Molecular Complex Detection Analysis Research Articles

Interpreting the Epidemiological Characteristics of HIV-1 in Heterosexually Transmitted Population Based on Molecular Transmission Network in Kunming, Yunnan: A Retrospective Cohort Study.

Heterosexuals have become the most prevalent group of HIV-1 in Kunming, Yunnan Province. Utilizing the principle of genetic similarity between their gene sequences, we built a molecular transmission network by gathering data from earlier molecular epidemiological studies. This allowed us to analyze the epidemiological features of this group and offer fresh concepts and approaches for the prevention and management of HIV-1 epidemics. Cytoscope was used to visualize and characterize the network following the processing of the sample gene sequences by BioEdit and HyPhy. The number of possible links and the size of the clusters were investigated as influencing factors using a zero-inflated Poisson model and a logistic regression model, respectively. A scikit-learn-based prediction model was developed to account for the dynamic changes in the HIV-1 molecular network. Six noteworthy modular clusters with network scores ranging from 4 to 9 were found from 150 clusters using Molecular Complex Detection analysis at a standard genetic distance threshold of 0.01. The size of the number of possible links and the network's clustering rate were significantly impacted by sampling time, marital status, and CD4+ T lymphocytes (all p < 0.05). The gradient boosting machine (GBM) model had the highest area under the curve value, 0.884 ± 0.051, according to scikit-learn. Though not all cluster subtypes grew equally, the network clusters were relatively specific and aggregated. The largest local transmission-risk group for HIV-1CRF08_BC is now the heterosexual transmission population. The most suitable model for constructing the HIV-1 molecular network dynamics prediction model was found to be the GBM model.

Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis.

Genomics and bioinformatics methods were used to screen genes and molecular markers correlated with colorectal cancer incidence and progression, and their biological functions were analyzed. Differentially expressed genes were obtained using the GEO2R program following colorectal cancer chip data GSE44076 retrieval from the Gene Expression Omnibus gene expression comprehensive database. An online database (David) that combines annotation, visualization, and gene discovery was utilized for investigating genes. Pathway and protein analyses were performed via resources from the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Visual analysis of the KEGG pathway was carried out according to ClueGO and CluePedia to establish the PPI network of gene interaction between pathways; the genes with the highest connectivity were screened by the molecular complex detection analysis method as Hub genes in this study; gene expression was verified by GEPIA online analysis tool, and Kaplan-Meier survival curve was drawn for prognosis analysis. By analyzing GSE44076 microarray data, 86 genes were selected, and colorectal cancer tissues' upregulation was observed in 27 genes and downregulation in 59 ones. GO assessment revealed that the differentially expressed genes were basically correlated with retinol dehydrogenase activity, carbon dehydrogenase activity, collagen-containing extracellular matrix, anchored component of memory, and cellular hormone metabolic process. Moreover, the KEGG assessment revealed that the differential genes contained various signal pathways such as retinol metabolism, chemical carotenogenesis, and nitrogen metabolism. Through further analysis of the PPI protein network, 4 clusters were obtained, and 16 Hub genes were screened out by combining the degree of each gene. Through the analysis of each gene on the prognosis of colon cancer through the GEPIA online analysis website, it was found that the expression levels of AQP8, CXCL8, and ZG16 genes were remarkably associated with colon cancer prognosis (P < 0.05). Genomics and bioinformatics methods can effectively analyze the genes and molecular markers correlated with colorectal cancer incidence and progression, help to systematically clarify the molecular mechanism of 16 key genes in colorectal cancer development and progression, and provide a theoretically valid insight for the screening of diagnostic markers of colorectal cancer and the selection of accurate targets for drug therapy.

Using the Molecular Transmission Networks to Analyze the Epidemic Characteristics of HIV-1 CRF08_BC in Kunming, Yunnan.

HIV-1CRF08_BC is the most prevalent epidemic subtype among heterosexual (HET) and intravenous drug users (IDUs) in Kunming, Yunnan. Using the pol region of gene sequences derived from molecular epidemiological surveys, we developed a molecular transmission network for the purpose of analyzing its epidemiological characteristics, assessing its epidemiological trends, identifying its potential transmission relationships, and developing targeted interventions. HyPhy 2.2.4 was used to calculate pairwise genetic distances between sequences; GraphPad-Prism 8.0 was employed to determine the standard genetic distance; and Cytoscope 3.7.2 was applied to visualize the network. We used the network analysis tools to investigate network characteristics and the Molecular Complex Detection (MCODE) tool to observe the growth of the network. We utilized a logistic regression model to examine the factors influencing clustering and a zero-inflated Poisson model to investigate the factors influencing potential transmission links. At the standard genetic distance threshold of 0.008, 406 out of 858 study participants were clustered in 132 dissemination networks with a total network linkage of 868, and the number of links per sequence ranged from 1 to 19. The MCODE analysis identified three significant modular clusters in the networks, with network scores ranging from 4.9 to 7. In models of logistic regression, HET, middle-aged and elderly individuals, and residents of northern and southeastern Kunming were more likely to enter the transmission network. According to the zero-inflated Poisson model, age, transmission category, sampling year, marital status, and CD4+ T level had a significant effect on the size of links. The molecular clusters in Kunming's molecular transmission network are specific and aggregate to a certain extent. HIV-1 molecular network analysis provided information on local transmission characteristics, and these findings helped to determine the priority of transmission-reduction interventions.

