Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.     

DNA methylation-based diagnostic and prognostic biomarkers of nonsmoking lung adenocarcinoma patients

Currently, there are few studies on patients with nonsmoking lung adenocarcinoma, and the pathogenesis is still unclear. The role of DNA methylation in the pathogenesis of cancer is gradually being recognized. The purpose of this study was to determine the abnormal methylation genes and pathways involved in nonsmoking lung adenocarcinoma patients. Gene expression microarray data (GSE10072, GSE43458) and gene methylation microarray data (GSE62948) were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes were obtained through GEO2R. Next, we analyzed the function and enrichment of the selected genes using Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) networks were constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized in Cytoscape. Finally, we performed module analysis of the PPI network using Molecular Complex Detection. And we obtained 10 hub genes by Cytoscape Centiscape. We analyzed the independent prognostic value of each hub gene in nonsmoking nonsmall cell lung cancer patients through Kaplan-Meier plotter. Seven hub genes (CXCL12, CDH1, CASP3, CREB1, COL1A1, ERBB2, and ENO2) were closely related to the overall survival time. This study provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of nonsmoking lung adenocarcinoma patients. Hub genes with prognostic value could be selected as effective biomarkers for timely diagnosis and prognostic of nonsmoking lung adenocarcinoma patients.     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

Novel key genes in triple-negative breast cancer identified by weighted gene co-expression network analysis

Triple-negative breast cancer (TNBC) is a special subtype of breast cancer (BC) with poor prognosis. Although some molecular mechanisms of TNBC have been elucidated, the efficacy of current treatments is limited. Therefore, it is urgently demanded to screen for novel biomarkers and drug targets for TNBC. In this study, we obtained four independent data sets (GSE76250, GSE31448, GSE43358, and METABRIC) from the Gene Expression Omnibus (GEO) database and the cBioPortal website. In the GSE76250 data set, 890 differentially expressed genes were identified and weighted gene co-expression network analysis was performed based on them. Then, two preserved modules associated with the KI67 score were detected. Gene ontology and pathway enrichment analyses showed genes in the modules participated in some cancer-related biological processes or pathways. Non-SMC condensin I complex subunit G (NCAPG) and ATP-binding cassette subfamily A member 9 (ABCA9) were identified as hub genes of the modules, and the significance of hub genes was validated in the GSE43358 data set. Finally, their prognostic value was assessed by survival analysis. These findings suggested that NCAPG and ABCA9 may be the key genes of TNBC. Moreover, ABCA9 was first reported in TNBC. They deserved further studies.     

Comprehensive analysis of gene expression profiles to identify differential prognostic factors of primary and metastatic breast cancer

Breast cancer accounts for nearly half of all cancer-related deaths in women worldwide. However, the molecular mechanisms that lead to tumour development and progression remain poorly understood and there is a need to identify candidate genes associated with primary and metastatic breast cancer progression and prognosis. In this study, candidate genes associated with prognosis of primary and metastatic breast cancer were explored through a novel bioinformatics approach. Primary and metastatic breast cancer tissues and adjacent normal breast tissues were evaluated to identify biomarkers characteristic of primary and metastatic breast cancer. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) dataset (ID: HS-01619) was downloaded using the mRNASeq platform. Genevestigator 8.3.2 was used to analyse TCGA-BRCA gene expression profiles between the sample groups and identify the differentially-expressed genes (DEGs) in each group. For each group, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to determine the function of DEGs. Networks of protein–protein interactions were constructed to identify the top hub genes with the highest degree of interaction. Additionally, the top hub genes were validated based on overall survival and immunohistochemistry using The Human Protein Atlas. Of the top 20 hub genes identified, four (KRT14, KIT, RAD51, and TTK) were considered as prognostic risk factors based on overall survival. KRT14 and KIT expression levels were upregulated while those of RAD51 and TTK were downregulated in patients with breast cancer. The four proposed candidate hub genes might aid in further understanding the molecular changes that distinguish primary breast tumours from metastatic tumours as well as help in developing novel therapeutics. Furthermore, they may serve as effective prognostic risk markers based on the strong correlation between their expression and patient overall survival.     

