Genomic and proteomic biomarkers for cancer: a multitude of opportunities

Emerging methods and techniques for cancer biomarker discovery

Modern cancer research depends heavily on the identification and validation of biomarkers because they provide important information about the diagnosis, prognosis, and response to treatment of the cancer. This review will provide a comprehensive overview of cancer biomarkers, including their development phases and recent breakthroughs in transcriptomics and computational techniques for detecting these biomarkers. Blood-based biomarkers have great potential for non-invasive tumor dynamics and treatment response monitoring. These include circulating tumor DNA, exosomes, and microRNAs. Comprehensive molecular profiles are provided by multi-omic technologies, which combine proteomics, metabolomics, and genomes to support the identification of biomarkers and the targeting of therapeutic interventions. Genetic changes are detected by next-generation sequencing, and patterns of protein expression are found by protein arrays and mass spectrometry. Tumor heterogeneity and clonal evolution can be understood using metabolic profiling and single-cell studies. It is projected that the use of several biomarkers-genetic, protein, mRNA, microRNA, and DNA profiles, among others-will rise, enabling multi-biomarker analysis and improving individualised treatment plans. Biomarker identification and patient outcome prediction are further improved by developments in AI algorithms and imaging techniques. Robust biomarker validation and reproducibility require cooperation between industry, academia, and doctors. Biomarkers can provide individualized care, meet unmet clinical needs, and enhance patient outcomes despite some obstacles. Precision medicine will continue to take shape as scientific research advances and the integration of biomarkers with cutting-edge technologies continues to offer a more promising future for personalized cancer care.

Applications of Digital Enzyme-Linked Immunosorbent Assays in Ophthalmology

Digital enzyme-linked immunosorbent assays (dELISAs) very sensitively detect biomarkers that cannot be measured using traditional methods. The molecules are confined within a small volume, their counts accurately computed, and the results rapidly delivered. Digital ELISAs find many applications. In recent years, such ELISAs have become increasingly used to aid ophthalmological diagnoses and treatments, and have revolutionized the field. This article reviews the applications of dELISAs in clinical practice, especially in the sphere of ophthalmology.

Exploring heterogeneous expression of beta-actin (ACTB) in bladder cancer by producing a monoclonal antibody 6D6

BACKGROUND

To predict outcomes and identify potential therapeutic targets for cancers, it is critical to find novel specific biomarkers. The objective of this study was to search for and explore novel bladder cancer-associated protein biomarkers.

METHODS

A library of monoclonal antibodies (mAbs) against the JAM-ICR cell line was first generated, and clones with high affinity were selected. Hybridomas were screened using bladder cancer (BLCA) cell lines and normal cells. The target of the selected mAb was then characterized through immunoaffinity purification, western blotting, and mass spectrometry analysis. Expression of the target antigen was assessed by flow cytometry and IHC methods. Several databases were also used to evaluate the target antigen in BLCA and other types of cancers.

RESULTS

Based on screenings, a 6D6 clone was selected that recognized an isoform of beta-actin (ACTB). Our data showed that ACTB expression on different cell lines was heterogeneous and varied significantly from low to high intensity. 6D6 bound strongly to epithelial cells while showing weak to no reactivity to stromal, endothelial, and smooth muscle cells. There was no association between ACTB intensity and related prognostic factors in BLCA. In silico evaluations revealed a significant correlation between ACTB and overexpressed genes and biomarkers in BLCA. Additionally, the differential expression of ACTB in tumor and healthy tissue as well as its correlation with survival time in a number of cancers were shown.

CONCLUSIONS

The heterogeneous expression of ACTB may suggest the potential value of this marker in the diagnosis or prognosis of cancer.

Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management

Lung cancer's enduring global significance necessitates ongoing advancements in diagnostics and therapeutics. Recent spotlight on proteomic and genetic biomarker research offers a promising avenue for understanding lung cancer biology and guiding treatments. This review elucidates genetic and proteomic lung cancer biomarker progress and their treatment implications. Technological strides in mass spectrometry-based proteomics and next-generation sequencing enable pinpointing of genetic abnormalities and abnormal protein expressions, furnishing vital data for precise diagnosis, patient classification, and customized treatments. Biomarker-driven personalized medicine yields substantial treatment improvements, elevating survival rates and minimizing adverse effects. Integrating omics data (genomics, proteomics, etc.) enhances understanding of lung cancer's intricate biological milieu, identifying novel treatment targets and biomarkers, fostering precision medicine. Liquid biopsies, non-invasive tools for real-time treatment monitoring and early resistance detection, gain popularity, promising enhanced management and personalized therapy. Despite advancements, biomarker repeatability and validation challenges persist, necessitating interdisciplinary efforts and large-scale clinical trials. Integrating artificial intelligence and machine learning aids analyzing vast omics datasets and predicting treatment responses. Single-cell omics reveal cellular connections and intratumoral heterogeneity, valuable for combination treatments. Biomarkers enable accurate diagnosis, tailored medicines, and treatment response tracking, significantly impacting personalized lung cancer care. This approach spurs patient-centered trials, empowering active patient engagement. Lung cancer proteomic and genetic biomarkers illuminate disease biology and treatment prospects. Progressing towards individualized efficient therapies is imminent, alleviating lung cancer's burden through ongoing research, omics integration, and technological strides.

Single-molecule immunoassay technology: Recent advances

Detecting diseases at the molecular level aids in early diagnosis and treatment. However, traditional immunological detection techniques, such as enzyme-linked immunosorbent assay (ELISA) and chemiluminescence, have detection sensitivities between 10^-16 and 10^-12 mol/L, which are inadequate for early diagnosis. Single-molecule immunoassays can reach detection sensitivities of 10^-18 mol/L and can detect biomarkers that are difficult to measure using conventional detection techniques. It can confine molecules to be detected in a small spatial area and provide absolute counting of the detected signal, offering the advantage of high efficiency and accuracy. Herein, we demonstrate the principles and equipment of two single-molecule immunoassay techniques and discuss their applications. It is shown that the detection sensitivity can be improved by 2-3 orders of magnitude compared to common chemiluminescence or ELISA assays. The microarray-based single-molecule immunoassay technique can test 66 samples in 1 h, which is more efficient than conventional immunological detection techniques. In contrast, microdroplet-based single-molecule immunoassay techniques can generate 107 droplets in 10 min, which is more than 100 times faster than a single droplet generator. By comparing the two single-molecule immunoassay techniques, we highlight our personal perspectives on the current limitations of point-of-care applications and future development trends.

