Differential profiling of breast cancer plasma proteome by isotope-coded affinity tagging method reveals biotinidase as a breast cancer biomarker

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.

Potential therapeutic targets for membranous nephropathy: proteome-wide Mendelian randomization and colocalization analysis

Background

The currently available medications for treating membranous nephropathy (MN) still have unsatisfactory efficacy in inhibiting disease recurrence, slowing down its progression, and even halting the development of end-stage renal disease. There is still a need to develop novel drugs targeting MN.

Methods

We utilized summary statistics of MN from the Kiryluk Lab and obtained plasma protein data from Zheng et al. We performed a Bidirectional Mendelian randomization analysis, HEIDI test, mediation analysis, Bayesian colocalization, phenotype scanning, drug bank analysis, and protein-protein interaction network.

Results

The Mendelian randomization analysis uncovered 8 distinct proteins associated with MN after multiple false discovery rate corrections. Proteins related to an increased risk of MN in plasma include ABO [(Histo-Blood Group Abo System Transferase) (WR OR = 1.12, 95%CI:1.05-1.19, FDR=0.09, PPH4 = 0.79)], VWF [(Von Willebrand Factor) (WR OR = 1.41, 95%CI:1.16-1.72, FDR=0.02, PPH4 = 0.81)] and CD209 [(Cd209 Antigen) (WR OR = 1.19, 95%CI:1.07-1.31, FDR=0.09, PPH4 = 0.78)], and proteins that have a protective effect on MN: HRG [(Histidine-Rich Glycoprotein) (WR OR = 0.84, 95%CI:0.76-0.93, FDR=0.02, PPH4 = 0.80)], CD27 [(Cd27 Antigen) (WR OR = 0.78, 95%CI:0.68-0.90, FDR=0.02, PPH4 = 0.80)], LRPPRC [(Leucine-Rich Ppr Motif-Containing Protein, Mitochondrial) (WR OR = 0.79, 95%CI:0.69-0.91, FDR=0.09, PPH4 = 0.80)], TIMP4 [(Metalloproteinase Inhibitor 4) (WR OR = 0.67, 95%CI:0.53-0.84, FDR=0.09, PPH4 = 0.79)] and MAP2K4 [(Dual Specificity Mitogen-Activated Protein Kinase Kinase 4) (WR OR = 0.82, 95%CI:0.72-0.92, FDR=0.09, PPH4 = 0.80)]. ABO, HRG, and TIMP4 successfully passed the HEIDI test. None of these proteins exhibited a reverse causal relationship. Bayesian colocalization analysis provided evidence that all of them share variants with MN. We identified type 1 diabetes, trunk fat, and asthma as having intermediate effects in these pathways.

Conclusions

Our comprehensive analysis indicates a causal effect of ABO, CD27, VWF, HRG, CD209, LRPPRC, MAP2K4, and TIMP4 at the genetically determined circulating levels on the risk of MN. These proteins can potentially be a promising therapeutic target for the treatment of MN.

Comparative profiling by data-independent acquisition mass spectrometry reveals featured plasma proteins in breast cancer: a pilot study

Purpose

Breast cancer is known to be influenced by genetic and environmental factors, and several susceptibility genes have been discovered. Still, the majority of genetic contributors remain unknown. We aimed to analyze the plasma proteome of breast cancer patients in comparison to healthy individuals to identify differences in protein expression profiles and discover novel biomarkers.

Methods

This pilot study was conducted using bioresources from Seoul National University Bundang Hospital's Human Bioresource Center. Serum samples from 10 breast cancer patients and 10 healthy controls were obtained. Liquid chromatography-mass spectrometry analysis was performed to identify differentially expressed proteins.

Results

We identified 891 proteins; 805 were expressed in the breast cancer group and 882 in the control group. Gene set enrichment and differential expression analysis identified 30 upregulated and 100 downregulated proteins in breast cancer. Among these, 10 proteins were selected as potential biomarkers. Three proteins were upregulated in breast cancer patients, including cluster of differentiation 44, eukaryotic translation initiation factor 2-α kinase 3, and fibronectin 1. Seven proteins downregulated in breast cancer patients were also selected: glyceraldehyde-3-phosphate dehydrogenase, α-enolase, heat shock protein member 8, integrin-linked kinase, tissue inhibitor of metalloproteinases-1, vasodilator-stimulated phosphoprotein, and 14-3-3 protein gamma. All proteins had been previously reported to be related to tumor development and progression.

Conclusion

The findings suggest that plasma proteome profiling can reveal potential diagnostic biomarkers for breast cancer and may contribute to early detection and personalized treatment strategies. A further validation study with a larger sample cohort of breast cancer patients is planned.

