scholarly journals Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Md. Zubbair Malik ◽  
Keilash Chirom ◽  
Shahnawaz Ali ◽  
Romana Ishrat ◽  
Pallavi Somvanshi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Ppi Network ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Levels Of Organization ◽  
Grassroots Level ◽  
Community Finding ◽  
Main Regulator ◽  
Cancer Network ◽  
Modular Structures

Abstract Background Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. Methods The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. Results The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. Conclusions The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

MODULAR STRUCTURES AND ROBUSTNESS OF PROTEIN NETWORKS

2006 ◽  
Vol 15 (02) ◽  
pp. 309-321
Author(s):  
KA-LOK NG ◽  
CHIEN-HUNG HUANG ◽  
PO-HAN LEE ◽  
JYWE-FEI FANG ◽  
JEFFREY J. P. TSAI
Keyword(s):  
Network Models ◽  
Good Evidence ◽  
Hierarchical Network ◽  
Edge Deletion ◽  
Scale Free ◽  
E Coli ◽  
Protein Protein Interaction ◽  
Topological Parameters ◽  
Random Graph Theory ◽  
Modular Structures

The topological structures and robustness of the protein-protein interaction networks (PINs) is studied via random graph theory. Several global topological parameters are used to characterize PINs for seven species. Good evidence by correlation analysis support the fact that the seven PINs are well approximated by scale-free networks. It is also found that two of the species' PINs are well described by the hierarchical models. In particular, it is determined that the E. coli and the yeast PINs are well represented by the stochastic and deterministic hierarchical network models respectively. These results suggest that the hierarchical network model is a better description for certain species' PINs. Furthermore, it is demonstrated that PINs are robust with respective to failure, attack, random rewiring and edge deletion perturbations.

scholarly journals TulsiPIN: an interologous protein interactome ofOcimum tenuiflorum

2019 ◽  
Author(s):  
Vikram Singh ◽  
Gagandeep Singh ◽  
Vikram Singh
Keyword(s):  
Small World ◽  
Ppi Network ◽  
Modular Architecture ◽  
High Confidence ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Ppi Networks ◽  
A Genome ◽  
Transcription Regulators

AbstractOcimum tenuiflorum, commonly known as holy basil or tulsi, is globally recognized for its multitude of medicinal properties. However, a comprehensive study revealing the complex interplay among its constituent proteins at subcellular level is still lacking. To bridge this gap, a genome scale interologous protein-protein interaction (PPI) network, TulsiPIN, is developed using 49 template plants. The reported network consists of 13, 660 nodes and 327, 409 binary interactions. A high confidence PPI network consisting of 7, 719 nodes having 95, 532 interactions was inferred using domain-domain interaction information along with interolog based statistics, and its reliability was further assessed using functional homogeneity and protein colocalization. 1, 625 vital proteins are predicted by statistically evaluating this high confidence TulsiPIN with two ensembles of corresponding random networks, each consisting of 10, 000 realizations of Erdős-Rényi and Barabási-Albert models. Topological features of TulsiPIN including small-world, scale-free and modular architecture are inspected and found to resemble with other plant PPI networks. Finally, numerous regulatory proteins like transcription factors, transcription regulators and protein kinases are profiled in TulsiPIN and a sub-network of proteins participating in 10 secondary metabolite biosynthetic pathways is studied. We believe, the methodology developed and insights imparted would be useful in understanding regulatory mechanisms in various plant species.

scholarly journals Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Naaila Tamkeen ◽  
Suliman Yousef AlOmar ◽  
Saeed Awad M. Alqahtani ◽  
Abdullah Al-jurayyan ◽  
Anam Farooqui ◽  
...  
Keyword(s):  
Spina Bifida ◽  
Experimental Validation ◽  
Hierarchical Organization ◽  
Ppi Network ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Rich Club ◽  
Spinal Cord Malformation ◽  
Graph Theoretical Approach ◽  
Multiple Etiology

