Proteomics in cancer biomarkers discovery: challenges and applications

Proteomic Profile of Endometrial Cancer: A Scoping Review

Proteomics can be a robust tool in protein identification and regulation, allowing the discovery of potential biomarkers. In clinical practice, the management of endometrial cancer can be challenging. Thus, identifying promising markers could be beneficial, helping both in diagnosis and prognostic stratification, even predicting the response to therapy. Therefore, this manuscript systematically reviews the existing evidence of the proteomic profile of human endometrial cancer. The literature search was conducted via Medline (through PubMed) and the Web of Science. The inclusion criteria were clinical, in vitro, and in vivo original studies reporting proteomic analysis using all types of samples to map the human endometrial cancer proteome. A total of 55 publications were included in this review. Most of the articles carried out a proteomic analysis on endometrial tissue, serum and plasma samples, which enabled the identification of several potential diagnostic and prognostic biomarkers. In addition, eight articles were analyzed regarding the identified proteins, where three studies showed a strong correlation, sharing forty-five proteins. This analysis also allowed the identification of the 10 most frequently reported proteins in these studies: EGFR, PGRMC1, CSE1L, MYDGF, STMN1, CASP3 ANXA2, YBX1, ANXA1, and MYH11. Proteomics-based approaches pointed out potential diagnostic and prognostic candidates for endometrial cancer. However, there is a lack of studies exploring novel therapeutic targets.

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Multiplexed quantitative proteomics in prostate cancer biomarker development

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Exploring protein profiles and hub genes in ameloblastoma

Ameloblastoma (AM) is a prominent benign odontogenic tumor characterized by aggressiveness, likely originating from tooth-generating tissue or the dental follicle (DF). However, proteomic distinctions between AM and DF remain unclear. In the present study, the aim was to identify the distinction between AM and DF in terms of their proteome and to determine the associated hub genes. Shotgun proteomics was used to compare the proteomes of seven fresh-frozen AM tissues and five DF tissues. Differentially expressed proteins (DEPs) were quantified and subsequently analyzed through Gene Ontology-based functional analysis, protein-protein interaction (PPI) analysis and hub gene identification. Among 7,550 DEPs, 520 and 216 were exclusive to AM and DF, respectively. Significant biological pathways included histone H2A monoubiquitination and actin filament-based movement in AM, as well as pro-B cell differentiation in DF. According to PPI analysis, the top-ranked upregulated hub genes were ubiquitin C (UBC), breast cancer gene 1 (BRCA1), lymphocyte cell-specific protein-tyrosine kinase (LCK), Janus kinase 1 and ATR serine/threonine kinase, whereas the top-ranked downregulated hub genes were UBC, protein kinase, DNA-activated, catalytic subunit (PRKDC), V-Myc avian myelocytomatosis viral oncogene homolog (MYC), tumor protein P53 and P21 (RAC1) activated kinase 1. When combining upregulated and downregulated genes, UBC exhibited the highest degree and betweenness values, followed by MYC, BRCA1, PRKDC, embryonic lethal, abnormal vision, Drosophila, homolog-like 1, myosin heavy chain 9, amyloid beta precursor protein, telomeric repeat binding factor 2, LCK and filamin A. In summary, these findings contributed to the knowledge on AM protein profiles, potentially aiding future research regarding AM etiopathogenesis and leading to AM prevention and treatment.

Protein Biomarker Discovery Studies on Urinary sEV Fractions Separated with UF-SEC for the First Diagnosis and Detection of Recurrence in Bladder Cancer Patients

Urinary extracellular vesicles (EVs) are an attractive source of bladder cancer biomarkers. Here, a protein biomarker discovery study was performed on the protein content of small urinary EVs (sEVs) to identify possible biomarkers for the primary diagnosis and recurrence of non-muscle-invasive bladder cancer (NMIBC). The sEVs were isolated by ultrafiltration (UF) in combination with size-exclusion chromatography (SEC). The first part of the study compared healthy individuals with NMIBC patients with a primary diagnosis. The second part compared tumor-free patients with patients with a recurrent NMIBC diagnosis. The separated sEVs were in the size range of 40 to 200 nm. Based on manually curated high quality mass spectrometry (MS) data, the statistical analysis revealed 69 proteins that were differentially expressed in these sEV fractions of patients with a first bladder cancer tumor vs. an age- and gender-matched healthy control group. When the discriminating power between healthy individuals and first diagnosis patients is taken into account, the biomarkers with the most potential are MASP2, C3, A2M, CHMP2A and NHE-RF1. Additionally, two proteins (HBB and HBA1) were differentially expressed between bladder cancer patients with a recurrent diagnosis vs. tumor-free samples of bladder cancer patients, but their biological relevance is very limited.

Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue

Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.

Molecular markers of recurrence of basal cell skin cancer

Basal cell carcinoma is the most common skin cancer worldwide, with rates increasing by almost 10% annually. It representing a growing public health problem with negative psychosocial and economic consequences. Analysis of gene expression and proteomic profiling of tumor cells and the tumor microenvironment strongly suggests that certain molecules involved in the pathogenetic pathways of skin cancer may represent novel prognostic biomarkers in basal cell skin cancer. The PubMed, MedLine, Web of Science and RSCI databases were used to search for the necessary literature.