Circulating microRNA biomarkers in melanoma and non-melanoma skin cancer

INTRODUCTION

Skin cancer is the most common type of cancer and is classified in melanoma and non-melanoma cancers, which include basal cell, squamous cell and Merkel cell carcinoma. Specific microRNAs are dysregulated in each skin cancer type. MicroRNAs act as oncogene or tumor suppressor gene regulators and are actively released from tumor cells in the circulation. Cell-free microRNAs serve many, and possibly yet unexplored, functional roles, but their presence and abundance in the blood has been investigated as disease biomarker. Indeed, specific microRNAs can be isolated and quantified in the blood, usually in serum or plasma fractions, where they are uncommonly stable. MicroRNA levels reflect underlying conditions and have been associated with skin cancer presence, stage, evolution, or therapy efficacy.

AREAS COVERED

In this review, we summarize the state of the art on circulating microRNAs detectable in skin cancer patients including all the studies that performed microRNA identification and quantification in the circulation using appropriate sample size and statistics and providing detailed methodology, with a specific focus on diagnostic and prognostic biomarkers.

EXPERT OPINION

Circulating microRNAs display a relevant biomarker potential. We expect the development of methodological guidelines and standardized protocols for circulating miRNA quantification in clinical settings.

Isolation of Single-Domain Antibodies to Transmembrane Proteins Using Magnetized Yeast Cell Targets

The isolation of binding ligands from yeast-displayed combinatorial libraries has typically relied on the use of a soluble, recombinantly expressed form of the target protein when performing magnetic selections or fluorescence-activated cell sorting. When identifying binding ligands, appropriate target protein expression and subsequent purification represents a significant bottleneck. As an alternative, we describe the use of target proteins expressed on the surface of magnetized yeast cells in the selection of yeast-displayed nanobody libraries. In this approach, yeast cells displaying the target protein also co-express an iron oxide-binding protein; incubation with iron oxide nanopowder results in magnetization of target-displaying cells. Alternatively, target-displaying cells are magnetized by nonspecific adsorption of iron oxide nanopowder. Subsequently, any library cells that interact with the magnetized target cells can be isolated using a magnet. Here, we detail protocols for the isolation of binders to membrane protein targets from a yeast display nanobody library using magnetized yeast cell targets. We provide guidance on how to generate magnetic yeast cell targets as well as library selection conditions to bias the isolation of high affinity binders. We also discuss how to assess the affinity and specificity of the isolated nanobodies using flow cytometry.

Attomolar analyte sensing techniques (AttoSens): a review on a decade of progress on chemical and biosensing nanoplatforms

Detecting the ultra-low abundance of analytes in real-life samples, such as biological fluids, water, soil, and food, requires the design and development of high-performance biosensing modalities. The breakthrough efforts from the scientific community have led to the realization of sensing technologies that measure the analyte's ultra-trace level, with relevant sensitivity, selectivity, response time, and sampling efficiency, referred to as Attomolar Analyte Sensing Techniques (AttoSens) in this review. In an AttoSens platform, 1 aM detection corresponds to the quantification of 60 target analyte molecules in 100 μL of sample volume. Herein, we review the approaches listed for various sensor probe design, and their sensing strategies that paved the way for the detection of attomolar (aM: 10^-18 M) concentration of analytes. A summary of the technological advances made by the diverse AttoSens trends from the past decade is presented.

Single-Cell Multiomics Analysis for Drug Discovery

Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

4H12, a Murine Monoclonal Antibody Directed against Myosin Heavy Chain-9 Expressed on Acinar Cell Carcinoma of Pancreas with Potential Therapeutic Application

Background:

PACC is a rare type of pancreatic exocrine neoplasm that is frequently diagnosed at late stages with a high rate of metastasis. Identification of new biomarkers for PACC can improve our knowledge of its biology, early detection, or targeted therapy. In this study, hybridoma technology was used to generate mAbs against Faraz-ICR, a pancreatic acinar cell carcinoma cell line.

Methods:

Cell ELISA and flow cytometry were used for screening, and the 4H12 hybridoma clone was selected for further analysis. The 4H12 mAb was specific for MYH9 as determined by Immunoprecipitation, Western blot, and mass spectrometry.

Results:

This antibody reacted variably with other cancer cells, in comparison to Faraz-ICR cell. Besides, by immunohistochemical staining, the acinar cell tumor, which was the source of Faraz-ICR, showed high MYH9 expression. Among 21 PDAC cases, nine (42.8%) expressed MYH9 with low intensity, while 10 (47.8%) and 2 (9.5%) cases expressed MYH9 with moderate to strong intensities, respectively. The 4H12 mAb inhibited the proliferation of Faraz-ICR cells in a dose-dependent manner from 0.75 to 12.5 μg/ml concentrations (p < 0.0001 and p < 0.002). IC₅₀ values were achieved at 12.09 ± 4.19 µg/ml and 7.74 ± 4.28 µg/ml after 24- and 48-h treatment, respectively.

Conclusion:

Our data suggest that the 4H12 mAb can serve as a tool for investigating the role of MYH9 pancreatic cancer biology and prognosis.

Diagnostic and prognostic value of stem cell factor plasma level in glioblastoma multiforme patients

BACKGROUND

Investigation of novel blood-circulating agents as potential biomarkers for glioblastoma multiforme (GBM) patients' diagnosis and monitoring has gained lots of attention, due to limitations of imaging modalities and invasive tissue biopsy procedures. The present study aims to assess the diagnostic and prognostic values of preoperative stem cell factor (SCF) plasma level in GBM patients.

METHODS

Preoperative plasma samples from 58 GBM patients and 20 patients with nonglial tumors and 30 healthy controls were obtained. SCF levels were measured by employing the enzyme-linked immunosorbent assay test and the values were compared between these three groups. Then, the association of SCF plasma level and tumor volume, progression-free survival (PFS), and overall survival (OS) for the GBM patients were evaluated.

RESULTS

Mean preoperative SCF plasma level of the GBM patients (2.80 ± 1.52 ng/ml) was significantly higher (p < 0.0001) than the healthy controls (0.80 ± 0.24 ng/ml) and patients with nonglial tumor (1.41 ± 0.76 ng/ml). Receiver operating characteristic analysis revealed that the preoperative SCF plasma level could distinguish the GBM patients from healthy controls and patients with nonglial tumors with the area under curve values of 0.915 and 0.790, respectively. However, no significant association was observed between the GBM patients' preoperative SCF plasma levels and tumors' volume (Spearman Rho correlation coefficient, 0.1847; 95% CI, p = 0.1652). The GBM patients were divided into two subgroups based on mean preoperative SCF plasma levels (2.80 ng/ml). No significant difference was observed between the patients' PFS (p = 0.3792) and OS (p = 0.1469) at these two subgroups.

CONCLUSION

Taking together, the SCF plasma level can serve as a novel diagnostic blood-circulating biomarker for patients with GBM. However, its plasma level is not correlated with GBM patients' tumor volume, PFS, or OS.

