Protein-protein Interaction Subnetworks Research Articles

Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID-19.

ObjectivesCoronavirus disease 2019 (COVID‐19) is rapidly spreading worldwide. Lianhua Qingwen capsule (LQC) has shown therapeutic effects in patients with COVID‐19. This study is aimed to discover its molecular mechanism and provide potential drug targets.Materials and MethodsAn LQC target and COVID‐19–related gene set was established using the Traditional Chinese Medicine Systems Pharmacology database and seven disease‐gene databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein‐protein interaction (PPI) network were performed to discover the potential mechanism. Molecular docking was performed to visualize the patterns of interactions between the effective molecule and targeted protein.ResultsA gene set of 65 genes was generated. We then constructed a compound‐target network that contained 234 nodes of active compounds and 916 edges of compound‐target pairs. The GO and KEGG indicated that LQC can act by regulating immune response, apoptosis and virus infection. PPI network and subnetworks identified nine hub genes. The molecular docking was conducted on the most significant gene Akt1, which is involved in lung injury, lung fibrogenesis and virus infection. Six active compounds of LQC can enter the active pocket of Akt1, namely beta‐carotene, kaempferol, luteolin, naringenin, quercetin and wogonin, thereby exerting potential therapeutic effects in COVID‐19.ConclusionsThe network pharmacological strategy integrates molecular docking to unravel the molecular mechanism of LQC. Akt1 is a promising drug target to reduce tissue damage and help eliminate virus infection.

Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium.

Delirium is a complex pathophysiological process, and multiple contributing mechanisms have been identified. However, it is largely unclear how the genes associated with delirium contribute and which of them play key roles. In this study, the genes associated with delirium were retrieved from the Comparative Toxicogenomics Database (CTD) and integrated through a protein–protein interaction (PPI) network. Delirium-associated genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate. Using the Molecular Complex Detection (MCODE) algorithm, we identified the top two delirium-relevant network modules, M1 and M5, that have the most significant enrichments for the delirium-related gene sets. Functional enrichment analysis showed that genes related to neurotransmitter receptor activity were enriched in both modules. Moreover, analyses with genes located in human accelerated regions (HARs) provided evidence that HAR-Brain genes were overrepresented in the delirium-relevant network modules. We found that four of the HAR-Brain genes, namely APP, PLCB1, NPY, and HTR2A, in the M1 module were highly connected and appeared to exhibit hub properties, which might play vital roles in delirium development. Further understanding of the function of the identified modules and member genes could help to identify therapeutic intervention targets and diagnostic biomarkers for delirium.

Identification of G2 and S Phase-Expressed-1 as a Potential Biomarker in Patients with Prostate Cancer.

BackgroundThis study aimed to predict and explore the possible clinical value and mechanism of genetic markers in prostate cancer (PCa) using a bioinformatics analysis method.Materials and MethodsThe RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database to identify the differentially expressed genes (DEGs). The hub genes were screened by building protein–protein interaction (PPI) subnetworks with four topological analysis methods. The overall survival analysis of hub genes was conducted using Kaplan–Meier curves. Furthermore, the bioinformatics results were confirmed in 102 PCa samples collected in our hospital. Gene Set Enrichment Analysis (GSEA) was performed to provide information about the molecular mechanisms underlying PCa.ResultsAmong 13 hub genes, the high expression of GTSE1 or KIF18B was associated with worse overall survival according to the TCGA samples. Immunoreactive scores for GTSE1 staining were significantly higher in PCa tissues than in paracancerous tissues (P<0.01). The overall survival time of patients with high GTSE1 expression was shorter than that of patients with low GTSE1 expression (P=0.015). GSEA demonstrated that high GTSE1 expression was mainly enriched in the cell cycle (P<0.001), DNA replication (P<0.001), mismatch repair (P<0.001), and p53 signaling pathway (P<0.001).ConclusionGTSE1 expression was significantly high in PCa and associated with poor prognosis. GTSE1 may serve as a potential biomarker and therapeutic target in PCa patients.

Integrative Analysis of MAPK14 as a Potential Biomarker for Cardioembolic Stroke.