The role of neutrophils in corneal nerve regeneration

BackgroundTo investigate the role of neutrophils in corneal nerve regeneration.MethodsA mouse model simulating corneal nerve injury was established and samples from corneal scraping with and without neutrophil closure were collected. These samples were used for corneal nerve staining, ribonucleic acid sequencing, and bioinformatics. Differential expression analysis was used to perform enrichment analysis to identify any significant differences between these two groups. The differential genes were then intersected with neutrophil-associated genes and a protein-protein interaction network was constructed using the intersected genes. The immune infiltration between the two groups was examined along with the immune cell variation between the high and low gene expression groups.ResultsNeutrophil removal delays corneal epithelial and nerve regeneration. A total of 546 differential genes and 980 neutrophil-associated genes, with 27 genes common to both sets were obtained. Molecular Complex Detection analysis yielded five key genes, namely integrin subunit beta 2 (ITGB2), matrix metallopeptidase 9 (MMP9), epidermal growth factor (EGF), serpin family E member 1 (SERPINE1), and plasminogen activator urokinase receptor (PLAUR). Among these genes, ITGB2, SERPINE1, and PLAUR exhibited increased expression in the neutrophil-confined group, while MMP9 and EGF showed decreased expression, with MMP9 and EGF displaying a more significant difference. Immune infiltration was also observed between the two groups, revealing significant differences in the infiltration of M0 macrophages, activated mast cells, and neutrophils. Moreover, the neutrophil levels were lower in the groups with low MMP9 and EGF expressions and higher in the high-expression group.ConclusionNeutrophil confinement might significantly affect the MMP9 and EGF expression levels. Strategies to inhibit MMP9 could potentially yield therapeutic benefits.

Identification of novel potential molecular targets associated with pediatric septic shock by integrated bioinformatics analysis and validation of in vitro septic shock model

Background/Aim: Sepsis is a major cause of morbidity, mortality, and healthcare utilization among children all over the world. Sepsis, characterized as life-threatening organ failure, results from a dysregulated host response to infection. When combined with critically low blood pressure, it causes septic shock, resulting in high mortality rates. The aim of this study was to perform a bioinformatic analysis of gene expression profiles to predict septic shock risk. Methods: Four datasets related to pediatric septic shock were retrieved from the Gene Expression Omnibus (GEO) database for a total of 240 patients and 83 controls. GEO2R tools based on R were used to find differentially expressed genes (DEGs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to examine the functional enrichment of DEGs. STRING was used to create a protein–protein interaction (PPI) network. After separately analyzing the four datasets, commonly affected genes were removed using the Venny program. Finally, human umbilical vein endothelial cells (HUVECs) were stimulated with supernatants of lipopolysaccharide (LPS)-stimulated RAW267.4 macrophage cells and expression of selected genes was confirmed by real-time reverse-transcriptase polymerase chain reaction (qRT-PCR) and used to construct an in vitro septic shock model. Results: Seven-hundred seventy-one common differentially expressed genes in the four groups were found. Of these, 433 genes showed increased expression, while 338 had reduced expression. In the DAVID analysis results, DEGs up-regulated according to gene ontology results were enriched in the regulation of innate and adaptive immune responses, complement receptor-mediated signaling, and cytokine secretion processes. Down-regulated DEGs were significantly enriched in the regulation of immune response, T-cell activation, antigen processing, and presentation and integral component of plasma membrane processes. According to The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Cystoscape Molecular Complex Detection (MCODE), nine down-regulated genes in the center of the PPI network, ZAP70, ITK, LAT, PRKCQ, LCK, IL2RB, FYN, CD8A, CD247 and four up-regulated genes, MMP9, TIMP1, LCN2, HGF, were associated with septic shock. Expressions of FYN and MMP9 genes in the in vitro septic shock model were consistent with the bioinformatic results. Conclusion: Comparative bioinformatics analysis of data from four different septic shock studies was performed. As a result, molecular processes and important signal networks and 13 genes that we think will play a role in the development and risk prediction of septic shock are proposed. Methods: Four datasets related to Pediatric septic shock were retrieved from the Gene Expression Omnibus (GEO) database for a total of 240 patients and 83 controls. GEO2R tools based on R were used to find differentially expressed genes (DEGs). DAVID was used to examine the functional enrichment of DEGs. STRING was used to create a protein-protein interaction (PPI) network. After separately analyzing the four datasets, commonly affected genes were removed using the Venny program. Finally, HUVECs were stimulated with supernatants of LPS-stimulated RAW267.4 macrophage cells and expression of selected genes was confirmed by qRT-PCR, constructing an in vitro septic shock model. Results: There were 771 common differentially expressed genes in the 4 groups. Of these, 433 genes showed increased expression, while 338 had reducing expression. In the DAVID analysis results, DEGs upregulated by gene ontology were enriched in the regulation of innate and adaptive immune responses, complement receptor-mediated signaling, and cytokine secretion processes. Downregulated DEGs are significantly enriched in the regulation of immune response, T cell activation, antigen processing, and presentation and integral component of plasma membrane processes. According to STRING, cystoscape MCODE, and cytohubba analysis, 9 downregulated genes in the center of the PPI network, ZAP70, ITK, LAT, PRKCQ, LCK, IL2RB, FYN, CD8A, CD247, and 4 upregulated genes, MMP9, TIMP1, LCN2, HGF, were associated with septic shock. Expressions of FYN and MMP9 genes in the in vitro septic shock model were consistent with bioinformatic results. Conclusion: Important signaling networks and 13 genes potentially indicating molecular processes for the incidence, development, and risk prediction in septic shock were found using bioinformatic analysis of gene expression profiles.