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

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

Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovaries. Epidemiological findings revealed that women with PCOS are prone to develop certain cancer types due to their shared metabolic and endocrine abnormalities. However, the mechanism that relates PCOS and oncogenesis has not been addressed. Herein, in this review article the genomic status, transcriptional and protein profiles of 264 strongly PCOS related genes (PRG) were evaluated in endometrial cancer (EC), ovarian cancer (OV) and breast cancer (BC) exploring oncogenic databases. The genomic alterations of PRG were significantly higher when compared with a set of non-diseases genes in all cancer types. PTEN had the highest number of mutations in EC, TP53, in OC, and FSHR, in BC. Based on clinical data, women older than 50 years and Black or African American females carried the highest ratio of genomic alterations among all cancer types. The most altered signaling pathways were p53 in EC and OC, while Fc epsilon RI in BC. After evaluating PRG in normal and cancer tissue, downregulation of the differentially expressed genes was a common feature. Less than 30 proteins were up and downregulated in all cancer contexts. We identified 36 highly altered genes, among them 10 were shared between the three cancer types analyzed, which are involved in the cell proliferation regulation, response to hormone and to endogenous stimulus. Despite limited PCOS pharmacogenomics studies, 10 SNPs are reported to be associated with drug response. All were missense mutations, except for rs8111699, an intronic variant characterized as a regulatory element and presumably binding site for transcription factors. In conclusion, in silico analysis revealed key genes that might participate in PCOS and oncogenesis, which could aid in early cancer diagnosis. Pharmacogenomics efforts have implicated SNPs in drug response, yet still remain to be found.

Analytical Challenges in Development of Chemoresistance Predictors for Precision Oncology

Chemoresistance, i.e., tumor insensitivity to chemotherapy, shortens life expectancy of cancer patients. Despite the availability of new treatment options, initial systemic regimens for solid tumors are dominated by a set of standard chemotherapy drugs, and alternative therapies are used only when a patient has demonstrated chemoresistance clinically. Chemoresistance predictors use laboratory parameters measured on tissue samples to predict the patient's response to chemotherapy and help to avoid application of chemotherapy to chemoresistant patients. Despite thousands of publications on putative chemoresistance predictors, there are only about a dozen predictors that are sufficiently accurate for precision oncology. One of the major reasons for inaccuracy of predictors is inaccuracy of analytical methods utilized to measure their laboratory parameters: an inaccurate method leads to an inaccurate predictor. The goal of this study was to identify analytical challenges in chemoresistance-predictor development and suggest ways to overcome them. Here we describe principles of chemoresistance predictor development via correlating a clinical parameter, which manifests disease state, with a laboratory parameter. We further classify predictors based on the nature of laboratory parameters and analyze advantages and limitations of different predictors using the reliability of analytical methods utilized for measuring laboratory parameters as a criterion. Our eventual focus is on predictors with known mechanisms of reactions involved in drug resistance (drug extrusion, drug degradation, and DNA damage repair) and using rate constants of these reactions to establish accurate and robust laboratory parameters. Many aspects and conclusions of our analysis are applicable to all types of disease biomarkers built upon the correlation of clinical and laboratory parameters.

Precision Medicine: Steps along the Road to Combat Human Cancer

The diagnosis and treatment of diseases such as cancer is becoming more accurate and specialized with the advent of precision medicine techniques, research and treatments. Reaching down to the cellular and even sub-cellular level, diagnostic tests can pinpoint specific, individual information from each patient, and guide providers to a more accurate plan of treatment. With this advanced knowledge, researchers and providers can better gauge the effectiveness of drugs, radiation, and other therapies, which is bound to lead to a more accurate, if not more positive, prognosis. As precision medicine becomes more established, new techniques, equipment, materials and testing methods will be required. Herein, we will examine the recent innovations in assays, devices and software, along with next generation sequencing in genomics diagnostics which are in use or are being developed for personalized medicine. So as to avoid duplication and produce the fullest possible benefit, all involved must be strongly encouraged to collaborate, across national borders, public and private sectors, science, medicine and academia alike. In this paper we will offer recommendations for tools, research and development, along with ideas for implementation. We plan to begin with discussion of the lessons learned to date, and the current research on pharmacogenomics. Given the steady stream of advances in imaging mass spectrometry and nanoLC-MS/MS, and use of genomic, proteomic and metabolomics biomarkers to distinguish healthy tissue from diseased cells, there is great potential to utilize pharmacogenomics to tailor a drug or drugs to a particular cohort of patients. Such efforts very well may bring increased hope for small groups of non-responders and those who have demonstrated adverse reactions to current treatments.

Identification of human peripheral blood monocyte gene markers for early screening of solid tumors

As cancer mortality is high in most regions of the world, early screening of cancer has become increasingly important. Minimally invasive screening programs that use peripheral blood mononuclear cells (PBMCs) are a new and reliable strategy that can achieve early detection of tumors by identifying marker genes. From 797 datasets, four (GSE12771, GSE24536, GSE27562, and GSE42834) including 428 samples, 236 solid tumor cases, and 192 healthy controls were chosen according to the inclusion criteria. A total of 285 genes from among 440 reported genes were selected by meta-analysis. Among them, 4 of the top significantly differentially expressed genes (ANXA1, IFI44, IFI44L, and OAS1) were identified as marker genes of PBMCs. Pathway enrichment analysis identified, two significant pathways, the 'primary immunodeficiency' pathway and the 'cytokine-cytokine receptor interaction' pathway. Protein- protein interaction (PPI) network analysis revealed the top 27 hubs with a degree centrality greater than 23 to be hub genes. We also identified 3 modules in Molecular Complex Detection (MCODE) analysis: Cluster 1 (related to ANXA1), Cluster 2 (related to IFI44 and IFI44L) and Cluster 3 (related to OAS1). Among the 4 marker genes, IFI44, IFI44L, and OAS1 are potential diagnostic biomarkers, even though their results were not as remarkable as those for ANXA1 in our study. ANXA1 is involved in the immunosuppressive mechanism in tumor-bearing hosts and may be used in a new strategy involving the use of the host's own immunity to achieve tumor suppression.

Drug Resistance Biomarkers and Their Clinical Applications in Childhood Acute Lymphoblastic Leukemia

Biomarkers are biological molecules found in body fluids or tissues, which can be considered as indications of a normal or abnormal process, or of a condition or disease. There are various types of biomarkers based on their application and molecular alterations. Treatment-sensitivity or drug resistance biomarkers include prognostic and predictive molecules with utmost importance in selecting appropriate treatment protocols and improving survival rates. Acute lymphoblastic leukemia (ALL) is the most prevalent hematological malignancy diagnosed in children with nearly 80% cure rate. Despite the favorable survival rates of childhood ALL (chALL), resistance to chemotherapeutic agents and, as a consequence, a dismal prognosis develops in a significant number of patients. Therefore, there are urgent needs to have robust, sensitive, and disease-specific molecular prognostic and predictive biomarkers, which could allow better risk classification and then better clinical results. In this article, we review the currently known drug resistance biomarkers, including somatic or germ line nucleic acids, epigenetic alterations, protein expressions and metabolic variations. Moreover, biomarkers with potential clinical applications are discussed.