Plasma proteome of growing tumors

Early detection of cancer is vital for the best chance of successful treatment, but half of all cancers are diagnosed at an advanced stage. A simple and reliable blood screening test applied routinely would therefore address a major unmet medical need. To gain insight into the value of protein biomarkers in early detection and stratification of cancer we determined the time course of changes in the plasma proteome of mice carrying transplanted human lung, breast, colon, or ovarian tumors. For protein measurements we used an aptamer-based assay which simultaneously measures ~ 5000 proteins. Along with tumor lineage-specific biomarkers, we also found 15 markers shared among all cancer types that included the energy metabolism enzymes glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phophate isomerase and dihydrolipoyl dehydrogenase as well as several important biomarkers for maintaining protein, lipid, nucleotide, or carbohydrate balance such as tryptophanyl t-RNA synthetase and nucleoside diphosphate kinase. Using significantly altered proteins in the tumor bearing mice, we developed models to stratify tumor types and to estimate the minimum detectable tumor volume. Finally, we identified significantly enriched common and unique biological pathways among the eight tumor cell lines tested.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

A Simple and Low-Cost Electrochemical Immunosensor for Ultrasensitive Determination of Calreticulin Biomarker in Human Serum

An early on time detection of breast cancer significantly affects the treatment process and outcome. Herein, a new label-free impedimetric biosensor is developed to determine the lowest change in the level of calreticulin (CALR), which is a new biomarker of breast carcinoma. The proposed immunosensor is fabricated by using reduced graphene oxide/amino substituted polypyrrole polymer (rGO-PPyNH₂ ) nanocomposite modified disposable electrode. The anti-CALR antibodies are first attached on the rGO-PPyNH₂ nanocomposite coated electrode through glutaraldehyde crosslinking; the CALR antigens are then immobilized with the addition of CALR antigens to form an immunocomplex on the sensing surface. This immunocomplex induces considerably larger interfacial electron transport resistance (R_ct ). The variation in the R_ct has a linear relationship with CALR level in the detection range of 0.025 to 75 pg mL^-1 , with a detection limit of 10.4 fg mL^-1 . The suggested biosensor shows high selectivity to CALR, good storage stability (at least 5 weeks) and suitable reproducibility results as shown in quality control chart. The designed immunosensor is utilized to analyze CALR levels in human sera with satisfying results. This immunosensor provides a novel way for the clinical determination of CALR and other cancer biological markers.

Diagnostic accuracy of a three-protein signature in women with suspicious breast lesions: a multicenter prospective trial

BACKGROUND

Mammography screening has been proven to detect breast cancer at an early stage and reduce mortality; however, it has low accuracy in young women or women with dense breasts. Blood-based diagnostic tools may overcome the limitations of mammography. This study assessed the diagnostic performance of a three-protein signature in patients with suspicious breast lesions.

FINDINGS

This trial (MAST; KCT0004847) was a prospective multicenter observational trial. Three-protein signature values were obtained using serum and plasma from women with suspicious lesions for breast malignancy before tumor biopsy. Additionally, blood samples from women who underwent clear or benign mammography were collected for the assays. Among 642 participants, the sensitivity, specificity, and overall accuracy values of the three-protein signature were 74.4%, 66.9%, and 70.6%, respectively, and the concordance index was 0.698 (95% CI 0.656, 0.739). The diagnostic performance was not affected by the demographic features, clinicopathologic characteristics, and co-morbidities of the participants.

CONCLUSIONS

The present trial showed an accuracy of 70.6% for the three-protein signature. Considering the value of blood-based biomarkers for the early detection of breast malignancies, further evaluation of this proteomic assay is warranted in larger, population-level trials. This Multi-protein Assessment using Serum to deTermine breast lesion malignancy (MAST) was registered at the Clinical Research Information Service of Korea with the identification number of KCT0004847 ( https://cris.nih.go.kr ).

Impedimetric Detection of Calreticulin by a Disposable Immunosensor Modified with a Single-Walled Carbon Nanotube-Conducting Polymer Nanocomposite

A label-free impedimetric immunosensing system was constructed for ultrasensitive determination of the calreticulin (CALR) biological marker in human serum samples utilizing an electrochemical impedance spectroscopy analysis technique for the first time. The new biosensor fabrication procedure consisted of electrodeposition of single-walled carbon nanotubes (SWCNTs) incorporating polymerization of an oxiran-2-yl methyl 3-(1H-pyrrol-1-yl) propanoate monomer (Pepx) onto a low-cost and disposable indium tin oxide (ITO) electrode. The SWCNTs-PPepx nanocomposite layer was prepared onto the ITO after the one-step fabrication procedure. The fabrication procedure of the immunosensor and the characteristic biomolecular interactions between the anti-CALR and CALR were characterized by electrochemical analysis and morphological monitoring techniques. Under optimum conditions, the proposed biosensor was responsive to CALR concentrations over the detection ranges of 0.015-60 pg/mL linearly, and it had a very low detection limit (4.6 fg/mL) and a favorable sensitivity (0.43 kΩ pg^-1 mL cm^-2). The reliability of the biosensor system in clinical analysis was investigated by successful quantification of CALR levels in human serum. Moreover, the repeatability and reproducibility results of the biosensor were evaluated by using Dixon, Grubbs, T-test, and F-tests. Consequently, the proposed biosensor was a promising method for scientific, rapid, and successful analysis of CALR in human serum samples.

Diagnostic performance improvement with combined use of proteomics biomarker assay and breast ultrasound

PURPOSE

To investigate the combined use of blood-based 3-protein signature and breast ultrasound (US) for validating US-detected lesions.

