Genes In Protein-protein Interaction Network Research Articles

Reconstruction of dynamic protein-protein interaction network via graph convolutional network

Traditional studies are mostly based on static protein network models. The highly averaged and idealized static protein network does not represent well this dynamic nature that encompasses different time, place and reaction conditions. Therefore, we propose PreDPPI, a method for convolving dynamic protein-protein interaction networks (PINs) using knowledge graph reconstruction of relational graphs, which achieves better performance in terms of dynamics compared to other methods. We found that the longer the length of timestamps used in reconstructing dynamic PINs, the better the prediction. In addition, we identified three hub genes (NUSAP1, SCG3, and CKAP2L) in dynamic PINs that were shown to be significantly associated with glioma prognosis. In conclusion, this work reconstructed a web-based knowledge graph convolving dynamic PINs and identified three hub genes in the GEO glioma dataset that were confirmed to be highly associated with glioma prognosis.

The Features of Shared Genes among Transcriptomes Probed in Atopic Dermatitis, Psoriasis, and Inflammatory Acne: S100A9 Selection as the Target Gene.

Atopic dermatitis (AD), psoriasis (PS), and inflammatory acne (IA) are well-known as inflammatory skin diseases. Studies of the transcriptome with altered expression levels have reported a large number of dysregulated genes and gene clusters, particularly those involved in inflammatory skin diseases. To identify genes commonly shared in AD, PS, and IA that are potential therapeutic targets, we have identified consistently dysregulated genes and disease modules that overlap with AD, PS, and IA. Microarray data from AD, PS, and IA patients were downloaded from Gene Expression Omnibus (GEO), and identification of differentially expressed genes from microarrays of AD, PS, and IA was conducted. Subsequently, gene ontology and gene set enrichment analysis, detection of disease modules with known disease-associated genes, construction of the protein-protein interaction (PPI) network, and PPI sub-mapping analysis of shared genes were performed. Finally, the computational docking simulations between the selected target gene and inhibitors were conducted. We identified 50 shared genes (36 up-regulated and 14 down-regulated) and disease modules for each disease. Among the shared genes, 20 common genes in PPI network were detected such as LCK, DLGAP5, SELL, CEP55, CDC20, RRM2, S100A7, S100A9, MCM10, AURKA, CCNB1, CHEK1, BTC, IL1F7, AGTR1, HABP4, SERPINB13, RPS6KA4, GZMB, and TRIP13. Finally, S100A9 was selected as the target gene for therapeutics. Docking simulations between S100A9 and known inhibitors indicated several key binding residues, and based on this result, we suggested several cannabinoids such as WIN-55212-2, JZL184, GP1a, Nabilone, Ajulemic acid, and JWH-122 could be potential candidates for a clinical study for AD, PS, and IA via inhibition of S100A9-related pathway. Overall, our approach may become an effective strategy for discovering new disease candidate genes for inflammatory skin diseases with a reevaluation of clinical data.

Bioinformatic and systems biology approach revealing the shared genes and molecular mechanisms between COVID-19 and non-alcoholic hepatitis.

Introduction: Coronavirus disease 2019 (COVID-19) has become a global pandemic and poses a serious threat to human health. Many studies have shown that pre-existing nonalcoholic steatohepatitis (NASH) can worsen the clinical symptoms in patients suffering from COVID-19. However, the potential molecular mechanisms between NASH and COVID-19 remain unclear. To this end, key molecules and pathways between COVID-19 and NASH were herein explored by bioinformatic analysis. Methods: The common differentially expressed genes (DEGs) between NASH and COVID-19 were obtained by differential gene analysis. Enrichment analysis and protein-protein interaction (PPI) network analysis were carried out using the obtained common DEGs. The key modules and hub genes in PPI network were obtained by using the plug-in of Cytoscape software. Subsequently, the hub genes were verified using datasets of NASH (GSE180882) and COVID-19 (GSE150316), and further evaluated by principal component analysis (PCA) and receiver operating characteristic (ROC). Finally, the verified hub genes were analyzed by single-sample gene set enrichment analysis (ssGSEA) and NetworkAnalyst was used for the analysis of transcription factor (TF)-gene interactions, TF-microRNAs (miRNA) coregulatory network, and Protein-chemical Interactions. Results: A total of 120 DEGs between NASH and COVID-19 datasets were obtained, and the PPI network was constructed. Two key modules were obtained via the PPI network, and enrichment analysis of the key modules revealed the common association between NASH and COVID-19. In total, 16 hub genes were obtained by five algorithms, and six of them, namely, Kruppel-like factor 6 (KLF6), early growth response 1 (EGR1), growth arrest and DNA-damage-inducible 45 beta (GADD45B), JUNB, FOS, and FOS-like antigen 1 (FOSL1) were confirmed to be closely related to NASH and COVID-19. Finally, the relationship between hub genes and related pathways was analyzed, and the interaction network of six hub genes was constructed with TFs, miRNAs, and compounds. Conclusion: This study identified six hub genes related to COVID-19 and NASH, providing a new perspective for disease diagnosis and drug development.