Screening Yeast Display Libraries against Magnetized Yeast Cell Targets Enables Efficient Isolation of Membrane Protein Binders

When isolating binders from yeast displayed combinatorial libraries, a soluble, recombinantly expressed form of the target protein is typically utilized. As an alternative, we describe the use of target proteins displayed as surface fusions on magnetized yeast cells. In our strategy, the target protein is coexpressed on the yeast surface with an iron oxide binding protein; incubation of these yeast cells with iron oxide nanoparticles results in their magnetization. Subsequently, binder cells that interact with the magnetized target cells can be isolated using a magnet. Using a known binder-target pair with modest binding affinity (K_D ≈ 400 nM), we showed that a binder present at low frequency (1 in 10⁵) could be enriched more than 100-fold, in a single round of screening, suggesting feasibility of screening combinatorial libraries. Subsequently, we screened yeast display libraries of Sso7d and nanobody variants against yeast displayed targets to isolate binders specific to the cytosolic domain of the mitochondrial membrane protein TOM22 (K_D ≈ 272-1934 nM) and the extracellular domain of the c-Kit receptor (K_D ≈ 93 to K_D > 2000 nM). Additional studies showed that the TOM22 binders identified using this approach could be used for the enrichment of mitochondria from cell lysates, thereby confirming binding to the native mitochondrial protein. The ease of expressing a membrane protein or a domain thereof as a yeast cell surface fusion-in contrast to recombinant soluble expression-makes the use of yeast-displayed targets particularly attractive. Therefore, we expect the use of magnetized yeast cell targets will enable efficient isolation of binders to membrane proteins.

Biomarkers in Stress Related Diseases/Disorders: Diagnostic, Prognostic, and Therapeutic Values

Various internal and external factors negatively affect the homeostatic equilibrium of organisms at the molecular to the whole-body level, inducing the so-called state of stress. Stress affects an organism's welfare status and induces energy-consuming mechanisms to combat the subsequent ill effects; thus, the individual may be immunocompromised, making them vulnerable to pathogens. The information presented here has been extensively reviewed, compiled, and analyzed from authenticated published resources available on Medline, PubMed, PubMed Central, Science Direct, and other scientific databases. Stress levels can be monitored by the quantitative and qualitative measurement of biomarkers. Potential markers of stress include thermal stress markers, such as heat shock proteins (HSPs), innate immune markers, such as Acute Phase Proteins (APPs), oxidative stress markers, and chemical secretions in the saliva and urine. In addition, stress biomarkers also play critical roles in the prognosis of stress-related diseases and disorders, and therapy guidance. Moreover, different components have been identified as potent mediators of cardiovascular, central nervous system, hepatic, and nephrological disorders, which can also be employed to evaluate these conditions precisely, but with stringent validation and specificity. Considerable scientific advances have been made in the detection, quantitation, and application of these biomarkers. The present review describes the current progress of identifying biomarkers, their prognostic, and therapeutic values.

Biomarkers in Stress Related Diseases/Disorders: Diagnostic, Prognostic, and Therapeutic Values

Various internal and external factors negatively affect the homeostatic equilibrium of organisms at the molecular to the whole-body level, inducing the so-called state of stress. Stress affects an organism's welfare status and induces energy-consuming mechanisms to combat the subsequent ill effects; thus, the individual may be immunocompromised, making them vulnerable to pathogens. The information presented here has been extensively reviewed, compiled, and analyzed from authenticated published resources available on Medline, PubMed, PubMed Central, Science Direct, and other scientific databases. Stress levels can be monitored by the quantitative and qualitative measurement of biomarkers. Potential markers of stress include thermal stress markers, such as heat shock proteins (HSPs), innate immune markers, such as Acute Phase Proteins (APPs), oxidative stress markers, and chemical secretions in the saliva and urine. In addition, stress biomarkers also play critical roles in the prognosis of stress-related diseases and disorders, and therapy guidance. Moreover, different components have been identified as potent mediators of cardiovascular, central nervous system, hepatic, and nephrological disorders, which can also be employed to evaluate these conditions precisely, but with stringent validation and specificity. Considerable scientific advances have been made in the detection, quantitation, and application of these biomarkers. The present review describes the current progress of identifying biomarkers, their prognostic, and therapeutic values.

Quantification of plasma cell-free DNA levels in dogs with various tumors

Circulating cell-free DNA (cfDNA) is extracellular DNA released into the bloodstream by apoptotic or necrotic tumor cells, with cfDNA determination proposed as a noninvasive, sensitive marker for the diagnosis of human cancer. We evaluated cfDNA quantification as a diagnostic and prognostic tool in dogs with various tumors. We quantified plasma cfDNA concentration by absolute real-time PCR of long interspersed nuclear elements in 50 dogs with malignant tumors, 13 dogs with benign tumors or nodules, and 11 healthy controls. Six patients with malignant tumors were followed-up, and plasma cfDNA was quantified throughout disease progression. We found that plasma cfDNA concentrations were significantly elevated in dogs with malignant tumors compared with dogs with benign nodules or healthy controls. The DNA integrity index (the ratio between long and short cfDNA fragments) was significantly lower in dogs with malignant tumors compared to healthy controls. Significantly higher cfDNA levels and a lower DNA integrity index were observed in dogs with lymphoma or leukemia, hemangiosarcoma, and distant metastasis; cfDNA levels correlated well with clinical stage and tended to increase during or before periods of disease progression, suggesting potential efficacy of cfDNA for the detection of distant metastasis and to monitor the clinical stage of neoplasia.

Discovering proteins for chemoprevention and chemotherapy by curcumin in liver fluke infection-induced bile duct cancer

Modulation or prevention of protein changes during the cholangiocarcinoma (CCA) process induced by Opisthorchis viverrini (Ov) infection may become a key strategy for prevention and treatment of CCA. Monitoring of such changes could lead to discovery of protein targets for CCA treatment. Curcumin exerts anti-inflammatory and anti-CCA activities partly through its protein-modulatory ability. To support the potential use of curcumin and to discover novel target molecules for CCA treatment, we used a quantitative proteomic approach to investigate the effects of curcumin on protein changes in an Ov-induced CCA-harboring hamster model. Isobaric labelling and tandem mass spectrometry were used to compare the protein expression profiles of liver tissues from CCA hamsters with or without curcumin dietary supplementation. Among the dysregulated proteins, five were upregulated in liver tissues of CCA hamsters but markedly downregulated in the CCA hamsters supplemented with curcumin: S100A6, lumican, plastin-2, 14-3-3 zeta/delta and vimentin. Western blot and immunohistochemical analyses also showed similar expression patterns of these proteins in liver tissues of hamsters in the CCA and CCA + curcumin groups. Proteins such as clusterin and S100A10, involved in the NF-κB signaling pathway, an important signaling cascade involved in CCA genesis, were also upregulated in CCA hamsters and were then suppressed by curcumin treatment. Taken together, our results demonstrate the important changes in the proteome during the genesis of O. viverrini-induced CCA and provide an insight into the possible protein targets for prevention and treatment of this cancer.