The aim of this study was to obtain the candidate genes and biomarkers that are significantly related to cardioembolic stroke (CS) by applying bioinformatics analysis. In accordance with the results of the weighted gene coexpression network analysis (WGCNA) in the GSE58294 dataset, 11 CS-related coexpression network modules were identified in this study. Correlation analysis showed that the black and pink modules are significantly associated with CS. A total of 18 core genes in the black module and one core gene in the pink module were determined. We then identified differentially expressed genes (DEGs) of CS at 3 h, 5 h, and 24 h postonset. After performing intersection, it was found that 311 genes were coexpressed at these three time points. These genes were majorly enriched in positive regulation of transferase activity and regulation of peptidase activity. The abovementioned coexpressed DEGs were subjected to protein-protein interaction analysis and subnetwork module analysis. Subsequently, we used cytoHubba to obtain 11 key genes from DEGs. The intersection of the core genes screened from WGCNA and the key genes selected from DEGs yielded the MAPK14 gene. The expression level of MAPK14 on the receiver operating characteristic (ROC) curves of CS at 3 h, 5 h, and 24 h showed that the area under the ROC curve (AUC) was 0.923, 0.934, and 0.941, respectively. In a nutshell, MAPK14 screened out by using WGCNA showed differential expression in CS. We conclude that MAPK14 can be used as a potential biological marker of CS and exhibits potential to predict the physiopathological condition of CS patients.

Exploring the Role of Chemokine Receptor 6 (Ccr6) in the BXD Mouse Model of Gulf War Illness.

Gulf War illness (GWI) is a chronic and multi-symptomatic disorder with persistent neuroimmune symptomatology. Chemokine receptor 6 (CCR6) has been shown to be involved in several inflammation disorders in humans. However, the causative relationship between CCR6 and neuroinflammation in GWI has not yet been investigated. By using RNA-seq data of prefrontal cortex (PFC) from 31 C57BL/6J X DBA/2J (BXD) recombinant inbred (RI) mouse strains and their parental strains under three chemical treatment groups – saline control (CTL), diisopropylfluorophosphate (DFP), and corticosterone combined with diisopropylfluorophosphate (CORT+DFP), we identified Ccr6 as a candidate gene underlying individual differences in susceptibility to GWI. The Ccr6 gene is cis-regulated and its expression is significantly correlated with CORT+DFP treatment. Its mean transcript abundance in PFC of BXD mice decreased 1.6-fold (p < 0.0001) in the CORT+DFP group. The response of Ccr6 to CORT+DFP is also significantly different (p < 0.0001) between the parental strains, suggesting Ccr6 is affected by both host genetic background and chemical treatments. Pearson product-moment correlation analysis revealed 1473 Ccr6-correlated genes (p < 0.05). Enrichment of these genes was seen in the immune, inflammation, cytokine, and neurological related categories. In addition, we also found five central nervous system-related phenotypes and fecal corticosterone concentration have significant correlation (p < 0.05) with expression of Ccr6 in the PFC. We further established a protein-protein interaction subnetwork for the Ccr6-correlated genes, which provides an insight on the interaction of G protein-coupled receptors, kallikrein-kinin system and neuroactive ligand-receptors. This analysis likely defines the heterogeneity and complexity of GWI. Therefore, our results suggest that Ccr6 is one of promising GWI biomarkers.

Revealing the key point of the temperature stress response of Arthrospira platensis C1 at the interconnection of C- and N- metabolism by proteome analyses and PPI networking

