scholarly journals Co-expression analysis of placental genes in the search for key signaling pathways and biomarkers of the great obstetrical syndromes

2022 ◽  
Vol 36 (4) ◽  
pp. 144-155
Author(s):  
E. A. Trifonova ◽  
A. V. Markov ◽  
A. A. Zarubin ◽  
A. A. Babovskaya ◽  
I. G. Kutsenko ◽  
...  
Keyword(s):  
Protein Interaction Network ◽  
Intrauterine Growth Restriction ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Placental Tissue ◽  
Hub Genes ◽  
Intrauterine Growth ◽  
Protein Protein Interaction ◽  
Human Placental Tissue ◽  
Protein Protein Interaction Network

Objective. To study the molecular mechanisms responsible for the development of diseases grouped within the great obstetrical syndromes (GOS) at the level of the transcriptome of human maternal placenta.Material and Methods. We gathered the results of genome-wide transcriptome studies of the human placental tissue using Gene Expression Omnibus (GEO) data repository for the following phenotypes: physiological pregnancy, preeclampsia (PE), premature birth, and intrauterine growth restriction (IUGR). Eleven data sets were selected and supplemented with our experimental data; a total of 481 samples of human placental tissue were included in the integrative analysis. Bioinformatic data processing and statistical analyses were performed in the R v3.6.1 software environment using the Bioconductor packages. The pooled dataset was used to search for common molecular targets for GOS via weighted gene co-expression network analysis (WGCNA). The functional annotation of genes and the resulting clusters was carried out with the DAVID database; protein-protein interaction network was built using the STRING software; and the hub genes for the network were identified using the MCC analysis with plugin cytoHubba in Cytoscape software 3.7.2.Results. We obtained a table of expression levels for 15,167 genes in 246 samples. Hierarchical clustering of this network allowed to find 55 modules of co-expressed genes in the group with PE, 109 modules in the group with PB, 75 modules in patients with IUGR, and 56 modules in the control group. The preservation analysis of co-expressed modules for the studied phenotypes suggested the presence of a common cluster comprising eight genes specific only for patients with PE and IUGR, as well as the module of 23 co-expressed genes typical only for patients with PB and IUGR. Protein-protein interaction network was built for these gene sets, and the SOD1, TXNRD1, and UBB genes were the central nodes in the network. Based on network topology evaluation with cytoHubba, six hub genes (rank ˂ 5) were identified as follows: SOD1, TKT, TXNRD1, GCLM, GOT1, and ACO1.Conclusion. The obtained results allowed to identify promising genetic markers for preeclampsia, intrauterine growth restriction, and miscarriage. Moreover, the study also made it possible to identify the most important overlapping molecular mechanisms of these diseases occurring in the placental tissue.

scholarly journals Exploring The Molecular Mechanisms of Classical Hodgkin Lymphoma Through Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhu Lili ◽  
Zhu YuKun ◽  
Zhuangzhuang Tian ◽  
Yongsheng Li ◽  
Liyu Cao
Keyword(s):  
Hodgkin Lymphoma ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background Classic Hodgkin lymphoma (CHL) is the most common HL in the modern society. Although the treatment of cHL has made great progress, its molecular mechanisms have yet to be deciphered. Objectives The purpose of this study is to find out the crucial potential genes and pathways associated with cHL. Methods We downloaded the cHL microarray dataset (GSE12453) from Gene Expression Omnibus (GEO) database and to identify the differentially expressed genes (DEGs) between cHL samples and normal samples through the limma package in R. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out. Finally, we constructed the protein-protein interaction network to screen out the hub genes using Search Tool for the Retrieval of Interacting Genes (STRING) database. Results We screened out 788 DEGs in the cHL dataset, such as BATF3, IER3, RAB13 and FCRL2. GO functional enrichment analysis indicated that the DEGs were related with regulation of lymphocyte activation, secretory granule lumen and chemokine activity. KEGG pathway analysis showed that the genes enriched in Prion disease, Complement and coagulation cascades and Parkinson disease Coronavirus disease-COVID-19 pathway. Protein-protein interaction network construction identified 10 hub genes (IL6, ITGAM, CD86, FN1, MMP9, CXCL10, CCL5, CD19, IFNG, SELL, UBB) in the network. Conclusions In the present investigation, we identified several pathways and hub genes related to the occurrence and development of cHL, which may provide an important basis for further research and novel therapeutic targets and prognostic indicators for cHL.

