جستجوی مقالات مرتبط با کلیدواژه « protein-protein interaction network » در نشریات گروه « پزشکی »

 Lung transplantation is a promising  therapy for patients with end-stage lung disease. Pulmonary surfactant is a lipid and protein complex which has  a key role in lung function. Molecular mechanisms mediating in rejection of lung transplantation related to surfactants are not still comprehensively understood. In this study, we applied bioinformatics approaches to identify genes and molecular mechanisms involved in surfactant function in rejection of lung transplantation.

 At first, transcriptomics data was extracted and analyzed to construct the protein-protein interaction network and gene regulatory network using Cytoscape. Then, networks analysis were performed to determine hubs, bottlenecks, clusters, and regulatory motifs to identify critical genes and molecular mechanisms involve in surfactant function in rejection of lung transplantation. Finally, critical genes selected for repuposing drugs.

 Analyzing the constructed PPIN and GRN identified SCD, FN1, ICAM1, ITGB8, FOXC1, SIX1, FHL2, KRT5, TFAP2A, GAS5, MALAT1, and lnrCXCR4 as critical genes. Enrichment analysis showed the genes are enriched for pulmonary surfactant metabolism dysfunction, defective CSF2RB causes pulmonary surfactant metabolism dysfunction 4 and 5, Interleukin-4 and Interleukin-13 signaling  may be the mechanisms for surfactant function in rejection of lung transplantation. We predicted some candidate drugs for preventing of lung transplantation rejection such as Sunitinib, Gemcitabine, Oxaliplatin, Hyaluronic acid, … .

 Following our model validation using the existing experimental data, our model suggested critical molecules and candidate medicines involve in  surfactant function in rejection of lung transplantation for furtur investigations.

 Renal transplantation plays an essential role in the quality of life of patients with end-stage renal disease. At least 12% of the renal patients receiving transplantations show graft rejection. One of the methods used to diagnose renal transplantation rejection is renal allograft biopsy. This procedure is associated with some risks such as bleeding and arteriovenous fistula formation. In this study, we applied a bioinformatics approach to identify serum markers for graft rejection in patients receiving a renal transplantation.

 Transcriptomic data were first retrieved from the blood of renal transplantation rejection patients using the GEO database. The data were then used to construct the protein-protein interaction and gene regulatory networks using Cytoscape software. Next, network analysis was performed to identify hub-bottlenecks, and key blood markers involved in renal graft rejection. Lastly, the gene ontology and functional pathways related to hub-bottlenecks were detected using PANTHER and DAVID servers.

 In PPIN and GRN, SYNCRIP, SQSTM1, GRAMD1A, FAM104A, ND2, TPGS2, ZNF652, RORA, and MALAT1 were the identified critical genes. In GRN, miR-155, miR17, miR146b, miR-200 family, and GATA2 were the factors that regulated critical genes. The MAPK, neurotrophin, and TNF signaling pathways, IL-17, and human cytomegalovirus infection, human papillomavirus infection, and shigellosis were identified as significant pathways involved in graft rejection.

 The above-mentioned genes can be used as diagnostic and therapeutic serum markers of transplantation rejection in renal patients.  The newly predicted biomarkers and pathways require further studies.

Tooth decay (TD) is a multifactorial disorder, and several factors are involved in its etiology.

The present study aimed to unravel the main genes and molecular mechanisms underlying TD.

The dataset GSE1629 in the Gene Expression Omnibus (GEO) database was analyzed to uncover differentially expressed genes (DEGs) in patients with TD compared to patients with sound teeth. A protein-protein interaction network was built, and the most important clusters, hub genes, transcription factors (TFs), and protein kinases involved in the regulation of TFs were disclosed. Signaling pathways and Gene Ontology terms dysregulated in TD were also identified.

A total of 196 DEGs were determined (false discovery rate<0.001; |Log2 fold change|>1). PTPRC, ITGB2, TYROBP, MMP9, CXCL8, CD44, CCL2, C1QB, C3, and SPP1 were considered hub genes. Further, BPTF and MAPK1 were demonstrated to be the highest TFs and protein kinases likely involved in the pathogenesis of TD, respectively.

