis, prognosis, and evaluation of outcomes in various disease populations. This article provides a review of proteomics and a rationale for utilizing urine proteomics in biobehavioral research. It addresses as well some of the challenges involved in data collection and sample preparation.

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Journal articles on the topic 'Biomarker discovery research'

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