Targeted proteomics using parallel reaction monitoring confirms salivary proteins indicative of metastatic triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype due to the absence of hormonal receptors. Our study aimed to identify and determine the effectiveness of salivary proteins as candidate markers for metastatic TNBC subtype using parallel reaction monitoring mass spectrometry (PRM-MS). Three salivary proteins (lipocalin-1, SMR3B, and plastin-2) that showed significant differential expression in label-free quantitation (LFQ) between TNBC (N = 6) and health subjects (HS; N = 6) were selected for further validation. The developed PRM assay was used to quantify peptides GLST and NNLE (lipocalin-1), VYAL and MINL (Plastin-2) and GPYP, and IPPP (SMR3B) on a different cohort of TNBC patients (N = 20) and HS (N = 20) for evaluating their discriminating performances. Quantitative validation using PRM correlated well with the LFQ results, and 5 peptides from three proteins showed a similar up-or down-regulation. Subsequently, these proteins were validated by Western blot analysis. Compared to one protein's performance as an individual marker, the five-signature panel with salivary GLST, VYAL, MINL, GPYP, and IPPP achieved better performance in differentiating aggressive TNBC and HS with sensitivity (80%) and specificity (95%). Targeted proteomic analysis of the prioritized proteins highlights a peptide-based signature in saliva as the potential predictor to distinguish between TNBC and HS. SIGNIFICANCE OF THE STUDY: This study was designed to identify and quantify potential markers in saliva from the triple-negative breast cancer (TNBC) patients using parallel reaction monitoring assay. Three salivary proteins, Lipocalin-1 (LCN-1), Submaxillary androgen-regulated protein 3B (SMR3B), and Plastin-2 (LCP-1) selected in the discovery-phase were further quantified by targeted proteomics and Western blots. The salivary proteins successfully differentiated TNBC patients from healthy subjects with a sensitivity (80%) and specificity (95%).

Application of peptide biomarkers in life analysis based on liquid chromatography-mass spectrometry technology

Biomedicine is developing rapidly in the 21st century. Among them, the qualitative and quantitative analysis of peptide biomarkers is of considerable importance for the diagnosis and therapy of diseases and the quality evaluation of drugs and food. The identification and quantitative analysis of peptides have been going on for decades. Traditionally, immunoassays or biological assays are generally used to quantify peptides in biological matrices. However, the selectivity and sensitivity of these methods cannot meet the requirements of the application. The separation and analysis technique of liquid chromatography-mass spectrometry (LC-MS) supplies a reliable alternative. In contrast to immunoassays, LC-MS methods are capable of providing the analytical prowess necessary to satisfy the demands of peptide biomarker research in the life sciences arena. This review article provides a historical account of the in-roads made by LC-MS technology for the detection of peptide biomarkers in the past 10 years, with the focus on the qualification/quantification developments and their applications.

Biotechnological tools to elucidate the mechanism of pesticide degradation in the environment

Pesticides are widely used in agriculture, households, and industries; however, they have caused severe negative effects on the environment and human health. To clean up pesticide contaminated sites, various technological strategies, i.e. physicochemical and biological, are currently being used throughout the world. Biological approaches have proven to be a viable method for decontaminating pesticide-contaminated soils and water environments. The biological process eliminates contaminants by utilizing microorganisms' catabolic ability. Pesticide degradation rates are influenced by a variety of factors, including the pesticide's structure, concentration, solubility in water, soil type, land use pattern, and microbial activity in the soil. There is currently a knowledge gap in this field of study because researchers are unable to gather collective information on the factors affecting microbial growth, metabolic pathways, optimal conditions for degradation, and genomic, transcriptomic, and proteomic changes caused by pesticide stress on the microbial communities. The use of advanced tools and omics technology in research can bridge the existing gap in our knowledge regarding the bioremediation of pesticides. This review provides new insights on the research gaps and offers potential solutions for pesticide removal from the environment through the use of various microbe-mediated technologies.

Proteomic Profiling and Biomarker Discovery in Colorectal Liver Metastases

Colorectal liver metastases (CRLM) are the leading cause of death among patients with metastatic colorectal cancer (CRC). As part of multimodal therapy, liver resection is the mainstay of curative-intent treatment for select patients with CRLM. However, effective treatment of CRLM remains challenging as recurrence occurs in most patients after liver resection. Proposed clinicopathologic factors for predicting recurrence are inconsistent and lose prognostic significance over time. The rapid development of next-generation sequencing technologies and decreasing DNA sequencing costs have accelerated the genomic profiling of various cancers. The characterisation of genomic alterations in CRC has significantly improved our understanding of its carcinogenesis. However, the functional context at the protein level has not been established for most of this genomic information. Furthermore, genomic alterations do not always result in predicted changes in the corresponding proteins and cancer phenotype, while post-transcriptional and post-translational regulation may alter synthesised protein levels, affecting phenotypes. More recent advancements in mass spectrometry-based technology enable accurate protein quantitation and comprehensive proteomic profiling of cancers. Several studies have explored proteomic biomarkers for predicting CRLM after oncologic resection of primary CRC and recurrence after curative-intent resection of CRLM. The current review aims to rationalise the proteomic complexity of CRC and explore the potential applications of proteomic biomarkers in CRLM.

The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms

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Most correct de novo peptides have ⩽1 missing fragmentation cleavages.

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DeepNovo outperforms Novor for peptide accuracy for both data types.

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Novor excels at amino acid recall when many fragmentation cleavages are missing.

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Deep learning allows DeepNovo to predict amino acids without adjacent peaks.