Identification of key genes and immune infiltration based on weighted gene co-expression network analysis in vestibular schwannoma.

Vestibular schwannomas are the most common tumors of the cerebellopontine angle, but their pathogenesis is still unclear. This study aimed to explore the molecular mechanisms and potential therapeutic target biomarkers in vestibular schwannoma. Two datasets (GSE141801 and GSE54934) were downloaded from the Gene Expression Omnibus database. Weighted gene coexpression network analysis was performed to find the key modules associated with vestibular schwannoma (VS). Functional enrichment analysis was applied to evaluate the gene enrichment signaling pathway in key modules. Protein-protein interaction networks in key modules were constructed using the STRING website. Hub genes were identified by intersecting candidate hub genes in protein-protein interaction network and candidate hub genes in key modules. Single-sample gene set enrichment analysis was utilized to quantify the abundance of tumor-infiltrating immune cells in VSs and normal control nerves. A Random forest classifier was developed based on hub genes identified in this study and validated on an independent dataset (GSE108524). Results of immune cell infiltration were also validated on GSE108524 by gene set enrichment analysis. Eight genes from coexpression modules were identified as hub genes, that is, CCND1, CAV1, GLI1, SOX9, LY86, TLR3, TREM2, and C3AR1, which might be potential therapeutic targets for VS. We also found that there were distinct differences in the infiltration levels of immune cells between VSs and normal control nerves. Overall, our findings may be useful for investigating the mechanisms underlying VS and provide noteworthy directions for future research.

Identification of hub genes related to Duchenne muscular dystrophy by weighted gene co-expression network analysis

The study was aimed to analyze the potential gene modules and hub genes of Duchenne muscular dystrophy (DMD) by weighted gene co-expression network analysis. Based on the muscular dystrophy tissue expression profiling microarray GSE13608 from gene expression omnibus, gene co-expression modules were analyzed using weighted gene co-expression network analysis, gene modules related to DMD were screened, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were performed, and signature genes in the modules were screened. The protein-protein interaction network was constructed through Cytoscape, and hub genes were identified. The expression of hub genes in DMD versus normal muscle tissue was calculated in GSE6011. 12 co-expressed gene modules were identified, among which black module was significantly related to DMD. The characteristic genes in the module were enriched in the regulation of immune effector processes, immune response mediated by immunoglobulin, immune response mediated by B cells, etc. SERPING1, F13A1, C1S, C1R, and HLA-DPA1 were considered as hub genes in protein-protein interaction network. Analysis of GSE6011 shows that expression of SERPING1, F13A1, C1S, C1R, and HLA-DPA1 in tissues of DMD patients were higher than normal. SERPING1, F13A1, C1S, C1R, and HLA-DPA1 may participate in the development of DMD by regulating innate immunity and inflammation, and they are expected to be a potential biomarker and novel therapeutic targets for DMD.