BackgroundGrowth-temperature stress causes biochemical changes in the cells and reduction of biomass yield. Quantitative proteome of Arthrospira platensis C1 in response to low- and high temperature stresses was previously analysed to elucidate the stress response mechanism. The data highlighted the linkage of signaling proteins and proteins involved in nitrogen and ammonia assimilation, photosynthesis and oxidative stress.ResultsAfter phosphoproteome analysis was carried out in this study, the tentative temperature response cascade of A. platensis C1 was drawn based on data integration of quantitative proteome and phosphoproteome analysis and protein-protein interaction (PPI) networks. The integration revealed 31 proteins regulated at the protein-expression and post-translational levels; thus, this group of proteins was designated bi-level regulated proteins. PPI networks were then constructed based on A. platensis C1 gene inference from publicly available interaction data. The key two-component system (TCS) proteins, SPLC1_S082010 and SPLC1_S230960, were identified as bi-level regulated proteins and were linked to SPLC1_S270380 or glutamate synthase, an important enzyme in nitrogen assimilation that synthesizes glutamate from 2-oxoglutarate, which is known as the signal compound that regulates the carbon/nitrogen (C/N) balance of cells. Moreover, the role of the p-site in the PPIs of some phosphoproteins of interest was determined using site-directed mutagenesis and a yeast two-hybrid system. Evidence showing the critical role of the p-site in the PPI was observed for the multi-sensor histidine kinase SPLC1_S041070 (Hik28) and glutamate synthase. PPI subnetwork also showed that the Hik28 involved with the enzymes in fatty acid desaturation and nitrogen metabolism. The effect of Hik28-deletion was validated by fatty acid analysis and measurement of photosynthetic activity under nitrogen depletion.ConclusionsTaken together, the data clearly represents (i) the multi-level regulation of proteins involved in the stress response mechanism and (ii) the key point of the temperature stress response at the interconnection of C- and N- metabolism.

Identification of co‑expression modules and hub genes of retinoblastoma via co‑expression analysis and protein‑protein interaction networks.

Retinoblastoma is a common intraocular malignant tumor in children. However, the molecular and genetic mechanisms of retinoblastoma remain unclear. The gene expression dataset GSE110811 was retrieved from Gene Expression Omnibus. After preprocessing, coexpression modules were constructed by weighted gene coexpression network analysis (WGCNA), and modules associated with clinical traits were identified. In addition, functional enrichment analysis was performed for genes in the indicated modules, and protein-protein interaction (PPI) networks and subnetworks were constructed based on these genes. Eight coexpression modules were constructed through WGCNA. Of these, the yellow module had the highest association with severity and age (r=0.82 and P=3e-07; r=0.72 and P=3e-05). The turquoise module had the highest association with months (r=−0.63 and P=5e-04). The genes in the two modules participate in multiple pathways of retinoblastoma, and by combining the PPI network and subnetworks; 10 hub genes were identified in the two modules. The present study identified coexpression modules and hub genes associated with clinical traits of retinoblastoma, providing novel insight into retinoblastoma progression.

Network-guided bioengineering of oncolytic immunovirotherapies for glioblastoma

Abstract Glioblastoma is the most common and lethal primary brain tumor in adults, with a median survival less than 15 months despite current standard of care. Oncolytic immunovirotherapies (OVs) have shown promise in early clinical trials. OVs are engineered to specifically target cancer cells, trigger autophagic programmed cell death, and release tumor antigens in the context of T-cell activating cytokines. Cellular communication network factor 1 (CCN1) is an extracellular matrix protein expressed in 70% of glioblastomas that has been shown to drastically reduce OV efficacy, particularly herpes simplex virus type 1 (HSV-1). Our objectives were to decode protein-protein interaction (PPI) networks activated by CCN1, identify critical nodes in resistance networks, and use this analysis to design OVs that overcome CCN1-mediated resistance. We used NetDecoder to elucidate phenotype-specific PPI subnetworks in LN229 human glioblastoma cells with tetracycline-inducible CCN1 expression. Publicly available microarray data on CCN1-induced and control samples established differentially expressed genes, which served as sources in our PPI network to derive prioritized context-specific subnetworks. We found that 11 source genes collaborate via 39 deep nodes to confer OV resistance to CCN1-expressing glioblastomas. Of these, a router (IKBKE) and a sink (YBX1) have been previously implicated in glioblastoma pathogenesis. We conclude that autophagy regulator HSP90AA1, a critical node, may be targeted to improve OV efficacy in CCN1-expressing glioblastomas. Furthermore, WebGestalt overrepresentation analysis of subnetwork nodes suggests that oncolytic adenovirus may be more effective than HSV-1 as a synthetic biology chassis.

