Understanding Ovarian Cancer: iTRAQ-Based Proteomics for Biomarker Discovery

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Integrated proteomic and phosphoproteomic analysis for characterization of colorectal cancer

Proteins and translationally modified proteins like phosphoproteins have essential regulatory roles in tumorigenesis. This study attempts to elucidate the dysregulated proteins driving colorectal cancer (CRC). To explore the differential proteins, we performed iTRAQ labeling proteomics and TMT labeling phosphoproteomics analysis of CRC tissues and adjacent non-cancerous tissues. The functions of quantified proteins were analyzed using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Subcellular localization analysis. Depending on the results, we identified 330 differential proteins and 82 phosphoproteins in CRC. GO and KEGG analyses demonstrated that protein changes were primarily associated with regulating biological and metabolic processes through binding to other molecules. Co-expression relationships between proteomic and phosphoproteomic analysis revealed that TMC5, SMC4, SLBP, VSIG2, and NDRG2 were significantly dysregulated differential proteins. Additionally, based on the predicted co-expression proteins, we identified that the stem-loop binding protein (SLBP) was up-regulated in CRC cells and promoted the proliferation and migration of CRC. This study reports an integrated proteomic and phosphoproteomic analysis of CRC to discern the functional impact of protein alterations and provides a candidate diagnostic biomarker or therapeutic target for CRC. SIGNIFICANCE: Combining one or more high-throughput omics technologies with bioinformatics to analyze biological samples and explore the links between biomolecules and their functions can provide more comprehensive and multi-level insights for disease mechanism research. Proteomics, phosphoproteomics, metabolomics and their combined analysis play an important role in the auxiliary diagnosis, the discovery of biomarkers and novel therapeutic targets for colorectal cancer. In this integrated proteomic and phosphoproteomic analysis, we identified proteins and phosphoproteins in colorectal cancer tissue and analyzed potential mechanisms contributing to progression in colorectal cancer. The results of this study provide a foundation to focus future experiments on the contribution of altered protein and phosphorylation patterns to prevention and treatment of colorectal cancer.

Proteomic Applications and Considerations: From Research to Patient Care

Despite technological advancements in the field of proteomics, the rate at which serum and plasma biomarkers identified using proteomic approaches are translated into clinical use remains extremely low. In this chapter, we describe recent technological advancements and analytical strategies in proteomic methods. We also describe the progress of proteomic blood-based biomarkers to date and discuss what the future of proteomics might entail with the use of multi-omic approaches and implementing machine learning on large proteomic datasets. Lastly, we provide several key considerations for biomarker studies, ranging from sample type to the use of reference samples, in order to achieve progress from bench to bedside, ultimately improving patient diagnosis, disease, and/or therapeutic monitoring and care.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

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

MALDI-TOF Mass Spectrometry in Clinical Analysis and Research

In the decade after being awarded the Nobel Prize in Chemistry in 2002, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used as an analytical chemistry tool for the detection of large and small molecules (e.g., polymers, proteins, peptides, nucleic acids, amino acids, lipids, etc.) and for clinical analysis and research (e.g., pathogen identification, genetic disorders screening, cancer diagnosis, etc.). In view of the fast development of MALDI-TOF MS in clinical usage, this review systematically summarizes the most important applications of MALDI-TOF MS in clinical analysis and research by analyzing MALDI TOF MS-related reviews collected in the Web of Science database. On the basis of the analysis of keyword co-occurrence of over 2000 review articles, four themes consisting of "pathogen identification", "disease diagnosis", "nucleic acids analysis", and "small molecules analysis" were found. For each theme, the review further outlined their application implications, analytical methods, and systems as well as limitations that need to be addressed. Overall, the review summarizes and elaborates on the clinical applications of MALDI-TOF MS, providing a comprehensive picture for researchers embarking on MALDI TOF MS-related clinical analysis and research.

Application of informatics in cancer research and clinical practice: Opportunities and challenges

Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high-throughput omics data mining, machine-learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes, and focus on how informatics can provide opportunities for cancer research and practices. Finally, we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices. It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics-specific insights.