METHODS

From July 2011 to April 2020, women who underwent whole-breast US within at least 6 months from sampling period were retrospectively included. Blood-based 3-protein signature (Mastocheck®) value and US findings were evaluated. Following outcome measures were compared between US alone and the combination of Mastocheck® value with US: sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the receiver operating characteristic curve (AUC), and biopsy rate.

RESULTS

Among the 237 women included, 59 (24.9%) were healthy individuals and 178 (75.1%) cancer patients. Mean size of cancers was 1.2 ± 0.8 cm. Median value of Mastocheck® was significantly different between nonmalignant (- 0.24, interquartile range [IQR] - 0.48, - 0.03) and malignant lesions (0.55, IQR - 0.03, 1.42) (P < .001). Utilizing Mastocheck® value with US increased the AUC from 0.67 (95% confidence interval [CI] 0.61, 0.73) to 0.81 (95% CI 0.75, 0.88; P < .001), and specificity from 35.6 (95% CI 23.4, 47.8) to 64.4% (95% CI 52.2, 76.6; P < .001) without loss in sensitivity. PPV was increased from 82.2 (95% CI 77.1, 87.3) to 89.3% (95% CI 85.0, 93.6; P < .001), and biopsy rate was significantly decreased from 79.3 (188/237) to 72.1% (171/237) (P < .001). Consistent improvements in specificity, PPV, and AUC were observed in asymptomatic women, in women with dense breast, and in those with normal/benign mammographic findings.

CONCLUSION

Mastocheck® is an effective tool that can be used with US to improve diagnostic specificity and reduce false-positive findings and unnecessary biopsies.

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

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.

StanDep: Capturing transcriptomic variability improves context-specific metabolic models

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep.

Hippocampal proteomic changes of susceptibility and resilience to depression or anxiety in a rat model of chronic mild stress

Chronic stressful occurrences are documented as a vital cause of both depression and anxiety disorders. However, the stress-induced molecular mechanisms underlying the common and distinct pathophysiology of these disorders remains largely unclear. We utilized a chronic mild stress (CMS) rat model to differentiate and subgroup depression-susceptible, anxiety-susceptible, and insusceptible rats. The hippocampus was analyzed for differential proteomes by combining mass spectrometry and the isobaric tags for relative and absolute quantitation (iTRAQ) labeling technique. Out of 2593 quantified proteins, 367 were aberrantly expressed. These hippocampal protein candidates might be associated with susceptibility to stress-induced depression or anxiety and stress resilience. They provide the potential protein systems involved in various metabolic pathways as novel investigative protein targets. Further, independent immunoblot analysis identified changes in Por, Idh2 and Esd; Glo1, G6pdx, Aldh2, and Dld; Dlat, Ogdhl, Anxal, Tpp2, and Sdha that were specifically associated to depression-susceptible, anxiety-susceptible, or insusceptible groups respectively, suggesting that identical CMS differently impacted the mitochondrial and metabolic processes in the hippocampus. Collectively, the observed alterations to protein abundance profiles of the hippocampus provided significant and novel insights into the stress regulation mechanism in a CMS rat model. This might serve as the molecular basis for further studies that would contributed to a better understanding of the similarities and differences in pathophysiologic mechanisms underlying stress-induced depression or anxiety, and stress resiliency.

The prospect of discovering new biomarkers for ovarian cancer based on current knowledge of susceptibility loci and genetic variation (Review)

Ovarian cancer is the most lethal gynaecological malignancy. The cancer initially presents with non‑specific symptoms; thus, it is typically not discovered until the patient has reached the late, considerably more lethal, stages of the disease. Research focus is currently on finding novel biomarkers, especially for early detection and stratification of the disease. One promising approach has been to focus on mutations or variations in the genetic code that are associated with the risk of developing ovarian cancer. A certain heritable component is already known regarding genes such as BRCA1/2, TP53, MSH6, BRIP1 and RAD51C, yet these are estimated to only account for ~3.1% of the total risk. Recent advances in sequencing technologies have enabled the investigation of hundreds of thousands of genetic variants in genome‑wide association studies in tens of thousands of patients, which has led to the discovery of 108 (39 loci with P<5.0x10‑8) novel susceptibility loci for ovarian cancer, presented in this review. Using the published variants in a patient cohort screening, together with variants identified in our ongoing whole exome sequencing project, future aims are to ascertain whether certain of the novel variants could be used as biomarkers for early diagnosis and/or treatment decisions.

Analytical techniques for characterization of biological molecules - proteins and aptamers/oligonucleotides

With the advent of the high-throughput technologies and exciting times for biology, the discipline of analytical methodology is experiencing a surge in the growth and the scope. Over the years, multitude of analytical techniques have evolved from a work-intensive, low sensitivity and high volume of reagent and sample consumption endeavor to automated, better selectivity, lower limit of quantification and cost-effective techniques for biological research. In this review, we give an overview of the currently available wide range of cell-based and noncell based and structural based analytical techniques, their principle and biological applications. The analytical techniques discussed in this paper includes surface plasmon resonance, electrophoresis, enzyme linked immunosorbent assay, Western blotting, flow cytometry, fluorescence activated cell sorting, mass spectrometry, nuclear magnetic resonance and x-ray crystallography.

Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes).

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.