A molecular signature of lung cancer: potential biomarkers for adenocarcinoma and squamous cell carcinoma

Adenocarcinoma (AC) and squamous cell carcinoma (SCC), sub-types of non-small cell lung cancer (NSCLC), both present unique features at the genome, epigenome, transcriptome and proteome levels, as well as shared clinical and histopathological characteristics, but differ in terms of treatment. To ensure proper treatment, one must be able to distinguish between these sub-types. Here, we identify novel biomarker proteins in NSCLC, allowing for distinguishing between the AC and SCC sub-types. Proteomics analysis distinguished between healthy and tumor tissues, with the expression level of 1,494 proteins being altered, 378 of which showed a ≥|100|-fold change. Enrichment of proteins related to protein synthesis and degradation, and of proteins associated with mitochondria, metabolism, and apoptosis, was found. Network analysis defined groups of proteins, such as those associated with cell metabolic processes or with fatty acid/lipid metabolism and transport. Several biomarkers that enable for distinguishing between AC and SCC were identified here for the first time, and together with previous reports confirmed here, led us to propose a list of proteins differentially expressed in SCC and AC. Some of these biomarkers are clear signatures for AC or SCC and four of them are secreted proteins. The presence of the mitochondrial protein SMAC/Diablo in the nucleus was found to be a signature for SCC. Precise diagnosis of AC and SCC is essential for selecting appropriate treatment and thus, increasing patient life expectancy. Finally, the search for drugs that target some of these biomarkers may lead to new treatments for lung cancer.

Advances in endometrial cancer protein biomarkers for use in the clinic

INTRODUCTION

Endometrial cancer (EC) is the fourth most common cancer in women in developed countries. The identification of sensitive and specific biomarkers to improve early detection of EC is crucial for an appropriate management of this disease, in which 30% of patients are diagnosed only at advanced stages, which is associated with high levels of morbidity and mortality. Despite major efforts and investments made to identify EC biomarkers, no protein has yet reached the stage of clinical application. Areas covered: This review gathers the numerous candidate biomarkers for EC diagnosis proposed in proteomic studies published from 1978 to 2017. Additionally, we summarize limitations associated with the proteomic technologies and study designs employed in those articles. Finally, we address new perspectives in EC biomarker research, including the comprehensive knowledge of previously suggested candidate biomarkers in conjunction with novel mass spectrometry-based proteomic technologies with enhanced sensitivity and specificity not yet applied to EC studies and a directed clinical perspective in the study design. Expert commentary: These ingredients could be the recipe to accelerate the application of protein biomarkers in the clinic.

snoRNA and piRNA expression levels modified by tobacco use in women with lung adenocarcinoma

Lung cancer is one of the most frequent types of cancer worldwide. Most patients are diagnosed at advanced stage and thus have poor prognosis. Smoking is a risk factor for lung cancer, however most smokers do not develop lung cancer while 20% of women with lung adenocarcinoma are non-smokers. Therefore, it is possible that these two groups present differences besides the smoking status, including differences in their gene expression signature. The altered expression patterns of non-coding RNAs in complex diseases make them potential biomarkers for diagnosis and treatment. We analyzed data from differentially and constitutively expressed PIWI-interacting RNAs and small nucleolar RNAs from publicly available small RNA high-throughput sequencing data in search of an expression pattern of non-coding RNA that could differentiate these two groups. Here, we report two sets of differentially expressed small non-coding RNAs identified in normal and tumoral tissues of women with lung adenocarcinoma, that discriminate between smokers and non-smokers. Our findings may offer new insights on metabolic alterations caused by tobacco and may be used for early diagnosis of lung cancer.

Comparative Proteomic Analysis of Whole-Gut Lavage Fluid and Pancreatic Juice Reveals a Less Invasive Method of Sampling Pancreatic Secretions

OBJECTIVES

There are currently no reliable, non-invasive screening tests for pancreatic ductal adenocarcinoma. The fluid secreted from the pancreatic ductal system ("pancreatic juice") has been well-studied as a potential source of cancer biomarkers. However, it is invasive to collect. We recently observed that the proteomic profile of intestinal effluent from the bowel in response to administration of an oral bowel preparation solution (also known as whole-gut lavage fluid, WGLF) contains large amounts of pancreas-derived proteins. We therefore hypothesized that the proteomic profile is similar to that of pancreatic juice. In this study, we compared the proteomic profiles of 77 patients undergoing routine colonoscopy with the profiles of 19 samples of pure pancreatic juice collected during surgery.

METHODS

WGLF was collected from patients undergoing routine colonoscopy, and pancreatic juice was collected from patients undergoing pancreatic surgery. Protein was isolated from both samples using an optimized method and analyzed by LC-MS/MS. Identified proteins were compared between samples and groups to determine similarity of the two fluids. We then compared our results with literature reports of pancreatic juice-based studies to determine similarity.

RESULTS

We found 104 proteins in our pancreatic juice samples, of which 90% were also found in our WGLF samples. The majority (67%) of the total proteins found in the WGLF were common to pancreatic juice, with intestine-specific proteins making up a smaller proportion.

CONCLUSIONS

WGLF and pancreatic juice appear to have similar proteomic profiles. This supports the notion that WGLF is a non-invasive, surrogate bio-fluid for pancreatic juice. Further studies are required to further elucidate its role in the diagnosis of pancreatic cancer.

Novel Biomarker Proteins in Chronic Lymphocytic Leukemia: Impact on Diagnosis, Prognosis and Treatment

In many cancers, cells undergo re-programming of metabolism, cell survival and anti-apoptotic defense strategies, with the proteins mediating this reprogramming representing potential biomarkers. Here, we searched for novel biomarker proteins in chronic lymphocytic leukemia (CLL) that can impact diagnosis, treatment and prognosis by comparing the protein expression profiles of peripheral blood mononuclear cells from CLL patients and healthy donors using specific antibodies, mass spectrometry and binary logistic regression analyses and other bioinformatics tools. Mass spectrometry (LC-HR-MS/MS) analysis identified 1,360 proteins whose expression levels were modified in CLL-derived lymphocytes. Some of these proteins were previously connected to different cancer types, including CLL, while four other highly expressed proteins were not previously reported to be associated with cancer, and here, for the first time, DDX46 and AK3 are linked to CLL. Down-regulation expression of two of these proteins resulted in cell growth inhibition. High DDX46 expression levels were associated with shorter survival of CLL patients and thus can serve as a prognosis marker. The proteins with modified expression include proteins involved in RNA splicing and translation and particularly mitochondrial proteins involved in apoptosis and metabolism. Thus, we focused on several metabolism- and apoptosis-modulating proteins, particularly on the voltage-dependent anion channel 1 (VDAC1), regulating both metabolism and apoptosis. Expression levels of Bcl-2, VDAC1, MAVS, AIF and SMAC/Diablo were markedly increased in CLL-derived lymphocytes. VDAC1 levels were highly correlated with the amount of CLL-cancerous CD19+/CD5+ cells and with the levels of all other apoptosis-modulating proteins tested. Binary logistic regression analysis demonstrated the ability to predict probability of disease with over 90% accuracy. Finally, based on the changes in the levels of several proteins in CLL patients, as revealed from LC-HR-MS/MS, we could distinguish between patients in a stable disease state and those who would be later transferred to anti-cancer treatments. The over-expressed proteins can thus serve as potential biomarkers for early diagnosis, prognosis, new targets for CLL therapy, and treatment guidance of CLL, forming the basis for personalized therapy.

Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor

Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model implements protein production, transport, and shedding based on tumor vascularization, cell proliferation, hypoxia, and necrosis, thus quantitatively relating the tumor and circulating proteomes. The results highlight the dynamics of shedding as a function of protein diffusivity and production. Linking the simulated tumor parameters to clinical tumor and vascularization measurements could potentially enable this approach to reveal the tumor-specific conditions based on the protein detected in circulation and thus help to more accurately manage cancer diagnosis and treatment.

Biomarkers for Bone Tumors: Discovery from Genomics and Proteomics Studies and Their Challenges

Diagnosis of bone tumor currently relies on imaging and biopsy, and hence, the need to find less invasive ways for its accurate detection. More recently, numerous promising deoxyribonucleic acid (DNA) and protein biomarkers with significant prognostic, diagnostic and/or predictive abilities for various types of bone tumors have been identified from genomics and proteomics studies. This article reviewed the putative biomarkers for the more common types of bone tumors (that is, osteosarcoma, Ewing sarcoma, chondrosarcoma [malignant] and giant cell tumor [benign]) that were unveiled from the studies. The benefits and drawbacks of these biomarkers, as well as the technology platforms involved in the research, were also discussed. Challenges faced in the biomarker discovery studies and the problems in their translation from the bench to the clinical settings were also addressed.

CD147 and Ki-67 overexpression confers poor prognosis in squamous cell carcinoma of oral tongue: a tissue microarray study

OBJECTIVE

Squamous cell carcinoma of the oral tongue (SCCOT) exhibits high risk for recurrence and regional metastasis even after surgical resection. We assessed the clinicopathologic and prognostic significance of a group of functionally related biomarkers.

STUDY DESIGN

We used a tissue microarray consisting of SCCOT from 32 patients for this study. These patients were treated at the University of Texas MD Anderson Cancer Center from 1995 to 2008. Biomarker expression levels were examined by immunohistochemistry and graded semiquantitatively to determine their prognostic significance.

RESULTS

CD147 and Tp63 expressions were significantly associated with a higher T stage and Ki-67 labeling index, as well as a shorter overall survival (OS) rate. Expression of Tp63 associated positively with poorly differentiated histology. There was significant association of Tp63 with the expression levels of CD147 and Glut-1. Glut-1 overexpression was marginally associated with a higher T stage. There was no prognostic significance of CD44 v6 expression in SCCOT.

CONCLUSION

SCCOT with CD147 overexpression in combination with high Ki-67 labeling index had poor OS. CD147 and Ki-67 overexpression is associated with aggressive disease with poor prognosis in SCCOT.

Microfluidic platforms for biomarker analysis

Biomarkers have been described as characteristics, most often molecular, that provide information about biological states, whether normal, pathological, or therapeutically modified. They hold great potential to assist diagnosis and prognosis, monitor disease, and assess therapeutic effectiveness. While a few biomarkers are routinely utilised clinically, these only reflect a very small percentage of all biomarkers discovered. Numerous factors contribute to the slow uptake of these new biomarkers, with challenges faced throughout the biomarker development pipeline. Microfluidics offers two important opportunities to the field of biomarkers: firstly, it can address some of these developmental obstacles, and secondly, it can provide the precise and complex platform required to bridge the gap between biomarker research and the biomarker-based analytical device market. Indeed, adoption of microfluidics has provided a new avenue for advancement, promoting clinical utilisation of both biomarkers and their analytical platforms. This review will discuss biomarkers and outline microfluidic platforms developed for biomarker analysis.

Circulating microRNA biomarkers for glioma and predicting response to therapy

The need for glioma biomarkers with improved sensitivity and specificity has sparked research into short non-coding RNA known as microRNA (miRNA). Altered miRNA biogenesis and expression in glioma plays a vital role in important signaling pathways associated with a range of tumor characteristics including gliomagenesis, invasion, and malignancy. This review will discuss current research into the role of miRNA in glioma and altered miRNA expression in biofluids as candidate biomarkers with a particular focus on glioblastoma, the most malignant form of glioma. The isolation and characterization of miRNA using cellular and molecular biology techniques from the circulation of glioma patients could potentially be used for improved diagnosis, prognosis, and treatment decisions. We aim to highlight the links between research into miRNA function, their use as biomarkers, and how these biomarkers can be used to predict response to therapy. Furthermore, increased understanding of miRNA in glioma biology through biomarker research has led to the development of miRNA therapeutics which could restore normal miRNA expression and function and improve the prognosis of glioma patients. A panel of important miRNA biomarkers for glioma in various biofluids discovered to date has been summarized here. There is still a need, however, to standardize techniques for biomarker characterization to bring us closer to clinically relevant miRNA-based diagnostic and therapeutic signatures. A clinically validated biomarker panel has potential to improve time to diagnosis, predicting response to treatment and ultimately the prognosis of glioma patients.

Cancer-associated autoantibodies to MUC1 and MUC4--a blinded case–control study of colorectal cancer in UK collaborative trial of ovarian cancer screening

Recent reports suggest that autoantibodies directed to aberrantly glycosylated mucins, in particular MUC1 and MUC4, are found in patients with colorectal cancer. There is, however, limited information on the autoantibody levels before clinical diagnosis, and their utility in cancer screening in the general population. In our study, we have generated O-glycosylated synthetic MUC1 and MUC4 peptides in vitro, to mimic cancer-associated glycoforms, and displayed these on microarrays. The assay’s performance was tested through an initial screening of serum samples taken from patients at the time of colorectal cancer diagnosis and healthy controls. Subsequently, the selected biomarkers were evaluated in a blinded nested case–control study using stored serum samples from among the 50,640 women randomized to the multimodal arm of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), where women gave annual blood samples for several years. Cases were 97 postmenopausal women who developed colorectal cancer after recruitment and were age-matched to 97 women without any history of cancer. MUC1-STn and MUC1-Core3 IgG autoantibodies identified cases with 8.2 and 13.4% sensitivity, respectively, at 95% specificity. IgA to MUC4 glycoforms were unable to discriminate between cases and controls in the UKCTOCS sera. Additional analysis was undertaken by combining the data of MUC1-STn and MUC1-Core3 with previously generated data on autoantibodies to p53 peptides, which increased the sensitivity to 32.0% at 95% specificity. These findings suggest that a combination of antibody signatures may have a role as part of a biomarker panel for the early detection of colorectal cancer.