Proteomics aims to characterise system-wide protein expression and typically relies on mass-spectrometry and peptide fragmentation, followed by a database search for protein identification. It has wide ranging applications from clinical to environmental settings and virtually impacts on every area of biology. In that context, de novo peptide sequencing is becoming increasingly popular. Historically its performance lagged behind database search methods but with the integration of machine learning, this field of research is gaining momentum. To enable de novo peptide sequencing to realise its full potential, it is critical to explore the mass spectrometry data underpinning peptide identification. In this research we investigate the characteristics of tandem mass spectra using 8 published datasets. We then evaluate two state of the art de novo peptide sequencing algorithms, Novor and DeepNovo, with a particular focus on their performance with regard to missing fragmentation cleavage sites and noise. DeepNovo was found to perform better than Novor overall. However, Novor recalled more correct amino acids when 6 or more cleavage sites were missing. Furthermore, less than 11% of each algorithms’ correct peptide predictions emanate from data with more than one missing cleavage site, highlighting the issues missing cleavages pose. We further investigate how the algorithms manage to correctly identify peptides with many of these missing fragmentation cleavages. We show how noise negatively impacts the performance of both algorithms, when high intensity peaks are considered. Finally, we provide recommendations regarding further algorithms’ improvements and offer potential avenues to overcome current inherent data limitations.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive prognoses and greater patient longevity, means for early detection and severity stratification have failed to keep pace. Detection and validation of cancer-specific biomarkers hinges on the ability to identify subtype-specific phenotypic and proteomic alterations and the systematic screening of diverse patient groups. For this reason, clinical and scientific research settings rely on high throughput and high sensitivity mass spectrometry methods to discover and quantify unique molecular perturbations in cancer patients. Discussed within is an overview of quantitative proteomics strategies and a summary of recent applications that enable revealing potential biomarkers and treatment targets in prostate, ovarian, breast, and pancreatic cancer in a high throughput manner.

Development and Validation of a LASSO Prediction Model for Better Identification of Ischemic Stroke: A Case-Control Study in China

Background

Timely diagnosis of ischemic stroke (IS) in the acute phase is extremely vital to achieve proper treatment and good prognosis. In this study, we developed a novel prediction model based on the easily obtained information at initial inspection to assist in the early identification of IS.

Methods

A total of 627 patients with IS and other intracranial hemorrhagic diseases from March 2017 to June 2018 were retrospectively enrolled in the derivation cohort. Based on their demographic information and initial laboratory examination results, the prediction model was constructed. The least absolute shrinkage and selection operator algorithm was used to select the important variables to form a laboratory panel. Combined with the demographic variables, multivariate logistic regression was performed for modeling, and the model was encapsulated within a visual and operable smartphone application. The performance of the model was evaluated on an independent validation cohort, formed by 304 prospectively enrolled patients from June 2018 to May 2019, by means of the area under the curve (AUC) and calibration.

Results

The prediction model showed good discrimination (AUC = 0.916, cut-off = 0.577), calibration, and clinical availability. The performance was reconfirmed in the more complex emergency department. It was encapsulated as the Stroke Diagnosis Aid app for smartphones. The user can obtain the identification result by entering the values of the variables in the graphical user interface of the application.

Conclusion

The prediction model based on laboratory and demographic variables could serve as a favorable supplementary tool to facilitate complex, time-critical acute stroke identification.

Proteomic Interrogation in Cancer Biomarker

Biomarkers factor into the diagnosis and treatment of almost every patient with cancer. The innovation in proteomics follows improvement of mass spectrometry techniques and data processing strategy. Recently, proteomics and typical biological studies have been the answer for clinical applications. The clinical proteomics techniques are now actively adapted to protein identification in large patient cohort, biomarker development for more sensitive and specific screening based on quantitative data. And, it is important for clinical, translational researchers to be acutely aware of the issues surrounding appropriate biomarker development, in order to facilitate entry of clinically useful biomarkers into the clinic. Here, we discuss in detail include the case research for clinical proteomics. Furthermore, we give an overview on the current developments and novel findings in proteomics-based cancer biomarker research.

Comprehensive Isotope Ratio Metabolomics: Gas chromatography Isotope Ratio Mass Spectrometry of urinary metabolites and exhaled breath

We have developed an analytical procedure to measure the carbon isotopic composition of multiple compounds even when there is a partial overlap in the chromatographic profiles and applied this procedure to measure the carbon isotopic composition of different metabolites in human urine and exhaled breath. Method development and validation was performed with CRM IAEA-600 caffeine after calibration of the reference CO₂ gas using a mixture of certified undecane, pentadecane and eicosane δ(¹³C) standards. The alternative data treatment procedure included the correction of time-lag between Faraday cup amplifiers (44 ms at mass 45 and -160 ms at mass 46), the calculation and correction of chromatographic isotope effects on each peak (isotope shifts) and the calculation of the isotope ratio for each compound using the linear regression slope procedure with data only at the top of the chromatographic peak. In that way, partial chromatographic overlap between different metabolites can be tolerated (resolution equal or higher than 1). The reproducibility (SD) of the carbon isotope composition of 93 metabolites in human urine (n = 8) from one volunteer was typically better than 0.5 δ(¹³C) (range 0.1-2.0 δ(¹³C), median 0.4 δ(¹³C)). The method was applied to follow the carbon isotope composition of different metabolites in human urine and exhaled breath after the oral administration of 100 mg of universally labelled ¹³C-glucose to another human volunteer. It was demonstrated that isotopically labelled compounds could be detected in both samples even 2 h after administration. So, the developed methodology can be applied to multiple types of samples containing a large number of partially overlapping analytes including environmental applications, anti-doping control or metabolomics studies, including the use of enriched isotope tracers.