PTPRC, ITGB2, TYROBP, MMP9, CXCL8, CD44, CCL2, C1QB, C3, SPP1, BPTF, and MAPK1 may be regarded as potential markers for the therapeutic purposes of TD.

Acute lymphoblastic leukemia (ALL) as the most common malignancy in children is associated with high mortality and significant relapse. Currently, the non-invasive diagnosis of pediatric ALL is a main challenge in the early detection of patients. In the present study, a systems biology approach was used through network-based analysis to identify the key candidate genes related to ALL development and relapse.

In this systems biology (experimental) study, main and validating datasets were retrieved from a gene expression omnibus (GEO). Gene expression analyses were done using a bioinformatics array research tool (BART) and ExAtlas. Gene ontology and pathway enrichment analysis were also performed via Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the Search Tool for the Retrieval of Interacting Genes (STRING) and cytoscape V.3.9.1 were used to network construction and analysis. The MCODE and NCMine Plugin of cytoscape were applied to find clusters and a functional module in the network. The Kaplan Myer curve was applied in order to survival analysis of the validated candidate genes. A P-value of < 0.05 was considered as significant.

A total of 671 differentially expressed genes (DEGs) mainly involved in transporter/channel activity functions, cell communication/signaling processes and fatty acid transport/PPAR signaling/eicosanoid metabolism pathways were identified (P-value < 0.05). The main cellular compartments were plasma membrane, cell periphery and cell surface (P-value <0.05). The network analysis revealed 68 hub genes, 29 of which were candidate genes. Five candidate genes were also validated in two independent experiments. These genes were considered as key candidate genes, and three of them (BCL2L11, IGF1, PDE5A) were predictors of pediatric ALL patients survival (P-value < 0.05). 

BCL2L11, IGF1 and PDE5A genes, as key candidate genes, are potentially good diagnostic biomarkers and therapeutic targets for pediatric ALL.

Uveal melanoma is the most prevalent melanoma in adults. This malignancy arises from melanocytes. Patients with uveal melanoma were investigated according to their metastases and non-metastases status to propose candidate drugs.

The studied data set included 63 patients (24 females and 39 males), among whom 35 patients were with metastases and 28 patients were without metastases. By comparing the metastases and non-metastases cases, 2000 genes were chosen as practical genes. A co-expression network was constructed, and protein complexes were extracted to find effective gene modules. Then drug-gene networks were constructed to find essential drugs.

The STRING database was utilized to construct a co-expression network. Then, protein complexes were extracted using the MCODE clustering algorithm. Four meaningful modules were chosen for the next step. Afterward, drug-gene subnetworks were constructed and illustrated, and high degree drugs were chosen as effective drugs for this disease.

As a result, 12 important drugs were introduced as the proposed candidate drugs. By detailed investigation of the obtained results, drugs that were not previously reported were introduced as new drugs, which would be studied experimentally in future works. These were Bortezomib, Oprozomib, Clofarabine, ME-344 and NV-128.

Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease worldwide. Left untreated, it can be a risk factor for developing cirrhosis or hepatocellular carcinoma (HCC). Although experts have made many efforts to find the underlying mechanisms of NAFLD, they remain a mystery.

This study aimed to distinguish common gene signatures and pathways in the human liver during NAFLD progression through systems biology.

In this study, the researchers selected three microarray datasets, GSE48452, GSE63067, and GSE89632, from the NCBI GEO database to explore differentially expressed genes (DEGs) among healthy controls, simple steatosis, and nonalcoholic steatohepatitis (NASH) patients. Furthermore, protein-protein interaction (PPI) networks and pathway enrichment analyses were used to detect common genes and biological pathways in different stages of NAFLD.

The current study included 45 healthy participants, 36 simple steatosis patients, and 46 NASH patients. Common genes for NAFLD progression were Chi3L1, ICAM1, MT1A, MT1H, ABCB11, ACOT1, CYP2C9, HSP90B1, and CPB2, which are involved in inflammation and oxidative stress pathways.

The present study investigated the shared vital genes and pathways between different stages of NAFLD, which may facilitate understanding NAFLD mechanisms and identifying potential therapeutic targets in this disease.