Identification of core genes and pathways between geriatric multimorbidity and renal insufficiency: potential therapeutic agents discovered using bioinformatics analysis

BackgroundGeriatric people are prone to suffer from multiple chronic diseases, which can directly or indirectly affect renal function. Through bioinformatics analysis, this study aimed to identify key genes and pathways associated with renal insufficiency in patients with geriatric multimorbidity and explore potential drugs against renal insufficiency.MethodsThe text mining tool Pubmed2Ensembl was used to detect genes associated with the keywords including "Geriatric", "Multimorbidity" and "Renal insufficiency". The GeneCodis program was used to specify Gene Ontology (GO) biological process terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape. Module analysis was performed using CytoHubba and Molecular Complex Detection (MCODE) plugins. GO and KEGG analysis of gene modules was performed using the Database for Annotation, Visualization and Integrated Discover (DAVID) platform database. Genes clustered in salient modules were selected as core genes. Then, the functions and pathways of core genes were visualized using ClueGO and CluePedia. Finally, the drug-gene interaction database was used to explore drug-gene interactions of the core genes to identify drug candidates for renal insufficiency in patients with geriatric multimorbidity.ResultsThrough text mining, 351 genes associated with "Geriatric", "Multimorbidity" and "Renal insufficiency" were identified. A PPI network consisting of 216 nodes and 1087 edges was constructed and CytoHubba was used to sequence the genes. Five gene modules were obtained by MCODE analysis. The 26 genes clustered in module1 were selected as core candidate genes primarily associated with renal insufficiency in patients with geriatric multimorbidity. The HIF-1, PI3K-Akt, MAPK, Rap1, and FoxO signaling pathways were enriched. We found that 21 of the 26 selected genes could be targeted by 34 existing drugs.ConclusionThis study indicated that CST3, SERPINA1, FN1, PF4, IGF1, KNG1, IL6, VEGFA, ALB, TIMP1, TGFB1, HGF, SERPINE1, APOA1, APOB, FGF23, EGF, APOE, VWF, TF, CP, GAS6, APP, IGFBP3, P4HB, and SPP1 were key genes potentially involved with renal insufficiency in patients with geriatric multimorbidity. In addition, 34 drugs were identified as potential agents for the treatment and management of renal insufficiency.

Identifying patterns of immune related cells and genes in the peripheral blood of acute myocardial infarction patients using a small cohort

BackgroundThe immune system plays a vital role in the pathophysiology of acute myocardial infarction (AMI). However, the exact immune related mechanism is still unclear. This research study aimed to identify key immune-related genes involved in AMI.MethodsCIBERSORT, a deconvolution algorithm, was used to determine the proportions of 22 subsets of immune cells in blood samples. The weighted gene co-expression network analysis (WGCNA) was used to identify key modules that are significantly associated with AMI. Then, CIBERSORT combined with WGCNA were used to identify key immune-modules. The protein–protein interaction (PPI) network was constructed and Molecular Complex Detection (MCODE) combined with cytoHubba plugins were used to identify key immune-related genes that may play an important role in the occurrence and progression of AMI.ResultsThe CIBERSORT results suggested that there was a decrease in the infiltration of CD8 + T cells, gamma delta (γδ) T cells, and resting mast cells, along with an increase in the infiltration of neutrophils and M0 macrophages in AMI patients. Then, two modules (midnightblue and lightyellow) that were significantly correlated with AMI were identified, and the salmon module was found to be significantly associated with memory B cells. Gene enrichment analysis indicated that the 1,171 genes included in the salmon module are mainly involved in immune-related biological processes. MCODE analysis was used to identify four different MCODE complexes in the salmon module, while four hub genes (EEF1B2, RAC2, SPI1, and ITGAM) were found to be significantly correlated with AMI. The correlation analysis between the key genes and infiltrating immune cells showed that SPI1 and ITGAM were positively associated with neutrophils and M0 macrophages, while they were negatively associated with CD8 + T cells, γδ T cells, regulatory T cells (Tregs), and resting mast cells. The RT-qPCR validation results found that the expression of the ITGAM and SPI1 genes were significantly elevated in the AMI samples compared with the samples from healthy individuals, and the ROC curve analysis showed that ITGAM and SPI1 had a high diagnostic efficiency for the recognition of AMI.ConclusionsImmune cell infiltration plays a crucial role in the occurrence and development of AMI. ITGAM and SPI1 are key immune-related genes that are potential novel targets for the prevention and treatment of AMI.