Proteomics-based identification of TMED9 is linked to vascular invasion and poor prognoses in patients with hepatocellular carcinoma

BACKGROUND

Due to the difficulties in early diagnosing and treating hepatocellular carcinoma (HCC), prognoses for patients remained poor in the past decade. In this study, we established a screening model to discover novel prognostic biomarkers in HCC patients.

METHODS

Candidate biomarkers were screened by liquid chromatography with tandem mass spectrometry (LC-MS/MS) analyses of five HCC normal (N)/tumor (T) paired tissues and preliminarily verified them through several in silico database analyses. Expression levels and functional roles of candidate biomarkers were respectively evaluated by immunohistochemical staining in N/T paired tissue (n = 120) and MTS, colony formation, and transwell migration/invasion assays in HCC cell lines. Associations of clinicopathological features and prognoses with candidate biomarkers in HCC patients were analyzed from GEO and TCGA datasets and our recruited cohort.

RESULTS

We found that the transmembrane P24 trafficking protein 9 (TMED9) protein was elevated in HCC tissues according to a global proteomic analysis. Higher messenger (m)RNA and protein levels of TMED9 were observed in HCC tissues compared to normal liver tissues or pre-neoplastic lesions. The TMED9 mRNA expression level was significantly associated with an advanced stage and a poor prognosis of overall survival (OS, p = 0.00084) in HCC patients. Moreover, the TMED9 protein expression level was positively correlated with vascular invasion (p = 0.026), OS (p = 0.044), and disease-free survival (p = 0.015) in our recruited Taiwanese cohort. In vitro, manipulation of TMED9 expression in HCC cells significantly affected cell migratory, invasive, proliferative, and colony-forming abilities.

CONCLUSIONS

Ours is the first work to identify an oncogenic role of TMED9 in HCC cells and may provide insights into the application of TMED9 as a novel predictor of clinical outcomes and a potential therapeutic target in patients with HCC.

Quantitative Proteomics Analysis for the Identification of Differential Protein Expression in Calf Muscles between Young and Old SD Rats Using Mass Spectrometry

Aging is associated with loss of muscle mass and strength that leads to a condition termed sarcopenia. Impaired conditions, morbidity, and malnutrition are the factors of devaluation of muscle fibers in aged animals. Satellite cells play an important role in maintaining muscle homeostasis during tissue regeneration and repair. Proteomic profiling on the skeletal muscle tissues of different age group rats helps to determine the differentially expressed (DE) proteins, which may eventually lead to the development of biomarkers in treating the conditions of sarcopenia. In this study, nanoscale liquid chromatography coupled to tandem mass spectrometry (nano-LC-MS/MS) analysis was implemented in the calf tissues of young and old groups of rats. The mass spectrometry (MS) analysis revealed the presence of 335 differentially expressed proteins between the two different age conditions, among which those based on log-fold change 25 proteins were upregulated and 77 were downregulated. The protein-protein interaction network analysis revealed 18 upregulated proteins with three distinct interconnected networks and 57 downregulated proteins with two networks. Further, gene ontology (GO) enrichment analysis showed the biological process, cellular component, and molecular function of the differential proteins. Pathway enrichment analysis of the DE proteins identified nine significantly enriched pathways with a list of eight significant genes (Cryab, Hspb2, Acat1, Ak1, Adssl1, Anxa5, Gys1, Ogdh, Gc, and Adssl1). Quantification of significant genes by quantitative real-time polymerase chain reaction (qRT-PCR) confirmed the downregulation at the mRNA level. Western blot analysis of their protein expression showed concordant results on two candidate proteins (Ogdh and annexin 5) confirming their differential regulation between the two age groups of rats. Thus, these proteomic approaches on young and aged rats provide insights into the development of protein targets in the treatment of sarcopenia (muscle loss).

Studies in lung cancer cytokine proteomics: a review

INTRODUCTION

Proteins are molecules that have role in the progression of the diseases. Proteomics is a tool that can play an effective role in identifying diagnostic and therapeutic biomarkers for lung cancer. Cytokines are proteins that play a decisive role in activating body's immune system in lung cancer. They can increase the growth of the tumor (oncogenic cytokines) or limit tumor growth (anti-tumor cytokines) by regulating related signaling pathways such as proliferation, growth, metastasis, and apoptosis.

AREAS COVERED

In the present study, a total of 223 papers including 196 research papers and 27 review papers, extracted from PubMed and Scopus and published from 1997 to present, are reviewed. The most important involved-cytokines in lung cancer including TNF-α, IFN- γ, TGF-β, VEGF and interleukins such as IL-6, IL-17, IL-8, IL-10, IL-22, IL-1β and IL-18 are introduced. Also, the pathological and biological role of such cytokines in cancer signaling pathways is explained.

EXPERT OPINION

In lung cancer, the cytokine expression changes under the physiological conditions of the immune system, and inflammatory cytokines are associated with the progression of lung cancer. Therefore, the cytokine expression profile can be used in the diagnosis, prognosis, prediction of therapeutic responses, and survival of patients with lung cancer.

Efficient pancreatic cancer detection through personalized protein corona of gold nanoparticles

Characterization of the personalized protein corona (PC) that forms around nanomaterials upon exposure to human plasma is emerging as powerful technology for early cancer detection. However, low material stability and interbatch variability have limited its clinical application so far. Here, we present a nanoparticle-enabled blood (NEB) test that uses 120 nm gold nanoparticles (NPs) as the accumulator of blood plasma proteins. In the test, the personalized PC of gold NPs is characterized by sodium dodecyl sulfate polyacrylamide gel electrophoresis. As a paradigmatic case study, pancreatic ductal adenocarcinoma (PDAC) was chosen due to the lack of effective detection strategies that lead to poor survival rate after diagnosis (<1 year) and extremely low 5-years survival rate (15-20%). Densitometric analysis of 75 protein patterns (28 from healthy subjects and 47 from PDAC patients) allowed us to distinguish nononcological and PDAC patients with good sensitivity (78.6%) and specificity (85.3%). The gold NEB test is completely aligned to affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free, and deliverable to end users criteria stated by the World Health Organization for cancer screening and detection. Thus, it could be very useful in clinical practice at the first level of investigation to decide whether to carry out more invasive analyses or not.