Exome genotyping arrays to identify rare and low frequency variants associated with epithelial ovarian cancer risk

Rare and low frequency variants are not well covered in most germline genotyping arrays and are understudied in relation to epithelial ovarian cancer (EOC) risk. To address this gap, we used genotyping arrays targeting rarer protein-coding variation in 8,165 EOC cases and 11,619 controls from the international Ovarian Cancer Association Consortium (OCAC). Pooled association analyses were conducted at the variant and gene level for 98,543 variants directly genotyped through two exome genotyping projects. Only common variants that represent or are in strong linkage disequilibrium (LD) with previously-identified signals at established loci reached traditional thresholds for exome-wide significance (P < 5.0 × 10 -  7). One of the most significant signals (Pall histologies = 1.01 × 10 -  13;Pserous = 3.54 × 10 -  14) occurred at 3q25.31 for rs62273959, a missense variant mapping to the LEKR1 gene that is in LD (r2 = 0.90) with a previously identified 'best hit' (rs7651446) mapping to an intron of TIPARP. Suggestive associations (5.0 × 10 -  5 > P≥5.0 ×10 -  7) were detected for rare and low-frequency variants at 16 novel loci. Four rare missense variants were identified (ACTBL2 rs73757391 (5q11.2), BTD rs200337373 (3p25.1), KRT13 rs150321809 (17q21.2) and MC2R rs104894658 (18p11.21)), but only MC2R rs104894668 had a large effect size (OR = 9.66). Genes most strongly associated with EOC risk included ACTBL2 (PAML = 3.23 × 10 -  5; PSKAT-o = 9.23 × 10 -  4) and KRT13 (PAML = 1.67 × 10 -  4; PSKAT-o = 1.07 × 10 -  5), reaffirming variant-level analysis. In summary, this large study identified several rare and low-frequency variants and genes that may contribute to EOC susceptibility, albeit with possible small effects. Future studies that integrate epidemiology, sequencing, and functional assays are needed to further unravel the unexplained heritability and biology of this disease.

Quantitative proteomic analysis exploring progression of colorectal cancer: Modulation of the serpin family

Colorectal cancer (CRC) remains a major cause of cancer related-death in developed countries. The mortality risk is correlated with the stage of CRC determined at the primary diagnosis and early diagnosis is associated with enhanced survival rate. Currently, only faecal occult blood tests are used to screen for CRC. Consequently, there is an incentive to identify specific markers of CRC. We used quantitative proteomic analysis of serum samples to characterize protein profiles in adenoma, CRC and healthy control samples. We identified 89 distinct proteins modulated between normal, colorectal adenoma and carcinoma patients. This list emphasizes proteins involved in enzyme regulator activities and in particular the serpin family. In serum samples, protein profiles of three members of the serpin family (SERPINA1, SERPINA3 and SERPINC1) were confirmed by ELISA assays. We obtained sensitivity/specificity values of 95%/95% for both SERPINA1 and SERPINC1, and 95%/55% for SERPINA3. This study supports the idea that serum proteins can discriminate adenoma and CRC patients from unaffected patients and reveals a panel of regulated proteins that might be useful for selecting patients for colonoscopy. By evaluating SERPINA1, SERPINA3 and SERPINC1, we highlight the potential role of the serpin family during the development and progression of CRC.

SIGNIFICANCE

Colorectal cancer (CRC) remains a major cause of cancer mortality throughout the world. However, very few CRC biomarkers have satisfactory sensitivity and specificity in clinical practice. To the best of our knowledge our study is the first to profile sera proteomes between adenoma, CRC and healthy patients. We report a comprehensive list of proteins that may be used as early diagnostic biomarkers of CRC. It is noteworthy that 17% of these modulated proteins have been previously described as candidate biomarkers in CRC. Enzyme regulator activity was found to be the main molecular function among these proteins and, in particular, there was an enrichment of members of the serpin family. The subsequent verification on a new cohort by ELISA demonstrates that these serpins could be useful to discriminate healthy from colorectal carcinoma patients with a high sensitivity and specificity. The combination of these biomarkers should increase predictive powers of CRC diagnosis. The remaining candidates form a reserve for further evaluation of additional biomarkers for CRC diagnosis.

Repression of Biotin-Related Proteins by Benzo[a]Pyrene-Induced Epigenetic Modifications in Human Bronchial Epithelial Cells

Benzo[a]pyrene (B[a]P) exposure has been associated with the alteration in epigenetic marks that are involved in cancer development. Biotinidase (BTD) and holocarboxylase synthetase (HCS) are 2 major enzymes involved in maintaining the homeostasis of biotinylation, and the deregulation of this pathway has been associated with a number of cancers. However, the link between B[a]P exposure and the dysregulation of BTD/HCS in B[a]P-associated tumorigenesis is unknown. Here we showed that the expression of both BTD and HCS was significantly decreased upon B[a]P treatment in human bronchial epithelial (16HBE) cells. Benzo[a]pyrene exposure led to the global loss of DNA methylation by immunofluorescence, which coincided with the reduction in acetylation levels on histones H3 and H4 in 16HBE cells. Consistent with decreased histone acetylation, histone deacetylases (HDACs) HDAC2 and HDAC3 were significantly upregulated in a dosage-dependent manner. When DNA methylation or HDAC activity was inhibited, we found that the reduction in BTD and HCS was separately regulated through distinct epigenetic mechanisms. Together, our results suggested the potential link between B[a]P toxicity and deregulation of biotin homeostasis pathway in B[a]P-associated cancer development.