Network pharmacology and molecular docking combined to investigate the mechanism of duck‐derived active peptides in preventing hypertension

SummaryThis study investigated the potential target and mechanism of duck‐derived active peptides in preventing hypertension through network pharmacology and molecular docking. Food‐derived active peptides proved to have potent anti‐hypertension activity, but the underlying mechanisms remain unclear. ‘Active peptide‐target’ and protein–protein interaction (PPI) networks were constructed, followed by GO and KEGG enrichment analyses. The top five genes in PPI network were selected as target genes, and associated peptides and targets were subjected to molecular docking and molecular dynamics simulations. Forty‐four active peptides were obtained, and 143 targets were uncovered, twenty‐two of which were associated with hypertension. In PPI network, ACE, MMP2, REN, NOS3 and VCAM1 were main potential targets. Moreover, GO and KEGG enrichment analyses documented that function involved in modulation of blood pressure, endocrine and renin–angiotensin cycle, including impact on membrane raft, membrane microdomain, membrane region and even G protein‐coupled peptide receptor activity, peptide receptor activity, endopeptidase activity and calcium signalling pathway. Molecular docking and molecular dynamics simulations proved that the interaction between active peptides and targets depended on hydrogen. IPIIDYEVSITLGDPEALRDLLGEWVPWQ, AEDEEEINAELTAK, LLCVAV, IQLESVAVNVTIQDFVADVESKLF and GGPDF exhibited the strongest binding ability with ACE, MMP2, NOS3, REN and VCAM1. These findings provided potential key targets for hypertension treatment by duck‐derived active peptides.

Study on the Mechanism of Radix Astragali against Renal Aging Based on Network Pharmacology.

Radix Astragali is widely used in the traditional Chinese medicine with the effect of antiaging. The purpose of this study is to explore the main active ingredients and targets of Radix Astragali against renal aging by network pharmacology and further to verify the mechanism of the main active ingredients in vitro. TCMSP, ETCM, and TCMID databases were used to screen active ingredients of Radix Astragali. Targets of active ingredients were predicted using BATMAN-TCM and cross validated using kidney aging-related genes obtained from GeneCards and NCBI database. Pathways enrichment and protein-protein interaction (PPI) analysis were performed on core targets. Additionally, a pharmacological network was constructed based on the active ingredients-targets-pathways. HK-2 cell was treated with D-galactose to generate a cell model of senescence. CCK-8 and β-galactosidase were used to detect the effect of Radix Astragali active components on cell proliferation and aging. ELISA was used to detect the expression of senescence-associated secreted protein (TGF-β and IL-6) in the cell culture supernatant. Western blot was used to detect the expression of key proteins in the SIRT1/p53 pathway. Five active ingredients (Astragaloside I, II, III, IV and choline) were identified from Radix Astragali, and all these active ingredients target a total of 128 genes. Enrichment analysis showed these genes were implicated in 153 KEGG pathways, including the p53, FoxO, and AMPK pathway. 117 proteins and 572 interactions were found in PPI network. TP53 and SIRT1 were two hub genes in PPI network, which interacted with each other. The pharmacological network showed that the five main active ingredients target on some coincident genes, including TP53 and SIRT1. These targeted genes were involved in the p53, FoxO, and AMPK pathway. Proliferation of HK-2 cells was increased by Astragaloside IV treatment compared with that of the D-Gal treatment group. However, the proliferation of the SA-β-gal positive cells were inhibited. The expression of TGF-β and IL-6 in the D-Gal group was higher than that in the normal group, and the treatment of Astragaloside IV could significantly reduce the expression of TGF-β and IL-6. The expression of SIRT1 in the Astragaloside IV group was higher than that in the D-Gal group. However, the expression of p53 and p21 was less in the Astragaloside IV group than that in the D-Gal group. This study suggested that Astragaloside IV is an important active ingredient of Radix Astragali in the treatment of kidney aging via the SITR1-p53 pathway.

Curcumin combined with arsenic trioxide in the treatment of acute myeloid leukemia: network pharmacology analysis and experimental validation.

