A Novel Core-Attachment-Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

PROTEOMICS ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1800129 ◽  
Author(s):  
Qianghua Xiao ◽  
Ping Luo ◽  
Min Li ◽  
Jianxin Wang ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Protein Complexes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Ppi Networks

scholarly journals Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Min Li ◽  
Weijie Chen ◽  
Jianxin Wang ◽  
Fang-Xiang Wu ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Computational Methods ◽  
Protein Complex ◽  
Protein Complexes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Protein Interaction ◽  
Cellular Life ◽  
Ppi Networks ◽  
Core Proteins

Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data ofSaccharomyces cerevisiaeand the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.

scholarly journals Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

2021 ◽  
Author(s):  
Zimeng Wei ◽  
Min Zhao ◽  
Linnan Zang
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Analysis Tool ◽  
Hub Genes ◽  
Kegg Analysis ◽  
Ppi Networks

Abstract Background Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Methods Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes. Results 74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05). Conclusions Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

scholarly journals Potential genes and pathways associated with heterotopic ossification derived from analyses of gene expression profiles

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zhanyu Yang ◽  
Delong Liu ◽  
Rui Guan ◽  
Xin Li ◽  
Yiwei Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Kinase ◽  
Heterotopic Ossification ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Heterotopic Bone ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Heterotopic ossification (HO) represents pathological lesions that refer to the development of heterotopic bone in extraskeletal tissues around joints. This study investigates the genetic characteristics of bone marrow mesenchymal stem cells (BMSCs) from HO tissues and explores the potential pathways involved in this ailment. Methods Gene expression profiles (GSE94683) were obtained from the Gene Expression Omnibus (GEO), including 9 normal specimens and 7 HO specimens, and differentially expressed genes (DEGs) were identified. Then, protein–protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for further analysis. Results In total, 275 DEGs were differentially expressed, of which 153 were upregulated and 122 were downregulated. In the biological process (BP) category, the majority of DEGs, including EFNB3, UNC5C, TMEFF2, PTH2, KIT, FGF13, and WISP3, were intensively enriched in aspects of cell signal transmission, including axon guidance, negative regulation of cell migration, peptidyl-tyrosine phosphorylation, and cell-cell signaling. Moreover, KEGG analysis indicated that the majority of DEGs, including EFNB3, UNC5C, FGF13, MAPK10, DDIT3, KIT, COL4A4, and DKK2, were primarily involved in the mitogen-activated protein kinase (MAPK) signaling pathway, Ras signaling pathway, phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway, and Wnt signaling pathway. Ten hub genes were identified, including CX3CL1, CXCL1, ADAMTS3, ADAMTS16, ADAMTSL2, ADAMTSL3, ADAMTSL5, PENK, GPR18, and CALB2. Conclusions This study presented novel insight into the pathogenesis of HO. Ten hub genes and most of the DEGs intensively involved in enrichment analyses may be new candidate targets for the prevention and treatment of HO in the future.

Integrating network topology, gene expression data and GO annotation information for protein complex prediction

2019 ◽  
Vol 17 (01) ◽  
pp. 1950001 ◽  
Author(s):  
Wei Zhang ◽  
Jia Xu ◽  
Yuanyuan Li ◽  
Xiufen Zou
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Complex ◽  
Complex Disease ◽  
Protein Complexes ◽  
Expression Profiles ◽  
Interaction Network ◽  
Ppi Networks ◽  
Annotation Information

The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as the mechanisms underlying complex disease. A great number of methods have been developed to predict protein complexes based on protein–protein interaction (PPI) networks in recent years. However, because the high throughput data obtained from experimental biotechnology are incomplete, and usually contain a large number of spurious interactions, most of the network-based protein complex identification methods are sensitive to the reliability of the PPI network. In this paper, we propose a new method, Identification of Protein Complex based on Refined Protein Interaction Network (IPC-RPIN), which integrates the topology, gene expression profiles and GO functional annotation information to predict protein complexes from the reconstructed networks. To demonstrate the performance of the IPC-RPIN method, we evaluated the IPC-RPIN on three PPI networks of Saccharomycescerevisiae and compared it with four state-of-the-art methods. The simulation results show that the IPC-RPIN achieved a better result than the other methods on most of the measurements and is able to discover small protein complexes which have traditionally been neglected.

scholarly journals Identification of crucial genes and pathways associated with prostate cancer in multiple databases

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110166
Author(s):  
Hanxu Guo ◽  
Zhichao Zhang ◽  
Yuhang Wang ◽  
Sheng Xue
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Expression Profiles ◽  
Malignant Neoplasm ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Core ◽  
Ppi Networks ◽  
Core Genes

Objective Prostate cancer (PCa) is a malignant neoplasm of the urinary system. This study aimed to use bioinformatics to screen for core genes and biological pathways related to PCa. Methods The GSE5957 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were constructed by R language. Furthermore, protein–protein interaction (PPI) networks were generated to predict core genes. The expression levels of core genes were examined in the Tumor Immune Estimation Resource (TIMER) and Oncomine databases. The cBioPortal tool was used to study the co-expression and prognostic factors of the core genes. Finally, the core genes of signaling pathways were determined using gene set enrichment analysis (GSEA). Results Overall, 874 DEGs were identified. Hierarchical clustering analysis revealed that these 24 core genes have significant association with carcinogenesis and development . LONRF1, CDK1, RPS18, GNB2L1 ( RACK1), RPL30, and SEC61A1 directly related to the recurrence and prognosis of PCa. Conclusions This study identified the core genes and pathways in PCa and provides candidate targets for diagnosis, prognosis, and treatment.