Analysis of key genes and their functions in placental tissue of patients with gestational diabetes mellitus

BackgroundThis study was aimed at screening out the potential key genes and pathways associated with gestational diabetes mellitus (GDM).MethodsThe GSE70493 dataset used for this study was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in the placental tissue of women with GDM in relation to the control tissue samples were identified and submitted to protein-protein interaction (PPI) network analysis and subnetwork module mining. Functional enrichment analyses of the PPI network and subnetworks were subsequently carried out. Finally, the integrated miRNA–transcription factor (TF)–DEG regulatory network was analyzed.ResultsIn total, 238 DEGs were identified, of which 162 were upregulated and 76 were downregulated. Through PPI network construction, 108 nodes and 278 gene pairs were obtained, from which chemokine (C-X-C motif) ligand 9 (CXCL9), CXCL10, protein tyrosine phosphatase, receptor type C (PTPRC), and human leukocyte antigen (HLA) were screened out as hub genes. Moreover, genes associated with the immune-related pathway and immune responses were found to be significantly enriched in the process of GDM. Finally, miRNAs and TFs that target the DEGs were predicted.ConclusionsFour candidate genes (viz., CXCL9, CXCL10, PTPRC, and HLA) are closely related to GDM. miR-223-3p, miR-520, and thioredoxin-binding protein may play important roles in the pathogenesis of this disease.

Aggregated network centrality shows non-random structure of genomic and proteomic networks.

Network analysis is a powerful tool for modelling biological systems. We propose a new approach that integrates the genomic interaction data at population level with the proteomic interaction data. In our approach we use chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data from human genome to construct a set of genomic interaction networks, considering the natural partitioning of chromatin into chromatin contact domains (CCD). The genomic networks are then mapped onto proteomic interactions, to create protein-protein interaction (PPI) subnetworks. Furthermore, the network-based topological properties of these proteomic subnetworks are investigated, namely closeness centrality, betweenness centrality and clustering coefficient. We statistically confirm, that networks identified by our method significantly differ from random networks in these network properties. Additionally, we identify one of the regions, namely chr6:32014923-33217929, as having an above-random concentration of the single nucleotide polymorphisms (SNPs) related to autoimmune diseases. Then we present it in the form of a meta-network, which includes multi-omic data: genomic contact sites (anchors), genes, proteins and SNPs. Using this example we demonstrate, that the created networks provide a valid mapping of genes to SNPs, expanding on the raw SNP dataset used.

Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.

Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients, and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high-throughput technologies. In this report, we explored data from human RNA-seq and protein–protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired control tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in tumor processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.

Identification of cancer/testis antigen 2 gene as a potential hepatocellular carcinoma therapeutic target by hub gene screening with topological analysis.

The 5-year survival rate of hepatocellular carcinoma (HCC) is <20%; thus, identifying new potential therapeutic targets or novel biomarkers for prognosis prediction is crucial. The present study aimed to screen hub genes by constructing protein-protein interaction (PPI) subnetworks using topological analysis methods, as well as reveal their clinical significance through big data analytics and their association with the clinicopathological features. Firstly, the PPI subnetworks were constructed using four topological analysis methods, including the MCC, DMNC, MNC and degree methods, to obtain 6 hub genes. Subsequently, the hub gene cancer/testis antigen 2 (CTAG2), which affects the prognosis of HCC (overall survival, P=0.035), was acquired by analysing clinical data in The Cancer Genome Atlas database. Meanwhile, CTAG2 expression was significantly associated with the age at diagnosis (P=0.003), T stage (P=0.028), TNM stage (P=0.028) and α-fetoprotein (AFP) expression (P=0.045). Further immunohistochemical analysis of samples collected in our hospital revealed that the expression level of CTAG2 in 46 HCC tissues was significantly higher in comparison with that in paired adjacent tissues. The clinical data indicated that the expression of CTAG2 was significantly correlated with the hepatitis B virus status (P=0.010) and AFP expression (P=0.004). These results were then found to be consistent with the results of big data analytics. Furthermore, Gene Set Enrichment Analysis demonstrated that the function of CTAG2 in HCC may be associated with the cell cycle. Taken together, these findings suggest that CTAG2 may serve as a new potential therapeutic target for HCC patients.