This study aimed to evaluate the effects of curcumin by co-administration of arsenic trioxide (As2O3) in acute myeloid leukemia (AML) treatment, using network pharmacology and experimental validation. Using Pubchem database, Traditional Chinese Medicine Information Database (TCMID) database, and Swiss target prediction database to predict compound-related targets, AML-associated targets were determined using GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. We identify overlapping common targets by comparing Compounds-related and AML-associated targets and using these targets to perform GO and KEGG functional enrichment analyses. Subsequently, these targets were input into the STRING database, and we used Cytoscape to construct protein-protein interaction (PPI) network. Finally, we used KG1-a cells and the AML mouse model to measure the anti-leukemia effects of curcumin and As2O3 and their combination. Compounds and targets screening hinted that 85 intersection targets were predicted in the curcumin treatment of AML, 75 targets in the As2O3 treatment of AML, and 48 targets in the curcumin combined with the As2O3 treatment of AML. GO and KEGG analyses indicated that the top 10 enriched biological processes and top 20 pathways implicated in the therapeutic effects of curcumin and As2O3 on AML, respectively. In addition, network pharmacology screening revealed STAT3, TP53, EP300, MAPK1, and PIK3CA as the top five genes in PPI network of curcumin treatment of AML and TP53, MAPK3, MAPK1, STAT3, and SRC as the top five genes in PPI network of As2O3 treatment of AML. Moreover, the in vitro experiment demonstrated that curcumin combined with As2O3 inhibited proliferation and induced apoptosis in KG1-a cells, and this effect is more substantial than curcumin or As2O3 alone. Mechanistically, the curcumin combined with As2O3 significantly down-regulated the protein expression of JAK2, STAT3, and Bcl-2, and up-regulated the levels of P53, P27, and Bax. In the mouse model, the survival time of mice in each administration group was drawn out to varying degrees, with the most significant prolongation in the curcumin combined with the As2O3 group. Our results suggested that curcumin and As2O3 combination therapy exerts more significant anti-leukemia effects in the treatment of AML than curcumin or As2O3 monotherapy by up-regulating p53 pathway and down-regulating the JAK2/STAT3 pathway.

Comprehensive Analysis of the Expression and Prognosis for Lipid Metabolism-Related Genes in Hepatocellular Carcinoma.

Hao DingBackground This study aimed to screen potential key genes associated with lipid metabolism and to evaluate their expressions and prognosis values in hepatocellular carcinoma (HCC). Methods Data sets GSE6764, GSE14520, and GSE112790 were used to identify the common differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed by STRING database. Hub genes in PPI network were identified and subjected to functional enrichment analysis to screen lipid metabolism-related genes. The expressions of selected genes and their associations with prognosis were analyzed using UALCAN, The Human Protein Atlas, and Kaplan-Meier plotter databases. The transcriptional factor (TF)-gene regulatory network was constructed using NetworkAnalyst. Results A total of 331 common DEGs including 106 upregulated and 225 downregulated genes were identified. PPI network analysis showed that 76 genes with high degrees were identified as hub genes, among which 14 genes were lipid metabolism-related genes. PON1, CYP2C9, and SPP1 were found to be the independent prognostic markers. Key TFs with close interactions with these prognostic genes, including HINFP, SRF, YY1, and NR3C1, were identified from the TF-gene regulatory network. Conclusion This study presented evidence for the prognostic capabilities of lipid metabolism-related genes in HCC, and newly identified HINFP and NR3C1 as potential biomarkers for HCC.

