Interaction network analysis revealed biomarkers in myocardial infarction

Myocardial infarction (MI) is a serious heart disease. The cardiac cells of patients with MI will die due to lack of blood for a long time. In this study, we aimed to find new targets for MI diagnosis and therapy. We downloaded GSE22229 including 12 blood samples from healthy persons and GSE29111 from Gene Expression Omnibus including 36 blood samples from MI patients. Then we identified differentially expressed genes (DEGs) in patients with MI compared to normal controls with p value < 0.05 and |logFC| > 1. Furthermore, interaction network and sub-network of these of these DEGs were constructed by NetBox. Linker genes were screened in the Global Network database. The degree of linker genes were calculated by igraph package in R language. Gene ontology and kyoto encyclopedia of genes and genomes pathway analysis were performed for DEGs and network modules. A total of 246 DEGs were identified in MI, which were enriched in the immune response. In the interaction network, LCK, CD247, CD3D, FYN, HLA-DRA, IL2, CD8A CD3E, CD4, CD3G had high degree, among which CD3E, CD4, CD3G were DEGs while others were linker genes screened from Global Network database. Genes in the sub-network were also enriched in the immune response pathway. The genes with high degree may be biomarkers for MI diagnosis and therapy.     

Steroidome and metabolome analysis in gilt saliva to identify potential biomarkers of boar effect receptivity

Optimal management of gilt reproduction requires oestrus synchronization. Hormonal treatments are used for this purpose, but there is a growing demand for non-hormonal alternatives, especially in organic farms. The boar effect is an important alternative opportunity to induce and synchronize oestrus without hormones. Before puberty, gilts exhibit a ‘waiting period’ during which boar exposure could induce and synchronize the first ovulation. We searched for salivary biomarkers of this period of boar effect receptivity to improve detection of the gilts to stimulate with the perspective of enhancing the efficacy of the boar effect. Saliva samples were collected from 30 Large-White × Landrace crossbred gilts between 140 and 175 days of age. Gilts were exposed twice a day to a boar and subjected to oestrus detection from 150 to 175 days of age. Among the 30 gilts, 10 were detected in oestrus 4 to 7 days after the first introduction of the boar and were considered receptive to the boar effect, 14 were detected in oestrus more than 8 days after first boar contact, and six did not show oestrus and were considered non-receptive. Saliva samples from six receptive and six non-receptive gilts were analyzed for steroidome and for metabolome using gas chromatography coupled to tandem mass spectrometry and ¹H nuclear magnetic resonance spectroscopy, respectively. Four saliva samples per gilt were analyzed: 25 days and 11 days before boar introduction, the day of boar introduction, 3 days later for receptive gilts or 7 days later for non-receptive gilts. Twenty-nine steroids and 31 metabolites were detected in gilt saliva. Salivary concentrations of six steroids and three metabolites were significantly different between receptive and non-receptive gilts: progesterone and glycolate 25 days before boar introduction, 3α5β20α- and 3β5α20β-hexahydroprogesterone, dehydroepiandrosterone, androstenediol, succinate, and butyrate 11 days before boar introduction, and 3β5α-tetrahydroprogesterone on the day of boar introduction. Thus, nine potential salivary biomarkers of boar effect receptivity were identified in our experimental conditions. Further studies with higher numbers of gilts and salivary sampling points are necessary to ascertain their reliability.     

Metabolomic profiling to identify potential serum biomarkers for schizophrenia and risperidone action

Despite recent advances in understanding the pathophysiology of schizophrenia and the mechanisms of antipsychotic drug action, the development of biomarkers for diagnosis and therapeutic monitoring in schizophrenia remains challenging. Metabolomics provides a powerful approach to discover diagnostic and therapeutic biomarkers by analyzing global changes in an individual's metabolic profile in response to pathophysiological stimuli or drug intervention. In this study, we performed gas chromatography-mass spectrometry based metabolomic profiling in serum of unmedicated schizophrenic patients before and after an 8-week risperidone monotherapy, to detect potential biomarkers associated with schizophrenia and risperidone treatment. Twenty-two marker metabolites contributing to the complete separation of schizophrenic patients from matched healthy controls were identified, with citrate, palmitic acid, myo-inositol, and allantoin exhibiting the best combined classification performance. Twenty marker metabolites contributing to the complete separation between posttreatment and pretreatment patients were identified, with myo-inositol, uric acid, and tryptophan showing the maximum combined classification performance. Metabolic pathways including energy metabolism, antioxidant defense systems, neurotransmitter metabolism, fatty acid biosynthesis, and phospholipid metabolism were found to be disturbed in schizophrenic patients and partially normalized following risperidone therapy. Further study of these metabolites may facilitate the development of noninvasive biomarkers and more efficient therapeutic strategies for schizophrenia.     

