scholarly journals Computational Identification of Guillain-Barre Syndrome Related Genes by an mRNA Gene Expression Profile and a Protein-Protein Interaction Network

2021 ◽  
Author(s):  
Chunyang Wang ◽  
Shiwei Liao ◽  
jing xu
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Shortest Path ◽  
Protein Interaction ◽  
Guillain Barre Syndrome ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Guillain Barré Syndrome ◽  
Guillain Barré

Abstract In this study, we developed a computational method to identify Guillain–Barré syndrome (GBS) related genes based on (i) a gene expression profile, and (ii) the shortest path analysis in a protein-protein interaction (PPI) network. The mRMR (Maximum Relevance Minimum Redundancy) method was employed to select significant genes from an mRNA profile dataset of GBS patients and healthy controls. The protein products of the significant genes were then mapped to a PPI network generated from the STRING database. Shortest paths were computed and all shortest path proteins were picked out and were ranked according to their betweenness. Related genes of the top-most proteins in the ordered list were then retrieved and were regarded as potential GBS related genes in this study. As a result, totally 30 GBS related genes were screened out, in which 20 were retrieved from PPI analysis of up-regulated expressed genes and 23 were from down-regulated expressed genes (13 overlap genes). GO enrichment and KEGG enrichment analysis were performed respectively. Results showed that there were some overlap GO terms and KEGG pathway terms in both up-regulated and down-regulated analysis, which indicated these terms may play critical role during GBS process. These results could shed some light on the understanding of the Genetic and molecular pathogenesis of GBS disease, providing basis for future experimental biology studies and for the development of effective genetic strategies for GBS clinical therapies.

scholarly journals Computational Identification of Guillain-Barre Syndrome Related Genes by an mRNA Gene Expression Profile and a Protein-Protein Interaction Network

2021 ◽  
Author(s):  
Chunyang Wang ◽  
Shiwei Liao ◽  
Xiaowei Hu ◽  
Jing Xu
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Protein Interaction ◽  
Critical Role ◽  
Computational Method ◽  
Guillain Barre Syndrome ◽  
Protein Protein Interaction ◽  
Guillain Barré Syndrome ◽  
Guillain Barré

Abstract Background In this study, we developed a computational method to identify Guillain–Barré syndrome (GBS) related genes based on (i) a gene expression profile, and (ii) the shortest path analysis in a protein-protein interaction (PPI) network. Results Totally 30 GBS related genes were screened out, in which 20 were retrieved from PPI analysis of up-regulated expressed genes and 23 were from down-regulated expressed genes (13 overlap genes). GO enrichment and KEGG enrichment analysis were performed respectively. Results showed that there were some overlap GO terms and KEGG pathway terms in both up-regulated and down-regulated analysis, which indicated these terms may play critical role during GBS process. Discussion These results could shed some light on the understanding of the Genetic and molecular pathogenesis of GBS disease, providing basis for future experimental biology studies and for the development of effective genetic strategies for GBS clinical therapies.

scholarly journals Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Lei Chen ◽  
Jian Zhang ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Shortest Path ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Shortest Paths ◽  
Gene Expression Profiles ◽  
Cancer Genes ◽  
Ppi Network ◽  
Protein Protein Interaction

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

scholarly journals Gene Expression Profile of the Human Colorectal Carcinoma LoVo Cells Treated With Sporamin and Thapsigargin

2021 ◽  
Vol 11 ◽  
Author(s):  
Chun Yang ◽  
Si-Jia Chen ◽  
Bo-Wen Chen ◽  
Kai-Wen Zhang ◽  
Jing-Jie Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Ipomoea Batatas ◽  
Expression Profiles ◽  
Rna Seq ◽  
Ppi Network ◽  
Illumina Platform

Sporamin, a proteinase inhibitor isolated from the sweet potato (Ipomoea batatas), has shown promising anticancer effect against colorectal cancer (CRC) in vitro and in vivo but its mechanisms of action are poorly understood. In the present study, high throughput RNA sequencing (RNA-seq) technology was applied to explore the transcriptomic changes induced by sporamin in the presence of thapsigargin (TG), a non-12-O-tetradecanolphorbol-13-acetate type cancer promoter, in the LoVo human CRC cells. Cellular total RNA was extracted from the cells after they were treated with vehicle (CTL), 1 μM of thapsigargin (TG), or 1 μM of TG plus 30 μM of sporamin (TGSP) for 24 h. The migratory capacity of the cells was determined by wound healing assay. The gene expression profiles of the cells were determined by RNA-seq on an Illumina platform. GO enrichment analysis, KEGG pathway analysis, protein-protein interaction (PPI) network construction, and transcription factors (TF) prediction were all performed based on the differentially expressed genes (DEGs) across groups with a series of bioinformatics tools. Finally, the effect and potential molecular targets of the sporamin at the transcriptome level were evaluated. Sporamin significantly inhibited the migration of cells induced by TG. Among the 17915 genes detected in RNA-seq, 46 DEGs were attributable to the effect of sporamin. RT-PCR experiment validated that the expression of RGPD2, SULT1A3, and BIVM-ERCC5 were up-regulated while NYP4R, FOXN1, PAK6, and CEACAM20 were down-regulated. Sporamin enhanced the mineral absorption pathway, worm longevity regulating pathway, and pyrimidine metabolism pathway. Two TFs (SMIM11A and ATOH8) were down-regulated by sporamin. HMOX1 (up-regulated) and NME1-NME2 (down-regulated) were the main nodes in a PPI network consisting of 16 DEGs that were modulated by sporamin in the presence of TG. Sporamin could favorably alter the gene expression profile of CRC cells, up-regulating the genes that contribute to the homeostasis of intracellular metal ions and the activities of essential enzymes and DNA damage repairment. More studies are warranted to verify its effect on specific genes and delineate the mechanism of action implicated in the process.