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer

Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.     

Integrative analyses of genes associated with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF), characterized by irreversible scarring and progressive destruction of the lung tissue, is one of the most common types of idiopathic interstitial pneumonia worldwide. However, there are no reliable candidates for curative therapies. Hence, elucidation of the mechanisms of IPF genesis and exploration of potential biomarkers and prognostic indicators are essential for accurate diagnosis and treatment of IPF. Recently, efficient microarray and bioinformatics analyses have promoted an understanding of the molecular mechanisms of disease occurrence and development, which is necessary to explore genetic alternations and identify potential diagnostic biomarkers. However, high false-positive rates results have been observed based on single microarray datasets. In the current study, we performed a comprehensive analysis of the differential expression, biological functions, and interactions of IPF-related genes. Three publicly available microarray datasets including 54 IPF samples and 34 normal samples were integrated by performing gene set enrichment analysis and analyzing differentially expressed genes (DEGs). Our results identified 350 DEGs genetically associated with IPF. Gene ontology analyses revealed that the changes in the modules were mostly enriched in the positive regulation of smooth muscle cell proliferation, positive regulation of inflammatory responses, and the extracellular space. Kyoto encyclopedia of genes and genomes enrichment analysis of DEGs revealed that IPF involves the TNF signaling pathway, NOD-like receptor signaling pathway, and PPAR signaling pathway. To identify key genes related to IPF in the protein-protein interaction network, 20 hub genes were screened out with highest scores. Our results provided a framework for developing new pathological molecular networks related to specific diseases in silico.     

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA‐associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease‐related networks based on 21756 gene expression correlation coefficients, hub‐genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits‐related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA‐associated genes. Moreover, 310 OA‐associated genes were found, and 34 of them were among hub‐genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)‐receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'‐kinase (PI3K)‐Akt signaling pathway (PI3K‐AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

A scale-free structure prior for graphical models with applications in functional genomics

The problem of reconstructing large-scale, gene regulatory networks from gene expression data has garnered considerable attention in bioinformatics over the past decade with the graphical modeling paradigm having emerged as a popular framework for inference. Analysis in a full Bayesian setting is contingent upon the assignment of a so-called structure prior-a probability distribution on networks, encoding a priori biological knowledge either in the form of supplemental data or high-level topological features. A key topological consideration is that a wide range of cellular networks are approximately scale-free, meaning that the fraction, , of nodes in a network with degree is roughly described by a power-law with exponent between and . The standard practice, however, is to utilize a random structure prior, which favors networks with binomially distributed degree distributions. In this paper, we introduce a scale-free structure prior for graphical models based on the formula for the probability of a network under a simple scale-free network model. Unlike the random structure prior, its scale-free counterpart requires a node labeling as a parameter. In order to use this prior for large-scale network inference, we design a novel Metropolis-Hastings sampler for graphical models that includes a node labeling as a state space variable. In a simulation study, we demonstrate that the scale-free structure prior outperforms the random structure prior at recovering scale-free networks while at the same time retains the ability to recover random networks. We then estimate a gene association network from gene expression data taken from a breast cancer tumor study, showing that scale-free structure prior recovers hubs, including the previously unknown hub SLC39A6, which is a zinc transporter that has been implicated with the spread of breast cancer to the lymph nodes. Our analysis of the breast cancer expression data underscores the value of the scale-free structure prior as an instrument to aid in the identification of candidate hub genes with the potential to direct the hypotheses of molecular biologists, and thus drive future experiments.     