Cyclin-Dependent Kinase 6 Identified as the Target Protein in the Antitumor Activity of Tetrastigma hemsleyanum.

Background Tetrastigma hemsleyanum (T. hemsleyanum) is widely used as an adjuvant drug for tumor therapy but its antitumor therapeutic targets and molecular mechanisms have remained unclear. The prediction and analysis of natural products has previously used only network pharmacology methods to identify potential target proteins from public databases. In this study, we use comprehensive bioinformatics analysis and experimental verification to determine the antitumor mechanism of T. hemsleyanum.MethodsNetwork pharmacology analysis was used to predict the potential in vivo target proteins of T. hemsleyanum. The expression matrix and clinical data to perform an analysis of hub genes were collected from the TCGA and GTEx databases, specifically the analysis of expression, prognosis, tumor immune cell infiltration analysis, immune checkpoint genes, microsatellite instability, tumor mutational burden, tumor neoantigen, and immune microenvironment, which identify the roles and biological functions of the hub genes in pan-cancer. Finally, gene set enrichment analysis was used to verify the biological processes and signaling pathways involved in the pan-cancer expression profile.ResultsWe found 124 potential in vivo target proteins of T. hemsleyanum through network pharmacological analysis, and five hub genes (AKR1C1, MET, PTK2, PIK3R1, and CDK6) were then screened by protein–protein interaction (PPI) network analysis and molecular complex detection analysis (MCODE). Experimental intervention with an aqueous extract of T. hemsleyanum verified that these hub genes are the target proteins involved in the regulation of T. hemsleyanum in cells. A pan-cancer analysis then confirmed that CDK6 and MET are potential targets upon which T. hemsleyanum may exert antitumor action, especially in ACC, CESC, LGG, and PAAD. The CDK6 protein targeted by T. hemsleyanum is also involved in the immune and mutation process of pan-cancer, especially in the regulation of immune cell infiltration, immune checkpoint gene expression, microsatellite instability, tumor mutation burdens, and tumor neoantigens. Together, these analyses show that T. hemsleyanum affects tumor immune regulation and genomic stability. Finally, a gene set enrichment analysis confirmed that T. hemsleyanum regulates the cell cycle checkpoint.ConclusionsWe found that T. hemsleyanum can behave as an antitumor agent by acting as a potential cell cycle checkpoint inhibitor in CDK6-driven tumors, such as ACC, CESC, LGG, and PAAD, and that it acts as a tyrosine kinase receptor inhibitor that inhibits the expression of the proto-oncogene MET. Combined with an analysis of immune and mutation correlations in pan-cancer, we determined that T. hemsleyanum may function biologically as an immune regulator and interfere with the stability of the tumor genome, which is worthy of further study.

Identification of MYH6 as the potential gene for human ischaemic cardiomyopathy.

The present study aimed to explore the potential hub genes and pathways of ischaemic cardiomyopathy (ICM) and to investigate the possible associated mechanisms. Two microarray data sets (GSE5406 and GSE57338) were downloaded from the Gene Expression Omnibus (GEO) database. The limma package was used to analyse the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology (DO) and Gene Ontology (GO) annotation analyses were performed. A protein‐protein interaction (PPI) network was set up using Cytoscape software. Significant modules and hub genes were identified by the Molecular Complex Detection (MCODE) app. Then, further functional validation of hub genes in other microarrays and survival analysis were performed to judge the prognosis. A total of 1065 genes were matched, with an adjusted p < 0.05, and 17 were upregulated and 25 were downregulated with|log2 (fold change)|≥1.2. After removing the lengthy entries, GO identified 12 items, and 8 pathways were enriched at adjusted p < 0.05 (false discovery rate, FDR set at <0.05). Three modules with a score >8 after MCODE analysis and MYH6 were ultimately identified. When validated in GSE23561, MYH6 expression was lower in patients with CAD than in healthy controls (p < 0.05). GSE60993 data suggested that MYH6 expression was also lower in AMI patients (p < 0.05). In the GSE59867 data set, MYH6 expression was lower in CAD patients than in AMI patients and lower in heart failure (HF) patients than in non‐HF patients. However, there was no difference at different periods within half a year, and HF was increased when MYH6 expression was low (p < 0.05–0.01). We performed an integrated analysis and validation and found that MYH6 expression was closely related to ICM and HF. However, whether this marker can be used as a predictor in blood samples needs further experimental verification.

