Version Of String Research Articles

Exploring Beta-sitosterol’s Role in Breast Cancer Treatment via Integrated Network Pharmacological Analysis and In Vitro Validation

Background Beta-sitosterol, a phytosterol similar to cholesterol, is found in various plants and is recognized for its potential anticancer properties; still, the mechanism of breast cancer (BC) remains elusive. Purpose This study investigates the potential targets of beta-sitosterol in BC using network pharmacological analysis and in vitro validation. Methods Targets of beta-sitosterol and BC were identified from online databases, including Swiss Target Prediction, SuperPred, GeneCards, and DisGeNET. Protein–protein interactions of common targets were analyzed using STRING version 11.0, and network construction and core target screening were performed with Cytoscape 3.8.0. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and gene ontology (GO) annotation were conducted using the ShinyGO web tool. Molecular docking analysis was done with AutoDock Vina and the in vitro validation, including cell viability, apoptosis, and gene expression analysis was done using MTT assay, acridine orange/ethidium bromide staining, and polymerase chain reaction (PCR), respectively. Results Sixty-eight common targets for beta-sitosterol and BC were identified from 209 beta-sitosterol targets and 1,350 BC-related genes. Five core targets (HIF1A, ESR1, STAT3, TNF, and MAPK3) were identified. GO analysis linked common targets to biological processes, cellular components, and molecular functions. KEGG pathway analysis showed enrichment in pathways such as central carbon metabolism in cancer, vascular endothelial growth factor signaling, and prolactin signaling. Beta-sitosterol’s impact on MCF7 cell viability was assessed via MTT assay, and its molecular effects were explored through PCR. Results demonstrated beta-sitosterol’s ability to hinder tumor progression by modulating genes involved in ESR1, mitogen-activated protein kinase, and tumor necrosis factor signaling pathways. Conclusion These findings highlight beta-sitosterol’s potential as an anticancer agent, offering new perspectives for clinical investigations through the modulation of key signaling pathways in BC.

Determination of the EGFR Gene Role in Lung Cancer Pathway using STRING and Cytoscape Software

The leading cause of cancer-related fatalities worldwide is lung cancer. Both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), the two main types of lung cancer, have successfully responded to chemotherapy, radiation therapy, and adjuvant therapies. Surgery to remove the tumor also seemed to be a successful treatment plan. However, these treatments showed undesirable side effects; therefore, new lung cancer-targeting medications have become crucial. Activation of EGFR-tyrosine kinases is the main reason for lung cancer development. In this study, the EGFR gene involved in the lung cancer pathway was mapped using bioinformatics applications such as STRING version 11.5 and Cytoscape version 3.8.2 to provide a clear visualization of all the related and involved proteins and genes that interact with the EGFR gene in the pathway. Using EGFR as a seed, a protein-protein interaction network was generated with 2343 interactions obtained from the STRING version 11.5 database. The protein network interaction was further clustered into 6 groups using the MCODE application. The molecular function and biological processes of the genes involved in the EGFR protein network were determined using Biological Networks Gene Ontology (BiNGO). The protein-protein network interaction map results show that the EGFR mechanism and co-expressed genes are interconnected with protein or enzyme binding, signaling receptor binding, MAPK cascade, and regulation of cell death. Understanding these protein-protein interactions can give us insight into biological processes and, in turn, help us understand how oncogenic diseases develop.

Bioinformatics analysis of potential pathogenesis and risk genes of neuroinflammation-promoted brain injury in intracerebral hemorrhage