The Isotope-Coded Affinity Tag Method for Quantitative Protein Profile Comparison and Relative Quantitation of Cysteine Redox Modifications

The isotope-coded affinity tag (ICAT) technique has been applied to measure pairwise changes in protein expression through differential stable isotopic labeling of proteins or peptides followed by identification and quantification using a mass spectrometer. Changes in protein expression are observed when the identical peptide from each of two biological conditions is identified and a difference is detected in the measurements comparing the peptide labeled with the heavy isotope to the one with a normal isotopic distribution. This approach allows the simultaneous comparison of the expression of many proteins between two different biological states (e.g., yeast grown on galactose versus glucose, or normal versus cancer cells). Due to the cysteine-specificity of the ICAT reagents, the ICAT technique has also been applied to perform relative quantitation of cysteine redox modifications such as oxidation and nitrosylation. This unit describes both protein quantitation and profiling of cysteine redox modifications using the ICAT technique.

Quantitative Proteomic Analysis Reveals Molecular Adaptations in the Hippocampal Synaptic Active Zone of Chronic Mild Stress-Unsusceptible Rats

BACKGROUND

While stressful events are recognized as an important cause of major depressive disorder, some individuals exposed to life stressors maintain normal psychological functioning. The molecular mechanism(s) underlying this phenomenon remain unclear. Abnormal transmission and plasticity of hippocampal synapses have been implied to play a key role in the pathoetiology of major depressive disorder.

METHODS

A chronic mild stress protocol was applied to separate susceptible and unsusceptible rat subpopulations. Proteomic analysis using an isobaric tag for relative and absolute quantitation coupled with tandem mass spectrometry was performed to identify differential proteins in enriched hippocampal synaptic junction preparations.

RESULTS

A total of 4318 proteins were quantified, and 89 membrane proteins were present in differential amounts. Of these, SynaptomeDB identified 81 (91%) having a synapse-specific localization. The unbiased profiles identified several candidate proteins within the synaptic junction that may be associated with stress vulnerability or insusceptibility. Subsequent functional categorization revealed that protein systems particularly involved in membrane trafficking at the synaptic active zone exhibited a positive strain as potential molecular adaptations in the unsusceptible rats. Moreover, through STRING and immunoblotting analysis, membrane-associated GTP-bound Rab3a and Munc18-1 appear to coregulate syntaxin-1/SNAP25/VAMP2 assembly at the hippocampal presynaptic active zone of unsusceptible rats, facilitating SNARE-mediated membrane fusion and neurotransmitter release, and may be part of a stress-protection mechanism in actively maintaining an emotional homeostasis.

CONCLUSIONS

The present results support the concept that there is a range of potential protein adaptations in the hippocampal synaptic active zone of unsusceptible rats, revealing new investigative targets that may contribute to a better understanding of stress insusceptibility.

Analytical platforms in vitreoretinal proteomics

Current proteomic technologies can effectively be used to study the proteins of the vitreous body and retina in health and disease. The use of appropriate samples, analytical platform and bioinformatic method are essential factors to consider when undertaking such studies. Certain proteins may hinder the detection and evaluation of more relevant proteins associated with pathological processes if not carefully considered, particularly in the sample preparation and data analysis stages. The utilization of more than one quantification technique and database search program to expand the level of proteome coverage and analysis will help to generate more robust and worthwhile results. This review discusses important aspects of sample processing and the use of label and label-free quantitative proteomics strategies applied to the vitreous and retina.

Biotin starvation causes mitochondrial protein hyperacetylation and partial rescue by the SIRT3-like deacetylase Hst4p

The essential vitamin biotin is a covalent and tenaciously attached prosthetic group in several carboxylases that play important roles in the regulation of energy metabolism. Here we describe increased acetyl-CoA levels and mitochondrial hyperacetylation as downstream metabolic effects of biotin deficiency. Upregulated mitochondrial acetylation sites correlate with the cellular deficiency of the Hst4p deacetylase, and a biotin-starvation-induced accumulation of Hst4p in mitochondria supports a role for Hst4p in lowering mitochondrial acetylation. We show that biotin starvation and knockout of Hst4p cause alterations in cellular respiration and an increase in reactive oxygen species (ROS). These results suggest that Hst4p plays a pivotal role in biotin metabolism and cellular energy homeostasis, and supports that Hst4p is a functional yeast homologue of the sirtuin deacetylase SIRT3. With biotin deficiency being involved in various metabolic disorders, this study provides valuable insight into the metabolic effects biotin exerts on eukaryotic cells.