IL2RA is a prognostic indicator and correlated with immune characteristics of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and incurable cancer with a dismal prognosis. In this study, we aimed to explore potential predictors for the prognosis and immunological characteristics of PDAC.Estimation of stromal and immune cells in malignant tumors, using expression data (ESTIMATE) method was applied to calculate the immune and stromal scores of 206 PDAC samples from GSE71729. R package of “limma” was utilized to identify differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were conducted for functional exploration. Protein-protein interaction (PPI) network and Univariate Cox analysis were conducted to select key prognostic genes of PDAC. Gene set enrichment analysis (GSEA) was applied to investigate the roles of IL2RA in PDAC. Single sample GSEA (ssGSEA) was performed to evaluate the immunological characteristics of PDAC samples. Wilcoxon rank sum test was conducted to compare the difference of immunological characteristics of PDAC samples between low IL2RA and high IL2RA. Spearman correlation analysis was used to explore the correlations of IL2RA expression and immune checkpoint genes.A total of 747 DEGs were identified between low and high immune/stromal groups. Functional exploration revealed upregulated DEGs were associated with immune-related activities, whereas downregulated DEGs were involved in inflammatory-related activities. IL2RA was selected as the critical gene by overlapping the hub genes in PPI network and prognostic genes. Significantly, IL2RA expression was significantly elevated in PDAC and patients with higher IL2RA expression had worse prognoses. The immunological and oncogenic roles of IL2RA in PDAC were evidenced by GSEA. Furthermore, PDAC samples with high IL2RA expression exhibited increased immune infiltration and better immunotherapy responses. IL2RA expression was positively correlated with PDCD1, CD274, CTLA4, IDO1, TDO2, and TIGT.Higher expression of IL2RA predicts worse survival outcomes and increased immune infiltration in PDAC. PDAC patients with high IL2RA expression might potentially benefit from immunotherapy.

Investigation on Potential Biomarkers and Immune-Related Mechanisms of Ischemic Stroke

This study aimed to reveal the detailed immune-related mechanisms underlying ischemic stroke (IS) and identify new immune-associated biomarkers for clinical management. Differentially expressed genes (DEGs) between IS samples and normal controls were identified using the GSE16561 dataset. The feature genes of the immune cells were investigated using the GSE72642 dataset. Weighted correlation network analysis (WGCNA) was performed to reveal module genes, followed by an investigation of common DEGs and a functional enrichment analysis. Potential biomarkers were identified based on hub genes in protein-protein interaction networks and WGCNA. Finally, GSE158312 was used for biomarker verification. In total, 1230 DEGs were identified between the IS samples and normal controls. Seven clinically significant modules were identified using WGCNA. The yellow module genes were positively correlated with polymorphonuclear cells (PMNC), whereas the brown module genes were positively correlated with CD4+ T cells. Eight genes were selected as hub genes. These genes are mainly involved in functions such as the innate immune response. Upregulated TLR2 and ARG1 levels were significantly different between the two groups in the verification dataset. Our findings suggest ARG1 and TLR2 as novel biomarkers for IS. Upregulated TLR2 might play a role in IS development by participating in the innate immune response function.

Microarray meta-analysis reveals IL6 and p38β/MAPK11 as potential targets of hsa-miR-124 in endothelial progenitor cells: Implications for stent re-endothelization in diabetic patients.

Circulating endothelial progenitor cells (EPCs) play an important role in the repair processes of damaged vessels, favoring re-endothelization of stented vessels to minimize restenosis. EPCs number and function is diminished in patients with type 2 diabetes, a known risk factor for restenosis. Considering the impact of EPCs in vascular injury repair, we conducted a meta-analysis of microarray to assess the transcriptomic profile and determine target genes during the differentiation process of EPCs into mature ECs. Five microarray datasets, including 13 EPC and 12 EC samples were analyzed, using the online tool ExpressAnalyst. Differentially expressed genes (DEGs) analysis was done by Limma method, with an | log2FC| > 1 and FDR < 0.05. Combined p-value by Fisher exact method was computed for the intersection of datasets. There were 3,267 DEGs, 1,539 up-regulated and 1,728 down-regulated in EPCs, with 407 common DEGs in at least four datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed enrichment for terms related to “AGE-RAGE signaling pathway in diabetic complications.” Intersection of common DEGs, KEGG pathways genes and genes in protein-protein interaction network (PPI) identified four key genes, two up-regulated (IL1B and STAT5A) and two down-regulated (IL6 and MAPK11). MicroRNA enrichment analysis of common DEGs depicted five hub microRNA targeting 175 DEGs, including STAT5A, IL6 and MAPK11, with hsa-miR-124 as common regulator. This group of genes and microRNAs could serve as biomarkers of EPCs differentiation during coronary stenting as well as potential therapeutic targets to improve stent re-endothelization, especially in diabetic patients.

M6Acancer-Net: Identification of m6A-mediated cancer driver genes from gene-site heterogeneous network.

