Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Low expression of interleukin-1 receptor antagonist correlates with poor prognosis via promoting proliferation and migration and inhibiting apoptosis in oral squamous cell carcinoma

BACKGROUND

Biomarkers are biochemical indicators that can identify changes in the structure or function of systems, organs, or cells and can be used to monitor a wide range of biological processes, including cancer. Interleukin-1 receptor antagonist (IL1RA) is an important inflammatory suppressor gene and tumor biomarker. The goal of this study was to investigate the expression of IL1RA, its probable carcinogenic activity, and its diagnostic targets in oral squamous cell carcinoma (OSCC).

RESULTS

We discovered that IL1RA was expressed at a low level in OSCC tumor tissues compared to normal epithelial tissues and that the expression declined gradually from epithelial hyperplasia through dysplasia to carcinoma in situ and invasive OSCC. Low IL1RA expression was associated not only with poor survival but also with various clinicopathological markers such as increased infiltration, recurrence, and fatalities. Following cellular phenotyping investigations in OSCC cells overexpressing IL1RA, we discovered that recovering IL1RA expression decreased OSCC cell proliferation, migration, and increased apoptosis.

CONCLUSIONS

In summary, our investigation highlighted the possible involvement of low-expression IL1RA in OSCC cells in promoting invasive as well as metastatic and inhibiting apoptosis, as well as the efficacy of IL1RA-focused monitoring in the early detection and treatment of OSCC.

Trypsin Encapsulation in the Zeolitic Imidazolate Framework for Low-Molecular Weight Protein Analysis with High Selectivity and Efficiency

Low-molecular weight proteins (LWPs) are important sources of biological information in biomarkers, signaling molecules, and pathology. However, the separation and analysis of LWPs in complex biological samples are challenging, mainly due to their low abundance and the complex sample pretreatment procedure. Herein, trypsin modified by poly(acrylic acid) (PAA) was encapsulated by a zeolitic imidazolate framework (ZIF-L). Mesopores were formed on the ZIF-L with the introduction of PAA. An alternative strategy for separation and pretreatment of LWPs was developed based on the prepared ZIF-L-encapsulated trypsin with adjustable pore size. The mesoporous structure of the prepared materials selectively excluded high-molecular weight proteins from the reaction system, allowing LWPs to enter the pores and react with the internal trypsin, resulting in an improved separation efficiency. The hydrophobicity of the ZIF-L simplified the digestion process by inducing significant structural changes in substrate proteins. In addition, the enzymatic activity was significantly enhanced by the developed encapsulation method that maintained the enzyme conformation, allowed low mass transfer resistance, and possessed a high enzyme-to-substrate ratio. As a result, the ZIF-L-encapsulated trypsin can achieve highly selective separation, valid denaturation, and efficient digestion of LWPs in a short time by simply mixing with substrate proteins, greatly simplifying the separation and pretreatment process of the traditional hydrolysis method. The prepared materials and the developed strategy demonstrated an excellent size-selective assay performance in model protein mixtures, showing great potential in the application of proteomics analysis.

An Innovative Approach for Tailoring Molecularly Imprinted Polymers for Biosensors-Application to Cancer Antigen 15-3

This work presents a novel approach for tailoring molecularly imprinted polymers (MIPs) with a preliminary stage of atom transfer radical polymerization (ATRP), for a more precise definition of the imprinted cavity. A well-defined copolymer of acrylamide and N,N'-methylenebisacrylamide (PAAm-co-PMBAm) was synthesized by ATRP and applied to gold electrodes with the template, followed by a crosslinking reaction. The template was removed from the polymer matrix by enzymatic/chemical action. The surface modifications were monitored via electrochemical impedance spectroscopy (EIS), having the MIP polymer as a non-conducting film designed with affinity sites for CA15-3. The resulting biosensor exhibited a linear response to CA15-3 log concentrations from 0.001 to 100 U/mL in PBS or in diluted fetal bovine serum (1000×) in PBS. Compared to the polyacrylamide (PAAm) MIP from conventional free-radical polymerization, the ATRP-based MIP extended the biosensor's dynamic linear range 10-fold, improving low concentration detection, and enhanced the signal reproducibility across units. The biosensor demonstrated good sensitivity and selectivity. Overall, the work described confirmed that the process of radical polymerization to build an MIP material influences the detection capacity for the target substance and the reproducibility among different biosensor units. Extending this approach to other cancer biomarkers, the methodology presented could open doors to a new generation of MIP-based biosensors for point-of-care disease diagnosis.

Dietary patterns, untargeted metabolite profiles and their association with colorectal cancer risk

We investigated data-driven and hypothesis-driven dietary patterns and their association to plasma metabolite profiles and subsequent colorectal cancer (CRC) risk in 680 CRC cases and individually matched controls. Dietary patterns were identified from combined exploratory/confirmatory factor analysis. We assessed association to LC-MS metabolic profiles by random forest regression and to CRC risk by multivariable conditional logistic regression. Principal component analysis was used on metabolite features selected to reflect dietary exposures. Component scores were associated to CRC risk and dietary exposures using partial Spearman correlation. We identified 12 data-driven dietary patterns, of which a breakfast food pattern showed an inverse association with CRC risk (OR per standard deviation increase 0.89, 95% CI 0.80-1.00, p = 0.04). This pattern was also inversely associated with risk of distal colon cancer (0.75, 0.61-0.96, p = 0.01) and was more pronounced in women (0.69, 0.49-0.96, p = 0.03). Associations between meat, fast-food, fruit soup/rice patterns and CRC risk were modified by tumor location in women. Alcohol as well as fruit and vegetables associated with metabolite profiles (Q2 0.22 and 0.26, respectively). One metabolite reflecting alcohol intake associated with increased CRC risk, whereas three metabolites reflecting fiber, wholegrain, and fruit and vegetables associated with decreased CRC risk.

Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine

Colorectal cancer (CRC) is one of the most heterogeneous and deadly diseases, with a global incidence of 1.5 million cases per year. Genomics has revolutionized the clinical management of CRC by enabling comprehensive molecular profiling of cancer. However, a deeper understanding of the molecular factors is needed to identify new prognostic and predictive markers that can assist in designing more effective therapeutic regimens for the improved management of CRC. Recent breakthroughs in single-cell analysis have identified new cell subtypes that play a critical role in tumor progression and could serve as potential therapeutic targets. Spatial analysis of the transcriptome and proteome holds the key to unlocking pathogenic cellular interactions, while liquid biopsy profiling of molecular variables from serum holds great potential for monitoring therapy resistance. Furthermore, gene expression signatures from various pathways have emerged as promising prognostic indicators in colorectal cancer and have the potential to enhance the development of equitable medicine. The advancement of these technologies for identifying new markers, particularly in the domain of predictive and personalized medicine, has the potential to improve the management of patients with CRC. Further investigations utilizing similar methods could uncover molecular subtypes specific to emerging therapies, potentially strengthening the development of personalized medicine for CRC patients.

Signaling-induced systematic repression of miRNAs uncovers cancer vulnerabilities and targeted therapy sensitivity

Targeted therapies are effective in treating cancer, but success depends on identifying cancer vulnerabilities. In our study, we utilize small RNA sequencing to examine the impact of pathway activation on microRNA (miRNA) expression patterns. Interestingly, we discover that miRNAs capable of inhibiting key members of activated pathways are frequently diminished. Building on this observation, we develop an approach that integrates a low-miRNA-expression signature to identify druggable target genes in cancer. We train and validate our approach in colorectal cancer cells and extend it to diverse cancer models using patient-derived in vitro and in vivo systems. Finally, we demonstrate its additional value to support genomic and transcriptomic-based drug prediction strategies in a pan-cancer patient cohort from the National Center for Tumor Diseases (NCT)/German Cancer Consortium (DKTK) Molecularly Aided Stratification for Tumor Eradication (MASTER) precision oncology trial. In conclusion, our strategy can predict cancer vulnerabilities with high sensitivity and accuracy and might be suitable for future therapy recommendations in a variety of cancer subtypes.

Ultrasensitive detection of miRNA-21 by click chemistry and fluorescein-mediated photo-ATRP signal amplification

The development of a convenient and efficient assay using miRNA-21 as a lung cancer marker is of great importance for the early prevention of cancer. Herein, an electrochemical biosensor for the detection of miRNA-21 was successfully fabricated under blue light excitation using click chemistry and photocatalytic atom transfer radical polymerization (photo-ATRP). By using hairpin DNA as a recognition probe, the electrochemical sensor deposits numerous electroactive monomers (ferrocenylmethyl methacrylate) on the electrode surface under the reaction of photocatalyst (fluorescein) and pentamethyldiethylenetriamine, thereby achieving signal amplification. This biosensor is sensitive, precise and selective for miRNA-21, and is highly specific for RNAs with different base mismatches. Under optimal conditions, the biosensor showed a linear relationship in the range of 10 fM ∼1 nM (R2 = 0.995), with a detection limit of 1.35 fM. Furthermore, the biosensor exhibits anti-interference performance when analyzing RNAs in serum samples. The biosensor is based on green chemistry and has the advantages of low cost, specificity and anti-interference ability, providing economic benefits while achieving detection objectives, which makes it highly promising for the analysis of complex samples.

Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

BACKGROUND

For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme.

METHODS

TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set.

RESULTS

Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%.

CONCLUSIONS

Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.

Nutritional Metabolomics in Diet-Breast Cancer Relations: Current Research, Challenges, and Future Directions-A Review

Breast cancer is one of the most common types of cancer in women worldwide, and its incidence is increasing. Diet has been identified as a modifiable risk factor for breast cancer, but the complex interplay between diet, metabolism, and cancer development is not fully understood. Nutritional metabolomics is a rapidly evolving field that can provide insights into the metabolic changes associated with dietary factors and their impact on breast cancer risk. The review's objective is to provide a comprehensive overview of the current research on the application of nutritional metabolomics in understanding the relationship between diet and breast cancer. The search strategy involved querying several electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. The search terms included combinations of relevant keywords such as "nutritional metabolomics", "diet", "breast cancer", "metabolites", and "biomarkers". In this review, both in vivo and in vitro studies were included, and we summarize the current state of knowledge on the role of nutritional metabolomics in understanding the diet-breast cancer relationship, including identifying specific metabolites and metabolic pathways associated with breast cancer risk. We also discuss the challenges associated with nutritional metabolomics research, including standardization of analytical methods, interpretation of complex data, and integration of multiple-omics approaches. Finally, we highlight future directions for nutritional metabolomics research in studying diet-breast cancer relations, including investigating the role of gut microbiota and integrating multiple-omics approaches. The application of nutritional metabolomics in the study of diet-breast cancer relations, including 2-amino-4-cyano butanoic acid, piperine, caprate, rosten-3β,17β-diol-monosulfate, and γ-carboxyethyl hydrochroman, among others, holds great promise for advancing our understanding of the role of diet in breast cancer development and identifying personalized dietary recommendations for breast cancer prevention, control, and treatment.