Nanomedicine: Photo-activated nanostructured titanium dioxide, as a promising anticancer agent

The multivariate condition of cancer disease has been approached in various ways, by the scientific community. Recent studies focus on individualized treatments, minimizing the undesirable consequences of the conventional methods, but the development of an alternative effective therapeutic scheme remains to be held. Nanomedicine could provide a solution, filling this gap, exploiting the unique properties of innovative nanostructured materials. Nanostructured titanium dioxide (TiO₂) has a variety of applications of daily routine and of advanced technology. Due to its biocompatibility, it has also a great number of biomedical applications. It is now clear that photo-excited TiO₂ nanoparticles, induce generation of pairs of electrons and holes which react with water and oxygen to yield reactive oxygen species (ROS) that have been proven to damage cancer cells, triggering controlled cellular processes. The aim of this review is to provide insights into the field of nanomedicine and particularly into the wide context of TiO₂-NP-mediated anticancer effect, shedding light on the achievements of nanotechnology and proposing this nanostructured material as a promising anticancer photosensitizer.

Genetic predisposition and prediction protocol for epithelial neoplasms in disease-free individuals: A systematic review

Background

Epithelial neoplasm is an important global health-care problem, with high morbidity and mortality rates. Early diagnosis and appropriate treatment are essential for increased life survival. Prediction of occurrence of malignancy in a disease-free individual by any means will be a great breakthrough for healthy living.

Aims and Objectives

The aims and objectives were to predict the genetic predisposition and propose a prediction protocol for epithelial malignancy of various systems in our body, in a disease-free individual.

Methods

We have searched databases both manually and electronically, published in English language in Cochrane group, Google search, MEDLINE and PubMed from 2000 to 2019. We have included all the published, peer-reviewed, narrative reviews; randomized controlled trials; case-control studies; and cohort studies and excluded the abstract-only articles and duplicates. Specific words such as "etiological factors," "pathology and mutations," "signs and symptoms," "genetics and IHC marker," and "treatment outcome" were used for the search. A total of 1032 citations were taken, and only 141 citations met the inclusion criteria and were analyzed.

Results

After analyzing various articles, the etiological factors, clinical signs and symptoms, genes and the pathology involved and the commonly used blood and tissue markers were analyzed. A basic investigation strategy using immunohistochemistry markers was established.

Conclusion

The set of proposed biomarkers should be studied in future to predict genetic predisposition in disease-free individuals.

Implementation of MALDI Mass Spectrometry Imaging in Cancer Proteomics Research: Applications and Challenges

Studying the proteome–the entire set of proteins in cells, tissues, organs and body fluids—is of great relevance in cancer research, as differential forms of proteins are expressed in response to specific intrinsic and extrinsic signals. Discovering protein signatures/pathways responsible for cancer transformation may lead to a better understanding of tumor biology and to a more effective diagnosis, prognosis, recurrence and response to therapy. Moreover, proteins can act as a biomarker or potential drug targets. Hence, it is of major importance to implement proteomic, particularly mass spectrometric, approaches in cancer research, to provide new crucial insights into tumor biology. Recently, mass spectrometry imaging (MSI) approaches were implemented in cancer research, to provide individual molecular characteristics of each individual tumor while retaining molecular spatial distribution, essential in the context of personalized disease management and medicine.

Serum proteomic predicts effectiveness and reveals potential biomarkers for complications in liver transplant patients

Sophisticated postoperative complications limit the long-term clinical success of liver transplantation. Hence, early identification of biomarkers is essential for graft and patient survival. High-throughput serum proteomics technologies provide an opportunity to identify diagnostic and prognostic biomarkers. This study is aimed to identify serum diagnosis biomarkers for complications and monitor effectiveness. Serum samples from 10 paired pre- and post-liver transplant patients, 10 acute rejection (AR) patients, 9 ischemic-type biliary lesion (ITBL) patients, and 10 healthy controls were screened using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to explore divergence in polypeptide. Then, we used ELISA and western blot analysis to validate the expression of these potential biomarkers, and studied the correlation of proteomic profiles with clinical parameters. ACLY, FGA, and APOA1 were significantly lower in pre-operative patients compared with healthy controls, and these patients had modest recovery after transplantation. Downregulation of both, ACLY and FGA, was also observed in AR and ITBL patients. Furthermore, bioinformatics analysis was performed and the results suggested that the identified proteins were involved in glucolipid metabolism and the clotting cascade. Together, these findings suggest that ACLY, FGA, and APOA1 could be novel non-invasive and early biomarkers to detect complications and predict effectiveness of liver transplantation.