Bridging the gap between biologic, individual, and macroenvironmental factors in cancer: a multilevel approach

To address the complex nature of cancer occurrence and outcomes, approaches have been developed to simultaneously assess the role of two or more etiologic agents within hierarchical levels including the: (i) macroenvironment level (e.g., health care policy, neighborhood, or family structure); (ii) individual level (e.g., behaviors, carcinogenic exposures, socioeconomic factors, and psychologic responses); and (iii) biologic level (e.g., cellular biomarkers and inherited susceptibility variants). Prior multilevel approaches tend to focus on social and environmental hypotheses, and are thus limited in their ability to integrate biologic factors into a multilevel framework. This limited integration may be related to the limited translation of research findings into the clinic. We propose a "Multi-level Biologic and Social Integrative Construct" (MBASIC) to integrate macroenvironment and individual factors with biology. The goal of this framework is to help researchers identify relationships among factors that may be involved in the multifactorial, complex nature of cancer etiology, to aid in appropriate study design, to guide the development of statistical or mechanistic models to study these relationships, and to position the results of these studies for improved intervention, translation, and implementation. MBASIC allows researchers from diverse fields to develop hypotheses of interest under a common conceptual framework, to guide transdisciplinary collaborations, and to optimize the value of multilevel studies for clinical and public health activities.

Bioinformatics of Cancer ncRNA in High Throughput Sequencing: Present State and Challenges

The numerous genome sequencing projects produced unprecedented amount of data providing significant information to the discovery of novel non-coding RNA (ncRNA). Several ncRNAs have been described to control gene expression and display important role during cell differentiation and homeostasis. In the last decade, high throughput methods in conjunction with approaches in bioinformatics have been used to identify, classify, and evaluate the expression of hundreds of ncRNA in normal and pathological states, such as cancer. Patient outcomes have been already associated with differential expression of ncRNAs in normal and tumoral tissues, providing new insights in the development of innovative therapeutic strategies in oncology. In this review, we present and discuss bioinformatics advances in the development of computational approaches to analyze and discover ncRNA data in oncology using high throughput sequencing technologies.

An engineered ClyA nanopore detects folded target proteins by selective external association and pore entry

Nanopores have been used in label-free single-molecule studies, including investigations of chemical reactions, nucleic acid analysis, and applications in sensing. Biological nanopores generally perform better than artificial nanopores as sensors, but they have disadvantages including a fixed diameter. Here we introduce a biological nanopore ClyA that is wide enough to sample and distinguish large analyte proteins, which enter the pore lumen. Remarkably, human and bovine thrombins, despite 86% sequence identity, elicit characteristic ionic current blockades, which at -50 mV differ in their main current levels by 26 ± 1 pA. The use of DNA aptamers or hirudin as ligands further distinguished the protein analytes. Finally, we constructed ClyA nanopores decorated with covalently attached aptamers. These nanopores selectively captured and internalized cognate protein analytes but excluded noncognate analytes, in a process that resembles transport by nuclear pores.

Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations

Most clinical blood biomarkers lack the necessary sensitivity and specificity to reliably detect cancer at an early stage, when it is best treatable. It is not yet clear how early a clinical blood assay can be used to detect cancer or how biomarker-based strategies can be improved to enable earlier detection of smaller tumors. To address these issues, we developed a mathematical model describing dynamic plasma biomarker kinetics in relation to the growth of a tumor, beginning with a single cancer cell. To exemplify a realistic scenario in which biomarker is shed by both cancerous and noncancerous cells, we primed the model on ovarian tumor growth and CA125 shedding data, for which tumor growth parameters and shedding rates are readily available in published literature. We found that a tumor could grow unnoticed for more than 10.1 years and reach a volume of about π/6(25.36 mm)(3), corresponding to a spherical diameter of about 25.36 mm, before becoming detectable by current clinical blood assays. Model parameters were perturbed over log orders of magnitude to quantify ideal shedding rates and identify other blood-based strategies required for early submillimeter tumor detectability. The detection times we estimated are consistent with recently published tumor progression time lines based on clinical genomic sequencing data for several cancers. Here, we rigorously showed that shedding rates of current clinical blood biomarkers are likely 10(4)-fold too low to enable detection of a developing tumor within the first decade of tumor growth. The model presented here can be extended to virtually any solid cancer and associated biomarkers.

The proteome of normal pancreatic juice

OBJECTIVES

The aims of this study were to characterize the proteome of normal pancreatic juice, to analyze the effect of secretin on the normal proteome, and to compare these results with published data from patients with pancreatic cancer.

METHODS

Paired pancreatic fluid specimens (before and after intravenous secretin stimulation) were obtained during endoscopic pancreatography from 3 patients without significant pancreatic pathology. Proteins were identified and quantified by mass spectrometry-based protein quantification technology. The human RefSeq (NCBI) database was used to compare the data in samples from patients without pancreatic disease with published data from 3 patients with pancreatic cancer.

RESULTS

A total of 285 proteins were identified in normal pancreatic juice. Ninety had sufficient amino acid sequences identified to characterize the protein with a high level of confidence. All 90 proteins were present before and after secretin administration but with altered relative concentrations, usually by 1 to 2 folds, after stimulation. Comparison with 170 published pancreatic cancer proteins yielded an overlap of only 42 proteins.

CONCLUSIONS

Normal pancreatic juice contains multiple proteins related to many biological processes. Secretin alters the concentration but not the spectrum of these proteins. The pancreatic juice proteome of patients without pancreatic disease and that of patients with pancreatic cancer differ markedly.

YY1 overexpression is associated with poor prognosis and metastasis-free survival in patients suffering osteosarcoma

Background

The polycomb transcription factor Yin Yang 1 (YY1) overexpression can be causally implicated in experimental tumor growth and metastasization. To date, there is no clinical evidence of YY1 involvement in outcome of patients with osteosarcoma. Prognosis of osteosarcoma is still severe and only few patients survive beyond five years. We performed a prospective immunohistochemistry analysis to correlate YY1 immunostaining with metastatic development and survival in a selected homogeneous group of patients with osteosarcoma.

Methods

We studied 41 patients suffering from osteosarcoma (stage II-IVa). Multivariate analysis was performed using Cox proportional hazard regression to evaluate the correlation between YY1 expression and both metastasis development and mortality.