Recent Advances in Ovarian Cancer: Therapeutic Strategies, Potential Biomarkers, and Technological Improvements

Aggressive and recurrent gynecological cancers are associated with worse prognosis and a lack of effective therapeutic response. Ovarian cancer (OC) patients are often diagnosed in advanced stages, when drug resistance, angiogenesis, relapse, and metastasis impact survival outcomes. Currently, surgical debulking, radiotherapy, and/or chemotherapy remain the mainstream treatment modalities; however, patients suffer unwanted side effects and drug resistance in the absence of targeted therapies. Hence, it is urgent to decipher the complex disease biology and identify potential biomarkers, which could greatly contribute to making an early diagnosis or predicting the response to specific therapies. This review aims to critically discuss the current therapeutic strategies for OC, novel drug-delivery systems, and potential biomarkers in the context of genetics and molecular research. It emphasizes how the understanding of disease biology is related to the advancement of technology, enabling the exploration of novel biomarkers that may be able to provide more accurate diagnosis and prognosis, which would effectively translate into targeted therapies, ultimately improving patients’ overall survival and quality of life.

OMICS Applications for Medicinal Plants in Gastrointestinal Cancers: Current Advancements and Future Perspectives

Gastrointestinal cancers refer to a group of deadly malignancies of the gastrointestinal tract and organs of the digestive system. Over the past decades, considerable amounts of medicinal plants have exhibited potent anticancer effects on different types of gastrointestinal cancers. OMICS, systems biology approaches covering genomics, transcriptomics, proteomics and metabolomics, are broadly applied to comprehensively reflect the molecular profiles in mechanistic studies of medicinal plants. Single- and multi-OMICS approaches facilitate the unravelling of signalling interaction networks and key molecular targets of medicinal plants with anti-gastrointestinal cancer potential. Hence, this review summarizes the applications of various OMICS and advanced bioinformatics approaches in examining therapeutic targets, signalling pathways, and the tumour microenvironment in response to anticancer medicinal plants. Advances and prospects in this field are also discussed.

Toward improvement of screening through mass spectrometry-based proteomics: ovarian cancer as a case study

Ovarian cancer is one of the leading causes of cancer related deaths affecting United States women. Early-stage detection of ovarian cancer has been linked to increased survival, however, current screening methods, such as biomarker testing, have proven to be ineffective in doing so. Therefore, further developments are necessary to be able to achieve positive patient prognosis. Ongoing efforts are being made in biomarker discovery towards clinical applications in screening for early-stage ovarian cancer. In this perspective, we discuss and provide examples for several workflows employing mass spectrometry-based proteomics towards protein biomarker discovery and characterization in the context of ovarian cancer; workflows include protein identification and characterization as well as intact protein profiling. We also discuss the opportunities to merge these workflows for a multiplexed approach for biomarkers. Lastly, we provide our insight as to future developments that may serve to enhance biomarker discovery workflows while also considering translational potential.

Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis

Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.

Malignant Ascites in Ovarian Cancer: Cellular, Acellular, and Biophysical Determinants of Molecular Characteristics and Therapy Response

Ascites refers to the abnormal accumulation of fluid in the peritoneum resulting from an underlying pathology, such as metastatic cancer. Among all cancers, advanced-stage epithelial ovarian cancer is most frequently associated with the production of malignant ascites and is the leading cause of death from gynecologic malignancies. Despite decades of evidence showing that the accumulation of peritoneal fluid portends the poorest outcomes for cancer patients, the role of malignant ascites in promoting metastasis and therapy resistance remains poorly understood. This review summarizes the current understanding of malignant ascites, with a focus on ovarian cancer. The first section provides an overview of heterogeneity in ovarian cancer and the pathophysiology of malignant ascites. Next, analytical methods used to characterize the cellular and acellular components of malignant ascites, as well the role of these components in modulating cell biology, are discussed. The review then provides a perspective on the pressures and forces that tumors are subjected to in the presence of malignant ascites and the impact of physical stress on therapy resistance. Treatment options for malignant ascites, including surgical, pharmacological and photochemical interventions are then discussed to highlight challenges and opportunities at the interface of drug discovery, device development and physical sciences in oncology.