A proteomics outlook towards the elucidation of epithelial-mesenchymal transition molecular events

The main cause of death in cancer is the spread, or metastasis, of cancer cells to distant organs with consequent tumor formation. Additionally, metastasis is a process that demands special attention, as the cellular transformations make cancer at this stage very difficult or occasionally even impossible to be cured. The main process that converts epithelial tumor cells to mesenchymal-like metastatic cells is the Epithelial to Mesenchymal Transition (EMT). This process allows stationary and polarized epithelial cells, which are connected laterally to several types of junctions as well as the basement membrane, to undergo multiple biochemical changes that enable disruption of cell-cell adherence and apical-basal polarity. Moreover, the cells undergo important reprogramming to remodel the cytoskeleton and acquire mesenchymal characteristics such as enhanced migratory capacity, invasiveness, elevated resistance to apoptosis and a large increase in the production of ECM components. As expected, the alterations of the protein complement are extensive and complex, and thus exploring this by proteomic approaches is of particular interest. Here we review the overall findings of proteome modifications during EMT, mainly focusing on molecular signatures observed in multiple proteomic studies as well as coordinated pathways, cellular processes and their clinical relevance for altered proteins. As a result, an interesting set of proteins is highlighted as potential targets to be further investigated in the context of EMT, metastasis and cancer progression.

Stromal interaction molecule 1 promotes tumor growth in Esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a disease with poor prognosis which urgently is in need of effective prognostic marker. To discover novel prognostic protein marker for ESCC, we applied a high-throughput monoclonal antibody microarray to compare tumor and adjacent non-tumor tissues from ESCC patients. Antibody #ESmAb270 was consistent higher expressed in tumors and it was identified via mass spectrometry to be stromal interaction molecule 1 (STIM1). STIM1 H scores in tumor tissues were significantly up-regulated in esophageal tumor tissues compared to non-tumor tissues in 105 ESCC patients. We also observed that high STIM1 expression was correlated with advanced tumor grade and poor prognosis of ESCC. In addition, attenuation of STIM1 by siRNA or chemical inhibitors significantly inhibited cell viability and migration of ESCC cells. Evidence from high-throughput monoclonal antibody microarray, IHC microarray with associated survival data and functional analysis show that STIM1 is an unfavorable prognostic biomarker in ESCC.

Converting the personalized biomolecular corona of graphene oxide nanoflakes into a high-throughput diagnostic test for early cancer detection

Advances in nanotechnology are introducing the exciting possibility of cancer identification at early stages via analysis of the personalized biomolecular corona (BC), i.e. the dynamic "halo" of proteins that adsorbs onto NPs following exposure to patients' plasma. In this study, we develop a blood test for early cancer detection based on the characterization of the BC that forms around Graphene Oxide (GO) nanoflakes. Among its elective properties, GO binds low amounts of albumin, the most abundant protein in the blood and one of the most enriched proteins in the BC of many nanomaterials. This unique property of GO allows strong adsorption of poorly concentrated plasma proteins without abundant protein depletion. In our study, GO nanometric flakes have been used to analyze BCs from 50 subjects, half of them diagnosed with pancreatic cancer and half of them being healthy volunteers. Pancreatic cancer was chosen as the model of a high mortality disease with poor survival rates due to its delayed diagnosis. The receiver operating characteristic (ROC) curve analysis was applied to measure the diagnostic accuracy of the BC-based test. We obtained an area under the curve (AUC) of 0.96 and the test discriminated cancer patients from healthy subjects with a sensitivity of 92%. Finally, a double-blind validation was made using a second test dataset (10 healthy subjects + 10 pancreatic cancer patients) and it confirmed the results obtained on the first training dataset. Being highly accurate, fast, inexpensive and easy to perform, we believe that the BC-enabled blood test has the potential to become a turning point in early detection of cancer and other diseases.

The Relevance of Mass Spectrometry Analysis for Personalized Medicine through Its Successful Application in Cancer "Omics"

Mass spectrometry (MS) is an essential analytical technology on which the emerging omics domains; such as genomics; transcriptomics; proteomics and metabolomics; are based. This quantifiable technique allows for the identification of thousands of proteins from cell culture; bodily fluids or tissue using either global or targeted strategies; or detection of biologically active metabolites in ultra amounts. The routine performance of MS technology in the oncological field provides a better understanding of human diseases in terms of pathophysiology; prevention; diagnosis and treatment; as well as development of new biomarkers; drugs targets and therapies. In this review; we argue that the recent; successful advances in MS technologies towards cancer omics studies provides a strong rationale for its implementation in biomedicine as a whole.

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology

In order to overcome the limitations of classic imaging in Histology during the actually era of multiomics, the multi-color "molecular microscope" by its emerging "molecular pictures" offers quantitative and spatial information about thousands of molecular profiles without labeling of potential targets. Healthy and diseased human tissues, as well as those of diverse invertebrate and vertebrate animal models, including genetically engineered species and cultured cells, can be easily analyzed by histology-directed MALDI imaging mass spectrometry. The aims of this review are to discuss a range of proteomic information emerging from MALDI mass spectrometry imaging comparative to classic histology, histochemistry and immunohistochemistry, with applications in biology and medicine, concerning the detection and distribution of structural proteins and biological active molecules, such as antimicrobial peptides and proteins, allergens, neurotransmitters and hormones, enzymes, growth factors, toxins and others. The molecular imaging is very well suited for discovery and validation of candidate protein biomarkers in neuroproteomics, oncoproteomics, aging and age-related diseases, parasitoproteomics, forensic, and ecotoxicology. Additionally, in situ proteome imaging may help to elucidate the physiological and pathological mechanisms involved in developmental biology, reproductive research, amyloidogenesis, tumorigenesis, wound healing, neural network regeneration, matrix mineralization, apoptosis and oxidative stress, pain tolerance, cell cycle and transformation under oncogenic stress, tumor heterogeneity, behavior and aggressiveness, drugs bioaccumulation and biotransformation, organism's reaction against environmental penetrating xenobiotics, immune signaling, assessment of integrity and functionality of tissue barriers, behavioral biology, and molecular origins of diseases. MALDI MSI is certainly a valuable tool for personalized medicine and "Eco-Evo-Devo" integrative biology in the current context of global environmental challenges.