A review on proteomic and genomic biomarkers for gelatin source authentication: Challenges and future outlook

Biomarkers are compounds that could be detected and used as indicators of normal and/or abnormal functioning of different biological systems, including animal tissues and food matrices. Gelatin products of animal origin, mainly bovine and porcine, are currently under scrutiny mainly due to the specific needs of some sectors of the population related to religious beliefs and their dietary prohibitions, as well as some potential health threats associated with these products. Thus, manufacturers are currently in need of a reliable, convenient, and easy procedure to discern and authenticate the origin of animal-based gelatins (bovine, porcine, chicken, or fish). This work aims to review current advances in the creation of reliable gelatin biomarkers for food authentication purposes based on proteomic and DNA biomarkers that could be applied in the food sector. Overall, the presence of specific proteins and peptides in gelatin can be chemically analysed (i.e., by chromatography, mass spectroscopy, electrophoresis, lateral flow devices, and enzyme-linked immunosorbent assay), and different polymerase chain reaction (PCR) methods have been applied for the detection of nucleic acid substances in gelatin. Altogether, despite the fact that numerous methods are currently being developed for the purpose of detecting gelatin biomarkers, their widespread application is highly dependent on the cost of the equipment and reagents as well as the ease of use of the various methods. Combining different methods and approaches targeting multiple biomarkers may be key for manufacturers to achieve reliable authentication of gelatin's origin.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Mucins as contrast agent targets for fluorescence-guided surgery of pancreatic cancer

Pancreatic cancer is difficult to resect due to its unique challenges, often leading to incomplete tumor resections. Fluorescence-guided surgery (FGS), also known as intraoperative molecular imaging and optical surgical navigation, is an intraoperative tool that can aid surgeons in complete tumor resection through an increased ability to detect the tumor. To target the tumor, FGS contrast agents rely on biomarkers aberrantly expressed in malignant tissue compared to normal tissue. These biomarkers allow clinicians to identify the tumor and its stage before surgical resection and provide a contrast agent target for intraoperative imaging. Mucins, a family of glycoproteins, are upregulated in malignant tissue compared to normal tissue. Therefore, these proteins may serve as biomarkers for surgical resection. Intraoperative imaging of mucin expression in pancreatic cancer can potentially increase the number of complete resections. While some mucins have been studied for FGS, the potential ability to function as a biomarker target extends to the entire mucin family. Therefore, mucins are attractive proteins to investigate more broadly as FGS biomarkers. This review summarizes the biomarker traits of mucins and their potential use in FGS for pancreatic cancer.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Rose bengal-mediated photoinduced atom transfer radical polymerization for high sensitivity detection of target DNA

Convenient and sensitive detection of biomolecules is of utmost importance in the field of early disease screening. In this study, a Rose Bengal-Mediated photoinduced atom transfer radical polymerization (photoATRP) method was used to achieve highly sensitive detection of target DNA (tDNA). The tDNA was specifically recognized using PNA with terminal modified sulfhydryl groups, and the initiator α-bromophenylacetic acid (BPAA) was attached to the electrode surface via a phosphate-Zr⁴⁺-carboxylate acid structure. Under the excitation of blue light, rose bengal (RB) acts as a photocatalyst, β-nicotinamide adenine dinucleotide (NADH) as an electron donor, and ferrocenylmethyl methacrylate (FMMA) as a monomer to activate the photoATRP reaction and generate a large number of electroactive polymer chains on the electrode surface. Under optimal conditions, the method can be used for the quantitative analysis of tDNA in the concentration range of 1-10⁵ fM (R² = 0.994) with a limit of detection (LOD) of 0.115 fM. This metal-free mediated photoATRP biosensor, with low cost and environmental friendliness, has great potential in the field of highly sensitive biomolecule detection.

Design and Preparation of Sensing Surfaces for Capacitive Biodetection

Despite their high sensitivity and their suitability for miniaturization, biosensors are still limited for clinical applications due to the lack of reproducibility and specificity of their detection performance. The design and preparation of sensing surfaces are suspected to be a cause of these limitations. Here, we first present an updated overview of the current state of use of capacitive biosensors in a medical context. Then, we summarize the encountered strategies for the fabrication of capacitive biosensing surfaces. Finally, we describe the characteristics which govern the performance of the sensing surfaces, along with recent developments that were suggested to overcome their main current limitations.

Systematic Investigation of the Diagnostic and Prognostic Impact of LINC01087 in Human Cancers

(1) Background: Long non-coding RNAs may constitute epigenetic biomarkers for the diagnosis, prognosis, and therapeutic response of a variety of tumors. In this context, we aimed at assessing the diagnostic and prognostic value of the recently described long intergenic non-coding RNA 01087 (LINC01087) in human cancers. (2) Methods: We studied the expression of LINC01087 across 30 oncological indications by interrogating public resources. Data extracted from the TCGA and GTEx databases were exploited to plot receiver operating characteristic curves (ROC) and determine the diagnostic performance of LINC01087. Survival data from TCGA and KM-Plotter directories allowed us to graph Kaplan-Meier curves and evaluate the prognostic value of LINC01087. To investigate the function of LINC01087, gene ontology (GO) annotation and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed. Furthermore, interactions between LINC01087 and both miRNA and mRNA were studied by means of bioinformatics tools. (3) Results: LINC01087 was significantly deregulated in 7 out of 30 cancers, showing a predominant upregulation. Notably, it was overexpressed in breast (BC), esophageal (ESCA), and ovarian (OV) cancers, as well as lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD), and uterine carcinosarcoma (UCS). By contrast, LINC01087 displayed downregulation in testicular germ cell tumors (TGCT). ROC curve analyses identified LINC01087 as a potential diagnostic indicator in BC, ESCA, OV, STAD, and TGCT. Moreover, high and low expression of LINC01087 predicted a favorable prognosis in BC and papillary cell carcinoma, respectively. In silico analyses indicated that deregulation of LINC01087 in cancer was associated with a modulation of genes related to ion channel, transporter, and peptide receptor activity. (4) Conclusions: the quantification of an altered abundance of LINC01087 in tissue specimens might be clinically useful for the diagnosis and prognosis of some hormone-related tumors, including BC, OV, and TGCT, as well as other cancer types such as ESCA and STAD. Moreover, our study revealed the potential of LINC01087 (and perhaps other lncRNAs) to regulate neuroactive molecules in cancer.