Abstract 1638: Whole-exome sequencing and protein interaction networks to prioritize candidate genes for cutaneous melanoma susceptibility

Abstract Background: Known cutaneous malignant melanoma (CMM) genes account for melanoma risk in less than 40% of melanoma-prone families, suggesting the existence of additional risk genes or other modifiers. Whole exome sequencing (WES) of high-risk families usually results in the identification of a large number of potential disease-causing genes. However, prioritizing these genes and finding the true disease-causing ones have been challenging. We hypothesize that physically interacting proteins might have a similar effect on cell function and therefore, might produce a shared phenotype or increase the risk when mutated. To test this hypothesis, we applied a network approach integrating WES and protein-protein interaction (PPI) data to detect and prioritize rare variants in melanoma-prone families. Methods: We conducted WES analysis on germline DNA of 212 patients from 45 CMM families (2-5 affected people in a family) without known mutations. Variants that were rare (&amp;lt;1% in public and our in-house control datasets), loss-of-function or missense predicted to be deleterious, and showed complete disease co-segregation (present in all affected members within a family) were included in the network analysis. For this analysis, we only focused on genes that interact with known CMM genes (gene list in Goldstein AM, et al. Hum Mol Genet, 2017) which we used as seed proteins. We then applied a degree-aware algorithm (DADA) to rank the set of candidate genes from WES with respect to the set of known CMM genes using human PPI networks. The goal of DADA is to prioritize a candidate set of genes based on their association level with genes known to be related to the disease. Network proximity calculations of the candidates with respect to the seed set were based on Random Walk with Restarts method. We used high quality and scored human proteins interactions collected from InWeb_IM. In the primary analysis, the network contains 625,641 interactions, aggregated from 8 source databases and spanning 87% of reviewed human proteins. We also conducted additional analyses with CMM PPI subnetworks generated using high confidence interaction data from different sources to compare results. Results: The WES analysis described above identified 546 genes that harbored rare variants, likely affect protein function and co-segregated in affected members in CMM families. After applying DADA across all networks, the rankings of these genes were fairly consistent. Top genes included both known CMM genes (such as MC1R, CDKN2B, KITLG, and TINF2) and genes that have not yet been associated with CMM susceptibility (such as FOXK1, AR, LEF1, RGL4, PABPC1, CHEK2, CTBP2, and BLM). These results highlight the importance of known CMM genes and their networks in CMM susceptibility, and demonstrate the potential value of network approaches in gene prioritization. Further evaluation of novel genes is in progress. Citation Format: Sally Yepes, Margaret Tucker, Hela Koka, Kristine Jones, Aurelie Vogt, Laurie Burdette, Wen Luo, Bin Zhu, Meredith Yeager, Belynda Hicks, Neal D. Freedman, Stephen J. Chanock, Alisa M. Goldstein, Xiaohong R. Yang. Whole-exome sequencing and protein interaction networks to prioritize candidate genes for cutaneous melanoma susceptibility [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1638.

Genetic effects of welding fumes on the development of respiratory system diseases

BackgroundThe welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WF exposure may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood. MethodsWe developed a quantitative framework to identify gene expression effects of WF exposure that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology. ResultsWe observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEG analysis, using disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC. ConclusionsOur network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.

PPIExp: A Web-Based Platform for Integration and Visualization of Protein-Protein Interaction Data and Spatiotemporal Proteomics Data.

Integrating spatiotemporal proteomics data with protein-protein interaction (PPI) data can help researchers make an in-depth exploration of their proteins of interest in a dynamic manner. However, there is still a lack of proper tools for the biologists who usually have few programming skills to construct a PPI network for a protein list, visualize active PPI subnetworks, and then select key nodes for further study. We propose a web-based platform named PPIExp that can automatically construct a PPI network, perform clustering analysis according to protein abundances, and perform functional enrichment analysis. More importantly, it provides multiple effective visualization interfaces, such as the interface to display the PPI network map, the interface to display a dendrogram and heatmap for the clustering result, and the interface to display the expression pattern of a selected protein. To visualize the active PPI subnetworks in specific space or time, it provides buttons to highlight the differentially expressed proteins under each condition on the network map. Additionally, to help researchers determine which proteins are worth further attention, PPIExp provides extensive one-click interactive operations to map node centrality measures to node size on the network and highlight three types of proteins, that is, the proteins in an enriched functional term, the coexpressed proteins selected from the dendgrogram and heatmap, and the proteins input by users. PPIExp is available at http://www.fgvis.com/expressvis/PPIExp .