Probing the endogenous peptidomes of cancer for biomarkers: A new endeavor

The life span of cancer patients can be prolonged with appropriate therapies if detected early. Mass screening for early detection of cancer, however, requires sensitive and specific biomarkers obtainable from body fluids such as blood or urine. To date, most biomarker discovery programs focus on the proteome rather than the endogenous peptidome. It has been long-established that tumor cells and stromal cells produce tumor resident proteases (TRPs) to remodel the surrounding tumor microenvironment in support of tumor progression. In fact, proteolytic products of TRPs have been shown to correlate with malignant behavior. Being of low molecular weight, these unique peptides can pass through the endothelial barrier of the vasculature into the bloodstream. As such, the cancer peptidome has increasingly become a focus for biomarker discovery. In this review, we discuss on the various aspects of the peptidome in cancer biomarker research.

Experimental Design in Clinical 'Omics Biomarker Discovery

This tutorial highlights some issues in the experimental design of clinical 'omics biomarker discovery, how to avoid bias and get as true quantities as possible from biochemical analyses, and how to select samples to improve the chance of answering the clinical question at issue. This includes the importance of defining clinical aim and end point, knowing the variability in the results, randomization of samples, sample size, statistical power, and how to avoid confounding factors by including clinical data in the sample selection, that is, how to avoid unpleasant surprises at the point of statistical analysis. The aim of this Tutorial is to help translational clinical and preclinical biomarker candidate research and to improve the validity and potential of future biomarker candidate findings.

Proteomic Differences in Feline Fibrosarcomas Grown Using Doxorubicin-Sensitive and -Resistant Cell Lines in the Chick Embryo Model

Proteomic analyses are rapid and powerful tools that are used to increase the understanding of cancer pathogenesis, discover cancer biomarkers and predictive markers, and select and monitor novel targets for cancer therapy. Feline injection-site sarcomas (FISS) are aggressive skin tumours with high recurrence rates, despite treatment with surgery, radiotherapy, and chemotherapy. Doxorubicin is a drug of choice for soft tissue sarcomas, including FISS. However, multidrug resistance is one of the major causes of chemotherapy failure. The main aim of the present study was to identify proteins that differentiate doxorubicin-resistant from doxorubicin-sensitive FISS using two-dimensional gel electrophoresis (2DE), followed by matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) analysis. Using the three-dimensional (3D) preclinical in ovo model, which resembles features of spontaneous fibrosarcomas, three significantly (p ≤ 0.05) differentially expressed proteins were identified in tumours grown from doxorubicin-resistant fibrosarcoma cell lines (FFS1 and FFS3) in comparison to the doxorubicin-sensitive one (FFS5): Annexin A5 (ANXA5), Annexin A3 (ANXA3), and meiosis-specific nuclear structural protein 1 (MNS1). Moreover, nine other proteins were significantly differentially expressed in tumours grown from the high doxorubicin-resistant cell line (FFS1) in comparison to sensitive one (FFS5). This study may be the first proteomic fingerprinting of FISS reported, identifying potential candidates for specific predictive biomarkers and research targets for doxorubicin-resistant FISS.

Selected reaction monitoring approach for validating peptide biomarkers

We here describe a selected reaction monitoring (SRM)-based approach for the discovery and validation of peptide biomarkers for cancer. The first stage of this approach is the direct identification of candidate peptides through comparison of proteolytic peptides derived from the plasma of cancer patients or healthy individuals. Several hundred candidate peptides were identified through this method, providing challenges for choosing and validating the small number of peptides that might prove diagnostically useful. To accomplish this validation, we used 2D chromatography coupled with SRM of candidate peptides. We applied this approach, called sequential analysis of fractionated eluates by SRM (SAFE-SRM), to plasma from cancer patients and discovered two peptides encoded by the peptidyl-prolyl cis-trans isomerase A (PPIA) gene whose abundance was increased in the plasma of ovarian cancer patients. At optimal thresholds, elevated levels of at least one of these two peptides was detected in 43 (68.3%) of 63 women with ovarian cancer but in none of 50 healthy controls. In addition to providing a potential biomarker for ovarian cancer, this approach is generally applicable to the discovery of peptides characteristic of various disease states.

Coatomer subunit beta 2 (COPB2), identified by label-free quantitative proteomics, regulates cell proliferation and apoptosis in human prostate carcinoma cells

Label-free quantitative proteomics has broad applications in the identification of differentially expressed proteins. Here, we applied this method to identify differentially expressed proteins (such as coatomer subunit beta 2 [COPB2]) and evaluated the functions and molecular mechanisms of these proteins in prostate cancer (PCA) cell proliferation. Proteins extracted from surgically resected PCA tissues and adjacent tissues of 3 patients were analyzed by label-free quantitative proteomics. The target protein was confirmed by bioinformatics and GEO dataset analyses. To investigate the role of the target protein in PCA, we used lentivirus-mediated small-interfering RNA (siRNA) to knockdown protein expression in the prostate carcinoma cell line, CWR22RV1 cells and assessed gene and protein expression by reverse transcription quantitative polymerase chain reaction and western blotting. CCK8 and colony formation assays were conducted to evaluate cell proliferation. Cell cycle distributions and apoptosis were assayed by flow cytometry. We selected the differentiation-related protein COPB2 as our target protein based on the results of label-free quantitative proteomics. High expression of COPB2 was found in PCA tissue and was related to poor overall survival based on a public dataset. Cell proliferation was significantly inhibited in COPB2-knockdown CWR22RV1 cells, as demonstrated by CCK8 and colony formation assays. Additionally, the apoptosis rate and percentage of cells in the G₁ phase were increased in COPB2-knockdown cells compared with those in control cells. CDK2, CDK4, and cyclin D1 were downregulated, whereas p21 Waf1/Cip1 and p27 Kip1 were upregulated, affecting the cell cycle signaling pathway. COPB2 significantly promoted CWR22RV1 cell proliferation through the cell cycle signaling pathway. Thus, silencing of COPB2 may have therapeutic applications in PCA.