Quantitative variability of 342 plasma proteins in a human twin population

The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis-SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood-based biomarker studies.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

Proteomics for breast cancer urine biomarkers

Although the survival of breast cancer (BC) patients has increased over the last two decades due to improved screening programs and postoperative adjuvant systemic therapies, many patients die from metastatic relapse. Current biomarkers used in the clinic are not useful for the early detection of BC, or monitoring its progression, and have limited value in predicting response to treatment. The development of proteomic techniques has sparked new searches for novel protein markers for many diseases including BC. Proteomic techniques allow for a high-throughput analysis of samples with the visualization and quantification of thousands of potential protein and peptide markers. Human urine is one of the most interesting and useful biofluids for routine testing and provides an excellent resource for the discovery of novel biomarkers, with the advantage over tissue biopsy samples due to the ease and less invasive nature of collection. In this review, we summarize the results from studies where urine was used as a source for BC biomarker research and discuss urine sample preparation, its advantage, challenges, and limitation. We focus on the gel-based proteomic approaches as well as the recent development of quantitative techniques in BC urine biomarker detection. Finally, the future use of modern proteomic techniques in BC biomarker identification will be discussed.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.

Proteomics analysis of tumor microenvironment: Implications of metabolic and oxidative stresses in tumorigenesis

Tumorigenesis is always concomitant with microenvironmental alterations. The tumor microenvironment is a heterogeneous and complex milieu, which exerts a variety of stresses on tumor cells for proliferation, survival, or death. Recently, accumulated evidence revealed that metabolic and oxidative stresses both play significant roles in tumor development and progression that converge on a common autophagic pathway. Tumor cells display increased metabolic autonomy, and the hallmark is the exploitation of aerobic glycolysis (termed Warburg effect), which increased glucose consumption and decreased oxidative phosphorylation to support growth and proliferation. This characteristic renders cancer cells more aggressive; they devour tremendous amounts of nutrients from microenvironment to result in an ever-growing appetite for new tumor vessel formation and the release of more "waste," including key determinants of cell fate like lactate and reactive oxygen species (ROS). The intracellular ROS level of cancer cells can also be modulated by a variety of stimuli in the tumor microenvironment, such as pro-growth and pro-inflammatory factors. The intracellular redox state serves as a double-edged sword in tumor development and progression: ROS overproduction results in cytotoxic effects and might lead to apoptotic cell death, whereas certain level of ROS can act as a second-messenger for regulation of such cellular processes as cell survival, proliferation, and metastasis. The molecular mechanisms for cancer cell responses to metabolic and oxidative stresses are complex and are likely to involve multiple molecules or signaling pathways. In addition, the expression and modification of these proteins after metabolic or oxidative stress challenge are diverse in different cancer cells and endow them with different functions. Therefore, MS-based high-throughput platforms, such as proteomics, are indispensable in the global analysis of cancer cell responses to metabolic and oxidative stress. Herein, we highlight recent advances in the understanding of the metabolic and oxidative stresses associated with tumor progression with proteomics-based systems biology approaches.

Advances in membrane proteomics and cancer biomarker discovery: current status and future perspective

Membrane proteomic analysis has been proven to be a promising tool for identifying new and specific biomarkers that can be used for prognosis and monitoring of various cancers. Membrane proteins are of great interest particularly those with functional domains exposed to the extracellular environment. Integral membrane proteins represent about one-third of the proteins encoded by the human genome and assume a variety of key biological functions, such as cell-to-cell communication, receptor-mediated signal transduction, selective transport, and pharmacological actions. More than two-thirds of membrane proteins are drug targets, highlighting their immensely important pharmaceutical significance. Most plasma membrane proteins and proteins from other cellular membranes have several PTMs; for example, glycosylation, phosphorylation, and nitrosylation, and moreover, PTMs of proteins are known to play a key role in tumor biology. These modifications often cause change in stoichiometry and microheterogeneity in a protein molecule, which is apparent during electrophoretic separation. Furthermore, the analysis of glyco- and phosphoproteome of cell membrane presents a number of challenges mainly due to their low abundance, their large dynamic range, and the inherent hydrophobicity of membrane proteins. Under pathological conditions, PTMs, such as phosphorylation and glycosylation are frequently altered and have been recognized as a potential source for disease biomarkers. Thus, their accurate differential expression analysis, along with differential PTM analysis is of paramount importance. Here we summarize the current status of membrane-based biomarkers in various cancers, and future perspective of membrane biomarker research.

Cancer proteomics

Cancer presents high mortality and morbidity globally, largely due to its complex and heterogenous nature, and lack of biomarkers for early diagnosis. A proteomics study of cancer aims to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, determine therapy efficacy, identify novel drug targets, and ultimately develop personalized medicine. The various sources of human samples such as cell lines, tissues, and plasma/serum are probed by a plethora of proteomics tools to discover novel biomarkers and elucidate mechanisms of tumorigenesis. Innovative proteomics technologies and strategies have been designed for protein identification, quantitation, fractionation, and enrichment to delve deeper into the oncoproteome. In addition, there is the need for high-throughput methods for biomarker validation, and integration of the various platforms of oncoproteome data to fully comprehend cancer biology.