N6-methyladenosine (m6A) is the most abundant eukaryotic modification internal mRNA, which plays the crucial roles in the occurrence and development of cancer. However, current knowledge about m6A-mediated functional circuit and key genes targeted by m6A methylation in cancer is mostly elusive. Thus, here we proposed a novel network-based approach (called m6Acancer-Net) to identify m6A-mediated driver genes and their associated network in specific type of cancer, such as acute myeloid leukemia. m6A-mediated cancer driver genes are defined as genes mediated by m6A methylation, significantly mutated, and functionally interacted in cancer. m6Acancer-Net identified the m6A-mediated cancer driver genes by combining gene functional interaction network with RNA methylation, gene expression and mutation information. A cancer-specific gene-site heterogeneous network was firstly constructed by connecting the m6A site co-methylation network with the functional interaction pruned gene co-expression network generated from large scale gene expression profile of specific cancer. Then, the functional m6A-mediated genes were identified by selecting the m6A regulators as seed genes to perform the random walk with restart algorithm on the gene-site heterogeneous network. Finally, m6A-mediated cancer driver gene subnetworks were constructed by performing the heat diffusion of mutation frequency for functional m6A-mediated genes in protein-protein interaction networks. The experimental results of m6Acancer-Net on the acute myeloid leukemia (AML) and glioblastoma multiforme (GBM) data from TCGA project show that the m6A-mediated caner driver genes identified by m6Acancer-Net are targeted by m6A regulators, and mediate significant cancer-related pathways. They play crucial roles in development and prognostic stratification of cancer. Moreover, 15 m6A-mediated cancer driver genes identified in AML are validated by literatures to mediate AML progress, and 14 m6A-mediated cancer driver genes identified in GBM are validated by literatures to participate in development of GBM. m6Acancer-Net is reliable to identify the functionally significant m6A-mediated driver genes in specific cancer, and it can effectively facilitate the understanding of regulatory and therapeutic mechanism of cancer driver genes in epitranscriptome layer.

Identification of hub genes predicting the development of prostate cancer from benign prostate hyperplasia and analyzing their clinical value in prostate cancer by bioinformatic analysis

Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in males. Studies showed that genetic factors are responsible for the occurrences of both diseases. However, the genetic association between them is still unclear. Gene Expression Omnibus (GEO) database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were utilized to find pathways DEGs enriched. The STRING database can provide a protein–protein interaction (PPI) network, and find hub genes in PPI network. R software was used to analyze the clinical value of hub genes in PCa. Finally, the function of these hub genes was tested in different databases, clinical samples, and PCa cells. Fifteen up-regulated and forty-five down-regulated genes were found from GEO database. Seven hub genes were found in PPI network. The expression and clinical value of hub genes were analyzed by The Cancer Genome Atlas (TCGA) data. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Exclude CXCR4, other hub genes have diagnostic value in predicting PCa and their mutations can cause PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect disease-free survival (DFS). Same results reflected in different databases. The expression and function of MYC, MYL9, and SNAI2, were validated in clinical samples and PCa cells. In conclusion, seven hub genes among sixty DEGs may be achievable targets for predicting which BPH patients may later develop PCa and they can influence the progression of PCa.

Pathway enrichment and network analysis of differentially expressed genes in pashmina goat

Gene expression, pathway analysis and cellular processes in Pashmina goat helps to understand the dynamics and production mechanisms of its long-fibre and in propagating its valued traits to other goat breeds. We obtained 8418 DEGs (p-value cutoff for FDR < 0.001 and log2FC > 2) from our previous differential expression analysis of Pashmina and Barbari goats. Here, out of the aforementioned total, 152 up-regulated (TMM ≥ 100) and 919 down-regulated genes (TMM 1 < 0) were searched for key GO enriched terms and pathways with the Benjamini p-value <0.05. These were further mapped to STRING database for acquiring protein-protein interaction (PPI) networks. The enriched biological processes with significant Padj values comprise skin development, keratinocyte differentiation and filament regulation. The molecular functions performed by down-regulated genes with highest Padj values are protein binding and GTPase regulator activity while others are present in microtubule cytoskeleton thus providing the structural or functional support. The adhesion molecules, calcium-binding genes, KRTs, KRTAPs, and collagen molecules exhibited maximum interactions that all serve as key regulators in hair growth. The maximum interaction exhibited by down-regulated genes in PPI network is involved in binding of ions, microtubule organization, development and replication processes. Implication of this whole computational framework makes it feasible to highlight the relevant genes and their functional interactive networks. The highly enriched terms may further be explored with inclusion of more samples for improving the quality of the analysis and how genetics, breeding, feeding and even husbandry practices may modulate these pathways for better productivity.