Comparison of matched formalin-fixed paraffin embedded and fresh frozen meningioma tissue reveals bias in proteomic profiles

Tissue biopsies are most commonly archived in a paraffin block following tissue fixation with formaldehyde (FFPE) or as fresh frozen tissue (FFT). While both methods preserve biological samples, little is known about how they affect the quantifiable proteome. We performed a 'bottom-up' proteomic analysis (N = 20) of short and long-term archived FFPE surgical samples of human meningiomas and compared them to matched FFT specimens. FFT facilitated a similar number of proteins assigned by MetaMorpheus compared with matched FFPE specimens (5378 vs. 5338 proteins, respectively (p = 0.053), regardless of archival time. However, marked differences in the proteome composition were apparent between FFPE and FFT specimens. Twenty-three percent of FFPE-derived peptides and 8% of FFT-derived peptides contained at least one chemical modification. Methylation and formylation were most prominent in FFPE-derived peptides (36% and 17% of modified FFPE peptides, respectively) while, most of phosphorylation and iron modifications appeared in FFT-derived peptides (p < 0.001). A mean 14% (± 2.9) of peptides identified in FFPE contained at least one modified Lysine residue. Importantly, larger proteins were significantly overrepresented in FFT specimens, while FFPE specimens were enriched with smaller proteins.

A Novel ssDNA Aptamer Targeting Carcinoembryonic Antigen: Selection and Characterization

One of the major causes of a drastically shorter life expectancy and one of the most prevalent diseases in the world today is cancer. Given the data on the rise in cancer cases throughout the world, it is obvious that, despite the diagnostic techniques currently being used, there is a pressing need to develop precise and sensitive techniques for early diagnosis of the disease. A high degree of affinity and specificity towards particular targets is maintained by the short nucleic acid molecules known as aptamers. Aptamers outperform antibodies due to their unique benefits, such as their simplicity in synthesis and modification, lack of toxicity, and long-term stability. Utilizing an accurate recognition element and a robust signal transduction mechanism, molecular diagnostics can be extremely sensitive and specific. In this study, development of new single-stranded DNA aptamers against CEA for use in cancer diagnostics was accomplished using SELEX and NGS methods. As a result of 12 iterative SELEX rounds, nine aptamer candidates against CEA were developed. NGS comparative analysis revealed that round twelve had an enriched number of aptamers that were specifically bound, as opposed to round eight. Among the selected nine sequences characterized by bioinformatics analysis and ELONA, an aptamer sequence with the highest specificity and affinity for the target protein was identified and further examined. Aptamer sequence (6) was screened in a concentration-dependent assay, specificity analysis was performed, and its potential secondary and tertiary structures were predicted, which enabled us to test one of the possible putative interactions with CEA. Finally, aptamer sequence (6) labelled with a Cy5 fluorescent tag was used in confocal microscopy to observe its binding towards the CEA expressed in HT-29 human colon adenocarcinoma cell line.

Mass spectroscopy-based proteomics and metabolomics analysis of triple-positive breast cancer cells treated with tamoxifen and/or trastuzumab

PURPOSE

HER2-enriched breast cancer with high levels of hormone receptor expression, known as "triple positive" breast cancer, may represent a new entity with a relatively favourable prognosis against which the combination of chemotherapy, HER-2 inhibition, and endocrine treatment may be considered overtreatment. We explored the effect of the anticancer drugs tamoxifen and trastuzumab, both separately and in combination, on the integrated proteomic and metabolic profile of "triple positive" breast cancer cells (BT-474).

METHOD

We employed ultra-high-performance liquid chromatography-quadrupole time of flight mass spectrometry using a Bruker timsTOF to investigate changes in BT-474 cell line treated with either tamoxifen, trastuzumab or a combination. Differentially abundant metabolites were identified using the Bruker Human Metabolome Database metabolite library and proteins using the Uniprot proteome for Homo sapiens using MetaboScape and MaxQuant, respectively, for identification and quantitation.

RESULTS

A total of 77 proteins and 85 metabolites were found to significantly differ in abundance in BT-474 treated cells with tamoxifen 5 μM/and or trastuzumab 2.5 μM. Findings suggest that by targeting important cellular signalling pathways which regulate cell growth, apoptosis, proliferation, and chemoresistance, these medicines have a considerable anti-growth effect in BT-474 cells. Pathways enriched for dysregulation include RNA splicing, neutrophil degranulation and activation, cellular redox homeostasis, mitochondrial transmembrane transport, ferroptosis and necroptosis, ABC transporters and central carbon metabolism.

CONCLUSION

Our findings in protein and metabolite level research revealed that anti-cancer drug therapy had a significant impact on the key signalling pathways and molecular processes in triple positive BT-474 cell lines.

Cancer-associated Fibroblasts Communicate with Breast Tumor Cells Through Extracellular Vesicles in Tumor Development

Breast cancer is the leading cause of cancer death among women worldwide. In solid tumors, the microenvironment plays a critical role in tumor development, and it has been described a communication between the different cell types that conform the stroma, including fibroblasts, pericytes, adipocytes, immune cells and cancer-associated fibroblasts. Intercellular communication is bidirectional, complex, multifactorial and is mediated by the secretion of molecules and extracellular vesicles. The extracellular vesicles are vesicles limited by two membranes that are secreted by normal and cancer cells into the extracellular space. Extracellular vesicle cargo is complex and includes proteins, miRNAs, DNA and lipids, and their composition is specific to their parent cells. Extracellular vesicles are taken up for neighboring or distant cells. Particularly, extracellular vesicles from breast cancer cells are taken up for fibroblasts and it induces the activation of fibroblasts into cancer-associated fibroblasts. Interestingly, cancer associated fibroblasts release extracellular vesicles that are taken up for breast cancer cells and promote migration, invasion, proliferation, epithelial-mesenchymal transition, changes in metabolism, chemoresistance, evasion of immune system and remodeling of extracellular matrix. In addition, the enrichment of specific cargos in extracellular vesicles of breast cancer patients has been suggested to be used as biomarkers of the disease. Here we review the current literature about the intercommunication between tumor cells and cancer associated fibroblasts through extracellular vesicles in breast cancer.