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

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

Plasma-Derived Exosomal ALIX as a Novel Biomarker for Diagnosis and Classification of Pancreatic Cancer

Background

Pancreatic cancer (PC) has a dismal prognosis due to its insidious early symptoms and poor early detection rate. Exosomes can be released by various cell types and tend to be a potential novel biomarker for PC detection. In this study, we explored the proteomic profiles of plasma exosomes collected from patients with PC at different stages and other pancreatic diseases.

Methods

Plasma samples were collected from six groups of patients, including PC at stage I/II, PC at stage III/IV, well-differentiated pancreatic neuroendocrine tumor (P-NET), pancreatic cystic lesions (PCLs), chronic pancreatitis (CP), and healthy controls (HCs). Plasma-derived exosomes were isolated by ultracentrifugation and identified routinely. Isobaric tags for relative and absolute quantification (iTRAQ) based proteomic analysis along with bioinformatic analysis were performed to elucidate the biological functions of proteins. The expression of exosomal ALIX was further confirmed by enzyme-linked immunosorbent assay in a larger cohort of patients. Furthermore, receiver operating characteristic curve analysis was applied to evaluate the potential of ALIX as a novel diagnostic biomarker.

Results

The proteomic profile revealed a total of 623 proteins expressed among the six groups, and 16 proteins with differential degrees of abundance were found in PC vs. other pancreatic diseases (including P-NET, PCLs, and CP). Based on the results of proteomic and bioinformatic analyses, exosomal ALIX was subsequently selected as a novel biomarker for PC detection and validated in another clinical cohort. We noticed that ALIX expression was elevated in PC patients compared with patients with other pancreatic diseases or HC, and it was also closely associated with TNM stage and distant metastasis. Interestingly, the combination of exosomal ALIX and serum CA199 has greater values in differentiating both early vs. late PC (AUC value 0.872) and PC vs. other pancreatic diseases (AUC value 0.910) than either ALIX or CA199 alone.

Conclusion

In summary, our study demonstrated that based on proteomic profiling, proteins isolated from the plasma-derived exosomes may function as ideal non-invasive biomarkers for the clinical diagnosis of PC. Importantly, exosomal ALIX combined with CA199 has great potentials in detection of PC, especially in distinguishing PC patients at early stages from advanced stages.

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

Poor Clearance of Free Hemoglobin Due to Lower Active Haptoglobin Availability is Associated with Osteoarthritis Inflammation

Introduction

Circulating plasma proteins play an important role in various diseases, and analysis of the plasma proteome has led to the discovery of various disease biomarkers. Osteoarthritis (OA) is the most common chronic joint disease, mostly affecting people of older age. OA typically starts as a focal disease (in a single compartment, typically treated with unicompartmental knee replacement), and then progresses to the other compartments (if not treated in time, typically treated with total knee replacement). For this, identification of differential proteins was carried out in plasma samples of OA cases and compared with healthy controls. The aim of this study was to identify circulatory differentially expressed proteins (DEPs) in knee-OA patients undergoing total knee replacement or unicompartmental knee replacement compared to healthy controls and assess their role, in order to have better understanding of the etiology behind OA pathophysiology.

Methods

DEPs were identified with two-dimensional gel electrophoresis (2DE) and isobaric tags for relative and absolute quantification (iTRAQ), followed by liquid chromatography with tandem mass spectrometry. Validation of DEPs was carried out using Western blot and ELISA. Posttranslational modifications were checked after running native gel using purified protein from patients, followed by detection of autoantibodies.

Results

In total, 52 DEPs were identified, among which 45 were distinct DEPs. Haptoglobin (Hp) was identified as one of the most significantly upregulated proteins in OA (P=0.005) identified by both 2DE and iTRAQ. Decreased levels of Hp tetramers and increased levels of autoantibodies against Hpβ were observed in OA plasma.

Conclusion

Our data suggest that poor clearance of free hemoglobin and low levels of Hp tetramers may be associated with OA pathogenesis and inflammation.

