Modules In Protein-protein Interaction Networks Research Articles

SEMtree: tree-based structure learning methods with structural equation models.

With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework. Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance. SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa)

BackgroundThe root system is vital to plant growth and survival. Therefore, genetic improvement of the root system is beneficial for developing stress-tolerant and improved plant varieties. This requires the identification of proteins that significantly contribute to root development. Analyzing protein-protein interaction (PPI) networks is vastly beneficial in studying developmental phenotypes, such as root development, because a phenotype is an outcome of several interacting proteins. PPI networks can be analyzed to identify modules and get a global understanding of important proteins governing the phenotypes. PPI network analysis for root development in rice has not been performed before and has the potential to yield new findings to improve stress tolerance. ResultsHere, the network module for root development was extracted from the global Oryza sativa PPI network retrieved from the STRING database. Novel protein candidates were predicted, and hub proteins and sub-modules were identified from the extracted module. The validation of the predictions yielded 75 novel candidate proteins, 6 sub-modules, 20 intramodular hubs, and 2 intermodular hubs. ConclusionsThese results show how the PPI network module is organized for root development and can be used for future wet-lab studies for producing improved rice varieties.

Bioinformatics and Raman spectroscopy-based identification of key pathways and genes enabling differentiation between acute myeloid leukemia and T cell acute lymphoblastic leukemia.

Acute myeloid leukemia (AML) and T cell acute lymphoblastic leukemia (T-ALL) are two of the most prevalent hematological malignancies diagnosed among adult leukemia patients, with both being difficult to treat and associated with high rates of recurrence and mortality. In the present study, bioinformatics approaches were used to analyze both of these types of leukemia in an effort to identify characteristic gene expression patterns that were subsequently validated via Raman spectroscopy. For these analyses, four Gene Expression Omnibus datasets (GSE13204, GSE51082, GSE89565, and GSE131184) pertaining to acute leukemia were downloaded, and differentially expressed genes (DEGs) were then identified through comparisons of AML and T-ALL patient samples using the R Bioconductor package. Shared DEGs were then subjected to Gene Ontology (GO) enrichment analyses and were used to establish a protein-protein interaction (PPI) network analysis. In total, 43 and 129 upregulated and downregulated DEGs were respectively identified. Enrichment analyses indicated that these DEGs were closely tied to immune function, collagen synthesis and decomposition, inflammation, the synthesis and decomposition of lipopolysaccharide, and antigen presentation. PPI network module clustering analyses further led to the identification of the top 10 significantly upregulated and downregulated genes associated with disease incidence. These key genes were then validated in patient samples via Raman spectroscopy, ultimately confirming the value of these genes as tools that may aid the differential diagnosis and treatment of AML and T-ALL. Overall, these results thus highlight a range of novel pathways and genes that are linked to the incidence and progression of AML and T-ALL, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of these devastating malignancies.

AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein–protein interaction network

Identifying unknown protein functional modules, such as protein complexes and biological pathways, from protein-protein interaction (PPI) networks, provides biologists with an opportunity to efficiently understand cellular function and organization. Finding complex nonlinear relationships in underlying functional modules may involve a long-chain of PPI and pose great challenges in a PPI network with an unevenly sparse and dense node distribution. To overcome these challenges, we propose AdaPPI, an adaptive convolution graph network in PPI networks to predict protein functional modules. We first suggest an attributed graph node presentation algorithm. It can effectively integrate protein gene ontology attributes and network topology, and adaptively aggregates low- or high-order graph structural information according to the node distribution by considering graph node smoothness. Based on the obtained node representations, core cliques and expansion algorithms are applied to find functional modules in PPI networks. Comprehensive performance evaluations and case studies indicate that the framework significantly outperforms state-of-the-art methods. We also presented potential functional modules based on their confidence.

Insight into Shenqi Jiangtang Granule on the improved insulin sensitivity by integrating in silico and in vivo approaches