IntroductionSpontaneous (non-traumatic) intracerebral hemorrhage (ICH) is one of the major causes of global death. The purpose of our bioinformatics analysis was to detect viable pathophysiological targets and small-molecule drug candidates and to identify the precise secondary mechanisms of brain injury in ICH. Material and methodsThe GSE24265 dataset, consisting of data from four perihematomal brain tissues and seven contralateral brain tissues, was downloaded from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs) in ICH. Online analysis tool GEO2R and Drug Susceptibility Assessment Module within the ACBI Bioinformation tool was used for data differential expression analysis. TargetScan, miRDB, and RNA22 were used to investigate the miRNAs regulating the DEGs. The functional annotation of DEGs was performed using Gene Ontology (GO) resources, and the cell signaling pathway analysis of DEGs was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). DAVID is used to perform GO function enrichment analysis and KEGG pathway analysis of candidate target genes. Enrichment analysis was performed for delving the molecular mechanism of DEGs, and protein–protein interaction (PPI) networks and microRNA (miRNA)-messenger RNA (mRNA) networks were used to reveal the hub nodes and the related interaction relationships. Hub genes and miRNA-mRNA interaction of PPI network were identified by STRING version 12.0 online software and Cytoscape. Next, the DEGs were analyzed using the L1000CDS2 database to identify small-molecule compounds with potential therapeutic effects. ResultsA total of 325 upregulated genes and 103 downregulated genes associated with ICH were identified. The biological functions of DEGs associated with ICH are mainly involved in the inflammatory response, chemokine activity, and immune response. The KEGG analysis identified several pathways significantly associated with ICH, including but not limited to cytokine-cytokine receptor interaction and MAPK signaling pathway. A PPI network consisting of 188 nodes and 563 edges was constructed using STRING, and 27 hub genes were identified with Cytoscape software. The miRNA-mRNA network with high connectivity contained key 27 mRNAs (from C-C motif chemokine ligand 5 (CCL5), C-C motif chemokine ligand 8 (CCL8), …., to dishevelled-associated activator of morphogenesis 1 (DAAM1), and FRAT regulator of WNT signaling pathway 1 (FRAT1)) and 135 candidate miRNAs. These genes and miRNAs are closely related to secondary brain injury induced by ICH. In addition, a L1000CDS2 analysis of six small-molecule compounds revealed their therapeutic potential. ConclusionsOur study explores the pathogenesis of brain tissue injury promoted by neuroinflammation in ICH and extends the clinical utility of its key genes. At the same time, we constructed a miRNA-mRNA network which may play crucial roles in the pathogenesis of ICH. In addition, we obtained six small molecule compounds that will have anti-inflammatory effects on ICH, including Geldanamycin, Dasatinib, BMS-345541, Saracatinib, and Afatinib.

Characteristic comparison of human induced pluripotent stem cell-derived adult and fetal β-like cells: a differential gene expression analysis

Differentiating human induced pluripotent stem cells (hiPSCs) into β cells for type 1 diabetes (T1D) management is a crucial step. Functionality characterization of hiPSC-derived β cells in some cases, however, only considers morphology and proliferation aspect without examining their distinct molecular properties. Thus, we aimed to investigate the difference between hiPSC-derived adult and fetal β-like cells by differentially expressed gene (DEG) analysis. We retrieved one Gene Expression Omnibus (GEO) dataset with the ID GSE70901 comprising 16 samples and GEO2RAnalyze menu performed the analysis. Network clustering was conducted through the STRING version 12.0, Cytoscape version 3.10.0, and CytoCluster 1.0 plugin by considering overall centrality parameters. Enrichment analysis was performed in DAVID 2021 and updated Enrichr tools. Two main clusters were each related to ribosome and carbohydrate metabolism. Enrichment results showed that some molecular pathways might contrast hiPSC-derived adult from fetal β-like cells, notably ribosome (p value <0.001). Cytoscape identified five significant subclusters with the densest one being ribosomal complex genes, such as RPS2, RPL5, and RPLP0 (p value <0.001). This in silico analysis provides insights into genetic signatures with their potential role in pancreatic β cell maturation, which should be validated in more thorough studies.

System Biology Approach to Identify the Hub Genes and Pathways Associated with Human H5N1 Infection.