Clinical applications of mass spectrometry-based proteomics in cancer: where are we?

Tumor tissue processing methodologies in combination with data-independent acquisition mass spectrometry (DIA-MS) have emerged that can comprehensively analyze the proteome of multiple tumor samples accurately and reproducibly. Increasing recognition and adoption of these technologies has resulted in a tranche of studies providing novel insights into cancer classification systems, functional tumor biology, cancer biomarkers, treatment response and drug targets. Despite this, with some limited exceptions, MS-based proteomics has not yet been implemented in routine cancer clinical practice. Here, we summarize the use of DIA-MS in studies that may pave the way for future clinical cancer applications, and highlight the role of alternative MS technologies and multi-omic strategies. We discuss limitations and challenges of studies in this field to date and propose steps for integrating proteomic data into the cancer clinic. This article is protected by copyright. All rights reserved.

Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hampers their use in research and clinical application. Mass spectrometry measures of fifty N-glycans, on seven serum proteins in liver disease, were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized FDA-supported BioCompute Object. Using the biomarker data model allows capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers, can integrate N-glycan biomarker data with multi-source biomedical data, and can foster discovery and insight within a unified data framework for glycan biomarker representation thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

LC–MS-Based Urine Metabolomics Analysis for the Diagnosis and Monitoring of Medulloblastoma

Medulloblastoma (MB) is the most common type of brain cancer in pediatric patients. Body fluid biomarkers will be helpful for clinical diagnosis and treatment. In this study, liquid chromatography–mass spectrometry (LC–MS)-based metabolomics was used to identify specific urine metabolites of MB in a cohort, including 118 healthy controls, 111 MB patients, 31 patients with malignant brain cancer, 51 patients with benign brain disease, 29 MB patients 1 week postsurgery and 80 MB patients 1 month postsurgery. The results showed an apparent separation for MB vs. healthy controls, MB vs. benign brain diseases, and MB vs. other malignant brain tumors, with AUCs values of 0.947/0.906, 0.900/0.873, and 0.842/0.885, respectively, in the discovery/validation group. Among all differentially identified metabolites, 4 metabolites (tetrahydrocortisone, cortolone, urothion and 20-oxo-leukotriene E4) were specific to MB. The analysis of these 4 metabolites in pre- and postoperative MB urine samples showed that their levels returned to a healthy state after the operation (especially after one month), showing the potential specificity of these metabolites for MB. Finally, the combination of two metabolites, tetrahydrocortisone and cortolone, showed diagnostic accuracy for distinguishing MB from non-MB, with an AUC value of 0.851. Our data showed that urine metabolomics might be used for MB diagnosis and monitoring.

Targeted proteomics for cancer biomarker verification and validation

Targeted proteomics is a method that measures the amount of target proteins via liquid chromatography-tandem mass spectrometry and is used to verify and validate the candidate cancer biomarker proteins. Compared with antibody-based quantification methods such as ELISA, targeted proteomics enables rapid method development, simultaneous measurement of multiple proteins, and high-specificity detection of modifications. Moreover, by spiking the internal standard peptide, targeted proteomics detects the absolute amounts of marker proteins, which is essential for determining the cut-off values for diagnosis and thus for multi-institutional validation. With these unique features, targeted proteomics can seamlessly transfer cancer biomarker candidate proteins from the discovery phase to the verification and validation phases, thereby resulting in an accelerated cancer biomarker pipeline. Furthermore, understanding the basic principles, advantages, and disadvantages is necessary to effectively utilize targeted proteomics in cancer biomarker pipelines. This review aimed to introduce the technical principles of targeted proteomics for cancer biomarker verification and validation.

Early detection of cancer

Survival improves when cancer is detected early. However, ~50% of cancers are at an advanced stage when diagnosed. Early detection of cancer or precancerous change allows early intervention to try to slow or prevent cancer development and lethality. To achieve early detection of all cancers, numerous challenges must be overcome. It is vital to better understand who is at greatest risk of developing cancer. We also need to elucidate the biology and trajectory of precancer and early cancer to identify consequential disease that requires intervention. Insights must be translated into sensitive and specific early detection technologies and be appropriately evaluated to support practical clinical implementation. Interdisciplinary collaboration is key; advances in technology and biological understanding highlight that it is time to accelerate early detection research and transform cancer survival.

Nanosecond Photochemical Reaction for Enhanced Identification, Quantification, and Visualization of Primary Amine-Containing Metabolites by MALDI-Mass Spectrometry

Many metabolites, including amino acids, neurotransmitters, and pharmaceuticals, contain primary amine functional groups. The analysis of these molecules by mass spectrometry (MS) plays an important role in the study of cancers and psychogenic diseases. However, the MS-based detection and visualization of these bioactive metabolites directly from real biological systems still suffer from challenges such as low ionization efficiency and/or matrix interference effects. Here, we introduce a simple and efficient strategy, the nanosecond photochemical reaction (nsPCR)-enabled fast chemical derivatization, enabling direct MS analysis of primary amine-containing metabolites, with enhanced detection sensitivity for numerous metabolites from cell culture medium and rat brain sections. Furthermore, this nsPCR-based chemical derivatization strategy was demonstrated to be a useful visualizing tool that could provide improved spatial information for these metabolites, potentially offering alternative tools for gaining novel insights into metabolic events.

MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses

Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-source, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork's utility by employing it to identify groups of proteins associated with prostate cancer from a proteomic analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments with 15 or more samples.

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.

Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Identification of Protein Biomarker Signatures for Acute Myeloid Leukemia (AML) Using Both Nontargeted and Targeted Approaches

Acute myeloid leukemia (AML) is characterized by an increasing number of clonal myeloid blast cells which are incapable of differentiating into mature leukocytes. AML risk stratification is based on genetic background, which also serves as a means to identify the optimal treatment of individual patients. However, constant refinements are needed, and the inclusion of significant measurements, based on the various omics approaches that are currently available to researchers/clinicians, have the potential to increase overall accuracy with respect to patient management. Using both nontargeted (label-free mass spectrometry) and targeted (multiplex immunoassays) proteomics, a range of proteins were found to be significantly changed in AML patients with different genetic backgrounds. The inclusion of validated proteomic biomarker panels could be an important factor in the prognostic classification of AML patients. The ability to measure both cellular and secreted analytes, at diagnosis and during the course of treatment, has advantages in identifying transforming biological mechanisms in patients, assisting important clinical management decisions.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.

The role of PI3K signaling pathway and its associated genes in papillary thyroid cancer

BACKGROUND

One of the well-differentiated types of thyroid cancer is papillary thyroid cancer (PTC), often developed by genetic mutations and radiation.

METHODS

In this study, the public NCBI-GEO database was systematically searched. The eligible datasets were the targets for biomarker discovery associated with PI3K signaling pathway.

RESULTS

Only two datasets were suitable and passed the inclusion criteria. The meta-analysis outcomes revealed eleven upregulation and thirteen downregulation genes differentially expressed between PTC and healthy tissues. Moreover, the outcomes for survival and disease-free rates for each gene were illustrated.

CONCLUSIONS

The present research suggests a panel signature of 24 gene biomarkers in diagnosing the PTC.

Discovering Panel of Autoantibodies for Early Detection of Lung Cancer Based on Focused Protein Array

Substantial studies indicate that autoantibodies to tumor-associated antigens (TAAbs) arise in early stage of lung cancer (LC). However, since single TAAbs as non-invasive biomarkers reveal low diagnostic performances, a panel approach is needed to provide more clues for early detection of LC. In the present research, potential TAAbs were screened in 150 serum samples by focused protein array based on 154 proteins encoded by cancer driver genes. Indirect enzyme-linked immunosorbent assay (ELISA) was used to verify and validate TAAbs in two independent datasets with 1,054 participants (310 in verification cohort, 744 in validation cohort). In both verification and validation cohorts, eight TAAbs were higher in serum of LC patients compared with normal controls. Moreover, diagnostic models were built and evaluated in the training set and the test set of validation cohort by six data mining methods. In contrast to the other five models, the decision tree (DT) model containing seven TAAbs (TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1), built in the training set, yielded the highest diagnostic value with the area under the receiver operating characteristic curve (AUC) of 0.897, the sensitivity of 94.4% and the specificity of 84.9%. The model was further assessed in the test set and exhibited an AUC of 0.838 with the sensitivity of 89.4% and the specificity of 78.2%. Interestingly, the accuracies of this model in both early and advanced stage were close to 90%, much more effective than that of single TAAbs. Protein array based on cancer driver genes is effective in screening and discovering potential TAAbs of LC. The TAAbs panel with TP53, NPM1, FGFR2, PIK3CA, GNA11, HIST1H3B, and TSC1 is excellent in early detection of LC, and they might be new target in LC immunotherapy.

Efficacy of Analytical Technologies in Metabolomics Studies of the Gastrointestinal Cancers

According to the reports of the World Health Organization and the International Agency for Research on Cancer, cancer is the second leading cause of human death worldwide. However, early-stage detection of cancers can efficiently enhance the chance of therapy and saving lives. Metabolomics strategies apply a variety of approaches to discover new potential diagnoses, prognoses, and/or therapeutic biomarkers of various diseases. Metabolomics aims to identify and measure different low-molecular-weight biomolecules in physiological environments. In these studies, special metabolites are extracted from biological samples and identified using analytical techniques. Afterward, using data processing programs discovering significantly associated biomarkers is pursued. In the present review, we aimed to discuss recently reported analytical approaches on the metabolomics studies of gastrointestinal cancers including gastric, colorectal, and esophageal cancers. The gas- and liquid-chromatography with different detectors have been shown that are the main analytical techniques and for metabolites quantification, nuclear magnetic resonance has been utilized as a master method.

Blueprint for cancer research: Critical gaps and opportunities

We are experiencing a revolution in cancer. Advances in screening, targeted and immune therapies, big data, computational methodologies, and significant new knowledge of cancer biology are transforming the ways in which we prevent, detect, diagnose, treat, and survive cancer. These advances are enabling durable progress in the goal to achieve personalized cancer care. Despite these gains, more work is needed to develop better tools and strategies to limit cancer as a major health concern. One persistent gap is the inconsistent coordination among researchers and caregivers to implement evidence-based programs that rely on a fuller understanding of the molecular, cellular, and systems biology mechanisms underpinning different types of cancer. Here, the authors integrate conversations with over 90 leading cancer experts to highlight current challenges, encourage a robust and diverse national research portfolio, and capture timely opportunities to advance evidence-based approaches for all patients with cancer and for all communities.

Screening of Potential Biomarkers for Gastric Cancer with Diagnostic Value Using Label-free Global Proteome Analysis

Gastric cancer (GC) is known as a top malignant type of tumors worldwide. Despite the recent decrease in mortality rates, the prognosis remains poor. Therefore, it is necessary to find novel biomarkers with early diagnostic value for GC. In this study, we present a large-scale proteomic analysis of 30 GC tissues and 30 matched healthy tissues using label-free global proteome profiling. Our results identified 537 differentially expressed proteins, including 280 upregulated and 257 downregulated proteins. The ingenuity pathway analysis (IPA) results indicated that the sirtuin signaling pathway was the most activated pathway in GC tissues whereas oxidative phosphorylation was the most inhibited. Moreover, the most activated molecular function was cellular movement, including tissue invasion by tumor cell lines. Based on IPA results, 15 hub proteins were screened. Using the receiver operating characteristic curve, most of hub proteins showed a high diagnostic power in distinguishing between tumors and healthy controls. A four-protein (ATP5B-ATP5O-NDUFB4-NDUFB8) diagnostic signature was built using a random forest model. The area under the curve (AUC) values of this model were 0.996 and 0.886 for the training and testing sets, respectively, suggesting that the four-protein signature has a high diagnostic power. This signature was further tested with independent datasets using plasma enzyme-linked immune sorbent assays, resulting in an AUC value of 0.778 for distinguishing GC tissues from healthy controls, and using immunohistochemical tissue microarray analysis, resulting in an AUC value of 0.805. In conclusion, this study identifies potential biomarkers and improves our understanding of the pathogenesis, providing novel therapeutic targets for GC.

Characterization of the intestinal mucosal proteome in cats with inflammatory bowel disease and alimentary small cell lymphoma

BACKGROUND

Current tests for diagnosis and differentiation of lymphoplasmacytic enteritis (LPE) and small cell lymphoma (SCL) in cats are expensive, invasive, and lack specificity. The identification of less invasive, more reliable biomarkers would facilitate diagnosis.

OBJECTIVES

To characterize the mucosal proteome in endoscopically obtained, small intestinal tissue biopsy specimens. We hypothesized that differentially expressed proteins could be identified and serve as biomarker candidates for the differentiation of LPE and SCL in cats.

ANIMALS

Six healthy control cats, 6 cats with LPE, and 8 cats with SCL.

METHODS

The mucosal proteome was analyzed using 2-dimensional fluorescence difference gel electrophoresis (2D DIGE) and nanoflow liquid chromatography tandem mass spectrometry. For 5 proteins, results were verified by Western blot analysis.

RESULTS

A total of 2349 spots were identified, of which 9 were differentially expressed with a ≥2-fold change between healthy cats and cats with LPE and SCL (.01 < P < .001). Eight of these 9 spots were also differentially expressed between cats with LPE and cats with SCL (P .001 < P < .04). However, Western blot analysis for malate dehydrogenase-1, malate dehydrogenase-2, apolipoprotein, annexin IV, and annexin V did not confirm significant differential protein expression for any of the 5 proteins assessed.

CONCLUSIONS AND CLINICAL IMPORTANCE

Two-D DIGE did not identify potential biomarker candidates in the intestinal mucosa of cats with LPE and SCL. Future studies should focus on different techniques to identify biomarker candidates for cats with chronic enteropathies (CE).

Rapid measurement of cardiac neuropeptide dynamics by capacitive immunoprobe in the porcine heart

Sympathetic control of regional cardiac function occurs through postganglionic innervation from stellate ganglia and thoracic sympathetic chain. Whereas norepinephrine (NE) is their primary neurotransmitter, neuropeptide Y (NPY) is an abundant cardiac cotransmitter. NPY plays a vital role in homeostatic processes including angiogenesis, vasoconstriction, and cardiac remodeling. Elevated sympathetic stress, resulting in increased NE and NPY release, has been implicated in the pathogenesis of several cardiovascular disorders including hypertension, myocardial infarction, heart failure, and arrhythmias, which may result in sudden cardiac death. Current methods for the detection of NPY in myocardium are limited in their spatial and temporal resolution and take days to weeks to provide results [e.g., interstitial microdialysis with subsequent analysis by enzyme-linked immunosorbent assay (ELISA), high performance liquid chromatography (HPLC), or mass spectrometry]. In this study, we report a novel approach for measurement of interstitial and intravascular NPY using a minimally invasive capacitive immunoprobe (C.I. probe). The first high-spatial and temporal resolution, multichannel measurements of NPY release in vivo are provided in both myocardium and transcardiac vascular space in a beating porcine heart. We provide NPY responses evoked by sympathetic stimulation and ectopic ventricular pacing and compare these to NE release and hemodynamic responses. We extend this approach to measure both NPY and vasoactive intestinal peptide (VIP) and show differential release profiles under sympathetic stimulation. Our data demonstrate rapid and local changes in neurotransmitter profiles in response to sympathetic cardiac stressors. Future implementations include real-time intraoperative determination of cardiac neuropeptides and deployment as a minimally invasive catheter.NEW & NOTEWORTHY The sympathetic nervous system regulates cardiac function through release of neurotransmitters and neuropeptides within the myocardium. Neuropeptide Y (NPY) acts as an acute cardiac vasoconstrictor and chronically to regulate angiogenesis and cardiac remodeling. Current methodologies for the measure of NPY are not capable of providing rapid readouts on a single-sample basis. Here we provide the first in vivo methodology to report dynamic, localized NPY levels within both myocardium and vascular compartments in a beating heart.

Capillary Electrophoresis-Mass Spectrometry for Cancer Metabolomics

This chapter presents the fundamentals, instrumentation, methodology, and applications of capillary electrophoresis-mass spectrometry (CE-MS) for cancer metabolomics. CE offers fast and high-resolution separation of charged analytes from a very small amount of sample. When coupled to MS, it represents a powerful analytical technique enabling identification and quantification of metabolites in biological samples. Several issues need to be addressed when combining CE with MS, especially the interface between CE and MS and the selection of a proper separation methodology, sample pretreatment, and capillary coatings. We will discuss these aspects of CE-MS and detail representative applications for cancer metabolomic analysis.