Gene expression profiling has high potential in the identification of diagnostic, predictive, and therapeutic gene targets in human cancers such as colorectal cancer (CRC).  Accordingly, in this study, an integrated systems biology analysis was done on several microarray datasets to identify key genes involved in CRC chemoresistance and also to differentiate peoples who benefit from chemotherapy. Subsequently, the findings were validated experimentally.  Datasets were retrieved from Gene Expression Omnibus (GEO). Gene expression analysis was performed using the ExAtlas software. Gene enrichment analysis was done using g: profiler. Protein-Protein Interaction Network (PPIN) was constructed in STRING and visualized/analyzed by Cytoscape 3.8.0. Significant modules were identified using the MCODE plugin in Cytoscape. The clinical importance of candidate genes was evaluated using ROC analysis and immunohistochemistry. Key candidate genes were validated using Real-Time  PCR.  According to findings, 26 datasets were selected. Gene expression analysis revealed 6463 Differentially Expressed Genes (DEGs), among which 4323 were unique and 2140 were related to overlapping DEGs between datasets. The overlapping DEGs with at least four shared datasets (n=217 DEGs) were selected for further analysis. Overlapping DEGs were mainly enriched in the cellular process of response to chemicals stimulus. Most selected DEGs were enriched in KEGG pathways of cancer Benzo(a)pyrene metabolism and glucocorticoid receptor signaling. Fourteen hub genes and two significant modules were identified. Six hub genes (candidate genes) were contributed in significant modules. Among candidate genes, LCN2, CXCL8, and EGR1 expression were significantly associated with chemotherapy response of CRC patients and chemosensitivity of CRC cell lines (P<0.05).  This study revealed three genes signature for predicting chemotherapy responsiveness and treatment decision-making in CRC patients and also for therapeutic purposes.

This paper investigates the effects of potential drugs on differentially expressed genes (DEGs) associated with substantial alterations in retinoblastoma malignancy.

The GSE125903 dataset consisting of ten samples was used in this study (seven cancer patients and three control samples). The genes were ordered according to their adjusted p value, and 2000 top differential expressed genes with adj p values less than 0.01 were chosen as statistically significant. The STRING database version 11.0 was used to display the interaction among genes. The Cytoscape3.8.2 and the Clusterviz plugin software were used to construct the modules for the PPI network, and five clusters of genes were formed. The DGIdb v4.2.0 database was used to study drug-gene interactions and identify potentially beneficial medicines for retinoblastoma malignancy. The DAVID v.6.8 database was used to study gene ontology (GO) and important biological pathways.

CISPLATIN, TAMOXIFEN, and CYCLOPHOSPHAMIDE are the medicines that have been shown to be successful in treating retinoblastoma in our study. Additionally, we conducted a research on three other drugs: GEMCITABINE, OLAPARIB, and MITOXANTRONE. Although it is used to treat other diseases, it seems to have no apparent effects on retinoblastoma cancer treatment.

CISPLATIN, a drug that causes apoptosis in tumors, has been proven to be the most effective therapy for retinoblastoma and should be included in treatment regimens for this illness. Of course, we obtained this information based on bioinformatics techniques, and more clinical trials are needed for more reliable results.

Breast cancer is the most common malignancy worldwide. Doxorubicin is an anthracycline used to treat breast cancer as the first treatment choice. Nevertheless, the molecular mechanisms underlying the response to Doxorubicin and its side effects are not comprehensively understood so far. We used systems biology and bioinformatics methods to identify essential genes and molecular mechanisms behind the body response to Doxorubicin and its side effects in breast cancer patients.

Omics data were extracted and analyzed to construct the protein-protein interaction and gene regulatory networks. Network analysis was performed to identify hubs, bottlenecks, clusters, and regulatory motifs to evaluate crucial genes and molecular mechanisms behind the body response to Doxorubicin and its side effects.

Analyzing the constructed PPI and gene-TF-miRNA regulatory network showed that MCM3, MCM10, and TP53 are key hub-bottlenecks and seed proteins. Enrichment analysis also revealed cell cycle, TP53 signaling, Forkhead box O (FoxO) signaling, and viral carcinogenesis as essential pathways in response to this drug. Besides, SNARE interactions in vesicular transport and neurotrophin signaling were identified as pathways related to the side effects of Doxorubicin. The apoptosis in-duction, DNA repair, invasion inhibition, metastasis, and DNA replication are sug-gested as critical molecular mechanisms underlying Doxorubicin anti-cancer effect. SNARE interactions in vesicular transport and neurotrophin signaling and FoxO signaling pathways in glucose metabolism are probably the mechanisms responsible for side effects of Doxorubicin.