Potential Genes and Pathways Associated with Heterotopic Ossification, Derived from Analyses of Gene Expression Profiles

2021 ◽  
Author(s):  
Zhanyu Yang ◽  
Delong Liu ◽  
Rui Guan ◽  
Xin Li ◽  
Yiwei Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Kinase ◽  
Heterotopic Ossification ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Heterotopic Bone ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Heterotopic ossification (HO) represents pathological lesions, referred to the development of heterotopic bone in extraskeletal tissues around joints. This study will investigate the genetic characteristics of bone marrow mesenchymal stem cells (BMSCs) from HO tissues and explore the potential pathways involved. Methods: The gene expression profiles (GSE94683) was obtained from the Gene Expression Omnibus (GEO), including 9 normal specimens and 7 HO specimens, and differentially expressed genes (DEGs) were identified. Then, the protein‑protein interaction (PPI) networks, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis were performed for further analysis. Results: Totally 275 DEGs were differentially expressed, of which 153 were upregulated and 122 were downregulated. In the biological process (BP), the majority of DEGs, including EFNB3, UNC5C, TMEFF2, PTH2, KIT, FGF13 and WISP3, were intensively enriched in cell signal transmission items, including axon guidance, negative regulation of cell migration, peptidyl-tyrosine phosphorylation and cell-cell signaling. Moreover, KEGG analysis indicated that the majority of DEGs, including EFNB3, UNC5C, FGF13, MAPK10, DDIT3, KIT, COL4A4 and DKK2, primarily involved in mitogen-activated protein kinase (MAPK) signaling pathway, Ras signaling pathway, phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) signaling pathway and Wnt signaling pathway. 10 hub genes were identified, including CX3CL1, CXCL1, ADAMTS3, ADAMTS16, ADAMTSL2, ADAMTSL3, ADAMTSL5, PENK, GPR18, CALB2.Conclusions: This study presents a novel insight into the pathogenesis of HO. 10 hub genes and most of the DEGs intensively involved in enrichment analyses may be the new candidate targets for the prevention and treatment of HO in the future.

Gene expression in rat placenta after exposure to di(2-ethylhexyl) phthalate

2020 ◽  
pp. 096032712095425
Author(s):  
Wan Xu ◽  
Hongyan Wu ◽  
Lixin Shang
Keyword(s):  
Gene Expression ◽  
Mirna Target ◽  
Corn Oil ◽  
Expression Profiles ◽  
Somatostatin Receptor ◽  
Gene Expression Profiles ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Ppi Networks ◽  
Ethylhexyl Phthalate

The organic compound di(2-ethylhexyl) phthalate (DEHP) is widely used as a plasticizer in many products. Exposure to DEHP has been reported to lead to adverse pregnancy outcomes by suppressing placenta growth and development. The aim of this study was to determine the gene expression profiles of rat placenta exposed to (DEHP) and identify genes crucial for the DEHP response. Three groups of Wistar rats were administered an intragastric dose of 1,000 mg/kg DEHP, 500 mg/kg DEHP, or corn oil, RNA was isolated from placenta tissue, and hybridization was performed. Gene expression profiles were analyzed by identifying functional enrichment, differentially expressed genes (DEGs), protein–protein interaction (PPI) networks and modules, and transcription factor (TF)-miRNA-target regulatory networks. We obtained 2,032 DEGs, including cytochrome P450, family 2, subfamily R, polypeptide 1 (CYP2R1), sterol O-acyltransferase 2 (SOAT2), and 24-dehydrocholesterol reductase (DHCR24) from the steroid biosynthesis pathway and somatostatin receptor 4 (SSTR4) and somatostatin receptor 2 (SSTR2) in the neuroactive ligand-receptor interaction pathway. The PPI network included 476 nodes, 2,682 interaction pairs, and three sub-network modules. Moreover, eight miRNAs, three TFs, and 176 regulatory pairs were obtained from the TF-miRNA-target regulatory network. CYP2R1, SOAT2, DHCR24, SSTR4, and SSTR2 may affect DEHP influence on rat placenta development.

1327: Gene Expression Profiles in Benign Prostatic Hyperplasia

2004 ◽  
Vol 171 (4S) ◽  
pp. 349-350
Author(s):  
Gaelle Fromont ◽  
Michel Vidaud ◽  
Alain Latil ◽  
Guy Vallancien ◽  
Pierre Validire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Benign Prostatic Hyperplasia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostatic Hyperplasia
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