Integrative analyses of biomarkers and pathways for heart failure

BackgroundHeart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated.MethodsThe microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF.ResultsWe identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine/threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules.ConclusionsThe enrichment pathway of DEGs and GO may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

Key factors and potential drug combinations of nonalcoholic steatohepatitis: Bioinformatic analysis and experimental validation-based study

BackgroundNonalcoholic fatty liver disease and its advanced stage, nonalcoholic steatohepatitis (NASH), are the major cause of hepatocellular carcinoma (HCC) and other end-stage liver disease. However, the potential mechanism and therapeutic strategies have not been clarified. This study aimed to identify potential roles of miRNA/mRNA axis in the pathogenesis and drug combinations in the treatment of NASH. MethodsMicroarray GSE59045 and GSE48452 were downloaded from the Gene Expression Omnibus and analyzed using R. Then we obtained differentially expressed genes (DE-genes). DAVID database was used for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis. Protein-protein interaction (PPI) networks were used for the identification of hub genes. We found upstream regulators of hub genes using miRTarBase. The expression and correlation of key miRNA and its targets were detected by qPCR. Drug Pair Seeker was employed to predict drug combinations against NASH. The expression of miRNA and hub genes in HCC was identified in the Cancer Genome Atlas database and Human Protein Atlas database. ResultsNinety-four DE-genes were accessed. GO and KEGG analysis showed that these predicted genes were linked to lipid metabolism. Eleven genes were identified as hub genes in PPI networks, and they were highly expressed in cells with vigorous lipid metabolism. hsa-miR-335-5p was the upstream regulator of 9 genes in the 11 hub genes, and it was identified as a key miRNA. The hub genes were highly expressed in NASH models, while hsa-miR-335-5p was lowly expressed. The correlation of miRNA-mRNA was established by qPCR. Functional verification indicated that hsa-miR-335-5p had inhibitory effect on the development of NASH. Finally, drug combinations were predicted and the expression of miRNA and hub genes in HCC was identified. ConclusionsIn the study, potential miRNA-mRNA pathways related to NASH were identified. Targeting these pathways may be novel strategies against NASH.

Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis.

Background Osteosarcoma (OS) is the most primary malignant bone cancer in children and adolescents with a high mortality rate. This work aims to screen novel potential gene signatures associated with OS by integrated microarray analysis of the Gene Expression Omnibus (GEO) database.Material and MethodsThe OS microarray datasets were searched and downloaded from GEO database to identify differentially expressed genes (DEGs) between OS and normal samples. Afterwards, the functional enrichment analysis, protein–protein interaction (PPI) network analysis and transcription factor (TF)-target gene regulatory network were applied to uncover the biological function of DEGs. Finally, two published OS datasets (GSE39262 and GSE126209) were obtained from GEO database for evaluating the expression level and diagnostic values of key genes.Results In total 1,059 DEGs (569 up-regulated DEGs and 490 down-regulated DEGs) between OS and normal samples were screened. Functional analysis showed that these DEGs were markedly enriched in 214 GO terms and 54 KEGG pathways such as pathways in cancer. Five genes (CAMP, METTL7A, TCN1, LTF and CXCL12) acted as hub genes in PPI network. Besides, METTL7A, CYP4F3, TCN1, LTF and NETO2 were key genes in TF-gene network. Moreover, Pax-6 regulated four key genes (TCN1, CYP4F3, NETO2 and CXCL12). The expression levels of four genes (METTL7A, TCN1, CXCL12 and NETO2) in GSE39262 set were consistent with our integration analysis. The expression levels of two genes (CXCL12 and NETO2) in GSE126209 set were consistent with our integration analysis. ROC analysis of GSE39262 set revealed that CYP4F3, CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients. ROC analysis of GSE126209 set revealed that CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients.