Following our model validation using the existing experimental data, we recommend our other newly predicted biomarkers and pathways as possible molecular mechanisms and side effects underlying the response to Doxorubicin in breast cancer requiring further investigations.

Glaucoma is recognized as one of the most common causes of global blindness observed in various types, such as primary open-angle glaucoma (POAG). This condition is characterized by progressive optic neuropathy, leading to the damage of optic nerve fibers. With no symptoms at the beginning, glaucoma results in decreased vision and eventually blindness over several years. Early treatment can prevent the progression of the disease.

The researchers performed a study to evaluate differential gene expression in normal control and POAG cases. A total of 179 DEGs were discovered with 60 up-regulated and 119 down-regulated genes. After the selection of DEGs, the protein-protein interaction network was constructed. The result of GO enrichment showed the DEGs were involved in antioxidant activity, haptoglobin binding, and oxygen carrier activity. Then four modules of the primary protein network were obtained using a STRING database, using the K-means method. Next, gene ontology analysis and Kyoto encyclopedia of genes and genomes pathway enrichment were performed for four modules.

The results showed that the selected module (Yellow module) is highly related to glaucoma pathogenesis genes. Among the genes identified in this module are TYRP1, FMOD, OGN, PAX6, COL8A2, HLA-DPA1, and HLA-DMB, which may be involved in the direct development of glaucoma.

Using integrated bioinformatical analysis, the researchers identified DEGs candidate genes and pathways involved in glaucoma, which improved our understanding of the cause and underlying molecular events. These candidate genes and pathways could be therapeutic targets for glaucoma.

Type 1 diabetes (T1D) occurs as a consequence of an autoimmune attack against pancreatic-β cells. Due to a lack of a clear understanding of the T1D pathogenesis, the identification of effective therapies for T1D is the active area in the research. The study purpose was to prioritize potential drugs and targets in T1D via systems biology approach. Gene expression data of peripheral blood mononuclear cells (PBMCs) and pancreatic-β cells in T1D were analyzed and differential expressed genes were integrated with protein-protein interactions (PPI) data. Multiple topological centrality parameters of extracted query-query PPI (QQPPI) networks were calculated and the interaction of more central proteins with drugs was investigated. Molecular docking was performed to further predict the interactions between drugs and the binding sites of targets. Central proteins were identified by the analysis of PBMC (MYC, ERBB2, PSMA1, ABL1 and HSP90AA1) and pancreatic β-cells QQPPI networks (HSP90AB1, ESR1, RELA, RAC1, NFKB1, NFKB2, IKBKE, ARRB2, SRC). Thirteen drugs targeted eight central proteins were identified by further analysis of drug-target interactions. Some drugs which investigated for diabetes treatment in the experimental models of T1D were prioritized by literature verification, including melatonin, resveratrol, lapatinib, geldanamycin, eugenol and fostaminib. Finally, according on molecular docking analysis, lapatinib-ERBB2 and eugenol-ESR1 exhibited highest and lowest binding energy, respectively. This study presented promising results for the prioritization of potential drug-targets which might facilitate T1D targeted therapy and its drug discovery process more effectively.

Diverse microbiotas which have some contributions to gene expression reside in human skin. To identify the protective role of the skin microbiome against UV exposure, protein-protein interaction (PPI) network analysis is used to assess gene expression alteration.

A microarray dataset, GEO accession number GSE117359, was considered in this respect. Differentially expressed genes (DEGs) in the germ-free (GF) and specific pathogen-free (SPF) groups are analyzed by GEO2R. The top significant DEGs were assigned for network analysis via Cytoscape 3.7.2 and its applications.

A total of 28 genes were identified as significant DEGs and the centrality analysis of the network indicated that only one of the seven hub-bottlenecks was from queried genes. The gene ontology analysis of Il6, Cxcl2, Cxcl1, TNF, Il10, Cxcl10, and Mmp9 showed that the crucial genes were highly enriched in the immune system.

