Protein-protein interaction analysis of distinct molecular pathways in two subtypes of colorectal carcinoma.
ABSTRACT: The aim of this study was to identify the molecular events that distinguish serrated colorectal carcinoma (SCRC) from conventional colorectal carcinoma (CCRC) through differential gene expression, pathway and protein-protein interaction (PPI) network analysis. The GSE4045 and GSE8671 microarray datasets were downloaded from the Gene Expression Omnibus database. We identified the genes that are differentially expressed between SCRC and normal colon tissues, CCRC and healthy tissues, and between SCRC and CCRC using Student's t-tests and Benjamini?Hochberg (BH) multiple testing corrections. The differentially expressed genes (DEGs) were then mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and their enrichment for specific pathways was investigated using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool with a significance threshold of 0.1. Analysis of the potential interactions between the protein products of 220 DEGs (between CCRC and SCRC) was performed by constructing a PPI network using data from the high performance RDF database (P<0.1). The interaction between pathways was also analyzed in CCRC based on the PPI network. Our study identified thousands of genes differentially expressed in SCRC and CCRC compared to healthy tissues. The DEGs in SCRC and CCRC were enriched in cell cycle, DNA replication, and base excision repair pathways. The proteasome pathway was significantly enriched in SCRC but not in CCRC after BH adjustment. The PPI network showed that tumour necrosis factor receptor-associated factor 6 (TRAF6) and atrophin 1 (ATN1) were the most central genes in the network, with respective degrees of node predicted at 90 and 88. In conclusion, the preoteasome pathway was shown to be specifically enriched in SCRC. Furthermore, TRAF6 and ATN1 may be promising biomarkers for the distinction between serrated and conventional CRC.
Project description:BACKGROUND: Primary osteoporosis is an age-related disease, and the main cause of this disease is the failure of bone homeostasis. Previous studies have shown that primary osteoporosis is associated with gene mutations.To explore the functional modules of the PPI (protein-protein interaction) network of differentially expressed genes (DEGs), and the related pathways participating in primary osteoporosis. METHODS: The gene expression profile of primary osteoporosis GSE35956 was downloaded from the GEO (Gene Expression Omnibus) database and included five MSC (mesenchymal stem cell) specimens of normal osseous tissue and five MSC specimens of osteoporosis. The DEGs between the two types of MSC specimens were identified by the samr package in R language. In addition, the functions and pathways of DEGs were enriched. Then the DEGs were mapped to String to acquire PPI pairs and the PPI network was constructed with by these PPI pairs. Topological properties of the network were calculated by Network Analyzer, and modules in the network were screened by Cluster ONE software. Subsequently, the fronting five modules whose P-value was less than 1.0e-05 were identified and function analysis was conducted. RESULTS: A total of 797 genes were filtered as DEGs from these ten specimens of GSE35956 with 660 up-regulated genes and 137 down-regulated genes. Meanwhile, up-regulated DEGs were mainly enriched in functions and pathways related to cell cycle and DNA replication. Furthermore, there were 4,135 PPI pairs and 377 nodes in the PPI network. Four modules were enriched in different pathways, including cell cycle and DNA replication pathway in module 2. CONCLUSIONS: In this paper, we explored the genes and pathways involved in primary osteoporosis based on gene expression profiles, and the present findings have the potential to be used clinically for the future treatment of primary osteoporosis.
Project description:The aim of the present study was to explore the critical genes and molecular mechanisms in pancreatic cancer (PC) cells with glutamine. By analyzing microarray data GSE17632 from the Gene Expression Omnibus database, the DEGs between PC cells treated with glutamine and without glutamine were evaluated. Additionally, function enrichment analyses and protein-protein interaction (PPI) network construction of DEGs were performed. Network module and literature mining analyses were performed to analyze the critical DEGs in PC cells. In total, 495 genes were selected as DEGs between control and glutamine cells in PC. These DEGs were mainly enriched in several Gene Ontology (GO) terms in biological process, cellular components and molecular function. Additionally, they were also enriched in certain pathways, including metabolic pathways and the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway. MYC, heat shock 70kDa protein 5 (HSPA5), interleukin 8 (IL8), and chemokine (C-X-C motif) receptor 4 (CXCR4) were hub genes in the PPI network. Furthermore, two sub-network modules of PPI network and two co-occurrence networks were obtained. The DEGs of MYC, HSPA5, IL18 and CXCR4 may exert important roles in molecular mechanisms of PC cells with glutamine.