Biotinidase is a novel marker for papillary thyroid cancer aggressiveness

Biotinidase was identified in secretome analysis of thyroid cancer cell lines using proteomics. The goal of the current study was to analyze the expression of biotinidase in thyroid cancer tissues and fine needle aspiration (FNA) samples to evaluate its diagnostic and prognostic potential in thyroid cancer. Immunohistochemical analysis of biotinidase was carried out in 129 papillary thyroid cancer (PTC, 34 benign thyroid tissues and 43 FNA samples and correlated with patients' prognosis. Overall biotinidase expression was decreased in PTC compared to benign nodules (p = 0.001). Comparison of aggressive and non-aggressive PTC showed decrease in overall biotinidase expression in the former (p = 0.001). Loss of overall biotinidase expression was associated with poor disease free survival (p = 0.019, Hazards ratio (HR) = 3.1). We examined the effect of subcellular compartmentalization of nuclear and cytoplasmic biotinidase on patient survival. Decreased nuclear expression of biotinidase was observed in PTC as compared to benign tissues (p<0.001). Upon stratification within PTC, nuclear expression was reduced in aggressive as compared to non-aggressive tumors (p<0.001). Kaplan-Meier survival analysis showed significant association of loss of nuclear biotinidase expression with reduced disease free survival (p = 0.014, HR = 5.4). Cytoplasmic biotinidase expression was reduced in aggressive thyroid cancers in comparison with non-aggressive tumors (p = 0.002, Odds ratio (OR) = 0.29) which was evident by its significant association with advanced T stage (p = 0.003, OR = 0.28), nodal metastasis (p<0.001, OR = 0.16), advanced TNM stage (p<0.001, OR = 0.21) and extrathyroidal extension (p = 0.001, OR = 0.23). However, in multivariate analysis extrathyroidal extension emerged as the most significant prognostic marker for aggressive thyroid carcinomas (p = 0.015, HR = 12.8). In conclusion, loss of overall biotinidase expression is a novel marker for thyroid cancer aggressiveness.

Comparative profiling of plasma proteome from breast cancer patients reveals thrombospondin-1 and BRWD3 as serological biomarkers

Breast cancer is the most common cancer in women worldwide. It is necessary to identify biomarkers for early detection, to make accurate prognoses, and to monitor for any recurrence of the cancer. In order to identify potential breast cancer biomarkers, we analyzed the plasma samples of women diagnosed with breast cancer and age-matched normal healthy women by mTRAQ-based stable isotope-labeling mass spectrometry. We identified and quantified 204 proteins including thrombospondin-1 (THBS1) and bromodomain and WD repeat-containing protein 3 (BRWD3) which were increased by more than 5-fold in breast cancer plasma. The plasma levels of the two proteins were evaluated by Western blot assay to confirm for their diagnostic value as serum markers. A 1.8-fold increase in BRWD3 was observed while comparing the plasma levels of breast cancer patients (n = 54) with age-matched normal healthy controls (n = 30), and the area under the receiver operating characteristic curve (AUC) was 0.917. THBS1 was detected in pooled breast cancer plasma at the ratio similar to mTRAQ ratio (> 5-fold). The AUC value for THBS1 was 0.875. The increase of THBS1 was more prominent in estrogen receptor negative and progesterone receptor negative patients than receptor-positive patients. Our results are evidence of the diagnostic value of THBS1 in detecting breast cancer. Based on our findings, we suggest a proteomic method for protein identification and quantification lead to effective biomarker discovery.

Proteomic technologies for the study of osteosarcoma

Osteosarcoma is the most common primary bone cancer of children and is established during stages of rapid bone growth. The disease is a consequence of immature osteoblast differentiation, which gives way to a rapidly synthesized incompletely mineralized and disorganized bone matrix. The mechanism of osteosarcoma tumorogenesis is poorly understood, and few proteomic studies have been used to interrogate the disease thus far. Accordingly, these studies have identified proteins that have been known to be associated with other malignancies, rather than being osteosarcoma specific. In this paper, we focus on the growing list of available state-of-the-art proteomic technologies and their specific application to the discovery of novel osteosarcoma diagnostic and therapeutic targets. The current signaling markers/pathways associated with primary and metastatic osteosarcoma that have been identified by early-stage proteomic technologies thus far are also described.

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Profiling for novel proteomics biomarkers in neurodevelopmental disorders

Protein biomarker discovery from biological fluids, such as serum, has been widely applied to disorders such as cancer and has more recently also been utilized in neuro-psychiatric disorders with relatively clear biological causes, such as Alzheimer's disease and schizophrenia. The application of the associated technologies for the identification of protein biomarker signatures in neurodevelopmental disorders, such as autism spectrum disorder and attention deficit hyperactivity disorder, is comparatively less well established. The aim of this article is to provide an overview of the various protocols available for such analysis, discuss reports in which these techniques have been previously applied in biomarker discovery/validation in neurodevelopmental disorders, and consider the future development of this area of research.