Bioinformatics Analysis of ZBTB16 as a Prognostic Marker for Ewing’s Sarcoma

Objective The purpose of this study is to identify novel biomarkers for the prognosis of Ewing's sarcoma based on bioinformatics analysis. Methods The GSE63157 and GSE17679 datasets contain patient and healthy control microarray data that were downloaded from the Gene Expression Omnibus (GEO) database and analyzed through R language software to obtain differentially expressed genes (DEGs). Firstly, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment, protein-protein interaction (PPI) networks, and Cytoscape Molecular Complex Detection (MCODE) plug-in were then used to compute the highest scores of the module. After survival analysis, the hub genes were lastly obtained from the two module genes. Results A total of 1181 DEGs were identified from the two GSEs. Through MCODE and survival analysis, we obtain 53 DEGs from the module and 29 overall survival- (OS-) related genes. ZBTB16 was the only downregulated gene after Venn diagrams. Survival analysis indicates that there was a significant correlation between the high expression of ZBTB16 and the OS of Ewing's sarcoma (ES), and the low expression group had an unfavorable OS when compared to the high expression group. Conclusions High expression of ZBTB16 may serve as a predictor biomarker of poor prognosis in ES patients.

A Novel Diagnostic Predictive Model for Idiopathic Short Stature in Children.

ObjectiveIdiopathic short stature (ISS), an endocrine-related disease, is difficult to diagnose. Previous studies have shown that many children with some inflammation-related diseases often have short stature, but whether inflammation is the underlying mechanism of ISS has not been studied. Here, we attempt to explore the role of inflammation in the occurrence and development of ISS and to demonstrate an available clinical diagnostic model of ISS.MethodsFrozen serum samples were collected from ISS patients (n = 4) and control individuals (n = 4). Isobaric tags for relative and absolute quantitation (iTRAQ) combined with LC-MS/MS analysis were applied to quantitative proteomics analysis. To assess clusters of potentially interacting proteins, functional enrichment (GO and KEGG) and protein-protein interaction network analyses were performed, and the crucial proteins were detected by Molecular Complex Detection (MCODE). Furthermore, serum levels of two selected proteins were measured by ELISA between ISS patients (n = 80) and controls (n = 80). In addition, experiments in vitro were used to further explore the effects of crucial proteins on endochondral ossification.ResultsA total of 437 proteins were quantified, and 84 DEPs (60 upregulated and 24 downregulated) were identified between patients with ISS and controls. Functional enrichment analysis showed that the DEPs were primarily enriched in blood microparticle, acute inflammatory response, protein activation cascade, collagen-containing extracellular matrix, platelet degranulation, etc. According to the results of top 10 fold change DEPs and MCODE analysis, C1QA and C1QB were selected to further experiment. The expression levels of C1QA and C1QB were validated in serum samples. Based on the logistic regression analysis and ROC curve analysis, we constructed a novel diagnostic model by serum levels of C1QA and C1QB with a specificity of 91.2% and a sensitivity of 75% (AUC = 0.900, p <0.001). Finally, the western blotting analysis confirmed the expression levels of OCN, OPN, RUNX2, and Collagen X were downregulated in chondrocytes, and the outcome of Collagen II was upregulated.ConclusionOur study is the first to demonstrate the significant role of inflammation in the development of ISS. In addition, we identify C1QA and C1QB as novel serum biomarkers for the diagnosis of ISS.

Relationship between miR-7-5p expression and 125I seed implantation efficacy in pancreatic cancer and functional analysis of target genes*

Abstract Objective The aim of this study was to investigate the relationship between miR-7-5p expression and intertissue-125I irradiation sensitivity in pancreatic cancer tissues and to analyze the function of target genes. Methods Thirty-seven patients with unresectable pancreatic ductal adenocarcinoma (PDAC) treated with radioactive 125I seed implantation were enrolled. RT-PCR was used to detect the expression level of miR-7-5p in cancer tissues and analyze the relationship between miR-7-5p expression and 125I radiation sensitivity. Bioinformatic software and online tools were used to predict the miR-7-5p target genes and analyze their functional annotation and pathway enrichment. Results Radioactive 125I seed implantation was followed up for 2 months. The objective response rate of the miR-7-5p high expression group was 65.0% (13/20), whereas the objective response rate of the miR-7-5p low expression group was 5.88% (1/17), and the difference between the two groups was statistically significant (χ 2 = 13.654, P &lt; 0.001). A total of 187 target genes were predicted using three databases. GO functional annotation showed that target genes were mainly involved in cellular response to insulin stimulus, regulation of gene expression by genetic imprinting, cytosol, peptidyl-serine phosphorylation, bHLH transcription factor binding, cargo loading into vesicles, cellular response to epinephrine stimulus, and nucleoplasm. KEGG pathway enrichment analysis showed that target genes were mainly involved in the ErbB signaling pathway, HIF-1 signaling pathway, axon guidance, longevity regulatory pathway, endocrine resistance, glioma, choline metabolism in cancer, and EGFR tyrosine kinase inhibitor drug resistance. Molecular complex detection analysis by Cytoscape revealed that PIGH, RAF1, EGFR, NXT2, PIK3CD, PIK3R3, ERBB4, TRMT13, and C5orf22 were the key modules of miR-7-5p target gene clustering. Conclusion The expression of miR-7-5p in pancreatic cancer tissues positively correlated with the radiosensitivity of 125I seeds. Via targeted gene regulation, miR-7-5p acts on the network of multiple signaling pathways in PDAC and participates in its occurrence and development. Thus, miR-7-5p may become a predictive index of 125I seed implantation therapy sensitivity in PDAC patients.