scholarly journals Resilience and evolvability of protein-protein interaction networks

2020 ◽  
Author(s):  
Brennan Klein ◽  
Ludvig Holmér ◽  
Keith M. Smith ◽  
Mackenzie M. Johnson ◽  
Anshuman Swain ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Preferential Attachment ◽  
Network Resilience ◽  
Biological Noise ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks

AbstractProtein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. One such factor is gene expression, which controls the simultaneous presence of proteins for allowed extant interactions and the possibility of novel associations. Here, we explore the influence of gene expression and network properties on a PPI network’s resilience, focusing especially on ribosomal proteins—vital molecular-complexes involved in protein synthesis, which have been extensively and reliably mapped in many species. Using publicly-available data of ribosomal PPIs for E. coli, S.cerevisae, and H. sapiens, we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network. This holds in all three species regardless of their distributions of gene expressions or their network community structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.1Author SummaryProteins in organismal cells are present at different levels of concentration and interact with other proteins to provide specific functional roles. Accumulating lists of all of these interactions, complex networks of protein interactions become apparent. This allows us to begin asking whether there are network-level mechanisms at play guiding the evolution of biological systems. Here, using this network perspective, we address two important themes in evolutionary biology (i) How are biological systems able to successfully incorporate novelty? (ii) What is the evolutionary role of biological noise in evolutionary novelty? We consider novelty to be the introduction of a new protein, represented as a new “node”, into a network. We simulate incorporation of novel proteins into Protein-Protein Interaction (PPI) networks in different ways and analyse how the resilience of the PPI network alters. We find that novel interactions guided by gene expression (indicative of concentration levels of proteins) creates a more resilient network than either uniformly random interactions or interactions guided solely by the network structure (preferential attachment). Moreover, simulated biological noise in the gene expression increases network resilience. We suggest that biological noise induces novel structure in the PPI network which has the effect of making it more resilient.

scholarly journals ITGB2 as a Novel Biomarker Correlating With Prognosis and Immune Infiltrates in Ovarian Cancer

2021 ◽  
Author(s):  
chanyuan li ◽  
Ting Wan ◽  
Ting Deng ◽  
Junya Cao ◽  
He Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Protein Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background: Epithelial ovarian cancer is nowadays one of the malignancies in women, this study aimed to identify novel biomarkers to predict prognosis and immunotherapy efficacy.Methods: The differentially expressed genes (DEGs) obtained from online database Gene Expression Omnibus (GEO)were screened via GEO2R and Venn diagram software, gene enrichment was analysed by Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG), then protein protein interaction(PPI)network and Cytoscape software were used to confirm the genes closely related to ovarian cancer. Survival analysis of hub genes were obtained from Kaplan–Meier plotter, with their differential expression in specimen validated by Gene Expression Profiling Interactive Analysis (GEPIA) and an integrated repository portal for tumor-immune system interactions (TISIDB). Finally, we used the Tumor Immune Estimation Resource 2.0 (TIMER2.0) and application Estimate the Proportion of Immune and Cancer cells (EPIC) to search the immune infiltration characteristics of the genes.Results: 355 DEGs between epithelial ovarian cancer and normal ovarian tissue were screened out. These DEGs were associated with extracellular exosome, bicellular tight junction and cell-cell junction, and remarkably enriched in molecules of cell adhesion and leukocyte transendothelial migration activity. Ten hub genes were identified via protein protein interaction (PPI) network: PTAFR, HLA-DRA, OAS2, OAS3, PTPN6, LYN, VAMP8, IRF6, ITGB2, CD47. Furthermore, the Kaplan–Meier plotter was conducted, overexpression of four genes was positively connected to poor prognosis in ovarian cancer:OAS2, OAS3, ITGB2, CD47,which were also correlated with immune infiltrates in ovarian cancer and had the highest degree of correlation with tumor associated macrophages (TAMs) infiltration, among which ITGB2 was highly correlated with TAMs infiltration level.Conclusion: ITGB2, OAS2, OAS3, and CD47 are upregulated with unfavorable prognosis in ovarian cancer, and ITGB2 may act as a novel prognostic biomarker with immune infiltration values.

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