scholarly journals The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins

2019 ◽  
Author(s):  
Jarmila Nahálková
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Age Related ◽  
P53 Signaling Pathway ◽  
Protein Protein Interaction Network

The protein-protein interaction network of seven pleiotropic proteins (PIN7) contains proteins with multiple functions in the aging and age-related diseases (TPPII, CDK2, MYBBP1A, p53, SIRT6, SIRT7, and BSG). At the present work, the pathway enrichment, the gene function prediction and the protein node prioritization analysis were applied for the examination of main molecular mechanisms driving PIN7 and the extended network. Seven proteins of PIN7 were used as an input for the analysis by GeneMania, a Cytoscape application, which constructs the protein interaction network. The software also extends it using the interactions retrieved from databases of experimental and predicted protein-protein and genetic interactions. The analysis identified the p53 signaling pathway as the most dominant mediator of PIN7. The extended PIN7 was also analyzed by Cytohubba application, which showed that the top-ranked protein nodes belong to the group of histone acetyltransferases and histone deacetylases. These enzymes are involved in the reverse epigenetic regulation mechanisms linked to the regulation of PTK2, NFκB, and p53 signaling interaction subnetworks of the extended PIN7. The analysis emphasized the role of PTK2 signaling, which functions upstream of the p53 signaling pathway and its interaction network includes all members of the sirtuin family. Further, the analysis suggested the involvement of molecular mechanisms related to metastatic cancer (prostate cancer, small cell lung cancer), hemostasis, the regulation of the thyroid hormones and the cell cycle G1/S checkpoint. The additional data-mining analysis showed that the small protein interaction network MYBBP1A-p53-TPPII-SIRT6-CD147 controls Warburg effect and MYBBP1A-p53-TPPII-SIRT7-BSG influences mTOR signaling and autophagy. Further investigations of the detail mechanisms of these interaction networks would be beneficial for the development of novel treatments for aging and age-related diseases.

scholarly journals Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias

2021 ◽  
Author(s):  
Nikoleta Vavouraki ◽  
James E. Tomkins ◽  
Eleanna Kara ◽  
Henry Houlden ◽  
John Hardy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Topological Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Hereditary Spastic Paraplegias ◽  
Core Proteins ◽  
Protein Protein Interaction Network

AbstractThe Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human protein-protein interactions were used to create a protein-protein interaction network based on the causative Hereditary Spastic Paraplegia genes. Network evaluation as a combination of both topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, suggesting there is scope to further classify conditions currently described under the same umbrella term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

Exploring Shared Pathogenesis of Alzheimer’s Disease and Type 2 Diabetes Mellitus via Co-expression Networks Analysis

2020 ◽  
Vol 17 (6) ◽  
pp. 566-575 ◽  
Author(s):  
Yukun Zhu ◽  
Xuelu Ding ◽  
Zhaoyuan She ◽  
Xue Bai ◽  
Ziyang Nie ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Background: Alzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (T2DM) have an increased incidence in modern society. Although increasing evidence has supported the close linkage between these two disorders, the inter-relational mechanisms remain to be fully elucidated. Objective: The primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and T2DM. Methods: We downloaded the microarray data of AD and T2DM from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene network modules related to AD and T2DM. Then, Gene Ontology (GO) and pathway enrichment analysis were performed on the common genes existing in the AD and T2DM related modules by clusterProfiler and DOSE package. Finally, we utilized the STRING database to construct the protein-protein interaction network and found out the hub genes in the network. Results: Our findings indicated that seven and four modules were the most significant with AD and T2DM, respectively. Functional enrichment analysis showed that AD and T2DM common genes were mainly enriched in signaling pathways such as circadian entrainment, phagosome, glutathione metabolism and synaptic vesicle cycle. Protein-protein interaction network construction identified 10 hub genes (CALM1, LRRK2, RBX1, SLC6A1, TXN, SNRPF, GJA1, VWF, LPL, AGT) in AD and T2DM shared genes. Conclusions: Our work identified common pathogenesis of AD and T2DM. These shared pathways might provide a novel idea for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosis and treatment of AD and T2DM.

scholarly journals Identification of Hub Genes Associated With Clinical Characteristics and Potential Therapy of Diabetic Nephropathy by Bioinformatics Analysis.