The skin microbiome plays a significant role in the protection of the skin against UV irradiation and the role of TNF and IL6 is prominent in this regard.

Photobiomodulation (PBM) is known as low-level laser (or light) therapy and is applied in different fields of medicine. However, it is required that its molecular and cellular mechanism be investigated. This study aims to assess the neuroprotective properties of PBM in the rat retina.

GSE22818 was downloaded from Gene Expression Omnibus (GEO) and the regulation of the significant differentially expressed genes (DEGs) which are produced by light damage in the rat retina by the pretreatment of PBM application was assessed via network analysis and gene ontology enrichment.

The 78 produced DEGs by light-damage in the rat retina were protected via PBM pretreatment action. Among these determined DEGs, 53 individuals were included in the main connected component of the constructed protein-protein interaction (PPI) network. Ccl2, Icam1, Cxcl10, Timp1, and Fos were determined as hub nodes. Eight clusters including 26 regulated biochemical pathways by PBM pretreatment were identified. The critical DEGs based on the action maps were introduced.

The finding indicates that PBM treatment protects rat retina against light damage via the prevention of Fos, Ccl2, Icam1, Cxcl10, and Myc dysregulation.

We aimed to explore potential molecular mechanisms of clear cell renal cell carcinoma (ccRCC) and provide candidate target genes for ccRCC gene therapy.

This is a bioinformatics-based study. Microarray datasets of GSE6344, GSE781 and GSE53000 were downloaded from Gene Expression Omnibus database. Using meta-analysis, differentially expressed genes (DEGs) were identified between ccRCC and normal samples, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) function analyses. Then, protein-protein interaction (PPI) networks and modules were investigated. Furthermore, miRNAs-target gene regulatory network was constructed.

Total of 511 up-regulated and 444 down-regulated DEGs were determined in the present gene expression microarray data meta-analysis. These DEGs were enriched in functions like immune system process and pathways like Toll-like receptor signaling pathway. PPI network and eight modules were further constructed. A total of 10 outstanding DEGs including TYRO protein tyrosine kinase binding protein (TYROBP), interferon regulatory factor 7 (IRF7) and PPARG co-activator 1 alpha (PPARGC1A) were detected in PPI network. Furthermore, the miRNAs-target gene regulation analyses showed that miR-412 and miR-199b respectively targeted IRF7 and PPARGC1A to regulate the immune response in ccRCC.

TYROBP, IRF7 and PPARGC1A might play important roles in ccRCC via taking part in the immune system process.

Thyroid cancer is the most malignant type of endocrine tumor. The molecular investigation of applied treatments for this type of neoplasm could provide a better understanding of their mechanisms of action.

Here, the differentially expressed proteins from a 2D gel-based proteomics of thyroid cancer cells treated by retinoic acid (RA) were considered for protein-protein interaction (PPI) network analysis.

Eight proteins related to the thyroid cancer cell line (FTC-133) treated with RA were extracted from an investigation by Trojanowicz et al. The query proteins and 50 neighbors were interacted by Cytoscape software via the STRING database. The network was analyzed, and hub-bottlenecks were identified. GeneMANIA determined the relationship between hub-bottlenecks. Protein complex analysis was done via MCODE. ClueGO + CluePedia was used to analyze gene ontology enrichment for hub-bottlenecks, differentially expressed hub-bottlenecks and the central cluster of the constructed network.

GAPDH, ENO1, and PKM proteins as hub-bottleneck and PDHB as the seed protein of the main protein complex were introduced as central proteins. However, the query proteins were not included oncogenic proteins, several oncogene genes such as MYC, STAT3, and AKT1among neighbor proteins were connected to the query proteins. The Glycolytic process through fructose-6- phosphate was the leading group of biological processes that were related to the central proteins.