Transcriptomic analysis of castration, chemo-resistant and metastatic prostate cancer elucidates complex genetic crosstalk leading to disease progression.

Prostate adenocarcinoma, with its rising numbers and high fatality rate, is a daunting healthcare challenge to clinicians and researchers alike. The mainstay of our meta-analysis was to decipher differentially expressed genes (DEGs), their corresponding transcription factors (TFs), miRNAs (microRNA) and interacting pathways underlying the progression of prostate cancer (PCa). We have chosen multiple datasets from primary, castration-resistant, chemo-resistant and metastatic prostate cancer stages for investigation. From our tissue-specific and disease-specific co-expression networks, fifteen hub genes such as ACTB, ACTN1, CDH1, CDKN1A, DDX21, ELF3, FLNA, FLNC, IKZF1, ILK, KRT13, KRT18, KRT19, SVIL and TRIM29 were identified and validated by molecular complex detection analysis as well as survival analysis. In our attempt to highlight hub gene-associated mutations and drug interactions, FLNC was found to be most commonly mutated and CDKN1A gene was found to have highest druggability. Moreover, from DAVID and gene set enrichment analysis, the focal adhesion and oestrogen signalling pathways were found enriched which indicates the involvement of hub genes in tumour invasiveness and metastasis. Finally by Enrichr tool and miRNet, we identified transcriptional factors SNAI2, TP63, CEBPB and KLF11 and microRNAs, namely hsa-mir-1-3p, hsa-mir-145-5p, hsa-mir-124-3p and hsa-mir-218-5p significantly controlling the hub gene expressions. In a nutshell, our report will help to gain a deeper insight into complex molecular intricacies and thereby unveil the probable biomarkers and therapeutic targets involved with PCa progression.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease

This investigation seeks to dissect coronary artery disease molecular target candidates along with its underlying molecular mechanisms. Data on patients with CAD across three separate array data sets, GSE66360, GSE19339 and GSE97320 were extracted. The gene expression profiles were obtained by normalizing and removing the differences between the three data sets, and important modules linked to coronary heart disease were identified using weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were applied in order to identify statistically significant genetic modules with the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). The online STRING tool was used to construct a protein–protein interaction (PPI) network, followed by the use of Molecular Complex Detection (MCODE) plug-ins in Cytoscape software to identify hub genes. Two significant modules (green-yellow and magenta) were identified in the CAD samples. Genes in the magenta module were noted to be involved in inflammatory and immune-related pathways, based on GO and KEGG enrichment analyses. After the MCODE analysis, two different MCODE complexes were identified in the magenta module, and four hub genes (ITGAM, degree = 39; CAMP, degree = 37; TYROBP, degree = 28; ICAM1, degree = 18) were uncovered to be critical players in mediating CAD. Independent verification data as well as our RT-qPCR results were highly consistent with the above finding. ITGAM, CAMP, TYROBP and ICAM1 are potential targets in CAD. The underlying mechanism may be related to the transendothelial migration of leukocytes and the immune response.

SILAC quantitative proteomics analysis of ivermectin-related proteomic profiling and molecular network alterations in human ovarian cancer cells.

The antiparasitic agent ivermectin offers more promises to treat a diverse range of diseases. However, a comprehensive proteomic analysis of ivermectin-treated ovarian cancer (OC) cells has not been performed. This study sought to identify ivermectin-related proteomic profiling and molecular network alterations in human OC cells. Stable isotope labeling with amino acids in cell culture (SILAC)-based quantitative proteomics was used to study the human OC TOV-21G cells. After TOV-21G cells underwent 10 passages in SILAC-labeled growth media, TOV-21G cells were treated with 10 ml of 20 μmol/L ivermectin in cell growing medium for 24 h. The SILAC-labeled proteins were digested with trypsin; tryptic peptides were identified with mass spectrometry (MS). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was used to mine signaling pathway alterations with ivermectin-related proteins in TOV-21G cells. Gene ontology (GO) analysis was used to explore biological functions of ivermectin-related proteins, including biological processes (BPs), cellular components (CCs), and molecular functions (MFs). The protein-protein interaction network was analyzed with molecular complex detection (MCODE) to identify hub modules. In total, 4,447 proteins were identified in ivermectin-treated TOV-21G cells. KEGG analysis revealed 89 statistically significant signaling pathways. Interestingly, the clustering analysis of these pathways showed that ivermectin was involved in various cancer pathogenesis processes, including modulation of replication, RNA metabolism, and translational machinery. GO analysis revealed 69 statistically significant CCs, 87 MFs, and 62 BPs. Furthermore, MCODE analysis identified five hub modules, including 147 hub molecules. Those hub modules involved ribosomal proteins, RNA-binding proteins, cell-cycle progression-related proteins, proteasome subunits, and minichromosome maintenance proteins. These findings demonstrate that SILAC quantitative proteomics is an effective method to analyze ivermectin-treated cells, provide the first ivermectin-related proteomic profiling and molecular network alterations in human OC cells, and provide deeper insights into molecular mechanisms and functions of ivermectin to inhibit OC cells.