Ethnopharmacological relevancePresently, insulin resistance has been a growing concern that urgently needs to be addressed, because it not only places patients at risk of developing type 2 diabetes mellitus but also results in metabolic syndrome and different aspects of cardiovascular diseases. Shenqi Jiangtang Granule (SJG) is a classic traditional Chinese medicine (TCM) prescription that is widely used to treat diabetes mellitus and its complications in clinical practice. While studies have revealed that SJG with multi-ingredients and multi-targets characteristics possesses potential anti-insulin resistance pharmacological properties, its mechanisms of action and molecular targets for the treatment of insulin resistance are still obscure, which prompt us to conduct an in-depth research. Aim of the studyThis study was purposed to uncover the pharmacological mechanism of SJG against insulin resistance through integrating network pharmacology and experimental validation. Materials and methodsThe putative ingredients of SJG and its related targets were discerned from the TCMSP database. Subsequently, insulin resistance-associated targets were retrieved from GeneCard, OMIM, and GEO database. Compound-target, protein-protein interaction (PPI), and compound-target-pathway networks were established using Cytoscape software. GO and KEGG pathway analyses were performed to identify possible enrichment of genes with specific biological themes. Molecular docking was used to verify the correlation between the main active ingredients and hub targets. Optimal docking conformation was further analyzed by molecular dynamics (MD) simulation. Finally, the potential molecular mechanisms of SJG acting on insulin resistance, as predicted by the network pharmacology analyses, were validated experimentally in insulin-resistant rat model. Results136 active compounds, 211 corresponding targets in addition to 1463 disease-related targets were collected, of which 94 intersection targets were obtained. 29 key targets including AKT1, VEGFA, IL-6, CASP3, and PTGS2 were identified through PPI network analysis. Hub module of PPI network was closely associated with inflammation. GO and KEGG analyses also revealed that inflammation-related pathways may be a central factor for SJG to modulate insulin resistance. Molecular docking test showed a good binding potency between primary active ingredients and core targets, and the binding mode of optimal docking conformation was stable in MD simulation. A rat model of insulin resistance was successfully induced by chronic high-fat diet (HFD) consumption. Through a series of in vivo studies, including HEC, ITT, and HOMA-IR measurement, it was revealed that SJG exhibited a beneficial effect on ameliorating insulin resistance, as demonstrated by a significant increase of GIR and a significant decrease of AUCITT and HOMA-IR index value. Further molecular biological analysis showed that SJG can decrease the mRNA expression level and serum concentration of inflammatory cytokines (TNF-α, IL-6, and IL-1β), along with suppressing the p-NFκB protein overexpression, indicating its anti-inflammatory activity. Also, it can contribute to the reversal of the impaired hepatic insulin signaling pathway, as evidenced by up-regulated protein expression of p-Akt and GLUT2. ConclusionsThrough in silico and in vivo approaches, the present study not only provides a unique insight into the possible mechanism of SJG in insulin resistance after successfully filtering out associated key target genes and signaling pathways, but also suggests a novel promising therapeutic strategy for curing insulin resistance.

Comprehensive analysis and identification of key genes and signaling pathways in the occurrence and metastasis of cutaneous melanoma.

BackgroundMelanoma is a malignant tumor of melanocytes, and the incidence has increased faster than any other cancer over the past half century. Most primary melanoma can be cured by local excision, but metastatic melanoma has a poor prognosis. Cutaneous melanoma (CM) is prone to metastasis, so the research on the mechanism of melanoma occurrence and metastasis will be beneficial to diagnose early, improve treatment, and prolong life survival. In this study, we compared the gene expression of normal skin (N), primary cutaneous melanoma (PM) and metastatic cutaneous melanoma (MM) in the Gene Expression Omnibus (GEO) database. Then we identified the key genes and molecular pathways that may be involved in the development and metastasis of cutaneous melanoma, thus to discover potential markers or therapeutic targets.MethodsThree gene expression profiles (GSE7553, GSE15605 and GSE46517) were downloaded from the GEO database, which contained 225 tissue samples. R software identified the differentially expressed genes (DEGs) between pairs of N, PM and MM samples in the three sets of data. Subsequently, we analyzed the gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the DEGs, and constructed a protein-protein interaction (PPI) network. MCODE was used to seek the most important modules in PPI network, and then the GO function and KEGG pathway of them were analyzed. Finally, the hub genes were calculated by the cytoHubba in Cytoscape software. The Cancer Genome Atlas (TCGA) data were analyzed using UALCAN and GEPIA to validate the hub genes and analyze the prognosis of patients.ResultsA total of 134, 317 and 147 DEGs were identified between N, PM and MM in pair. GO functions and KEGG pathways analysis results showed that the upregulated DEGs mainly concentrated in cell division, spindle microtubule, protein kinase activity and the pathway of transcriptional misregulation in cancer. The downregulated DEGs occurred in epidermis development, extracellular exosome, structural molecule activity, metabolic pathways and p53 signaling pathway. The PPI network obtained the most important module, whose GO function and KEGG pathway were enriched in oxidoreductase activity, cell division, cell exosomes, protein binding, structural molecule activity, and metabolic pathways. 14, 18 and 18 DEGs were identified respectively as the hub genes between N, PM and MM, and TCGA data confirmed the expression differences of hub genes. In addition, the overall survival curve of hub genes showed that the differences in these genes may lead to a significant decrease in overall survival of melanoma patients.ConclusionsIn this study, several hub genes were found from normal skin, primary melanoma and metastatic melanoma samples. These hub genes may play an important role in the production, invasion, recurrence or death of CM, and may provide new ideas and potential targets for its diagnosis or treatment.

Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation.

Background Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. The purpose of this study is to search for genes related to the prognosis of LUAD through methylation based on a linear mixed model (LMM). Methods Gene expression, methylation, and survival data of LUAD patients were downloaded from the TCGA database. Based on the LMM model, the GEMMA algorithm was used to screen the predictive genes related to LUAD survival. The Cox model was used to further screen the predicted genes, and then, protein-protein interaction (PPI) network was constructed. Through the software plugin Cytoscape MCODE 3.8.0, the most closely related genes in the PPI network module were selected for in-depth biological function analysis to further explore the interaction and correlation between genes. Results We screened out 97 predictive genes from 18,834 genes and eliminated one gene associated with lung squamous cell carcinoma from previous studies, leaving 96 genes. The MCODE and the Kaplan-Meier curve analysis were used to finally identify two genes ASB16 and NEDD4 that are related to the prognosis of LUAD. Conclusions The newly identified two genes associated with the prognosis of LUAD may provide a basis for the treatment of patients.

The potential biomarkers for the formation and development of intracranial aneurysm

This study is aimed to understand the pathogenesis of intracranial aneurysm (IA), which has a risk of rupture and is the primary cause of subarachnoid hemorrhage. From Gene Expression Omnibus (GEO) database, GSE75436 was extracted (15 IA tissues and 15 superficial temporal artery tissues). The differentially expressed genes (DEGs) was conducted through limma package, which followed by the enrichment analysis. Combining STRING database, protein–protein interaction (PPI) network was constructed. The modules in PPI network were performed utilizing molecular complex detection (MCODE) algorithm. With Cytoscape software, the transcription factor-miRNA-target regulatory network was constructed. Finally, microarray dataset GSE54083 was downloaded (13 IA tissues and 10 superficial temporal artery tissues) for the verification test. A total of 1332 DEGs were screened in IA tissues compared with superficial temporal artery tissues. Besides, the up-regulated TNF, IL10, IL1B, and CTSS, as well as down-regulated IL6 were included in the top 20 nodes in the PPI networks. Furthermore, in the module A of up-regulated PPI network, TNF, IL10, IL1B, and VCAM1 were interact with each other. In the regulatory network, miR-29A/B/C targeted up-regulated genes. Besides, VCAM1 was implicated in the pathway of leukocyte transendothelial migration. In the verification analysis, between GSE75436 and GSE54083, there were 444 up-regulated and 543 down-regulated co-existence DEGs and 11 co-existence genes involved the Leukocyte transendothelial migration pathway. VCAM1, TNF, CTSS, IL10, IL1B, IL6, and miR-29A/B/C might be the potential biomarkers for the formation and development of IA.

Maximizing cohesion and separation for detecting protein functional modules in protein-protein interaction networks.

Protein Function Module (PFM) identification in Protein-Protein Interaction Networks (PPINs) is one of the most important and challenging tasks in computational biology. The quick and accurate detection of PFMs in PPINs can contribute greatly to the understanding of the functions, properties, and biological mechanisms in research on various diseases and the development of new medicines. Despite the performance of existing detection approaches being improved to some extent, there are still opportunities for further enhancements in the efficiency, accuracy, and robustness of such detection methods. Based on the uniqueness of the network-clustering problem in the context of PPINs, this study proposed a very effective and efficient model based on the Lin-Kernighan-Helsgaun algorithm for detecting PFMs in PPINs. To demonstrate the effectiveness and efficiency of the proposed model, computational experiments are performed using three different categories of species datasets. The computational results reveal that the proposed model outperforms existing detection techniques in terms of two key performance indices, i.e., the degree of polymerization inside PFMs (cohesion) and the deviation degree between PFMs (separation), while being very fast and robust. The proposed model can be used to help researchers decide whether to conduct further expensive and time-consuming biological experiments and to select target proteins from large-scale PPI data for further detailed research.