H5N1 is a highly pathogenic avian influenza virus that can infect humans and has an estimated fatality rate of 53%. As shown by the current situation of the COVID-19 pandemic, emerging and re-emerging viruses such as H5N1 have the potential to cause another pandemic. Thus, this study outlined the hub genes and pathways associated with H5N1 infection in humans. The genes associated with H5N1 infection in humans were retrieved from the NCBI Gene database using "H5N1 virus infection" as the keyword. The genes obtained were investigated for protein-protein interaction (PPI) using STRING version 11.5 and studied for functional enrichment analysis using DAVID 2021. Further, the PPI network was visualised and analysed using Cytoscape 3.7.2, and the hub genes were obtained using the local topological analysis method of the cytoHubba plugin. A total of 39 genes associated with H5N1 infection in humans significantly interacted with each other, forming a PPI network with 38 nodes and 149 edges modulating 74 KEGG pathways, 76 biological processes, 13 cellular components, and 22 molecular functions. Further, the PPI network analysis revealed that 33 nodes interacted, forming 1056 shortest paths at 0.282 network density, along with a 1.947 characteristic path length. The local topological analysis predicted IFNA1, IRF3, CXCL8, CXCL10, IFNB1, and CHUK as the critical hub genes in human H5N1 infection. The hub genes associated with the H5N1 infection and their pathways could serve as diagnostic, prognostic, and therapeutic targets for H5N1 infection among humans.

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan-Meier curves analysis.

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest.

Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein-protein interactions-both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.

Differentially Expressed Genes Study Shown Potential for BCG Stimulation in Reducing the Severity of COVID-19.

The incidence of COVID-19 infection and death is known to be lower in tuberculosis (TB) endemic countries than in nonendemic countries. The Bacillus Calmette-Guerin (BCG) vaccination, which is commonly administered in TB endemic countries, was previously reported to have a nonspecific protective effect against several infections, including COVID-19. In this study, we used a differentially expressed genes (DEG) approach to analyze the genes modulated by BCG vaccination and COVID-19 infection. The Gene Expression Omnibus (GEO) database was used to select a COVID-19 gene expression data set with GSE164805, GSE14408, and GSE58636, and DEG in each data set were identified using the GEO2R online tools and selected using the adjusted p value (padj) 0.05 criteria. The protein-protein interaction (PPI) network was constructed from DEGs with the same trend of expression (upregulation or downregulation) using STRING version 11. The PPI network was performed by using the highest confidence number (0.9). DEGs that have a high-trust network were collected and functional cluster analysis of biological processes from Gene Ontology (GO), pathway analysis from the Human KEGG pathway, and COVID-19-related gene analysis was carried out using the Enrichr database. We found that either BCG or tuberculin increased the expression of several genes related to hyperinflammation, such as CCL3, CCL4, CSF2, IL1B, and LTA. In severe COVID-19, these genes were downregulated. This leads to the hypothesis that revaccination may have a protective effect against the severity of COVID-19 by reducing the hyperinflammatory status.

Characterization of differentially expressed and lipid metabolism-related lncRNA-mRNA interaction networks during the growth of liver tissue through rabbit models.

BackgroundCharacterization the long non-coding RNAs (lncRNAs) and their regulated mRNAs involved in lipid metabolism during liver growth and development is of great value for discovering new genomic biomarkers and therapeutic targets for fatty liver and metabolic syndrome.Materials and methodsLiver samples from sixteen rabbit models during the four growth stages (birth, weaning, sexual maturity, and somatic maturity) were used for RNA-seq and subsequent bioinformatics analyses. Differentially expressed (DE) lncRNAs and mRNAs were screened, and the cis/trans-regulation target mRNAs of DE lncRNAs were predicted. Then the function enrichment analyses of target mRNAs were performed through Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, respectively. The target protein interaction (PPI) and lncRNA-mRNA co-expression networks were constructed using string version 11.0 platform and R Stats. Finally, six lncRNAs and six mRNAs were verified taking RT-qPCR.ResultsLiver Oil Red O detection found that the liver showed time-dependent accumulation of lipid droplets. 41,095 lncRNAs, 30,744 mRNAs, and amount to 3,384 DE lncRNAs and 2980 DE mRNAs were identified from 16 cDNA sequencing libraries during the growth of liver. 689 out of all DE lncRNAs corresponded to 440 DE mRNAs by cis-regulation and all DE mRNAs could be regulated by DE lncRNAs by trans-regulation. GO enrichment analysis showed significant enrichment of 892 GO terms, such as protein binding, cytosol, extracellular exsome, nucleoplasm, and oxidation-reduction process. Besides, 52 KEGG pathways were significantly enriched, including 11 pathways of lipid metabolism were found, like Arachidonic acid metabolism, PPAR signaling pathway and Biosynthesis of unsaturated fatty acids. After the low expression DE mRNAs and lncRNAs were excluded, we further obtained the 54 mRNAs were regulated by 249 lncRNAs. 351 interaction pairs were produced among 38 mRNAs and 215 lncRNAs through the co-expression analysis. The PPI network analysis found that 10 mRNAs such as 3β-Hydroxysteroid-Δ24 Reductase (DHCR24), lathosterol 5-desaturase (SC5D), and acetyl-CoA synthetase 2 (ACSS2) were highly interconnected hub protein-coding genes. Except for MSTRG.43041.1, the expression levels of the 11 genes by RT-qPCR were the similar trends to the RNA-seq results.ConclusionThe study revealed lncRNA-mRNA interation networks that regulate lipid metabolism during liver growth, providing potential research targets for the prophylaxis and treatment of related diseases caused by liver lipid metabolism disorders.