Spina Bifida (SB) is a congenital spinal cord malformation. Efforts to discern the key regulators (KRs) of the SB protein-protein interaction (PPI) network are requisite for developing its successful interventions. The architecture of the SB network, constructed from 117 manually curated genes was found to self-organize into a scale-free fractal state having a weak hierarchical organization. We identified three modules/motifs consisting of ten KRs, namely, TNIP1, TNF, TRAF1, TNRC6B, KMT2C, KMT2D, NCOA3, TRDMT1, DICER1, and HDAC1. These KRs serve as the backbone of the network, they propagate signals through the different hierarchical levels of the network to conserve the network’s stability while maintaining low popularity in the network. We also observed that the SB network exhibits a rich-club organization, the formation of which is attributed to our key regulators also except for TNIP1 and TRDMT1. The KRs that were found to ally with each other and emerge in the same motif, open up a new dimension of research of studying these KRs together. Owing to the multiple etiology and mechanisms of SB, a combination of several biomarkers is expected to have higher diagnostic accuracy for SB as compared to using a single biomarker. So, if all the KRs present in a single module/motif are targetted together, they can serve as biomarkers for the diagnosis of SB. Our study puts forward some novel SB-related genes that need further experimental validation to be considered as reliable future biomarkers and therapeutic targets.

scholarly journals Scale-free structure of cancer networks and their vulnerability to hub-directed combination therapy

2020 ◽  
Author(s):  
Andrew X. Chen ◽  
Christopher J. Zopf ◽  
Jerome Mettetal ◽  
Wen Chyi Shyu ◽  
Joseph Bolen ◽  
...  
Keyword(s):  
Combination Therapies ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Effective Combination ◽  
Scale Free Networks ◽  
New Strategy ◽  
Cancer Networks ◽  
Law Degree ◽  
Cancer Network ◽  
Lethal Genes

AbstractBackgroundThe effectiveness of many targeted therapies is limited by toxicity and the rise of drug resistance. A growing appreciation of the inherent redundancies of cancer signaling has led to a rise in the number of combination therapies under development, but a better understanding of the overall cancer network topology would provide a conceptual framework for choosing effective combination partners. In this work, we explore the scale-free nature of cancer protein-protein interaction networks in 14 indications. Scale-free networks, characterized by a power-law degree distribution, are known to be resilient to random attack on their nodes, yet vulnerable to directed attacks on their hubs (their most highly connected nodes).ResultsConsistent with the properties of scale-free networks, we find that lethal genes are associated with ∼5-fold higher protein connectivity partners than non-lethal genes. This provides a biological rationale for a hub-centered combination attack. Our simulations show that combinations targeting hubs can efficiently disrupt 50% of network integrity by inhibiting less than 1% of the connected proteins, whereas a random attack can require inhibition of more than 30% of the connected proteins.ConclusionsWe find that the scale-free nature of cancer networks makes them vulnerable to focused attack on their highly connected protein hubs. Thus, we propose a new strategy for designing combination therapies by targeting hubs in cancer networks that are not associated with relevant toxicity networks.

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.

scholarly journals Investigation of Immune‐related Makers Associated with Tumor-infiltrating T Cells in Ovarian Cancer

2020 ◽  
Author(s):  
Haihong Liao ◽  
Shuwen Han ◽  
Yuefen Pan ◽  
Jiamin Xu ◽  
Quan Qi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
T Cells ◽  
Network Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Target Network ◽  
Patient Prognosis