2020 ◽  
Author(s):  
Si Xu ◽  
Xiaoning Li ◽  
Sha Wu ◽  
Min Yang
Keyword(s):  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Molecular Mechanism ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Theoretical Basis ◽  
Interaction Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background: To provide theoretical basis for the molecular mechanism of the development of diabetic nephropathy and targeted molecular therapy by screening expressed genes based on bioinformatic analysis. Methods: We analyzed diabetic nephropathy microarray datasets derived from GEO database. Perl and R programming packages were used for data processing and analysis and for drawing. STRING online database and Cytoscape software were utilized for protein-protein interaction network analysis and screened for hub genes. Also, WebGestalt was used to analyze the relationship between genes and microRNAs. Nephroseq online tool was used to visualize the correlation between genes and clinical properties.Results: We found 91 differentially expressed genes between diabetic nephropathy tissues and normal control tissues. Protein-protein interaction network analysis screened out 5 key modules and a total of 14 hub genes were identified by integration, also11 microRNAs were associated with hub genes. Especially mir29 could regulate COL6A3 and COL15A1.Conclusions: The internal biological information in diabetic nephropathy can be revealed by integrative bioinformatical analysis, providing theoretical basis for further research on molecular mechanism and potential targets for diagnosis and therapeutics of diabetic nephropathy.

scholarly journals Integrated analysis and the identification of a circRNA-miRNA-mRNA network in the progression of abdominal aortic aneurysm

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12682
Author(s):  
Ke Si ◽  
Da Lu ◽  
Jianbo Tian
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Abdominal Aortic ◽  
Protein Protein Interaction Network

Background Abdominal aortic aneurysm (AAA) is a disease commonly seen in the elderly. The aneurysm diameter increases yearly, and the larger the AAA the higher the risk of rupture, increasing the risk of death. However, there are no current effective interventions in the early stages of AAA. Methods Four gene expression profiling datasets, including 23 normal artery (NOR) tissue samples and 97 AAA tissue samples, were integrated in order to explore potential molecular biological targets for early intervention. After preprocessing, differentially expressed genes (DEGs) between AAA and NOR were identified using LIMMA package. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were conducted using the DAVID database. The protein-protein interaction network was constructed and hub genes were identified using the STRING database and plugins in Cytoscape. A circular RNA (circRNA) profile of four NOR tissues versus four AAA tissues was then reanalyzed. A circRNA-miRNA-mRNA interaction network was constructed after predictions were made using the Targetscan and Circinteractome databases. Results A total of 440 DEGs (263 up-regulated and 177 down-regulated) were identified in the AAA group, compared with the NOR group. The majority were associated with the extracellular matrix, tumor necrosis factor-α, and transforming growth factor-β. Ten hub gene-encoded proteins (namely IL6, RPS27A, JUN, UBC, UBA52, FOS, IL1B, MMP9, SPP1 and CCL2) coupled with a higher degree of connectivity hub were identified after protein‐protein interaction network analysis. Our results, in combination with the results of previous studies revealed that miR-635, miR-527, miR-520h, miR-938 and miR-518a-5p may be affected by circ_0005073 and impact the expression of hub genes such as CCL2, SPP1 and UBA52. The miR-1206 may also be affected by circ_0090069 and impact RPS27A expression. Conclusions This circRNA-miRNA-mRNA network may perform critical roles in AAA and may be a novel target for early intervention.