Differential expression of a set of microRNA genes reveals the potential mechanism of papillary thyroid carcinoma

BackgroundOur aim was to explore the potential mechanism underlying papillary thyroid carcinoma (PTC) development. MethodsGene expression profile data GSE3467 and microRNA (miRNA) expression profile data E-TABM-68 were downloaded from Gene Expression Omnibus and Array Express database respectively. The differentially expressed genes (DEGs) and miRNAs between PTC patients and normal individuals were screened. Then, the significant target DEGs regulated by differentially expressed miRNAs were mapped to protein-protein interaction (PPI) network and functional modules were screened. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis for miRNA genes were performed using DAVID (the Database for Annotation, Visualization and Integration Discovery) tool. ResultsTotal 4307 DEGs and 23 differentially expressed miRNAs were identified. A PPI subnetwork containing 612 nodes and 713 edges was constructed. Total 5 DEGs such as SPARC (secreted protein acidic and rich in cysteine), FN1 (fibronectin 1), THBS1 (thrombospondin 1), COL1A1 (collagen, type I, alpha 1) and COL7A1 (collagen, type VII, alpha 1) were found in module M1. The up-regulated DEGs were significantly related with cell adhesion molecules (CAMs), response to wounding and immune response. The down-regulated DEGs were significantly enriched in metabolism related pathways and transcription related with GO terms. ConclusionsECM-receptor interaction and amino acid degradation may play key roles in the mechanism of PTC progression.

A random forest classifier predicts recurrence risk in patients with ovarian cancer.

Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non-recurrent OC samples) and GSE49997 (204 OC samples), were obtained from the Gene Expression Omnibus database. Differentially expressed genes were screened using the differential expression via distance synthesis algorithm, followed by gene ontology enrichment analysis and weighted gene coexpression network analysis (WGCNA). Furthermore, subnetwork analysis was conducted for the protein-protein interaction (PPI) network using the BioNet package. Finally, a random forest classifier was constructed based on the subnetwork nodes, and its reliability was validated using the GSE44104 and GSE49997 validation datasets. A total of 44 upregulated and 117 downregulated genes were identified in the recurrent samples. Enrichment analysis indicated that cytochrome P450 family 17 subfamily A member 1 (CYP17A1) was associated with ‘positive regulation of steroid hormone biosynthetic processes’. WGCNA identified turquoise and grey modules that were significantly correlated with status and prognosis. A significant PPI subnetwork containing 16 nodes was also identified, including: Transcription factor GATA-4; fibroblast growth factor 9; aromatase; 3β-hydroxysteroid dehydrogenase/δ5-4-isomerase type 2; corticosteroid 11β-dehydrogenase isozyme 1; CYP17A1; pituitary homeobox 2; left-right determination factor 1; homeobox protein ARX; estrogen receptor β; steroidogenic factor 1; forkhead box protein L2; myocardin; steroidogenic acute regulatory protein mitochondrial; vesicular inhibitory amino acid transporter; and twist-related protein 1. A random forest classifier was constructed using the subnetwork nodes as feature genes, which exhibited a 92% true positive rate when classifying recurrent and non-recurrent OC samples. The classifying efficiency of the random forest classifier was validated using the two other independent datasets. Overall, 44 upregulated and 117 downregulated genes associated with OC recurrence were identified. Furthermore, the 16 subnetwork node genes that were identified may be important molecules in OC recurrence.

PCD-DPPI: Protein complex detection from dynamic PPI using shuffled frog-leaping algorithm