MicroRNA expression profiling reveals potential roles for microRNA in the liver during pigeon (Columba livia) development

The liver is the central organ for metabolism and influence the growth and development of the animals. To date, little is known about the microRNA (miRNA) in pigeon livers, particularly in different developmental stages. A comprehensive investigation into miRNA transcriptomes in livers across 3 pigeon developmental stages (1, 14, 28 d old) and an adult stage (2 y old) was performed by small RNA sequencing. We identified 312 known miRNA, 433 conserved miRNA, and 192 novel miRNA in pigeon livers. A set of differentially expressed (DE) miRNA in livers were screened out during pigeon development. This set of miRNA might be involved in hepatospecific phenotype and liver development. A Short Time-series Expression Miner analysis indicated significant expression variations in DE miRNA during liver development of pigeons. These DE miRNA with different expression patterns might play essential roles in response to growth factor, cell morphogenesis, and gland development, etc. Protein–protein interaction network and Molecular Complex Detection analysis identified several vital target genes (e.g., TNRC6B, FRS2, PTCH1, etc.) of DE miRNA, which is closely linked in liver development and enriched in PI3K cascade and regulation of growth. Our results expanded the repertoire of pigeon miRNA and may be of help in better understanding the mechanism of squab's rapid development from the perspective of liver development.

Quantitative proteomics reveals a broad-spectrum antiviral property of ivermectin, benefiting for COVID-19 treatment.

Viruses such as human cytomegalovirus (HCMV), human papillomavirus (HPV), Epstein–Barr virus (EBV), human immunodeficiency virus (HIV), and coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS‐CoV‐2]) represent a great burden to human health worldwide. FDA‐approved anti‐parasite drug ivermectin is also an antibacterial, antiviral, and anticancer agent, which offers more potentiality to improve global public health, and it can effectively inhibit the replication of SARS‐CoV‐2 in vitro. This study sought to identify ivermectin‐related virus infection pathway alterations in human ovarian cancer cells. Stable isotope labeling by amino acids in cell culture (SILAC) quantitative proteomics was used to analyze human ovarian cancer cells TOV‐21G treated with and without ivermectin (20 μmol/L) for 24 h, which identified 4447 ivermectin‐related proteins in ovarian cancer cells. Pathway network analysis revealed four statistically significant antiviral pathways, including HCMV, HPV, EBV, and HIV1 infection pathways. Interestingly, compared with the reported 284 SARS‐CoV‐2/COVID‐19‐related genes from GencLip3, we identified 52 SARS‐CoV‐2/COVID‐19‐related protein alterations when treated with and without ivermectin. Protein–protein network (PPI) was constructed based on the interactions between 284 SARS‐CoV‐2/COVID‐19‐related genes and between 52 SARS‐CoV‐2/COVID‐19‐related proteins regulated by ivermectin. Molecular complex detection analysis of PPI network identified three hub modules, including cytokines and growth factor family, MAP kinase and G‐protein family, and HLA class proteins. Gene Ontology analysis revealed 10 statistically significant cellular components, 13 molecular functions, and 11 biological processes. These findings demonstrate the broad‐spectrum antiviral property of ivermectin benefiting for COVID‐19 treatment in the context of predictive, preventive, and personalized medicine in virus‐related diseases.

Identification of key genes in ruptured atherosclerotic plaques by weighted gene correlation network analysis