scholarly journals FRI0584 SCREENING AND IDENTIFICATION OF BIOMARKERS IN MYOSITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 896-897
Author(s):  
W. Liu ◽  
X. Zhang
Keyword(s):  
Gene Expression ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background:Myositis, including dermatomyositis and polymyositis, is autoimmune disorders that is characterized by muscle degeneration in the proximal extremities, with the complications of weakness of muscles, interstitial lung disease and vascular lesions, even leading to death in an acute progressive process[1,2]. However, the molecular mechanisms of myositis are rarely understood.Objectives:Identify the candidate genes in myositis.Methods:Microarray datasets GSE128470, GSE48280 and GSE39454 were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and function enrichment analyses were conducted. The protein-protein interaction network and the analyses of hub genes were performed with STRING and Cytoscape.Results:There were 98 DEGs, of which the function and pathways enrichment analyses showed defense response, immune response, response to virus, inflammatory response, response to wounding, cell adhesion, cell proliferation, cell death and macromolecule metabolic process. 20 hub genes were identified, of which 7 including IRF9 TRIM22 MX2 IFITM1 IFI6 IFI44 IFI44L had not been reported in the literature, related to the response to virus, immune response, transcription from RNA polymerase II promoter, cell apoptosis, cell death. The verification analysis about the 7 genes in GSE128314 showed significant differences in myositis.Conclusion:In conclusion, DEGs and hub genes identified in our study showed the potential molecular mechanisms in myositis, providing the helpful targets for diagnosis and clinical strategy of myositis.References:[1] Wu H, Geng D, Xu J. An approach to the development of interstitial lung disease in dermatomyositis: a study of 230 cases in China[J]. Journal of International Medical Research. 2013;41(2):493–501.[2] Fathi M, Dastmalchi M, Rasmussen E, Lundberg IE, Tornling G. Interstitial lung disease, a common manifestation of newly diagnosed polymyositis and dermatomyositis[J]. Annals of the Rheumatic Diseases. 2004;63(3):297–301.Figure 1.The protein-protein interaction network of 20 hub genesFigure 2.7 genes in GSE128314 showed significant differences in myositisAcknowledgments:The authors acknowledge the efforts of the Gene Expression Omnibus (GEO) database. The interpretation and reporting of these data are the sole responsibility of the authors.Disclosure of Interests:None declared

scholarly journals MYBPC2 and MYL1 as Significant Gene Markers for Rhabdomyosarcoma

2021 ◽  
Vol 20 ◽  
pp. 153303382097966
Author(s):  
Zihang Chen ◽  
Xing-yu Li ◽  
Peng Guo ◽  
Dong-lai Wang
Keyword(s):  
Overall Survival ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Tumor Tissues ◽  
Protein Protein Interaction Network

Background: Rhabdomyosarcoma is the most common soft tissue tumor in children. Rhabdomyosarcoma commonly results in pain and bleeding caused by tumor compression and is prone to early metastasis and recurrence, which can seriously affect the therapeutic outcomes and long-term prognosis. Up to 37.7% of rhabdomyosarcomas may metastasize. Therefore, the molecular mechanisms underlying rhabdomyosarcoma must be explored to identify an effective target for its early diagnosis and specific treatment. Methods: A dataset of 18 rhabdomyosarcoma tissue samples and 6 healthy skeletal muscle samples was downloaded. Differentially expressed genes between rhabdomyosarcoma and healthy tissue samples were identified by GEO2R. Kyoto Encyclopedia of Genes and Genomes and gene ontology pathway enrichment analyses were performed. A protein–protein interaction network was constructed, and hub genes were identified. Expression and survival analyses of hub genes were performed. Additionally, 30 patients with rhabdomyosarcoma were recruited, and overall survival information and samples were collected. Reverse transcription quantitative real-time polymerase chain reaction assays were performed to verify the expression of MYBPC2 and MYL1 in rhabdomyosarcoma tumor tissues. The Kaplan–Meier method was used to explore overall survival based on our clinical data. Results: In total, 164 genes were up-regulated and 394 were down-regulated in rhabdomyosarcoma tumor tissues. Gene ontology analysis revealed that variations were predominantly enriched in the cell cycle, muscle contraction, muscle system processes, cytoskeleton, nucleotide binding, and cytoskeletal protein binding. The protein–protein interaction network revealed 3274 edges, and 441 nodes were constructed. Ten hub genes were identified; of these, MYBPC2 and MYL1 were significantly up-regulated in rhabdomyosarcoma. Compared with the healthy group, patients with rhabdomyosarcoma exhibiting high expression of MYBPC2 and MYL1 exhibited significantly worse overall survival. Conclusions: We found differentially expressed genes between rhabdomyosarcoma and healthy tissue samples. MYBPC2 and MYL1 may be involved in the pathogenesis of rhabdomyosarcoma and therefore deserve further exploration.

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