Results

YY1 protein is not usually present in normal bone; in contrast, a high number of patients (61%) showed a high score of YY1 positive cells (51-100%) and 39% had a low score (10-50% positive cells). No statistical difference was found in histology, anatomic sites, or response to chemotherapy between the two degrees of YY1 expression. Cox regression analysis demonstrated that the highest score of YY1 expression was predictive of both low metastasis-free survival (HR = 4.690, 95%CI = 1.079-20.396; p = 0.039) and poor overall survival (HR = 8.353, 95%CI = 1.863-37.451 p = 0.006) regardless of the effects of covariates such as age, gender, histology and chemonecrosis.

Conclusion

Overexpression of YY1 in primary site of osteosarcoma is associated with the occurrence of metastasis and poor clinical outcome.

Ion-permeable membrane for on-chip preconcentration and separation of cancer marker proteins

Cancer marker proteins have been electrophoretically concentrated and then separated in a microfluidic device. On-chip preconcentration was achieved using an ion-permeable membrane, consisting of acrylamide, N,N'-methylene-bisacrylamide and 2-(acrylamido)-2-methylpropanesulfonate. This negatively charged membrane was photopolymerized in the microdevice near the injection intersection. Anionic proteins were excluded from the porous membrane based on both size and charge, which concentrated target components in the injection intersection prior to separation by microchip capillary electrophoresis (μ-CE). Bovine serum albumin was used in the initial characterization of the system and showed a 40-fold enrichment in the μ-CE peak with 4 min of preconcentration. Adjustment of buffer pH enabled baseline resolution of two cancer biomarkers, α-fetoprotein (AFP) and heat shock protein 90 (HSP90), while fine control over preconcentration time limited peak broadening. Our optimized preconcentration and μ-CE approach was applied to AFP and HSP90, where enrichment factors of >10-fold were achieved with just 1 min of preconcentration. Overall, the process was simple and rapid, providing a useful tool for improving detection in microscale systems.

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.

Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer

A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.

Omics-based molecular target and biomarker identification

Genomic, proteomic, and other omic-based approaches are now broadly used in biomedical research to facilitate the understanding of disease mechanisms and identification of molecular targets and biomarkers for therapeutic and diagnostic development. While the Omics technologies and bioinformatics tools for analyzing Omics data are rapidly advancing, the functional analysis and interpretation of the data remain challenging due to the inherent nature of the generally long workflows of Omics experiments. We adopt a strategy that emphasizes the use of curated knowledge resources coupled with expert-guided examination and interpretation of Omics data for the selection of potential molecular targets. We describe a downstream workflow and procedures for functional analysis that focus on biological pathways, from which molecular targets can be derived and proposed for experimental validation.

Deciphering squamous cell carcinoma using multidimensional genomic approaches

Squamous cell carcinomas (SqCCs) arise in a wide range of tissues including skin, lung, and oral mucosa. Although all SqCCs are epithelial in origin and share common nomenclature, these cancers differ greatly with respect to incidence, prognosis, and treatment. Current knowledge of genetic similarities and differences between SqCCs is insufficient to describe the biology of these cancers, which arise from diverse tissue origins. In this paper we provide a general overview of whole genome approaches for gene and pathway discovery and highlight the advancement of integrative genomics as a state-of-the-art technology in the study of SqCC genetics.

A high-throughput O-glycopeptide discovery platform for seromic profiling

Biomarker microarrays are becoming valuable tools for serological screening of disease-associated autoantibodies. Post-translational modifications (PTMs) such as glycosylation extend the range of protein function, and a variety of glycosylated proteins are known to be altered in disease progression. Here, we have developed a synthetic screening microarray platform for facile display of O-glycosylated peptides (O-PTMs). By introduction of a capping step during chemical solid-phase glycopeptide synthesis, selective enrichment of N-terminal glycopeptide end products was achieved on an amine-reactive hydrogel-coated microarray glass surface, allowing high-throughput display of large numbers of glycopeptides. Utilizing a repertoire of recombinant glycosyltransferases enabled further diversification of the array libraries in situ and display of a new level of potential biomarker candidates for serological screening. As proof-of-concept, we have demonstrated that MUC1 glycopeptides could be assembled and used to detect autoantibodies in vaccine-induced disease-free breast cancer patients and in patients with confirmed disease at time of diagnosis.

A combined proteomics and metabolomics profiling of gastric cardia cancer reveals characteristic dysregulations in glucose metabolism

Gastric cardia cancer (GCC), which occurs at the gastric-esophageal boundary, is one of the most malignant tumors. Despite its high mortality and morbidity, the molecular mechanism of initiation and progression of this disease is largely unknown. In this study, using proteomics and metabolomics approaches, we found that the level of several enzymes and their related metabolic intermediates involved in glucose metabolism were deregulated in GCC. Among these enzymes, two subunits controlling pyruvic acid efflux, lactate dehydrogenase A (LDHA) and pyruvate dehydrogenase B (PDHB), were further analyzed in vitro. Either down-regulation of LDH subunit LDHA or overexpression of PDH subunit PDHB could force pyruvic acid into the Krebs cycle rather than the glycolysis process in AGS gastric cancer cells, which inhibited cell growth and cell migration. Our results reflect an important glucose metabolic signature, especially the dysregulation of pyruvic acid efflux in the development of GCC. Forced transition from glycolysis to the Krebs cycle had an inhibitory effect on GCC progression, providing potential therapeutic targets for this disease.

Biomarkers for Basal-like Breast Cancer

Initially recognized through microarray-based gene expression profiling, basal-like breast cancer, for which we lack effective targeted therapies, is an aggressive form of carcinoma with a predilection for younger women. With some success, immunohistochemical studies have attempted to reproduce the expression profile classification of breast cancer through identification of subtype-specific biomarkers. This review aims to present an in depth summary and analysis of the current status of basal-like breast cancer biomarker research. While a number of biomarkers show promise for future clinical application, the next logical step is a comprehensive investigation of all biomarkers against a gene expression profile gold standard for breast cancer subtype assignment.

Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer

Advances in high-throughput, genome-wide profiling technologies have allowed for an unprecedented view of the cancer genome landscape. Specifically, high-density microarrays and sequencing-based strategies have been widely utilized to identify genetic (such as gene dosage, allelic status, and mutations in gene sequence) and epigenetic (such as DNA methylation, histone modification, and microRNA) aberrations in cancer. Although the application of these profiling technologies in unidimensional analyses has been instrumental in cancer gene discovery, genes affected by low-frequency events are often overlooked. The integrative approach of analyzing parallel dimensions has enabled the identification of (a) genes that are often disrupted by multiple mechanisms but at low frequencies by any one mechanism and (b) pathways that are often disrupted at multiple components but at low frequencies at individual components. These benefits of using an integrative approach illustrate the concept that the whole is greater than the sum of its parts. As efforts have now turned toward parallel and integrative multidimensional approaches for studying the cancer genome landscape in hopes of obtaining a more insightful understanding of the key genes and pathways driving cancer cells, this review describes key findings disseminating from such high-throughput, integrative analyses, including contributions to our understanding of causative genetic events in cancer cell biology.