Serum protein profiling to identify biomarkers for small renal cell carcinoma

Diagnostic biomarkers for early detection of renal cell carcinoma (RCC) are in great need. In the present study, we compared the serum protein profiles of patients with small RCC to those of healthy individuals to identify the differentially expressed proteins with potential to serve as biomarkers. Serum samples were collected from 10 patients with small RCC and 10 healthy individuals. The serum protein expression profiles were analyzed by two-dimensional (2-D) gel electrophoresis. Twenty-seven proteins with differences in expression levels between RCC patients and healthy volunteers were identified. Of these, 19 were expressed at different levels and eight were expressed in serum from the RCC group, but not from the control group. Six differentially expressed proteins identified by using mass spectrometry included coagulation factor XIII B, complement C3 and its precursor, misato homolog 1 (isoform CRA_b), hemopexin, and alpha-1-B-glycoprotein. Some of these serum proteins are known regulators of tumor progression in human malignancies. In conclusion, we successfully applied 2-D gel electrophoresis and identified six serum proteins differentially expressed between patients with small RCC and healthy volunteers. These proteins may provide novel biomarkers for early detection and diagnosis of human RCC.     

Development of protein microarray technology to monitor biomarkers of rheumatoid arthritis disease

Most biological processes are mediated by complex networks of molecular interactions involving proteins. The analysis of protein expression in biological samples is especially important in the identification and monitoring of biomarkers for disease progression and therapeutic endpoints. In this paper, the development of a protein microarray format for multiplexed quantitative analysis of several potential markers for rheumatoid arthritis (RA) is described. Development of a high-performance protein microarray system depends on several key parameters such as surface chemistry, capture agents, immobilization technology, and methods used for signal detection and quantification. Several technical possibilities were investigated and compared: poly-L-lysine versus self-assembled monolayer of octadecyl phosphoric acid ester for surface chemistries; noncontact piezoelectric versus contact printing technology for antibody deposition; CCD camera capture versus fluorescent scanning for image detection; and the concentration of coating antibody. On the basis of reproducibility, signal-to-noise ratio, and sensitivity we have selected self-assembled monolayer, noncontact piezoelectric printer, and high-read-out fluorescence scanning for our microarray format. This format was used to perform multiplexed quantitative analysis of several potential markers of disease progression of rheumatoid arthritis: IL-1, IL-6, IL-8, MCP-1, and SAA. Some assays, such as MCP-1, provided a working range that covered physiologically relevant concentrations. Other assays, such as IL-6 and SAA, lacked sensitivity or were too sensitive for measuring biological concentrations, respectively. The results described demonstrate the applicability of protein microarrays to monitor RA markers; however, sandwich assay methodologies need to be further optimized to measure the appropriate biological ranges of these markers on one chip.     

Integrated network pharmacology to investigate the mechanism of Salvia miltiorrhiza Bunge in the treatment of myocardial infarction

Salvia miltiorrhiza Bunge is a natural drug for treating myocardial infarction (MI). However, the targets and mechanisms of S. miltiorrhiza Bunge in the treatment of MI are yet to be elucidated. Traditional Chinese medicine systems pharmacology (TCMSP) data were used to screen out chemical constituents, and UniProt was used to predict relevant targets. Disease targets were obtained from the Online Mendelian Inheritance in Man and GeneCards databases. We used the STRING platform to build a protein–protein interaction network and used Cytoscape_v3.8.1 software to make a Drug–Ingredients–Gene Symbols–Disease network map. The Metascape database was used to perform gene ontology and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses for drug–disease overlapping gene symbols. The targets identified by network pharmacology were further verified by in vitro and in vivo experiments. Seventy-five active components of S. miltiorrhiza Bunge were obtained from the TCMSP database, while 370 disease targets and 29 cross-targets were obtained from the Genecards database. The KEGG pathway enrichment results suggested that the mechanism of S. miltiorrhiza Bunge in the treatment of MI was significantly related to the VEGF signalling pathway. In vitro and in vivo experiments were used to evaluate the reliability of some important active ingredients and targets. S. miltiorrhiza Bunge alleviated the damage to cardiac function, attenuated myocardial fibrosis and protected endothelial cell function by increasing the expression of TGF-β and VEGFA. S. miltiorrhiza Bunge showed the therapeutic effect of MI by promoting the expression of VEGFA signalling pathway, providing a reliable basis for exploring herbal treatment of MI.     