Immune-related prognosis biomarkers associated with osteosarcoma microenvironment

BackgroundOsteosarcoma is a highly aggressive bone tumor that most commonly affects children and adolescents. Treatment and outcomes for osteosarcoma have remained unchanged over the past 30 years. The relationship between osteosarcoma and the immune microenvironment may represent a key to its undoing.MethodsWe calculated the immune and stromal scores of osteosarcoma cases from the Target database using the ESTIMATE algorithm. Then we used the CIBERSORT algorithm to explore the tumor microenvironment and analyze immune infiltration of osteosarcoma. Differentially expressed genes (DEGs) were identified based on immune scores and stromal scores. Search Tool for the Retrieval of Interacting Genes Database (STRING) was utilized to assess protein–protein interaction (PPI) information, and Molecular Complex Detection (MCODE) plugin was used to screen hub modules of PPI network in Cytoscape. The prognostic value of the gene signature was validated in an independent GSE39058 cohort. Gene set enrichment analysis (GSEA) was performed to study the hub genes in signaling pathways.ResultsFrom 83 samples of osteosarcoma obtained from the Target dataset, 137 DEGs were identified, including 134 upregulated genes and three downregulated genes. Functional enrichment analysis and PPI networks demonstrated that these genes were mainly involved in neutrophil degranulation and neutrophil activation involved in immune response, and participated in neuroactive ligand–receptor interaction and staphylococcus aureus infection.ConclusionsOur study established an immune-related gene signature to predict outcomes of osteosarcoma, which may be important targets for individual treatment.

Proteomic study of sulfated polysaccharide from Enterobacter cloacae Z0206 against H2O2-induced oxidative damage in murine macrophages

The present study aimed to unveil potential protective mechanisms of SEPS-4, a novel sulfated derivative of exopolysaccharide produced by Enterobacter cloacae Z0206, against H2O2-induced oxidative damage in murine macrophages based on proteomic approaches. SEPS-4 pre-treatment was found to be capable of alleviating H2O2-induced reduction of RAW264.7 cell viability. Two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry were used to evaluate proteins with significant expression alterations in H2O2-challenged RAW264.7 cells following pre-incubation with SEPS-4. Here 12 up-regulated proteins and 12 down-regulated proteins were successfully identified. Bio-informatic analysis was applied for further mechanistic studies. GO annotation and KEGG pathway enrichment analyses demonstrated that differentially expressed proteins were mainly clustered in stress-related biological processes, including metabolic process, stimulus to response, cell growth and death. Peroxiredoxin-2 and Eef1g were core modules of protein-protein interaction network. Collectively, our data indicated that SEPS-4 may exert protective effects against H2O2-induced oxidative damage via the regulation of these functional proteins and related biological processes.

Identification of potential miRNA biomarkers for traumatic osteonecrosis of femoral head.

Traumatic osteonecrosis of femoral head (TONFH) is a common orthopedic disease caused by physical injury in hip. However, the unclear pathogenesis mechanism of TONFH and lacking of simple noninvasive early diagnosis method cause the necessity of hip replacement for most patients with TONFH. In this study, we aimed to identify circulating microRNAs (miRNAs) by integrated bioinformatics analyses as potential biomarker of TONFH. mRNA expression profiles were downloaded from the Gene Expression Omnibus database. Then we combined two miRNA screen methods: Weighted gene co-expression network analysis and fold change based differentially expressed miRNAs analysis. As a result, we identified 14 key miRNAs as potential biomarkers for TONFH. Besides, 302 target genes of these miRNAs were obtained and the miRNA-mRNA interaction network was constructed. Furthermore, the results of Kyoto Encyclopedia of Gene and Genome pathway analysis, Gene Ontology function analysis, protein-protein interaction (PPI) network analysis and PPI network module analysis showed close correlation between these 14 key miRNAs and TONFH. Then we established receiver operating characteristic curves and identified 6-miRNA signature with highly diagnosis value including miR-93-5p (area under the curve [AUC] = 0.93), miR-1324 (AUC = 0.92), miR-4666a-3p (AUC = 0.92), miR-5011-3p (AUC = 0.92), and miR-320a (AUC = 0.89), miR-185-5p (AUC = 0.89). Finally, the results of quantitative real-time polymerase chain reaction confirmed the significantly higher expression of miR-93-5p and miR-320a in the serum of patients with ONFH. These circulating miRNAs could serve as candidate early diagnosis markers and potential treatment targets of TONFH.

A Robust Algorithm Based on Link Label Propagation for Identifying Functional Modules From Protein-Protein Interaction Networks.