Bioinformatic analysis identifies potential key genes in the pathogenesis of age-related macular degeneration.

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in older individuals. More studies focused on screening the genes, which may be correlated with the development of AMD. With advances in various technologies like multiple microarray datasets, researchers could identify differentially expressed genes (DEGs) more accurately. Exploring abnormal gene expression in disease status can help to understand pathophysiological changes in complex diseases. This study aims to identify the key genes and upstream regulators in AMD and reveal factors, especially genetic association, and the prognosis of the development of this disease. Data from expression profile GSE125564 and profile GSE29801 were obtained from the Gene Expression Omnibus (GEO) database. We analyzed DEGs using R software (version 3.6.3). Functional enrichment and PPI network analysis were performed using the R package and online database STRING (version 11.0). We compared AMD with normal and found 68 up-regulated genes (URGs) and 25 down-regulated genes (DRGs). We also compared wet AMD with dry AMD and found 41 DRGs in dry AMD. Further work including PPI network analysis, GO classification, and KEGG analysis was done to find connections with AMD. The URGs were mainly enriched in the biological process such as DNA replication, nucleoplasm, extracellular exosome, and cadherin binding. Besides, DRGs were mainly enriched in these functions such as an integral component of membrane and formation of the blood-aqueous barrier (BAB). This study implied that core genes might involve in the process of AMD. Our findings may contribute to revealing the pathogenesis, developing new biomarkers, and raising strategies of treatment for AMD.

D-Branes in Para-Hermitian Geometries

We introduce T-duality invariant versions of D-branes in doubled geometry using a global covariant framework based on para-Hermitian geometry and metric algebroids. We define D-branes as conformal boundary conditions for the open string version of the Born sigma-model, where they are given by maximally isotropic vector bundles which do not generally admit the standard geometric picture in terms of submanifolds. When reduced to the conventional sigma-model description of a physical string background as the leaf space of a foliated para-Hermitian manifold, integrable branes yield D-branes as leaves of foliations which are interpreted as Dirac structures on the physical spacetime. We define a notion of generalised para-complex D-brane, which realises our D-branes as para-complex versions of topological A/B-branes. We illustrate how our formalism recovers standard D-branes in the explicit example of reductions from doubled nilmanifolds.

Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease.

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.

Kallikrein-11, in Association with Coiled-Coil Domain Containing 25, as a Potential Prognostic Marker for Cholangiocarcinoma with Lymph Node Metastasis.