Integrated analysis of a competing endogenous RNA network reveals key long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

Growing evidence has revealed that long noncoding RNAs (lncRNAs) have an important impact on tumorigenesis and tumor progression via a mechanism involving competing endogenous RNAs (ceRNAs). However, their use in predicting the survival of a patient with hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to develop a novel lncRNA expression–based risk score system to accurately predict the survival of patients with HCC. In our study, using expression profiles downloaded from The Cancer Genome Atlas database, the differentially expressed messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) were explored in patients with HCC and normal liver tissues, and then a ceRNA network constructed. A risk score system was established between lncRNA expression of the ceRNA network and overall survival (OS) or recurrence-free survival (RFS); it was further analyzed for associations with the clinical features of patients with HCC. In HCC, 473 differentially expressed lncRNAs, 63 differentially expressed miRNAs, and 1417 differentially expressed mRNAs were detected. The ceRNA network comprised 41 lncRNA nodes, 12 miRNA nodes, 24 mRNA nodes, and 172 edges. The lncRNA expression–based risk score system for OS was constructed based on six lncRNAs (MYLK-AS1, AL359878.1, PART1, TSPEAR-AS1, C10orf91, and LINC00501), while the risk score system for RFS was based on four lncRNAs (WARS2-IT1, AL359878.1, AL357060.1, and PART1). Univariate and multivariate Cox analyses showed the risk score systems for OS or RFS were significant independent factors adjusted for clinical factors. Receiver operating characteristic curve analysis showed the area under the curve for the risk score system was 0.704 for OS, and 0.71 for RFS. Our result revealed a lncRNA expression–based risk score system for OS or RFS can effectively predict the survival of patients with HCC and aid in good clinical decision-making.     

Combinations of cytokine gene network polymorphic markers as potential predictors of myocardial infarction

With the intent to identify informative predictors of myocardial infarction (MI) development in an ethnically homogenous group of Russian men after MI (255 subjects) and in a corresponding control group (257 subjects), an analysis of genotype frequency distribution for polymorphic DNA markers (SNP) rs16944 (?511C>T, IL1B gene), rs1800796 (?572G>C, IL6 gene), rs1800872 (?592C>A, IL10 gene), rs3212227 (1159A>C, IL12B gene), rs1800629 (?308G>A, TNF), rs909253 (252A>G, LTA), rs767455 (36A>G, TNFRSF1A) was conducted. Using the Monte Carlo method and a Markov chain (APSampler), allele combinations associated both with decreased and increased MI risk were revealed. The most significant results were obtained for IL6*C/C (P = 3 × 10^?4, OR = 6.3 CI 2.37–16.75), LTA*A + IL6*G/G (FDR = 2.3 × 10^?4, OR = 0.25 CI 0.14–0.46), LTA*G/G + IL12B*A/A (FDR = 0.0053, OR = 4.92 CI 1.8–13.33), TNF*G + LTA*G/G + TNFRSF1A*A (FDR = 0.013, OR = 4.38, CI 1.6–12.01), TNFRSF1A*G + IL10*A/A + IL12B*C (FDR = 0.016, OR = 8.79, CI 2.17–35.63), TNF*G + LTA*G/G + IL10*C (FDR = 0.0105, OR = 3.54 CI 1.55–8.09).     

In‐depth proteomics approach reveals novel biomarkers of cardiac remodelling after myocardial infarction: An exploratory analysis

Cardiac remodelling following myocardial infarction (MI) is a maladaptive change associated with progressive heart failure and compromises long‐term clinical outcome. A substantial proportion of patients afflicted by MI still develop adverse outcomes associated with cardiac remodelling. Therefore, it is crucial to identify biomarkers for the early prediction of cardiac remodelling. An in‐depth proteomics approach, including both semi‐quantitative and quantitative antibody arrays, was used to identify circulating biomarkers that may be associated with detrimental cardiac remodelling. Furthermore, statistical correlation analysis was performed between the candidate biomarkers and clinical cardiac remodelling data to demonstrate their clinical utility. A systematic proteomics approach revealed that sclerostin (SOST), growth differentiation factor‐15 (GDF‐15), urokinase‐type plasminogen activator (uPA), and midkine (MK) were increased, while monocyte chemotactic protein‐3 (MCP‐3) was uniquely decreased in MI patients who developed cardiac remodelling, compared to MI patients who did not develop cardiac remodelling and healthy humen. Moreover, correlation analyses between serum proteomes and cardiac remodelling echocardiographic parameters demonstrated a moderate positive association between left ventricular end‐diastolic volume index (LVEDVi) and the three serum proteins, uPA, MK and GDF‐15 (P < .05, respectively), and a moderate negative correlation between LV ejection fraction (LVEF) and these serum proteins (P < .05, respectively). Importantly, uPA and MK were firstly identified to be associated with the development of cardiac remodelling. The present study contributes to a better understanding of the various cytokines expressed during adverse cardiac remodelling. The identified biomarkers may facilitate early identification of patients at high risk of ischaemic heart failure pending further confirmation through larger clinical trials.     