Molecular mechanisms, immune cell infiltration, and potential drugs for prostate cancer.

The molecular mechanisms involved in the prostate cancer and their relationship with immune cell infiltration are not fully understood. The prostate cancer patients undergoing standard androgen deprivation therapy eventually develop castration resistant prostate cancer (CRPC) for which there is no effective treatment currently available, and the hub genes involved in this process remain unclear. To study prostate cancer systematically and comprehensively. Differentially expressed genes (DEGs) of prostate cancer were screened in The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Connectivity Map (Cmap) software was applied to discover potential treatment drugs. A protein-protein interaction (PPI) analysis was performed to obtained the hub genes, and the relationship between hub genes and immune cell infiltration was investigated. Next, RNAseq data of hormone-sensitive prostate cancer samples and CRPC samples obtained from TCGA database was further analyzed to identify DEGs. Finally, a PPI analysis was performed to obtain the hub genes. A total of 319 DEGs were identified between prostate cancer samples and normal adjacent samples from TCGA database using comparative analysis. The KEGG pathway analysis showed significant correlations with drug metabolism, metabolism of xenobiotics by cytochrome P450, and chemical carcinogenesis. AMACR, FOLH1 and NPY, three hub genes, were found to be upregulated. FOLH1 was positively correlated with CD8+ T cell infiltration. FOLH1, AMACR, and NPY were negatively correlated with CD4+ T cell infiltration. A total of 426 DEGs were identified from RNAseq data of hormone-sensitive prostate cancer samples and CRPC samples using further comparative analysis. KEGG pathway enrichment analysis showed significant correlations with arachidonic acid metabolism, PPAR signaling pathway, AMPK signaling pathway, and metabolic pathways. The top 10 hub genes in PPI network were screened out, including PPARG, SREBF1, SCD, HMGCR, FASN, PTGS2, HMGCS2, SREBF2, FDFT1, and INSIG1. Among them, SCD and FASN are expected to be the potential therapeutic targets for CRPC. AMACR, FOLH1 and NPY may be effective therapeutic targets and specific diagnostic markers for prostate cancer. AMACR, FOLH1, and NPY are also closely associated with immune cell infiltration in prostate cancer. Moreover, aminoglutethimide and resveratrol were found to be the promising drugs for treating prostate cancer. The progression of hormone-sensitive prostate cancer to CRPC may be related to arachidonic acid metabolism, PPAR signaling pathway, AMPK signaling pathway, and other metabolic pathways. SCD and FASN are expected to be the potential therapeutic targets for CRPC.

Identification of TNIK as a novel potential drug target in thyroid cancer based on protein druggability prediction.

Thyroid cancer is a common endocrine malignancy; however, surgery remains its primary treatment option. A novel targeted drug for the development and application of targeted therapy in thyroid cancer treatment remain underexplored.We obtained RNA sequence data of thyroid cancer from The Cancer Genome Atlas database and identified differentially expressed genes (DEGs). Then, we constructed co-expression network with DEGs and combined it with differentially methylation analysis to screen the key genes in thyroid cancer. PockDrug-Server, an online tool, was applied to predict the druggability of the key genes. Finally, we constructed protein-protein interaction (PPI) network to observe potential targeted drugs for thyroid cancer.We identified 3 genes correlated with altered DNA methylation level and oncogenesis of thyroid cancer. According to the druggable analysis and PPI network, we predicted TRAF2 and NCK-interacting protein kinase (TNIK) sever as the drug targeted for thyroid cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that genes in protein-protein interaction network of TNIK enriched in mitogen-activated protein kinase signaling pathway. For drug repositioning, we identified a targeted drug of genes in PPI network.Our study provides a bioinformatics method for screening drug targets and provides a theoretical basis for thyroid cancer targeted therapy.