Cholangiocarcinoma (CCA) is a malignancy arising from cholangiocytes. Currently, the treatment and prognosis for CCA are mostly poor. Recently, we have reported that coiled-coil domain containing 25 (CCDC25) protein level in the sera may be a diagnostic marker for CCA. Subsequently, we identified three binding proteins of CCDC25 and found that kallikrein-11 (KLK11) expression was highest among those binding proteins. In this study, we investigated CCDC25 and KLK11 expression in CCA and adjacent normal tissues (n = 18) using immunohistochemistry. The results demonstrated that the expressions of CCDC25 and KLK11 in CCA tissues were both significantly higher than the adjacent tissues (p < 0.001 and p = 0.001, respectively). Then, using GEPIA bioinformatics analysis, KLK11 mRNA was significantly overexpressed in CCA tumor tissues compared with normal tissues (p < 0.05). Moreover, CCDC25 expression was positively correlated with KLK11 expression in CCA with lymph node metastasis (p = 0.028, r = 0.593). An analysis for the interaction of KLK11 with CCDC25 and other proteins, using STRING version 11.0, revealed that CCDC25 and KLK11 correlated with metastasis-related proteins. In addition, Kaplan-Meier survival curve analysis revealed that a high expression of KLK11 was associated with the poor prognosis of CCA. In conclusion, KLK11 is, as a binding protein for CCDC25, possibly involved in the metastatic process of CCA. KLK11 may be used as a prognostic marker for CCA.

The mechanisms of action of WeiChang'An Pill (WCAP) treat diarrhoea-predominant irritable bowel syndrome (IBS-D) using network pharmacology approach and in vivo studies

Ethnopharmacological relevanceWeiChang’An Pill (WCAP) is used in Traditional Chinese Medicine (TCM) to clinically treat diarrhoea-predominant irritable bowel syndrome (IBS-D); however, the underlying pharmacological mechanisms are unclear to date. Aim of the studyTo explore the mechanism underlying the therapeutic action of WCAP in IBS-D using a network pharmacology approach and in vivo experiments. Materials and methodsThe active compounds of WCAP were selected from the TCM Systems Pharmacology Database and TCM Integrated Database, and the potential targets were identified using the Swiss Target Prediction and Similarity Ensemble Approach (SEA) databases. The targets related to IBS-D were mined from the Therapeutic Target Database (TTD), National Center for Biotechnology Information Search database (NCBI), DrugBank database, and DisGeNET database. The intersecting protein-protein interactions (PPIs) of the drug-disease crossover genes were analysed, and the central PPI network was constructed using the String database, version 11.0, and Cytoscape version 3.7.2. Following Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway analyses, the gene-pathway network was constructed for identifying the key target genes and pathways. Based on the results and existing evidence, it was selected the cyclic adenosine monophosphate (cAMP) signalling pathway for further validation using in vivo experiments. ResultsA total of 872 targets were identified from the 77 active compounds in WCAP, which shared 78 targets that were predicted to be related to IBS-D. Twenty-one core targets were identified from the PPI network, which was constructed from the common targets. The results of enrichment analysis revealed that HRT2B, ADRA1A, ADRA1D, and CHRM2 could be the key targets of WCAP in IBS-D, and 11 signalling pathways, including the neuroactive ligand-receptor interaction, calcium signalling, and cAMP signalling pathways, were identified as crucial for the therapeutic activity of WCAP in IBS-D. We also identified the possibility of several interactions and crosstalk between the different pathways. Subsequent molecular biology experiments revealed that the expression levels of cAMP, phospho-(Ser/Thr) protein kinase A substrates (p-PKA), 5-hydroxytryptamine, and proteins in the cAMP signalling pathway, including G protein-coupled receptor (GPCR), adenylyl cyclase 5 (AC5), and cAMP-response element binding protein (CREB), were significantly upregulated in rat models of IBS-D following treatment with WCAP (P < 0.05). However, a reverse trend was observed in the expression of nuclear factor kappa-B (NF-κB) (P < 0.05), which could be attributed to the low-grade inflammation that occurs in IBS-D. ConclusionWe demonstrated that WCAP may alleviate the symptoms of diarrhoea and visceral sensitivity in IBS-D by regulating the cAMP signalling pathway.

Identification and analysis of key genes associated with acute myocardial infarction by integrated bioinformatics methods.

Background:Acute myocardial infarction (AMI) is a common disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in AMI through adopting integrated bioinformatics tools.Methods:The gene expression Omnibus (GEO) database was used to obtain genes data of AMI and no-AMI whole blood. Furthermore, differentially expressed genes (DEGs) were screened using the “Limma” package in R 3.6.1 software. Functional and pathway enrichment analyses of DEGs were performed via “Bioconductor” and “GOplot” package in R 3.6.1 software. In order to screen hub DEGs, the STRING version 11.0 database, Cytoscape and molecular complex detection (MCODE) were applied. Correlation among the hub DEGs was evaluated using Pearson's correlation analysis.Results:By performing DEGs analysis, 289 upregulated and 62 downregulated DEGs were successfully identified from GSE66360, respectively. And they were mainly enriched in the terms of neutrophil activation, immune response, cytokine, nuclear factor kappa-B (NF-κB) signaling pathway, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Based on the data of protein–protein interaction (PPI), the top 10 hub genes were ranked, including interleukin-8 (CXCL8), TNF, N-formyl peptide receptor 2 (FPR2), growth-regulated alpha protein (CXCL1), transcription factor AP-1 (JUN), interleukin-1 beta (IL1B), platelet basic protein (PPBP), matrix metalloproteinase-9 (MMP9), toll-like receptor 2 (TLR2), and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G). What's more, the results of correlation analysis demonstrated that there was positive correlation between the 10 hub DEGs.Conclusion:Ten DEGs were identified as potential candidate diagnostic biomarkers for patients with AMI in present study. However, further experiments are needed to confirm the functional pathways and hub genes associated with AMI.

MicroRNA related prognosis biomarkers from high throughput sequencing data of kidney renal clear cell carcinoma

BackgroundKidney renal clear cell carcinoma (KIRC) is the most common type of kidney cell carcinoma which has the worst overall survival rate. Almost 30% of patients with localized cancers eventually develop to metastases despite of early surgical treatment carried out. MicroRNAs (miRNAs) play a critical role in human cancer initiation, progression, and prognosis. The aim of our study was to identify potential prognosis biomarkers to predict overall survival of KIRC.MethodsAll data were downloaded from an open access database The Cancer Genome Atlas. DESeq2 package in R was used to screening the differential expression miRNAs (DEMs) and genes (DEGs). RegParallel and Survival packages in R was used to analysis their relationships with the KIRC patients. David version 6.8 and STRING version 11 were used to take the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis.ResultsWe found 2 DEGs (TIMP3 and HMGCS1) and 3 DEMs (hsa-miR-21-5p, hsa-miR-223-3p, and hsa-miR-365a-3p) could be prognosis biomarkers for the prediction of KIRC patients. The constructed prognostic model based on those 2 DEGs could effectively predict the survival status of KIRC. And the constructed prognostic model based on those 3 DEMs could effectively predict the survival status of KIRC in 3-year and 5-year.ConclusionThe current study provided novel insights into the miRNA related mRNA network in KIRC and those 2 DEGs biomarkers and 3 DEMs biomarkers may be independent prognostic signatures in predicting the survival of KIRC patients.

The roles of PARP-1 and XPD and their potential interplay in repairing bupivacaine-induced neuron oxidative DNA damage.

Bupivacaine has been widely used in clinical Anesthesia, but its neurotoxicity has been frequently reported, implicating cellular oxidative DNA damage as the major underlying mechanism. However, the mechanism underlying bupivacaine-induced oxidative DNA damage is unknown. We, thus, exposed SH-SY5Y cells to 1.5mM bupivacaine to induce neurotoxicity. Then, iTRAQ proteomic analysis was used to explore the repair of neuronal oxidative DNA damage. By analyzing the STRING version 11.0 database, the bioinformatics relationship between key repair enzymes was tracked. Subsequently, immunofluorescence co-localization and immunoprecipitation were used to investigate the interaction between key repair enzymes. The iTRAQ showed that Poly [ADP-ribose] polymerase 1 (PARP-1) from the base excision repair pathway participated closely in the repair of oxidative DNA damage induced by bupivacaine, and inhibition of PARP-1 expression significantly aggravated bupivacaine-induced DNA damage and apoptosis. Interestingly, this study showed that there were interactions and co-expression between PARP-1 and XPD (xeroderma pigmentosum D), another key protein of the nucleic acid excision repair pathway. After inhibiting XPD, PARP-1 expression was significantly reduced. However, simultaneous inhibition of both XPD and PARP-1 did not further increase DNA damage. It is concluded that PARP-1 may repair bupivacaine-induced oxidative DNA damage through XPD-mediated interactions.