Identifying functional modules in protein-protein interaction (PPI) networks elucidates cellular organization and mechanism. Various methods have been proposed to identify the functional modules in PPI networks, but most of these methods do not consider the noisy links in PPI networks. They achieve a competitive performance on the PPI networks without noisy links, but the performance of these methods considerably deteriorates in the noisy PPI networks. Furthermore, the noisy links are inevitable in the PPI networks. In this paper, we propose a novel link-driven label propagation algorithm (LLPA) to identify functional modules in PPI networks. The LLPA first find link clusters in PPI networks, and then the functional modules are identified from the link clusters. Two strategies aimed to ensure the robustness of LLPA are proposed. One strategy involves the proposed LLPA updating the link labels in accordance with the designed weight of the link, which can reduce the incidence of noisy links. The other strategy involves the filtration of some noisy labels from the link clusters to further reduce the influence of noisy links. The performance evaluation on three real PPI networks shows that LLPA outperforms other eight state-of-the-art detection algorithms in terms of accuracy and robustness.

Refining modules to determine functionally significant clusters in molecular networks

BackgroundModule detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks.ResultsThe module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. We also compare our results with six existing methods available for clustering biological networks.ConclusionThe proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.

Integrative analysis of mRNA and miRNA expression profiles reveals seven potential diagnostic biomarkers for non‑small cell lung cancer.

The present study aimed to explore specific molecular targets for the diagnosis and treatment of non-small cell lung cancer (NSCLC). The expression profiles of microRNAs (miRNAs) and mRNAs were downloaded from the GEO (GSE102286 and GSE101929) and TCGA databases. After data preprocessing, differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in cancer and normal tissues were selected and used to construct a DEM-DEG regulatory network and a protein-protein interaction (PPI) network. The genes and miRNAs in these networks were subjected to functional enrichment and survival analyses. Several key DEMs and DEGs were verified using RT-qPCR, and the results were statistically interpreted using a multivariate logistic regression analysis. In this study, 25 DEMs and 789 DEGs common to all datasets were identified, which were then used for the construction of a DEM-DEG regulatory network and a PPI network module. Survival analyses of 19 DEMs in the DEM-DEG regulatory network and 36 DEGs in the PPI network module revealed that 34 DEGs (including TOP2A, CCNB1, BIRC5, and TTK) and two miRNAs (miR-21-5p and miR-31-5p) were significantly associated with NSCLC prognosis. Moreover, RT-qPCR analysis identified three DEGs and five DEMs that had changes in expression consistent with those observed in the bioinformatic analysis. Finally, a multivariate logistic regression analysis of the data showed that TOP2A, CCNB1, BIRC5, miR-21-5p, miR-193b-3p, miR-210-3p and miR-31-5p could be combined for the diagnosis of NSCLC. In conclusion, TOP2A, CCNB1, BIRC5, miR-21-5p, miR-193b-3p, miR-210-3p and miR-31-5p may therefore serve as important biomarkers and diagnostic targets for NSCLC.

Analysis of modularity in proteome-wide protein interaction networks of Methanothermobacter thermautotrophicus strain ΔH and metal-loving bacteria

The proteins perform their functions independently and control all biological systems through protein–protein interaction (PPI) networks. The majority of proteins interact with others for proper biological activity in complex cellular and metabolic processes. A proteome-scale study affords the importance to reveal the function of molecular complexes and modularity of network architectures in microorganisms. In the present study, a PPI network has been developed for Methanothermobacter thermautotrophicus ΔH (MTH). The modular structure and hubs of the resulted network have been compared with PPI networks of metal-loving bacteria including Geobacter metallireducens and Geobacter sulfurreducens. Network quality and robustness were assessed with gene ontology terms. The predicted network of MTH consisted of 2450 edges and 256 nodes interacting with 564 metabolic genes. The metabolic link of interacting proteins was evaluated with efforts to mine experimental PPI data from the literature. The topological properties of each network model were robust and consistent for studying the network modularity. Besides, 172 different protein complexes were identified from the PPI network model of MTH in which 87 poorly characterized proteins characterized with certain functions. Core PPI networks in MTH and metal-loving bacteria were separately evolved and established for the organism-specific functions. Results of our study revealed that both MTH and metal-loving bacteria have a specialized PPI network module for electrical interplay systems in the geothermal environment.