In the post-genome period, the identification of a protein complex from large PPI (protein-protein interaction) networks is a challenging task. Protein complexes plays a vital character in numerous molecular processes of the cell. Although, the maximum computational techniques of protein complex detection have aimed at static PPI networks that cannot represent the logical dynamicity of protein interactions. In recent times, the dynamicity of PPI networks has been exploited by building a set of dynamic PPI subnetworks with respect to every time-point in a gene expression matrix. This paper presents a new technique, called Protein Complex Detection based on Dynamic PPI (PCD-DPPI) to accomplish dynamicity in protein complex detection. The initial stage of the proposed technique is based on shuffled frog-leaping algorithm, an Optimization technique that take out a few sets of genes that are co-regulated beneath some conditions from the input gene expression matrix. An individual extracted gene set is defined as a bicluster. In the second stage, depending on the biclusters, few dynamic PPI subnetworks are taken out from the input static PPI network. By employing a complex detection approach on every dynamic PPI subnetworks, protein complexes are identified and the result is aggregated. The proposed and existing algorithms were applied to various datasets such as DIP, Krogan “Extended”, Krogan “Core”, Gavin2, Gavin6, PPI 1, Collins and Gavin + Krogan. Experimental results have been proved that the proposed PCD-DPPI efficiently depicts the dynamicity characteristics in static PPI networks and attains expressively better results when compared to other existing standard methods.

Identification of potential target genes in pancreatic ductal adenocarcinoma by bioinformatics analysis.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most complicated and fatally pathogenic human malignancies. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of PDAC. The aim of the present study was to identify the key genes and signaling pathways associated with PDAC using bioinformatics analysis. Four transcriptome microarray datasets (GSE15471, GSE55643, GSE62165 and GSE91035) were acquired from Gene Expression Omnibus datasets, which included 226 PDAC samples and 65 normal pancreatic tissue samples. We screened differentially expressed genes (DEGs) with GEO2R and investigated their biological function by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. The overall survival data was obtained from UALCAN, which calculated the data shared with The Cancer Genome Atlas. In addition, a protein-protein interaction (PPI) network of the DEGs was constructed by STRING and Cytoscape software. The four sets of DEGs exhibited an intersection consisting of 205 genes (142 up-regulated and 63 down-regulated), which may be associated with PDAC. GO analysis showed that the 205 DEGs were significantly enriched in the plasma membrane, cell adhesion molecule activity and the Energy pathways, and glycine, serine, threonine metabolism were the most enriched pathways according to KEGG pathway analysis. Kaplan-Meier survival analysis revealed that 22 of 205 common genes were significantly associated with the overall survival of pancreatic cancer patients. In the PPI network and sub-network, DKK1 and HMGA2 were considered as hub genes with high connectivity degrees. DKK1 and HMGA2 are strongly associated with WNT3A and TP53 separately, which indicates that they may play an important role in the Wnt and P53 signaling pathways. Using integrated bioinformatics analysis, we identified DKK1 and HMGA2 as candidate genes in PDAC, which may improve our understanding of the mechanisms of the pathogenesis and integration; the two genes may be therapeutic targets and prognostic markers for PDAC.

Functional analysis of gene expression profiling-based prediction in bladder cancer.

The present study aimed to analyze the modification of gene expression in bladder cancer (BC) by identifying significant differentially expressed genes (DEGs) and functionally assess them using bioinformatics analysis. To achieve this, two microarray datasets, GSE24152 (which included 10 fresh tumor tissue samples from urothelial bladder carcinoma patients and 7 benign mucosa samples from the bladder), and GSE42089 (which included 10 tissues samples from urothelial cell carcinoma patients and 8 tissues samples from the normal bladder), were downloaded from the Gene Expression Omnibus database for further analysis. Differentially expressed genes (DEGs) were screened between benign the mucosa and control groups in GSE24152 and GSE42089 datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on overlapping DEGs identified in GSE24152 and GSE42089. Protein-protein interaction (PPI) networks and sub-networks were then constructed to identify key genes and main pathways. GO terms analysis was also performed for the selected clusters. In total, 1,325 DEGs in GSE24152 and 647 DEGs in GSE42089 were screened, in which 619 common DEGs were identified. The DEGs were mainly enriched in pathways and GO terms associated with mitotic and chromosome assembly, including nucleosome assembly, spindle checkpoint and DNA replication. In the interaction network, progesterone receptor (PGR), MAF bZIP transcription factor G (MAFG), cell division cycle 6 (CDC6) and members of the minichromosome maintenance family (MCMs) were identified as key genes. Histones were also considered to be significant factors in BC. Nucleosome assembly and sequence-specific DNA binding were the most significant clustered GO terms. In conclusion, the DEGs, including PGR, MAFG, CDC6 and MCMs, and those encoding the core histone family were closely associated with the development of BC via pathways associated with mitotic and chromosome assembly.