Identification of differentially expressed proteins and clinicopathological significance of HMGB2 in cervical cancer

To investigate the complexity of proteomics in cervical cancer tissues, we used isobaric tags for relative and absolute quantitation (iTRAQ)-based mass spectrometry analysis on a panel of normal cervical tissues (N), high-grade squamous intraepithelial lesion tissues (HSIL) and cervical cancer tissues (CC). Total 72 differentially expressed proteins were identified both in CC vs N and CC vs HSIL. The expression of HMGB2 was markedly higher in CC than that in HSIL and N. High HMGB2 expression was significantly correlated with primary tumor size, invasion and tumor stage. The up-regulated HMGB2 was discovered to be associated with human cervical cancer. These findings suggest that HMGB2 may be a potentially prognostic biomarker and a target for the therapy of cervical cancer.

NAD(P)-dependent steroid dehydrogenase-like protein and neutral cholesterol ester hydrolase 1 serve as novel markers for early detection of gastric cancer identified using quantitative proteomics

BACKGROUND

Gastric cancer (GC) is the third most common cause of cancer deaths worldwide. In the present study, we aimed to identify novel GC biomarkers by integrating isobaric tags of relative and absolute quantitation (iTRAQ) for aberrantly expressed proteins in GC patients.

METHODS

Using stable isotope tags, we labeled an initial discovery group comprising four paired gastric cancer and adjacent gastric tissue samples, and subjected them to LC-ESI-MS/MS. We used a validation set comprising 129 paired gastric cancer and adjacent gastric tissues from patients and benign healthy controls to validate the candidate targets.

RESULTS

We identified two proteins, NAD(P)-dependent steroid dehydrogenase-like (NSDHL) and neutral cholesterol ester hydrolase 1 (NCEH1), that were significantly overexpressed in GC tissues. The sensitivity and specificity of NSDHL were 80.6% and 74.4%, respectively, in GC compared with a sensitivity of 25.6% in adjacent tissues and 24% in benign healthy controls. The area under the ROC curve (AUC) for NSDHL was 0.810 for GC detection. Overexpression of NSDHL in GC was significantly correlated with local tumor invasion. The sensitivity and specificity of NCEH1 were 77.5% and 73.6%, respectively, in GC compared with a sensitivity of 26.4% in adjacent tissues and 20% in benign controls. The AUC for NSDHL was 0.792. Overexpression of NCEH1 was significantly associated with tumor histological classification and local invasion. Moreover, a combined analysis of NSDHL and NCEH1 achieved a sensitivity and specificity of 85.7% and 83%, respectively, and the AUC was 0.872. The combined analysis of NSDHL and NCEH1 was significantly correlated with histological grade and TNM Ⅱ-Ⅳ staging.

CONCLUSIONS

iTRAQ-labeled quantitative proteomics represents a powerful method to identify novel cancer biomarkers. The present study identified NSDHL and NCEH1 as useful biomarkers for screening, diagnosis, and prognosis of patients with gastric cancer.

Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potential roles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.

Translational biomarkers in the era of precision medicine

In this chapter we discuss the past, present and future of clinical biomarker development. We explore the advent of new technologies, paving the way in which health, medicine and disease is understood. This review includes the identification of physicochemical assays, current regulations, the development and reproducibility of clinical trials, as well as, the revolution of omics technologies and state-of-the-art integration and analysis approaches.

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

Simple Summary

The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient’s systemic status, not only the tumor’s molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning.

Abstract

Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.

The Effect of Bee Venom Peptides Melittin, Tertiapin, and Apamin on the Human Erythrocytes Ghosts: A Preliminary Study

Red blood cells (RBCs) are the most abundant cells in the human blood that have been extensively studied under morphology, ultrastructure, biochemical and molecular functions. Therefore, RBCs are excellent cell models in the study of biologically active compounds like drugs and toxins on the structure and function of the cell membrane. The aim of the present study was to explore erythrocyte ghost’s proteome to identify changes occurring under the influence of three bee venom peptides-melittin, tertiapin, and apamin. We conducted preliminary experiments on the erythrocyte ghosts incubated with these peptides at their non-hemolytic concentrations. Such preparations were analyzed using liquid chromatography coupled with tandem mass spectrometry. It was found that when higher concentrations of melittin and apamin were used, fewer proteins were identified. Moreover, the results clearly indicated that apamin demonstrates the greatest influence on the RBCs ghosts proteome. Interestingly, the data also suggest that tertiapin exerted a stabilizing effect on the erythrocyte membrane. The experiments carried out show the great potential of proteomic research in the projects focused on the toxin’s properties as membrane active agents. However, to determine the specificity of the effect of selected bee venom peptides on the erythrocyte ghosts, further proteomic research should be focused on the quantitative analysis.

Performance of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in diagnosis of ovarian cancer: a systematic review and meta-analysis

BACKGROUND

To evaluate the diagnostic performance of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for ovarian cancer.

PATIENTS AND METHODS

A thorough research was conducted in PubMed, Web of Science and Embase (until November 2018) to identify studies evaluating the accuracy of MALDI-TOF-MS for ovarian cancer. Using Meta-Disc1.4, Review Manager 5.3 and Stata 15.1 software to analyze the pooled results: sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and 95% confidence intervals (CI). The summary receiver operating characteristic curves (SROC) and area under the curve (AUC) show the overall performance of MALDI-TOF-MS.

RESULTS

Eighteen studies were included in the meta-analysis. Methodological quality analysis of the included studies showed that these articles were at low risk of bias and applicability concerns in total. Summary estimates of the diagnostic parameters were as follows: sensitivity, 0.77 (95% CI: 0.73-0.80); specificity, 0.72 (95% CI: 0.70-0.74), PLR, 2.80 (95% CI: 2.41-3.24); NLR, 0.30 (95% CI: 0.22-0.40) and DOR, 10.71 (95% CI: 7.81-14.68). And the AUC was 0.8336. Egger's test showed no significant publication bias in this meta-analysis.

CONCLUSION

In conclusion, MALDI-TOF-MS shows a good ability for diagnosing ovarian cancer. Further evaluation and optimization of standardized procedures are necessary for complete relying on MALDI-TOF-MS to diagnose ovarian cancer.

The Need for Multi-Omics Biomarker Signatures in Precision Medicine

Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses—which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas—have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.

Wide spectrum targeted metabolomics identifies potential ovarian cancer biomarkers

AIMS

Despite of almost a hundred years of research on cancer metabolism, the biological background of cancerogenesis and cancer-related reprogramming of metabolism remains not fully understood. In order to comprehensively and effectively diagnose and treat the deadliest diseases, the mechanisms underlying these diseases have to be discovered urgently. Among the gynecological malignancies, ovarian cancer is the most common cause of death. The aim of the study was to search for potential cancer-related differences in concentrations of metabolites and interactions between them in serum of women with ovarian cancer and benign ovarian tumor in comparison with healthy controls using targeted metabolomics. These metabolites might serve as biomarkers in the future.

MAIN METHODS

We used wide spectrum targeted metabolomics to evaluate serum concentrations of metabolites related to ovarian cancer and compared them against benign ovarian tumors and healthy controls. The measurements were performed using high performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry technique in highly-selective multiple reaction monitoring mode.

KEY FINDINGS

In this study we confirmed our previous findings about the role of histidine and citrulline in ovarian cancer as well as we indicated new lipid compounds (lysoPC a C16:1, PC aa C32:2, PC aa C34:4 and PC aa C 36:6) potentially involved in cancer metabolism.

SIGNIFICANCES

We indicated interesting interactions between metabolites for further in-depth research which could potentially serve as clinically useful biomarkers in future. Moreover, the presented work attempts to visualize a possible 3D-network of relationships between the molecules found to be related to ovarian malignancy.

A Double-Edged Sword: Complement Component 3 in Toxoplasma gondii Infection

Sprague Dawley rats and Kunming (KM) mice are artificially infected with type II Toxoplasma gondii strain Prugniaud (Pru) to generate toxoplasmosis, which is a fatal disease mediated by T. gondii invasion of the central nervous system (CNS) by unknown mechanisms. The aim is to explore the mechanism of differential susceptibility of mice and rats to T. gondii infection. Therefore, a strategy of isobaric tags for relative and absolute quantitation (iTRAQ) is established to identify differentially expressed proteins (DEPs) in the rats' and the mice's brains compared to the healthy groups. In KM mice, which is susceptible to T. gondii infection, complement component 3 (C3) is upregulated and the tight junction (TJ) pathway shows a disorder. It is presumed that T. gondii-stimulated C3 disrupts the TJ of the blood-brain barrier in the CNS. This effect allows more T. gondii passing to the brain through the intercellular space.

From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology

Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field.