Eleven genes associated with progression and prognosis of endometrial cancer (EC) identified by comprehensive bioinformatics analysis

BackgroundEndometrial cancer (EC) is one of the female malignant tumors. Endometrial cancer predominately affects post-menopausal women. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression.MethodsDownload the gene expression profile from Gene Expression Omnibus (GEO). Calculate raw expression data according to pre-processing procedures. We performed the “limma” R language package to screen DEGs between Endometrial cancer tissue samples and normal uterus tissue samples. Enrichment of the functions and pathways was analyzed by using clusterprofiler. We utilized Search Tool for the Retrieval of Interacting Genes Database (STRING) to assess protein–protein interaction (PPI) information, and then we used plug-in Molecular Complex Detection (MCODE) to screen hub modules of PPI network in Cytoscape. We also performed functional analysis on the genes in the hub module by using clusterprofiler. Next, we utilized the “WGCNA” package in R to establish co-expression network for the DEGs. The Venn diagram was performed to overlap the gene in key module and hub PPI cluster. We validated the key genes in TCGA, GEPIA, UALCAN and Immunohistochemistry staining obtained from The Human Protein Atlas database. And then we did ROC curve analysis by SPSS. Gene set enrichment analysis (GSEA) and mutation analysis were also performed for hub genes.ResultsFunctional and pathway enrichment analysis demonstrated that the upregulated differentially expressed genes (DEGs) were significantly enriched in CXCR chemokine receptor binding, chemokine activity, chemokine receptor binding, G-protein coupled receptor binding, RAGE receptor binding, cytokine activity, microtubule binding, receptor regulator activity and microtubule motor activity, and the down-regulated genes were highly enriched in collagen binding. After using STRING software to construct PPI network, 30 prominent proteins were identified and the first two significant modules were selected. In co-expression network, 5 EC-related modules were identified. Among them, the turquoise module has the highest correlation with the EC. We further analyzed the genes in the PPI and turquoise module, and selected eleven key genes related to EC after validation of TCGA database, GEPIA, UALCAN and immunohistochemistry. Six of them had mutation significance.ConclusionsIn summary, these 11 genes may become new therapy targets for EC treatment.

Comparative gene expression profiling reveals key pathways and genes different in skin epidermal stem cells and corneal epithelial cells.

Corneal epithelial cells (CECs) are required for corneal transparency and visual function, and corneal injuries may cause corneal blindness. Skin epidermal stem cells (SESCs), which share the same origin with CECs and have the potential of multi-directional differentiation are ideal seed cells for tissue engineered corneal construction to treat corneal blindness. This study aims to investigate critical genes and pathways that may modulate the transdifferentiation from SESCs to CECs. Isolated SESCs and CECs were used for gene expression analysis by microarray. GO and KEGG pathway of differently expressed genes (DEGs) were enriched using DAVID. The protein-protein interaction (PPI) network were then constructed using Cytoscape and highly interconnected module was subsequently isolated from the network by Molecular Complex Detection. Expression of the hub genes and other selected genes were then verified by qRT-PCR. We found 112 upregulated and 105 downregulated genes in CECs compared with SESCs. These DEGs were significantly enriched in focal adhesion, PI3K-Akt and TNF signaling pathway. Highly interconnected module of PPI network contains ten genes. Further regulatory network of these genes showed that ESR1 and SLC2A4 were hub genes. Our study identified gene expression in SESCs and CECs and suggested that several crucial genes and pathways may play critical roles in transdifferentiation from SESCs to CECs. It may help uncover molecular mechanisms and offer a foundation for promoting tissue-engineered cornea into clinical application.

ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer.

BackgroundEpithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable.Materials and methodsGene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples.ResultsWe analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA.ConclusionIn this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.

Functional homogeneity and specificity of topological modules in human proteome

BackgroundFunctional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome.ResultsWe investigated functional homogeneity and specificity of topological modules of the human proteome and validated them with known biological and disease pathways. Specifically, we determined the effects on functional homogeneity and heterogeneity of topological modules (i) with both physical and functional protein-protein interactions; and (ii) with incorporation of functional similarities between proteins as weights of interactions. With functional enrichment analyses and a novel measure for functional specificity, we evaluated functional relevance and specificity of topological modules of the human proteome.ConclusionsThe topological modules ranked using specificity scores show high enrichment with gene sets of known functions. Physical interactions in PPIN contribute to high specificity of the topological modules of the human proteome whereas functional interactions contribute to high homogeneity of the modules. Weighted networks result in more number of topological modules but did not affect their functional propensity. Modules of human proteome are more homogeneous for molecular functions than biological processes.