Project description:Colorectal cancer (CRC) is one of the most common malignant diseases worldwide, but the involved signaling pathways and driven-genes are largely unclear. This study integrated four cohorts profile datasets to elucidate the potential key candidate genes and pathways in CRC. Expression profiles GSE28000, GSE21815, GSE44076 and GSE75970, including 319 CRC and 103 normal mucosa, were integrated and deeply analyzed. Differentially expressed genes (DEGs) were sorted and candidate genes and pathways enrichment were analyzed. DEGs-associated protein-protein interaction network (PPI) was performed. Firstly, 292 shared DEGs (165 up-regulated and 127 down-regulated) were identified from the four GSE datasets. Secondly, the DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Thirdly, 180 nodes/DEGs were identified from DEGs PPI network complex. Lastly, the most significant 2 modules were filtered from PPI, 31 central node genes were identified and most of the corresponding genes are involved in cell cycle process, chemokines and G protein-coupled receptor signaling pathways. Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in CRC, which could improve our understanding of the cause and underlying molecular events, and these candidate genes and pathways could be therapeutic targets for CRC.
Project description:BACKGROUND Spinal cord injury (SCI) is the most critical complication of spinal injury. We aimed to identify differentially expressed genes (DEGs) and to find associated pathways that may function as targets for SCI prognosis and therapy. MATERIAL AND METHODS Seven gene microarray expression profiles, downloaded from the GEO database (ID: GSE33886), were used to screen the DEGs of leg tissue and to compare these between SCI patients and corresponding normal specimens. Then, GO enrichment analysis was performed on these selected DEGs. Afterwards, interactions among these DEGs were analyzed by String database and then a PPI network was constructed to obtain topology character and modules in the PPI network. Finally, roles of the critical proteins in the pathway were explained by comparing the enrichment results of the genes in sub-modules and all the DEGs. RESULTS A total of 113 DEGs were determined. We found that 21 up-regulated genes were enriched in 7 biological processes, while 9 down-regulated genes were significantly enriched in 4 KEGG pathways. The PPI network was constructed, including 40 interacting genes and 73 interactions. Three obvious function modules were identified by exploring the PPI network, and ACTC1 was identified as the critical protein in the 3 enriched signal pathways. However, no obvious difference was found in the signal pathway in which both the 11 genes in module 1 and all 113 DEGs participated. CONCLUSIONS Core proteins in the signal pathway associated with spinal cord injury may serve as potential prognostic and predictive markers for the diagnosis and treatment of spinal cord injury in clinical applications.
Project description:BACKGROUND To provide a better understanding of anaplastic thyroid carcinoma (ATC) at the molecular level, this study aimed to identify the genes and key pathways associated with ATC by using integrated bioinformatics analysis. MATERIAL AND METHODS Based on the microarray data GSE9115, GSE65144, and GSE53072 derived from the Gene Expression Omnibus, the differentially expressed genes (DEGs) between ATC samples and normal controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein-protein interaction (PPI) network was constructed and visualized, with which the hub gene nodes were screened out. Finally, modules analysis for the PPI network was performed to further investigate the potential relationships between DEGs and ATC. RESULTS A total of 537 common DEGs were screened out from all 3 datasets, among which 247 genes were upregulated and 275 genes were downregulated. GO analysis indicated that upregulated DEGs were mainly involved in cell division and mitotic nuclear division and the downregulated DEGs were significantly enriched in ventricular cardiac muscle cell action potential. KEGG pathway analysis showed that the upregulated DEGs were mainly enriched in cell cycle and ECM-receptor interaction and the downregulated DEGs were mainly enriched in thyroid hormone synthesis, insulin resistance, and pathways in cancer. The top 10 hub genes in the constructed PPI network were CDK1, CCNB1, TOP2A, AURKB, CCNA2, BUB1, AURKA, CDC20, MAD2L1, and BUB1B. The modules analysis showed that genes in the top 2 significant modules of PPI network were mainly associated with mitotic cell cycle and positive regulation of mitosis, respectively. CONCLUSIONS We identified a series of key genes along with the pathways that were most closely related with ATC initiation and progression. Our results provide a more detailed molecular mechanism for the development of ATC, shedding light on the potential biomarkers and therapeutic targets.
Project description:The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non?allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co?expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co?expression network was constructed based on these pairs. A protein?protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co?expression gene pairs were obtained. A differential co?expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR.
Project description:This study aimed to explore the underlying genes and pathways associated with pancreatic ductal adenocarcinoma (PDAC) by bioinformatics analyses. Gene expression profile GSE43795 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between six PDAC and five non-neoplastic pancreatic tissue samples were analyzed using the limma package. Gene ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by functional annotation and protein-protein interaction (PPI) network construction. Finally, the sub-network was identified and pathway enrichment analysis was performed on the contained DEGs. A total of 374 downregulated and 559 upregulated DEGs were identified. The downregulated DEGs were enriched in GO terms associated with digestion and transport and pathways related to metabolism, while the upregulated DEGs were enriched in GO terms associated with the cell cycle and mitosis and pathways associated with the occurrence of cancer including the cell cycle pathway. Following functional annotation, the oncogene pituitary tumor-transforming 1 (PTTG1) was upregulated. In the PPI network and sub-network, cell division cycle 20 (CDC20) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were hub genes with high connectivity degrees. Additionally, DEGs in the sub-network including cyclin B1 (CCNB1) were mainly enriched in the cell cycle and p53 signaling pathways. In conclusion, the cell cycle and p53 signaling pathways may play significant roles in PDAC, and DEGs including CDC20, BUB1B, CCNB1 and PTTG1 may be potential targets for PDAC diagnosis and treatment.