Mass spectrometric characterization of protein structure details refines the proteome signature for invasive ductal breast carcinoma

Early diagnosis as well as individualized therapies are necessary to reduce the mortality of breast cancer, and personalized patient care strategies rely on novel prognostic or predictive factors. In this study, with six breast cancer patients, 2D gel analysis was applied for studying protein expression differences in order to distinguish invasive ductal breast carcinoma, the most frequent breast tumor subtype, from control samples. In total, 1203 protein spots were assembled in a 2D reference gel. Differentially abundant spots were subjected to peptide mass fingerprinting for protein identification. Twenty proteins with their corresponding 38 differentially expressed 2D gel spots were contained in our previously reported proteome signature, suggesting that distinct protein forms were contributing. In-depth MS/MS measurements enabled analyses of protein structure details of selected proteins. In protein spots that significantly contributed to our signature, we found that glyceraldehyde-3-phosphate dehydrogenase was N-terminally truncated, pyruvate kinase M2 and nucleoside diphosphate kinase A but not other isoforms of these proteins were of importance, and nucleophosmin phosphorylation at serine residues 106 and 125 were clearly identified. Principle component analysis and hierarchical clustering with normalized quantitative data from the 38 spots resulted in accurate separation of tumor from control samples. Thus, separation of tissue samples as in our initial proteome signature could be confirmed even with a different proteome analysis platform. In addition, detailed protein structure investigations enabled refining our proteome signature for invasive ductal breast carcinoma, opening the way to structure-/function studies with respect to disease processes and/or therapeutic intervention.

Age-related differences in plasma proteins: how plasma proteins change from neonates to adults

The incidence of major diseases such as cardiovascular disease, thrombosis and cancer increases with age and is the major cause of mortality world-wide, with neonates and children somehow protected from such diseases of ageing. We hypothesized that there are major developmental differences in plasma proteins and that these contribute to age-related changes in the incidence of major diseases. We evaluated the human plasma proteome in healthy neonates, children and adults using the 2D-DIGE approach. We demonstrate significant changes in number and abundance of up to 100 protein spots that have marked differences in during the transition of the plasma proteome from neonate and child through to adult. These proteins are known to be involved in numerous physiological processes such as iron transport and homeostasis, immune response, haemostasis and apoptosis, amongst others. Importantly, we determined that the proteins that are differentially expressed with age are not the same proteins that are differentially expressed with gender and that the degree of phosphorylation of plasma proteins also changes with age. Given the multi-functionality of these proteins in human physiology, understanding the differences in the plasma proteome in neonates and children compared to adults will make a major contribution to our understanding of developmental biology in humans.

Endothelin-1- and isoproterenol-induced differential protein expression and signaling pathway in HL-1 cardiomyocytes

It is well known that the two chemical compounds endothelin-1 (ET-1) and isoproterenol (ISO) can individually induce cardiac hypertrophy through G protein-coupled receptors in cardiomyocytes. However, the cardiac hypertrophy signaling pathway activated by ET-1 and ISO is not well defined. Therefore, we investigated the protein expression profile and signaling transduction in HL-l cardiomyocyte cells treated with ET-1 and ISO. Following separation of the cell lysates by using 2-DE and silver staining, we identified 16 protein spots that were differentially expressed as compared to the controls. Of these 16 spots, three changed only after treatment with ET-1, whereas four changed only after treatment with ISO, suggesting that these two stimuli could induce different signaling pathways. In order to reveal the differences between ET-1- and ISO-induced signaling, we studied the different events that occur at each step of the signaling pathways, when selected biocomponents were blocked by inhibitors. Our results indicated that ET-1 and ISO used different pathways for phosphorylation of glycogen synthase kinase-3β (GSK3β). ET-1 mainly used the mitogen-activated protein kinase and phosphatidylinositol-3-kinase/AKT pathways to activate GSK3β, whereas under ISO stimulation, only the phosphatidylinositol-3-kinase/AKT pathway was required to trigger the GSK3β pathway. Furthermore, the strength of the GSK3β signal in ISO-induced cardiac hypertrophy was stronger than that in ET-1-induced cardiac hypertrophy. We found that these two agonists brought about different changes in the protein expression of HL-1 cardiomyocytes through distinct signaling pathways even though the destination of the two signaling pathways was the same.

Secretome-based identification and characterization of potential biomarkers in thyroid cancer

In search of thyroid cancer biomarkers, proteins secreted by thyroid cancer cell lines, papillary-derived TPC-1 and anaplastic-derived CAL62, were analyzed using liquid chromatography-tandem mass spectrometry. Of 46 high-confidence identifications, 6 proteins were considered for verification in thyroid cancer patients' tissue and blood. The localization of two proteins, nucleolin and prothymosin-α (PTMA), was confirmed in TPC-1 and CAL62 cells by confocal microscopy and immunohistochemically in xenografts of TPC-1 cells in NOD/SCID/γ mice and human thyroid cancers (48 tissues). Increased nuclear and cytoplasmic expression of PTMA was observed in anaplastic compared to papillary and poorly differentiated carcinomas. Nuclear expression of nucleolin was observed in all subtypes of thyroid carcinomas, along with faint cytoplasmic expression in anaplastic cancers. Importantly, PTMA, nucleolin, clusterin, cysteine-rich angiogenic inducer 61, enolase 1, and biotinidase were detected in thyroid cancer patients' sera, warranting future analysis to confirm their potential as blood-based thyroid cancer markers. In conclusion, we demonstrated the potential of secretome analysis of thyroid cancer cell lines to identify novel proteins that can be independently verified in cell lines, xenografts, tumor tissues, and blood samples of thyroid cancer patients. These observations support their potential utility as minimally invasive biomarkers for thyroid carcinomas and their application in management of these diseases upon future validation.