Mass Spectrometric Studies of Apolipoprotein Proteoforms and Their Role in Lipid Metabolism and Type 2 Diabetes

Apolipoproteins function as structural components of lipoprotein particles, cofactors for enzymes, and ligands for cell-surface receptors. Most of the apoliporoteins exhibit proteoforms, arising from single nucleotide polymorphisms (SNPs) and post-translational modifications such as glycosylation, oxidation, and sequence truncations. Reviewed here are recent studies correlating apolipoproteins proteoforms with the specific clinical measures of lipid metabolism and cardiometabolic risk. Targeted mass spectrometric immunoassays toward apolipoproteins A-I, A-II, and C-III were applied on large cross-sectional and longitudinal clinical cohorts. Several correlations were observed, including greater apolipoprotein A-I and A-II oxidation in patients with diabetes and cardiovascular disease, and a divergent apoC-III proteoforms association with plasma triglycerides, indicating significant differences in the metabolism of the individual apoC-III proteoforms. These are the first studies of their kind, correlating specific proteoforms with clinical measures in order to determine their utility as potential clinical biomarkers for disease diagnosis, risk stratification, and therapy decisions. Such studies provide the impetus for the further development and clinical translation of MS-based protein tests.

Global Proteomics-based Identification and Validation of Thymosin Beta-4 X-Linked as a Prognostic Marker for Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) represents a major health concern worldwide. We applied the matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) to analyze paired normal (N) and tumor (T) samples from head and neck squamous cell carcinoma as well as liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis in HNSCC cell lines to identify tumor-associated biomarkers. Our results showed a number of proteins found to be over-expressed in HNSCC. We identified thymosin beta-4 X-linked (TMSB4X) is one of the most significant candidate biomarkers. Higher TMSB4X expression in the tumor was found by N/T-paired HNSCC samples at both RNA and protein level. Overexpression of TMSB4X was found significantly associated with poor prognosis of overall survival (OS, P = 0.006) and recurrence-free survival (RFS, P = 0.013) in HNSCC patients. Silencing of TMSB4X expression in HNSCC cell line reduced the proliferation and invasion ability in vitro, as well as inhibited the cervical lymph node metastasis in vivo. Altogether, our global proteomics analysis identified that TMSB4X is a newly discovered biomarker in HNSCC whose functions resulted in enhanced proliferation and metastasis in vitro and in vivo. TMSB4X may be a potential therapeutic target for treating HNSCC patients.

Reconstruction of pathway modification induced by nicotinamide using multi-omic network analyses in triple negative breast cancer

Triple negative breast cancer (TNBC) is characterized by an aggressive biological behavior in the absence of a specific target agent. Nicotinamide has recently been proven to be a novel therapeutic agent for skin tumors in an ONTRAC trial. We performed combinatory transcriptomic and in-depth proteomic analyses to characterize the network of molecular interactions in TNBC cells treated with nicotinamide. The multi-omic profiles revealed that nicotinamide drives significant functional alterations related to major cellular pathways, including the cell cycle, DNA replication, apoptosis and DNA damage repair. We further elaborated the global interaction networks of molecular events via nicotinamide-inducible expression changes at the mRNA and functional protein levels. This approach indicated that nicotinamide treatment rewires interaction networks toward dysfunction in DNA damage repair and away from a pro-growth state in TNBC. To our knowledge, the high-resolution network interactions identified in the present study provide the first evidence to comprehensively support the hypothesis of nicotinamide as a novel therapeutic agent in TNBC.

Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.

Precision medicine in cancer: challenges and recommendations from an EU-funded cervical cancer biobanking study

Background:

Cervical cancer (CC) remains a leading cause of gynaecological cancer-related mortality worldwide. CC pathogenesis is triggered when human papillomavirus (HPV) inserts into the genome, resulting in tumour suppressor gene inactivation and oncogene activation. Collecting tumour and blood samples is critical for identifying these genetic alterations.

Methods:

BIO-RAIDs is the first prospective molecular profiling clinical study to include a substantial biobanking effort that used uniform high-quality standards and control of samples. In this European Union (EU)-funded study, we identified the challenges that were impeding the effective implementation of such a systematic and comprehensive biobanking effort.

Results:

The challenges included a lack of uniform international legal and ethical standards, complexities in clinical and molecular data management, and difficulties in determining the best technical platforms and data analysis techniques. Some difficulties were encountered by all investigators, while others affected only certain institutions, regions, or countries.

Conclusions:

The results of the BIO-RAIDs programme highlight the need to facilitate and standardise regulatory procedures, and we feel that there is also a need for international working groups that make recommendations to regulatory bodies, governmental funding agencies, and academic institutions to achieve a proficient biobanking programme throughout EU countries. This represents the first step in precision medicine.