Abstract Background: Tumor-infiltrating T cells in the tumor microenvironment are the biological basis of immunotherapy and promising predictors of cancer prognosis. Aim: The aim of this study was to investigate immune‐related RNAs associated with tumor-infiltrating T cells in ovarian cancer (OV).Methods: The gene expression data of patients with OV were downloaded from The Cancer Genome Atlas (TCGA) database. The immune and stromal scores were calculated and the differentially expressed mRNAs (DEGs) were screened. The abundance of six types of infiltrating immune cells was investigated, and the immune-related DEGs associated with tumor-infiltrating CD4+ and CD8+ T cells were explored by correlation analyses. Subsequently, multiple analyses, i.e., protein-protein interaction (PPI) network analysis, competing endogenous RNA (ceRNA; lncRNA-miRNA-target) network analysis, and small-molecule target network analysis, were performed. Results: In total, 37 and 49 immune-related DEGs of CD4+ and CD8+ T cells were screened, respectively. PPI network results showed that granzyme B (GZMB) was a hub node in the two PPI networks constructed by immune-related DEGs of CD4+ and CD8+ T cells. Moreover, the ceRNA chr22-38_28785274-29006793.1/has-miR-1249-5p/CD3E was obtained from the two constructed ceRNA networks related to CD4+ and CD8+ T cells. Survival analysis revealed that key immune-related DEGs of CD4+ and CD8+ T cells, such as GZMB and CD3E, were positively correlated with patient prognosis. Conclusion: GZMB and ceRNAs, such as chr22-38_28785274-29006793.1/has-miR-1249-5p/CD3E, may mediate the role of tumor-infiltrating T cells in OV. GZMB and CD3E may be used as promising T cell-related biomarkers with prognostic value in OV.

scholarly journals Construction of a novel prognostic-predicting model correlated to ovarian cancer

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Weichun Tang ◽  
Jie Li ◽  
Xinxia Chang ◽  
Lizhou Jia ◽  
Qi Tang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Prognostic Model ◽  
Outcome Prediction ◽  
Risk Groups ◽  
Biological Pathways ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Term Enrichment ◽  
New Perspective

Abstract Background: Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide. The pathogenesis of the disease and outcomes prediction of OC patients remain largely unclear. The present study aimed to explore the key genes and biological pathways in ovarian carcinoma development, as well as construct a prognostic model to predict patients’ overall survival (OS). Results: We identified 164 up-regulated and 80 down-regulated differentially expressed genes (DEGs) associated with OC. Gene Ontology (GO) term enrichment showed DEGs mainly correlated with spindle microtubes. For Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cell cycle was mostly enriched for the DEGs. The protein–protein interaction (PPI) network yielded 238 nodes and 1284 edges. Top three modules and ten hub genes were further filtered and analyzed. Three candidiate drugs targeting for therapy were also selected. Thirteen OS-related genes were selected and an eight-mRNA model was present to stratify patients into high- and low-risk groups with significantly different survival. Conclusions: The identified DEGs and biological pathways may provide new perspective on the pathogenesis and treatments of OC. The identified eight-mRNA signature has significant clinical implication for outcome prediction and tailored therapy guidance for OC patients.

Protein-Protein Interaction (PPI) Network: Recent Advances in Drug Discovery

2017 ◽  
Vol 18 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Alexiou Athanasios ◽  
Vairaktarakis Charalampos ◽  
Tsiamis Vasileios ◽  
Ghulam Ashraf
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Recent Advances

scholarly journals Scarcity of scale-free topology is universal across biochemical networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Harrison B. Smith ◽  
Hyunju Kim ◽  
Sara I. Walker
Keyword(s):  
Large Body ◽  
Biochemical Networks ◽  
Biological Organization ◽  
Scale Free ◽  
Levels Of Organization ◽  
Common Structure ◽  
Domains Of Life ◽  
Ecosystem Level ◽  
Different Levels ◽  
Organizing Principles

AbstractBiochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological and technological systems, including biochemistry, can be described by power-law relationships between the numbers of nodes and edges, often described as “scale-free”. Recently, new statistical analyses have revealed true scale-free networks are rare. We provide a first application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confirm no more than a few biochemical networks are any more than super-weakly scale-free. Additionally, we test the distinguishability of individual and ecosystem-level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across different network projections. Our results indicate that while biochemical networks are not scale-free, they nonetheless exhibit common structure across different levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks.

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