The rupture of atherosclerotic plaques is essential for cardiovascular and cerebrovascular events. Identification of the key genes related to plaque rupture is an important approach to predict the status of plaque and to prevent the clinical events. In the present study, we downloaded two expression profiles related to the rupture of atherosclerotic plaques (GSE41571 and GSE120521) from GEO database. 11 samples in GSE41571 were used to identify the differentially expressed genes (DEGs) and to construct the weighted gene correlation network analysis (WGCNA) by R software. The gene oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment tool in DAVID website, and the Protein-protein interactions in STRING website were used to predict the functions and mechanisms of genes. Furthermore, we mapped the hub genes extracted from WGCNA to DEGs, and constructed a sub-network using Cytoscape 3.7.2. The key genes were identified by the molecular complex detection (MCODE) in Cytoscape. Further validation was conducted using dataset GSE120521 and human carotid endarterectomy (CEA) plaques. Results: In our study, 868 DEGs were identified in GSE41571. Six modules with 236 hub genes were identified through WGCNA analysis. Among these six modules, blue and brown modules were of the highest correlations with ruptured plaques (with a correlation of 0.82 and −0.9 respectively). 72 hub genes were identified from blue and brown modules. These 72 genes were the most likely ones being related to cell adhesion, extracellular matrix organization, cell growth, cell migration, leukocyte migration, PI3K-Akt signaling, focal adhesion, and ECM-receptor interaction. Among the 72 hub genes, 45 were mapped to the DEGs (logFC > 1.0, p-value < 0.05). The sub-network of these 45 hub genes and MCODE analysis indicated 3 clusters (13 genes) as key genes. They were LOXL1, FBLN5, FMOD, ELN, EFEMP1 in cluster 1, RILP, HLA-DRA, HLA-DMB, HLA-DMA in cluster 2, and SFRP4, FZD6, DKK3 in cluster 3. Further expression detection indicated EFEMP1, BGN, ELN, FMOD, DKK3, FBLN5, FZD6, HLA-DRA, HLA-DMB, HLA-DMA, and RILP might have potential diagnostic value.

Identification of the role of toxin B in the virulence of Clostridioides difficile based on integrated bioinformatics analyses

Clostridioides difficile toxin B (TcdB) plays a critical role in C. difficile infection (CDI), a common and costly healthcare-associated disease. The aim of the current study was to explore the intracellular and potent systemic effects of TcdB on human colon epithelial cells utilizing Gene Expression Omnibus and bioinformatic methods. Two datasets (GSE63880 and GSE29008) were collected to extract data components of mRNA of TcdB-treated human colon epithelial cells; "limma" package of "R" software was used to screen the differential genes, and "pheatmap" package was applied to construct heat maps for the differential genes; Metascape website was utilized for protein-protein interaction network and Molecular Complex Detection analysis, and Genome Ontology (GO) was used to analyze the selected differential genes. Quantitative real-time PCR (qRT-PCR) and Western blot were performed to validate the expression of hub genes. GO terms involved in DNA replication and cell cycle were identified significantly enriched in TcdB-treated human colon epithelial cells. Moreover, the decreased expression of DNA replication-related genes, MCM complex, and CDC45 in C. difficile (TcdA-/TcdB+)-infected Caco-2 cells were validated via qRT-PCR and Western blot assays. In conclusion, the integrated analysis of different gene expression datasets allowed us to identify a set of genes and GO terms underlying the mechanisms of CDI induced by TcdB. It would aid in understanding of the molecular mechanisms underlying TcdB-exposed colon epithelial cells and provide the basis for developing diagnosis biomarkers, treatment, and prevention strategies.

Genes associated with inflammation may serve as biomarkers for the diagnosis of coronary artery disease and ischaemic stroke

BackgroundThe current research aimed to expound the genes and pathways that are involved in coronary artery disease (CAD) and ischaemic stroke (IS) and the related mechanisms.MethodsTwo array CAD datasets of (GSE66360 and GSE97320) and an array IS dataset (GSE22255) were downloaded. Differentially expressed genes (DEGs) were identified using the limma package. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) (version 6.8; david.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the DEGs. A protein-protein interaction (PPI) network was constructed by Cytoscape software, and then Molecular Complex Detection (MCODE) analysis was used to screen for hub genes. The hub genes were also confirmed by RT-qPCR and unconditional logistic regression analysis in our CAD and IS patients.ResultsA total of 20 common DEGs (all upregulated) were identified between the CAD/IS and control groups. Eleven molecular functions, 3 cellular components, and 49 biological processes were confirmed by GO enrichment analysis, and the 20 common upregulated DEGs were enriched in 21 KEGG pathways. A PPI network including 24 nodes and 68 edges was constructed with the STRING online tool. After MCODE analysis, the top 5 high degree genes, including Jun proto-oncogene (JUN, degree = 9), C-X-C motif chemokine ligand 8 (CXCL8, degree = 9), tumour necrosis factor (TNF, degree = 9), suppressor of cytokine signalling 3 (SOCS3, degree = 8) and TNF alpha induced protein 3 (TNFAIP3, degree = 8) were noted. RT-qPCR results demonstrated that the expression levels of CXCL8 were increased in IS patients than in normal participants and the expression levels of SOCS3, TNF and TNFAIP were higher in CAD/IS patients than in normal participants. Meanwhile, unconditional logistic regression analysis revealed that the incidence of CAD or IS was positively correlated with the CXCL8, SOCS3, TNF and TNFAIP3.ConclusionsThe CXCL8, TNF, SOCS3 and TNFAIP3 associated with inflammation may serve as biomarkers for the diagnosis of CAD or IS. The possible mechanisms may involve the Toll-like receptor, TNF, NF-kappa B, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathways.