Alterations of the Platelet Proteome in Lung Cancer: Accelerated F13A1 and ER Processing as New Actors in Hypercoagulability

Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

Thrombotic events associated with immune checkpoint inhibitors and novel antithrombotic strategies to mitigate bleeding risk

Although immunotherapy is expanding treatment options for cancer patients, the prognosis of advanced cancer remains poor, and these patients must contend with both cancers and cancer-related thrombotic events. In particular, immune checkpoint inhibitors are associated with an increased risk of atherosclerotic thrombotic events. Given the fundamental role of platelets in atherothrombosis, co-administration of antiplatelet agents is always indicated. Platelets are also involved in all steps of cancer progression. Classical antithrombotic drugs can cause inevitable hemorrhagic side effects due to blocking integrin β3 bidirectional signaling, which regulates simultaneously thrombosis and hemostasis. Meanwhile, many promising new targets are emerging with minimal bleeding risk and desirable anti-tumor effects. This review will focus on the issue of thrombosis during immune checkpoint inhibitor treatment and the role of platelet activation in cancer progression as well as explore the mechanisms by which novel antiplatelet therapies may exert both antithrombotic and antitumor effects without excessive bleeding risk.

Identifying novel biomarkers using proteomics to predict cancer-associated thrombosis

Comprehensive protein analyses of plasma are made possible by high-throughput proteomic screens, which may help find new therapeutic targets and diagnostic biomarkers. Patients with cancer are frequently affected by venous thromboembolism (VTE). The limited predictive accuracy of current VTE risk assessment tools highlights the need for new, more targeted biomarkers. Although coagulation biomarkers for the diagnosis, prognosis, and treatment of VTE have been investigated, none of them have the necessary clinical validation or diagnostic accuracy. Proteomics holds the potential to uncover new biomarkers and thrombotic pathways that impact the risk of thrombosis. This review explores the fundamental methods used in proteomics and focuses on particular biomarkers found in VTE and cancer-associated thrombosis.

Proteomic response of A549 lung cancer cell line to protein-polysaccharide complex Venetin-1 isolated from earthworm coelomic fluid

Earthworms' celomic fluid has long attracted scientists' interest due to their toxic properties. It has been shown that the elimination of coelomic fluid cytotoxicity to normal human cells was crucial for the generation of the non-toxic Venetin-1 protein-polysaccharide complex, which exhibits selective activity against Candida albicans cells as well as A549 non-small cell lung cancer cells. To find the molecular mechanisms behind the anti-cancer properties of the preparation, this research investigated the proteome response of A549 cells to the presence of Venetin-1. The sequential window acquisition of all theoretical mass spectra (SWATH-MS) methodology was used for the analysis, which allows for a relative quantitative analysis to be carried out without radiolabelling. The results showed that the formulation did not induce significant proteome responses in normal BEAS-2B cells. In the case of the tumour line, 31 proteins were up regulated, and 18 proteins down regulated. Proteins with increased expression in neoplastic cells are mainly associated with the mitochondrion, membrane transport and the endoplasmic reticulum. In the case of altered proteins, Venetin-1 interferes with proteins that stabilise the structures, i.e., keratin, glycolysis/gluconeogenesis and metabolic processes.

A Practical and Analytical Comparative Study of Gel-Based Top-Down and Gel-Free Bottom-Up Proteomics Including Unbiased Proteoform Detection

Proteomics is an indispensable analytical technique to study the dynamic functioning of biological systems via different proteins and their proteoforms. In recent years, bottom-up shotgun has become more popular than gel-based top-down proteomics. The current study examined the qualitative and quantitative performance of these two fundamentally different methodologies by the parallel measurement of six technical and three biological replicates of the human prostate carcinoma cell line DU145 using its two most common standard techniques, label-free shotgun and two-dimensional differential gel electrophoresis (2D-DIGE). The analytical strengths and limitations were explored, finally focusing on the unbiased detection of proteoforms, exemplified by discovering a prostate cancer-related cleavage product of pyruvate kinase M2. Label-free shotgun proteomics quickly yields an annotated proteome but with reduced robustness, as determined by three times higher technical variation compared to 2D-DIGE. At a glance, only 2D-DIGE top-down analysis provided valuable, direct stoichiometric qualitative and quantitative information from proteins to their proteoforms, even with unexpected post-translational modifications, such as proteolytic cleavage and phosphorylation. However, the 2D-DIGE technology required almost 20 times as much time per protein/proteoform characterization with more manual work. Ultimately, this work should expose both techniques' orthogonality with their different contents of data output to elucidate biological questions.

Proteomic analysis of small extracellular vesicles from the plasma of patients with hepatocellular carcinoma

PURPOSE

Liver cancer is one of the most common tumors with the seventh-highest incidence and the third-highest mortality. Many studies have shown that small extracellular vesicles (sEVs) play an important role in liver cancer. Here, we report comprehensive signatures for sEV proteins from plasma obtained from patients with hepatocellular carcinoma (HCC), which might be valuable for the evaluation and diagnosis of HCC.

METHODS

We extracted sEVs from the plasma of controls and patients with HCC. Differentially expressed proteins in the sEVs were analyzed using label-free quantification and bioinformatic analyses. Western blotting (WB) was used to validate the abovementioned sEV proteins.

RESULTS

Proteomic analysis was performed for plasma sEVs from 21 patients with HCC and 15 controls. Among the 335 identified proteins in our study, 27 were significantly dysregulated, including 13 upregulated proteins that were involved predominantly in the complement cascade (complement C1Q subcomponent subunit B (C1QB), complement C1Q subcomponent subunit C (C1QC), C4B-binding protein alpha chain (C4BPA), and C4B-binding protein beta chain (C4BPB)) and the coagulation cascade (F13B, fibrinogen alpha chain (FGA), fibrinogen beta chain (FGB), and fibrinogen gamma chain (FGG)). We verified increased levels of the C1QB, C1QC, C4BPA, and C4BPB proteins in the plasma sEVs from patients with HCC in both the discovery cohort and validation cohort.

CONCLUSIONS

The complement cascade in sEVs was significantly involved in HCC progression. C1QB, C1QC, C4BPA, and C4BPB were highly abundant in the plasma sEVs from patients with HCC and might represent molecular signatures.

The Generation of a Lung Cancer Health Factor Distribution Using Patient Graphs Constructed From Electronic Medical Records: Retrospective Study

BACKGROUND

Electronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC.

OBJECTIVE

We aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients.

METHODS

The data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor's relationship to LC.

RESULTS

The patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient's health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature.

CONCLUSIONS

By collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

Systematic Analysis and Identification of Molecular Subtypes of TRP-Related Genes and Prognosis Prediction in Lung Adenocarcinoma

Background. Transient receptor potential channel (TRP) is a superfamily of nonselective cation channels, which is a member of calcium ion channels with a vital role in different calcium ion signal transduction pathways. TRP channel expression is often changed in the tumor, although the role of TRP proteins in lung cancer is unknown. Methods. Molecular Signatures Database (MsigDB) provided the TRP gene set. Univariate Cox regression analysis was performed on The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data collection set employing the coxph function of R package survival to find prognosis-related genes. The R package ConsumusClusterPlus was employed for doing the consistency cluster analysis of TCGA-LUAD samples according to the prognosis-related TRP gene. The R-package limma was utilized for investigating the differential expression of TRP subtypes. According to the differentially expressed genes between subtypes, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major genes and develop the risk model. CIBERPORT algorithm, R package maftools, gene set variation analysis (GSVA), and pRRophetic of R-package were employed for measuring the proportion of immune cells among subtypes, genomic mutation difference, pathway enrichment score, and drug sensitivity analysis. Results. A total of 15 TRP-related genes associated with the prognosis of lung adenocarcinoma were found. According to the expression value of 15 genes, lung adenocarcinoma can be sorted into two subcategories. The prognosis of cluster1 is considerably better in comparison with that of cluster2. There were 123 differentially expressed genes between C1 and C2 subtypes, including 6 up- and 117 downregulated genes. There were major variations in the tumor microenvironment between C1 and C2 subtypes. The proportion of CD8 T cells in the C1 subtype was considerably enhanced in comparison with that in the C2 subtype. We further discovered 123 differentially expressed genes among subtypes, and 8 key genes were obtained at the end. The risk score (RS) model developed by the 8-gene signature had good strength in the TCGA validation set, overall set, and Gene Expression Omnibus (GEO) external dataset. There were major variations in immune checkpoint gene expression, patient sensitivity to immunotherapeutic drugs, immune infiltration, and genomic mutations between high and low groups on the basis of RS. Conclusions. The risk model developed on the basis of TRP-related genes can help in predicting the prognosis of patients suffering from lung adenocarcinoma and guide immunotherapy.

Abnormal ECA-Binding Membrane Glycans and Galactosylated CAT and P4HB in Lesion Tissues as Potential Biomarkers for Hepatocellular Carcinoma Diagnosis

Hepatocellular carcinoma (HCC) is one of the most common types of cancer. Despite decades of research efforts, the search for novel biomarkers is still urgently needed for the diagnosis of HCC and the improvement of clinical outcomes. Previous studies of HCC clinical biomarkers have usually focused on serum and urine samples (e.g., serum Alpha-fetoprotein (AFP). However, cellular membrane proteins in lesion tissues are less used in HCC diagnosis. The abnormal expression of membrane glycoproteins in tumor lesions are considered as potential targets for tumor diagnosis and tumor therapies. Here, a lectin array has been employed to screen and identify abnormal glycopatterns and cellular membrane glycans in HCC lesion tissues compared with adjacent non-tumor tissues. We found that there was significantly less expression of Erythrina cristagalli (ECA) lectin binding (Galβ1-3/β1-4) glycans on the cellular membrane of HCC lesion tissues compared with those of adjacent non-tumor tissues. Immunohistochemistry analysis further showed that ECA-binding ability on the membrane proteins of HCC tissues progressively decreased in different tumor-node-metastasis (TNM) stages (stage I to stage III) as the malignancy of liver cancer increased. Receiver operating curve (ROC) analysis showed ECA-binding ability yielding a sensitivity of 85% and specificity of 75%, and a combination of ECA and AFP has better clinical diagnostic efficiency, yielding a sensitivity of 90% and specificity of 85%, than ECA or AFP assay alone. ECA pull-down followed by mass spectrometry further showed that there was significantly less expression of ECA binding membrane catalase (CAT) and prolyl 4-hydroxylase beta polypeptide (P4HB) in HCC tissues compared with the adjacent non-tumor tissues. The abnormally increased expression of total CAT and P4HB and decreased expression of galactosylated membrane CAT and P4HB in HCC cell lines were correlated with an HCC metastasis status. Our findings suggest that abnormal declined ECA-binding galatosylated membrane glycans and two galactosylated-CAT and P4HB glycoproteins in lesion tissues are potential biomarkers in the diagnosis and/or metastasis prediction for HCC.

Extracellular Vesicles Linking Inflammation, Cancer and Thrombotic Risks

Extracellular vesicles (EVs) being defined as lipid-bilayer encircled particles are released by almost all known mammalian cell types and represent a heterogenous set of cell fragments that are found in the blood circulation and all other known body fluids. The current nomenclature distinguishes mainly three forms: microvesicles, which are formed by budding from the plasma membrane; exosomes, which are released, when endosomes with intraluminal vesicles fuse with the plasma membrane; and apoptotic bodies representing fragments of apoptotic cells. Their importance for a great variety of biological processes became increasingly evident in the last decade when it was discovered that they contribute to intercellular communication by transferring nucleotides and proteins to recipient cells. In this review, we delineate several aspects of their isolation, purification, and analysis; and discuss some pitfalls that have to be considered therein. Further on, we describe various cellular sources of EVs and explain with different examples, how they link cancer and inflammatory conditions with thrombotic processes. In particular, we elaborate on the roles of EVs in cancer-associated thrombosis and COVID-19, representing two important paradigms, where local pathological processes have systemic effects in the whole organism at least in part via EVs. Finally, we also discuss possible developments of the field in the future and how EVs might be used as biomarkers for diagnosis, and as vehicles for therapeutics.

Platelet and Cancer-Cell Interactions Modulate Cancer-Associated Thrombosis Risk in Different Cancer Types

The first cause of death in cancer patients, after tumoral progression itself, is thrombo-embolic disease. This cancer-associated hypercoagulability state is known as Trousseau's syndrome, and the risk for developing thrombotic events differs according to cancer type and stage, as well as within patients. Massive platelet activation by tumor cells is the key mediator of thrombus formation in Trousseau's syndrome. In this literature review, we aimed to compare the interactions between cancer cells and platelets in three different cancer types, with low, medium and high thrombotic risk. We chose oral squamous cell carcinoma for the low-thrombotic-risk, colorectal adenocarcinoma for the medium-thrombotic-risk, and pancreatic carcinoma for the high-thrombotic-risk cancer type. We showcase that understanding these interactions is of the highest importance to find new biomarkers and therapeutic targets for cancer-associated thrombosis.

Platelet Phenotype Analysis of COVID-19 Patients Reveals Progressive Changes in the Activation of Integrin αIIbβ3, F13A1, the SARS-CoV-2 Target EIF4A1 and Annexin A5

Background: The fatal consequences of an infection with severe acute respiratory syndrome coronavirus 2 are not only caused by severe pneumonia, but also by thrombosis. Platelets are important regulators of thrombosis, but their involvement in the pathogenesis of COVID-19 is largely unknown. The aim of this study was to determine their functional and biochemical profile in patients with COVID-19 in dependence of mortality within 5-days after hospitalization. Methods: The COVID-19-related platelet phenotype was examined by analyzing their basal activation state via integrin αIIbβ3 activation using flow cytometry and the proteome by unbiased two-dimensional differential in-gel fluorescence electrophoresis. In total we monitored 98 surviving and 12 non-surviving COVID-19 patients over 5 days of hospital stay and compared them to healthy controls (n = 12). Results: Over the observation period the level of basal αIIbβ3 activation on platelets from non-surviving COVID-19 patients decreased compared to survivors. In line with this finding, proteomic analysis revealed a decrease in the total amount of integrin αIIb (ITGA2B), a subunit of αIIbβ3, in COVID-19 patients compared to healthy controls; the decline was even more pronounced for the non-survivors. Consumption of the fibrin-stabilizing factor coagulation factor XIIIA (F13A1) was higher in platelets from COVID-19 patients and tended to be higher in non-survivors; plasma concentrations of the latter also differed significantly. Depending on COVID-19 disease status and mortality, increased amounts of annexin A5 (ANXA5), eukaryotic initiation factor 4A-I (EIF4A1), and transaldolase (TALDO1) were found in the platelet proteome and also correlated with the nasopharyngeal viral load. Dysregulation of these proteins may play a role for virus replication. ANXA5 has also been identified as an autoantigen of the antiphospholipid syndrome, which is common in COVID-19 patients. Finally, the levels of two different protein disulfide isomerases, P4HB and PDIA6, which support thrombosis, were increased in the platelets of COVID-19 patients. Conclusion: Platelets from COVID-19 patients showed significant changes in the activation phenotype, in the processing of the final coagulation factor F13A1 and the phospholipid-binding protein ANXA5 compared to healthy subjects. Additionally, these results demonstrate specific alterations in platelets during COVID-19, which are significantly linked to fatal outcome.