Plasma metallomics reveals potential biomarkers and insights into the ambivalent associations of elements with acute myocardial infarction

Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Using a validated and efficient ICP-MS/MS-based workflow, a total of 30 metallomic features were profiled in a study comprising 101 AMI patients and 66 age-matched healthy controls. The metallomic features include 12 essential elements (Ca, Co, Cu, Fe, K, Mg, Mn, Na, P, S, Se, Zn), 8 non-essential/toxic elements (Al, As, Ba, Cd, Cr, Ni, Rb, Sr, U, V), and 10 clinically relevant element-pair product/ratios (Ca/Mg, Ca×P, Cu/Se, Cu/Zn, Fe/Cu, P/Mg, Na/K, Zn/Se). Preliminary linear regression with feature selection confirmed smoking status as a predominant determinant for the non-essential/toxic elements, and revealed potential routes of action. Univariate assessments with adjustments for covariates revealed insights into the ambivalent relationships of Cu, Fe, and P with AMI, while also confirming cardioprotective associations of Se. Also, beyond their roles as risk factors, Cu and Se may be involved in the response mechanism in AMI onset/intervention, as demonstrated via longitudinal data analysis with 2 additional time-points (1-/6-month follow-up). Finally, based on both univariate tests and multivariate classification modelling, potentially more sensitive markers measured as element-pair ratios were identified (e.g., Cu/Se, Fe/Cu). Overall, metallomics-based biomarkers may have utility for AMI prediction.     

Understanding cardiac extracellular matrix remodeling to develop biomarkers of myocardial infarction outcomes

Cardiovascular Disease (CVD) is the most common cause of death in industrialized countries, and myocardial infarction (MI) is a major CVD with significant morbidity and mortality. Following MI, the left ventricle (LV) undergoes a wound healing response to ischemia that results in extracellular matrix (ECM) scar formation to replace necrotic myocytes. While ECM accumulation following MI is termed cardiac fibrosis, this is a generic term that does not differentiate between ECM accumulation that occurs in the infarct region to form a scar that is structurally necessary to preserve left ventricle (LV) wall integrity and ECM accumulation that increases LV wall stiffness to exacerbate dilation and stimulate the progression to heart failure. This review focuses on post-MI LV ECM remodeling, targeting the discussion on ECM biomarkers that could be useful for predicting MI outcomes.     

Improving regenerating potential of the heart after myocardial infarction: Factor-based approach

The emerging evidence that the heart has the potential to regenerate, albeit not ideally, has stimulated considerable interest in the field of cardiac regenerative medicine. Several lines of research demonstrated that factor-based therapy is feasible and effective, whether it is used independently or as an adjunct to cell therapy. The ultimate goal of the factor-based approach is to improve the regenerating potential of the heart as a means to treat patients with cardiovascular disease. This article reviews recent approaches involving factor-based therapy for cardiac repair and regeneration including some of the advantages of this type of therapy as well as some of the hurdles that must be overcome before this therapeutic approach becomes a standard part of clinical medicine.     

Fine-needle aspiration of thyroid nodules: proteomic analysis to identify cancer biomarkers

At present, the clinical and pathological analysis used in the diagnosis of papillary thyroid cancer (PTC) are insufficient to discern tumor behavior, and new diagnostic and prognostic markers need to be identified. In this study, we performed a comparative proteome analysis to examine the global changes of fine needle aspiration fluid (FNA) protein patterns of two variants of malignant PTC (classical variant PTC (cPTC) and tall cell variant PTC (TCV)) with respect to the controls. Changes in protein expression were identified using two-dimensional electrophoresis (2DE) and peptide mass fingerprinting via MALDI-TOF mass spectrometry (MS), as well as Western blot analysis. A statistical significant up-regulation of 17 protein spots in cPTC and/or TCV with respect to controls was demonstrated. These proteins included transthyretin precursor (TTR), ferritin light chain (FLC), proteasome activator complex subunit 1 and 2, alpha-1-antitrypsin precursor, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), lactate dehydrogenase chain B (LDH-B), apolipoprotein A1 precursor (Apo-A1), annexin A1, DJ-1 protein and cofilin-1. In addition, 12 protein spots were found exclusively in cPTC and three exclusively in TCV. These latter proteins (ferritin heavy chain (FHC), peroxiredoxin 1 (PRX1) and 6-phosphogluconate dehydrogenase (6-PDGH)) correspond to stress response proteins and, until now, had not been described in thyroid tumors. These findings illustrate the potential use of FNA proteomics to identify protein changes associated with thyroid cancer and to advance potential protein biomarkers in the diagnostic classification of the disease.