It can be concluded that retinoic acid suppressed the activated glycolysis in thyroid cancer cells. The finding can be useful in the follow-up of patients. Additionally, RA regulates many oncogenes that act as a regulator of the determined central proteins

Testis-specific gene antigen 10 (TSGA10) is a less-known gene, which is involved in the vague biological paths of different cancers. Here, we investigated the TSGA10 expression using different concentrations of glucose under hypoxia and also its interaction with the hypoxia-inducible factor 1 (HIF-1). The breast cancer MDA-MB-231 and MCF-7 cells were cultured with different concentrations of glucose (5.5, 11.0 and 25.0 mM) under normoxia/hypoxia for 24, 48, and 72 hours and examined for the HIF-1α expression and cell migration by Western blotting and scratch assays. The qPCR was employed to analyze the expression of TSGA10. Three-dimensional (3D) structure and the energy minimization of the interacting domain of TSGA10 were performed by MODELLER v9.17 and Swiss-PDB viewer v4.1.0/UCSF Chimera v1.11. The UCSF Chimera v1.13.1 and Hex 6.0 were used for the molecular docking simulation. The Cytoscape v3.7.1 and STRING v11.0 were used for protein-protein interaction (PPI) network analysis. The HIF-1a related hypoxia pathways were obtained from BioModels database and reconstructed in CellDesigner v4.4.2. The increased expression of TSGA10 was found to be significantly associated with the reduced metastasis in the MDA-MB-231 cells, while an inverse relationship was seen between the TSGA10 mRNA level and cellular migration but not in the MCF-7 cells. The C-terminal domain of TSGA10 interacted with HIF-1α with high affinity, resulting in PPI network with 10 key nodes (HIF-1α, VEGFA, HSP90AA1, AKT1, ARNT, TP53, TSGA10, VHL, JUN, and EGFR). Collectively, TSGA10 functional expression alters under the hyper-/hypo-glycemia and hypoxia, which indicates its importance as a candidate bio-target for the cancer therapy.

ObjectiveDuchenne muscular dystrophy as one of the mortal diseases is prominent to study in terms of molecular investigation. In this study, the protein interaction map of this muscle-wasting condition is generated to gain a better knowledge of interactome profile of DMD.
Materials & Methods Applying Cytoscape and String Database, the protein-protein interaction network was constructed and the gene ontology of the constructed network was analyzed for biological process, molecular function, and cell component annotations.
ResultsThe results indicate that among 100 proteins that are related to DMD, Dystrophin, Utrophin, Caveolin 3, and Myogenic differentiation 1 play key roles in DMD network. In addition, the gene ontology analysis showed that regulation processes, kinase activity and sarcoplasmic reticulum are the highlighted biological processes, molecular function, and cell component enrichments respectively for the proteins related to DMD.
ConclusionIn conclusion, the central proteins and the enriched ontologies can be suggested as possible prominent agents in DMD; however, the validation studies may be required.

This study aimed to identify several potentially key genes associated with the pathogenesis of Takayasu’s arteritis (TA). This identification may lead to a deeper mechanistic understanding of TA etiology and pave the way for potential therapeutic approaches.
In this experimental study, the microarray dataset GSE33910, which includes expression data for peripheral blood mononuclear cell (PBMC) samples isolated from TA patients and normal volunteers, was downloaded from the publicly accessible Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified in PBMCs of TA patients compared with normal controls. Gene ontology (GO) enrichment analysis of DEGs and analysis of protein-protein interaction (PPI) network were carried out. Several hub proteins were extracted from the PPI network based on node degrees and random walk algorithm. Additionally, transcription factors (TFs) were predicted and the corresponding regulatory network was constructed.
A total of 932 DEGs (372 up- and 560 down-regulated genes) were identified in PBMCs from TA patients. Interestingly, up-regulated and down-regulated genes were involved in different GO terms and pathways. A PPI network of proteins encoded by DEGs was constructed and RHOA, FOS, EGR1, and GNB1 were considered to be hub proteins with both higher random walk score and node degree. A total of 13 TFs were predicted to be differentially expressed. A total of 49 DEGs had been reported to be associated with TA in the Comparative Toxicogenomics Database (CTD). The only TA marker gene in the CTD database was NOS2, confirmed by three studies. However, NOS2 was not significantly altered in the analyzed microarray dataset. Nevertheless, NOS3 was a significantly down regulated gene and was involved in the platelet activation pathway.
RHOA, FOS, and EGR1 are potential candidate genes for the diagnosis and therapy of TA.