Proteomic profiles and cytokeratin 13 as a potential biomarker of Ovis aries papillomavirus 3-positive and negative cutaneous squamous cell carcinomas

Ovis aries papillomavirus 3 (OaPV3) is an epidermotropic PV reported in sheep cutaneous squamous cell carcinoma (SCC). The presence of OaPV3 DNA and its transcriptional activity in cutaneous SCC, as well as its in vitro transforming properties, suggest a viral etiology for this neoplasm. Nevertheless, the reactome associated with viral-host interaction is still unexplored. Here, we investigated and compared the proteomic profiles of OaPV3-positive SCCs, OaPV3-negative SCCs, and non-SCC samples by liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis, bioinformatics tools, and immunohistochemistry (IHC). OaPV3-positive SCCs (n = 3), OaPV3-negative SCCs (n = 3), and non-SCCs samples (n = 3) were subjected to a shotgun proteomic analysis workflow to assess protein abundance differences among the three sample classes. Proteins involved in epithelial cell differentiation, extracellular matrix organization, and apoptotic signaling showed different abundances in OaPV3-positive SCCs tissues (P ≤ 0.05) when compared to the other tissues. Cytokeratin 13 (CK 13) was among the most increased proteins in OaPV3-positive SCC and was validated by immunohistochemistry on 10 samples per class, confirming its potential as a biomarker of OaPV3 infection in SCC. Collectively, results provide a preliminary insight into the reactome associated with viral-host interaction and pave the way to the development of specific biomarkers for viral-induced sheep SCC.

The New and the Old: Platform Cross-Validation of Immunoaffinity MASS Spectrometry versus ELISA for PromarkerD, a Predictive Test for Diabetic Kidney Disease

PromarkerD is a proteomics derived test for predicting diabetic kidney disease that measures the concentrations of three plasma protein biomarkers, APOA4, CD5L and IBP3. Antibodies against these proteins were developed and applied to a multiplexed immunoaffinity capture mass spectrometry assay. In parallel, and facilitating current clinical laboratory workflows, a standard ELISA was also developed to measure each protein. The performance characteristics of the two technology platforms were compared using a cohort of 100 samples, with PromarkerD test scores demonstrating a high correlation (R = 0.97). These technologies illustrate the potential for large scale, high throughput clinical applications of proteomics now and into the future.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

PURPOSE OF REVIEW

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer.

RECENT FINDINGS

Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Proteomic Profiles and Biological Processes of Relapsed vs. Non-Relapsed Pediatric Hodgkin Lymphoma

The identification of circulating proteins associated with relapse in pediatric Hodgkin lymphoma (HL) may help develop predictive biomarkers. We previously identified a set of predictive biomarkers by difference gel electrophoresis. Here we used label-free quantitative liquid chromatography-mass spectrometry (LC-MS/MS) on plasma collected at diagnosis from 12 children (age 12–16 years) with nodular sclerosis HL, including six in whom the disease relapsed within 5 years of treatment in the LH2004 trial. Plasma proteins were pooled in groups of three, separately for non-relapsing and relapsing HL, and differentially abundant proteins between the two disease states were identified by LC-MS/MS in an explorative and validation design. Proteins with a fold change in abundance >1.2 or ≤0.8 were considered “differentially abundant”. LC-MS/MS identified 60 and 32 proteins that were more abundant in non-relapsing and relapsing HL plasma, respectively, in the explorative phase; these numbers were 39 and 34 in the validation phase. In both analyses, 11 proteins were more abundant in non-relapsing HL (e.g., angiotensinogen, serum paraoxonase/arylesterase 1, transthyretin), including two previously identified by difference gel electrophoresis (antithrombin III and α-1-antitrypsin); seven proteins were more abundant in relapsing HL (e.g., fibronectin and thrombospondin-1), including two previously identified proteins (fibrinogen β and γ chains). The differentially abundant proteins participated in numerous biological processes, which were manually grouped into 10 biological classes and 11 biological regulatory subclasses. The biological class Lipid metabolism, and its regulatory subclass, included angiotensinogen and serum paraoxonase/arylesterase 1 (more abundant in non-relapsing HL). The biological classes Immune system and Cell and extracellular matrix architecture included fibronectin and thrombospondin-1 (more abundant in relapsing HL). These findings deepen our understanding of the molecular scenario underlying responses to therapy and provide new evidence about these proteins as possible biomarkers of relapse in pediatric HL.

Nanotechnology in Radiation Oncology

Nanotechnology has made remarkable contributions to clinical oncology. Nanotherapeutics and diagnostic tools have distinctive characteristics which allow them superior abilities to deliver therapeutics and imaging agents for radiation oncology. Compared to solid biopsies and imaging, the analysis of circulating tumor cells (CTCs) offers a more rapid, real-time, and less invasive method to monitor the dynamic molecular profiles of tumors. The potential of CTCs to be translated as a novel cancer biomarker has been demonstrated in numerous clinical studies. This review will discuss clinical applications of nanomaterials in radiation oncology and the implication of CTCs in cancer detection and monitoring.

Predictive value and clinical significance of increased SSAT-1 activity in healthy adults

AIM

Spermidine/spermine N1-acetyltransferase (SSAT-1) regulates cell growth, proliferation and death. Amantadine is converted by SSAT-1 to acetylamantadine (AA). In our earlier studies, although SSAT-1 was activated in patients with cancer, a number of ostensibly healthy adult volunteers had higher than expected AA concentration. This study was therefore undertaken to examine the outlier group.

MATERIALS & METHODS

A follow up of urine analysis for AA by liquid chromatography-tandem mass spectrometry as well as clinical assessments and additional blood analyses were conducted.

RESULTS

In some of the outlier controls, higher than expected AA concentration was linked to increased serum carcinoembryonic antigen. Clinical and radiographic assessments revealed underlying abnormalities in other cases that could represent premalignant conditions. Hematology tests revealed elevations in white blood cells and platelets, which are markers of inflammation.

CONCLUSION

High urine concentration of AA could be used as a simple and useful test for screening of cancer in high-risk populations.