Identifying the Mechanisms of α-Synuclein-Mediated Cytotoxicity in Parkinson’s Disease: New Insights from a Bioinformatics-Based Approach

Aim: A large body of evidence has implicated the cytotoxicity of α-synuclein in Parkinson’s disease (PD). We planned to use a bioinformatics-based approach to gain further insight into this process. Materials &amp; methods: Using STRING version 10, we identified interacting proteins of α-synuclein. Using α-synuclein and one of these interactors involved in apoptosis as query proteins, we identified other linked proteins. We further analyzed the interactions between some of these proteins by Protein–Protein Docking using ClusPro. Results: We identified BAX as an interacting protein of α-synuclein. Interactions of α-synuclein and BAX as well as BAX and BCL2L1 were determined. Conclusion: The interaction of α-synuclein and BAX could play a crucial role in the cell death process of PD where apoptosis and mitochondrial permeability transition-driven necrosis may coexist.

Evaluation of the role of kras gene in colon cancer pathway using string and Cytoscape software

Introduction: cancer is one of the top three most commonly occurring cancer worldwide with more than 1.8 million cases in 2018. In Malaysia, cancer is the most common cancer in males and the second most common cancer in females. Albeit being the second most common form of cancer in Malaysia, there is a lack of informal or structured national cancer screening program in Malaysia and it remains a low priority in healthcare planning and expenditure. The risk of developing colon cancer is greatly influenced by factors such as lifestyle habits, genetic inheritance, diet, weight, and exercise. KRAS, the most frequently mutated oncogene in cancer, occurs in about 50 percent of cancers. This study maps the KRAS gene involved in colon cancer pathway using applications such as STRING version 11.0 and version 3.7.0 to provide a clear visualization of all the related and involved proteins and genes that interact with KRAS gene in the pathway.&#x0D; Methods: Using KRAS as a seed, a protein-protein interaction network was constructed with 3391 interactions which were retrieved from the STRING version 11.0 database. The protein network interaction was further grouped into 6 clusters using the MCODE application. Molecular function and biological processes of the genes involved in the KRAS protein network were determined using Biological Networks Gene Ontology (BiNGO).&#x0D; Results: According to the resulting protein-protein network interaction map, it revealed that KRAS mechanism and co-expressed genes interconnected with protein or enzyme binding, receptor signaling protein activity and vascular endothelial growth factor (VEGF) receptor 2 binding.&#x0D; Conclusion: Understanding these protein-protein interactions provide insight into cellular activities and thus aid in the understanding of the cause of disease.&#x0D;

On modification of Boyer-Moore-horspool's algorithm for tree pattern matching in linearised trees

Tree pattern matching on ordered trees is an important problem in Computer Science. Ordered trees can be represented as strings with additional properties via various linearisations. We present a backward tree pattern matching algorithm for ordered trees for various linear representations of trees and tree patterns. The algorithm adaptations find all occurrences of a single given tree pattern which match an input tree regardless of the chosen linearisation. The algorithms preserve the properties and advantages of standard backward string pattern matching using Boyer-Moore-Horspool's bad character shift heuristics. The number of symbol comparisons in the backward tree pattern matching can be sublinear in the size of the input tree. As in the case of the string version of Boyer-Moore-Horspool's matching algorithm, the size of the bad character shift table used by the algorithm is linear in the size of the alphabet. We compare the algorithm adaptations with the algorithm using originally chosen linear representation and with the best performing previously existing algorithms based on (non-linearised) tree pattern matching using finite tree automata or stringpath matchers. We show that the presented backward tree pattern matching algorithms outperform the non-linearising ones for single pattern matching and they perform among themselves comparably.