Project description:The aim of the present study was to investigate the prognostic value of genes that participate in the development of gastric adenocarcinoma, via exploring gene cross talk in disease?related pathways. Differentially expressed genes (DEGs) in the gastric samples were identified by analyzing the expression data downloaded from the GEO database. The DEGs were subjected to the human protein?protein interaction (PPI) network to construct the PPI network of DEGs, which was then used for the identification of key genes in cancer samples via the expression deviation score and degree in the network. A total of 635 DEGs, including 432 downregulated and 203 upregulated ones were screened in the gastric adenocarcinomas samples. The PPI network of DEGs comprised 590 DEGs and 4,299 interaction pairs. A total of 200 key genes were obtained, which were significantly enriched in six downregulated and six upregulated pathways. Cross talk genes in the connected pathways were analyzed, and the Kyoto Encyclopedia of Genes and Genomes pathways hsa00980 (Metabolism of xenobiotics by cytochrome P450) and hsa00982 (Drug metabolism) were reported to share 8 cross talk genes: ADH7, ALDH3A1, GSTA1, GSTA2, UGT2B17, UGT2B10, ADH1B and CYP2C18. Among all cross talk genes, ADH7, ALDH3A1 and CLDN3 were the most specific genes. The high? and low?risk samples identified by the prognosis model presented a remarkable difference in total survival time, indicating its robustness and sensitivity as the prognosis genes for gastric adenocarcinoma. ADH7, ALDH3A1, GSTA1, GSTA2, UGT2B17, UGT2B10, ADH1B, CYP2C18ADH7, ALDH3A1 and CLDN3 may be used as the prognosis markers and target biomarkers for chemotherapies in gastric adenocarcinoma.
Project description:Acne is the eighth most frequent disease worldwide. Inflammatory response runs through all stages of acne. It is complicated and is involved in innate and adaptive immunity. This study aimed to explore the candidate genes and their relative signaling pathways in inflammatory acne using data mining analysis. Microarray data GSE6475 and GSE53795, including 18 acne lesion tissues and 18 matched normal skin tissues, were obtained. Differentially expressed genes (DEGs) were filtered and subjected to functional and pathway enrichment analyses. Protein-protein interaction (PPI) network and module analyses were also performed based on the DEGs. In this work, 154 common DEGs, including 145 upregulated and 9 downregulated, were obtained from two microarray profiles. Gene Ontology and pathway enrichment of DEGs were clustered using significant enrichment analysis. A PPI network containing 110 nodes/DEGs was constructed, and 31 hub genes were obtained. Four modules in the PPI network, which mainly participated in chemokine signaling pathway, cytokine-cytokine receptor interaction, and Fc gamma R-mediated phagocytosis, were extracted. In conclusion, aberrant DEGs and pathways involved in acne pathogenesis were identified using bioinformatic analysis. The DEGs included FPR2, ITGB2, CXCL8, C3AR1, CXCL1, FCER1G, LILRB2, PTPRC, SAA1, CCR2, ICAM1, and FPR1, and the pathways included chemokine signaling pathway, cytokine-cytokine receptor interaction, and Fc gamma R-mediated phagocytosis. This study could serve as a basis for further understanding the pathogenesis and potential therapeutic targets of inflammatory acne.
Project description:BACKGROUND:Aortic dissection (AD) is one of the most lethal cardiovascular diseases. The aim of this study was to identify core genes and pathways revealing pathogenesis in AD. METHODS:We screened differentially expressed mRNAs and miRNAs using mRNA and miRNA expression profile data of AD from Gene Expression Omnibus. Then functional and pathway enrichment analyses of differential expression genes (DEGs) was performed utilizing the database for annotation, visualization, and integrated discovery (DAVID). Target genes with differential expression miRNAs (DEMIs) were predicted using the miRWalk database, and the intersection between these predictions and DEGs was selected as differentially expressed miRNA-target genes. In addition, a protein-protein interaction (PPI) network and miRNA-mRNA regulatory network were constructed. RESULTS:In total, 130 DEGs and 47 DEMIs were identified from mRNA and miRNA microarray, respectively, and 45 DEGs were DEMI-target genes. The PPI and miRNA-mRNA network included 79 node genes and 74 node genes, respectively, while 23 hub genes and 2 hub miRNAs were identified. The DEGs, PPI and modules differential expression miRNA-target genes were all mainly enriched in cell cycle, cell proliferation and cell apoptosis signaling pathways. CONCLUSION:Taken above, the study reveals some candidate genes and pathways potentially involving molecular mechanisms of AD. These findings provide a new insight for research and treatment of AD.