Minireview: nuclear receptors and breast cancer

Until recently, the study of nuclear receptor (NR) function in breast cancer biology has been largely limited to estrogen and progesterone receptors. The development of reliable gene expression arrays, real-time quantitative RT-PCR, and immunohistochemical techniques for studying NR superfamily members in primary human breast cancers has now revealed the presence and potential importance of several additional NRs in the biology of breast cancer. These include receptors for steroid hormones (including androgens and corticosteroids), fat-soluble vitamins A and D, fatty acids, and xenobiotic lipids derived from diet. It is now clear that after NR activation, both genomic and nongenomic NR pathways can coordinately activate growth factor signaling pathways. Advances in our understanding of both NR functional networks and epithelial cell growth factor signaling pathways have revealed a frequent interplay between NR and epithelial cell growth factor family signaling that is clinically relevant to breast cancer. Understanding how growth factor receptors and their downstream kinases are activated by NRs (and vice-versa) is a central goal for maximizing treatment opportunities in breast cancer. In addition to the estrogen receptor, it is predicted that modulating the activity of other NRs will soon provide novel prevention and treatment approaches for breast cancer patients.     

Integrating Multi-Omics for Uncovering the Architecture of Cross-Talking Pathways in Breast Cancer

Cross-talk among abnormal pathways widely occurs in human cancer and generally leads to insensitivity to cancer treatment. Moreover, alterations in the abnormal pathways are not limited to single molecular level. Therefore, we proposed a strategy that integrates a large number of biological sources at multiple levels for systematic identification of cross-talk among risk pathways in cancer by random walk on protein interaction network. We applied the method to multi-Omics breast cancer data from The Cancer Genome Atlas (TCGA), including somatic mutation, DNA copy number, DNA methylation and gene expression profiles. We identified close cross-talk among many known cancer-related pathways with complex change patterns. Furthermore, we identified key genes (linkers) bridging these cross-talks and showed that these genes carried out consistent biological functions with the linked cross-talking pathways. Through identification of leader genes in each pathway, the architecture of cross-talking pathways was built. Notably, we observed that linkers cooperated with leaders to form the fundamentation of cross-talk of pathways which play core roles in deterioration of breast cancer. As an example, we observed that KRAS showed a direct connection to numerous cancer-related pathways, such as MAPK signaling pathway, suggesting that it may be a central communication hub. In summary, we offer an effective way to characterize complex cross-talk among disease pathways, which can be applied to other diseases and provide useful information for the treatment of cancer.     

Identification of downregulated circRNAs from tissue and plasma of patients with gastric cancer and construction of a circRNA-miRNA-mRNA network

The connection between circular RNAs (circRNAs) and gastric cancer has been reported widely in recent years. However, previous studies have focused mainly on circRNAs from gastric cancer tissue. The objectives of the present study were to detect dysregulated circRNAs from both tissue and plasma of patients with gastric cancer and to explore their potential roles in the pathogenesis of gastric cancer. Expression profiles of circRNAs were obtained from the Gene Expression Omnibus (GEO) and analyzed using the GEO2R tool to identify differential expressed circRNAs. The significance threshold was set as |log2 (fold change)| > 2 and adjusted P < .05. The microRNA (miRNA) binding sites of the differentially expressed circRNAs were predicted using the Circular RNA Interactome web tool. TargetScan and the miRNet database were used to predict the miRNA target genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed using Database for Annotation Visualization and Integrated Discovery. Hub genes were identified and a network was constructed with Cytoscape. The overall survival rates for the selected miRNAs and messenger RNAs were evaluated by Kaplan-Meier Plotter. A total of three downregulated circRNAs (hsa_circ_0001190, hsa_circ_0036287, and hsa_circ_0048607) were identified in this study. Six miRNAs and eight hub genes met the significance criteria and were selected for further analysis. A circRNA-miRNA-hub gene network was constructed based on three circRNAs, six miRNAs, and eight hub genes. Evaluation of overall survival rates for the hub genes showed that low expression levels of GADD45A, PPP1CB, PJA2, and KLF2 were associated with poor overall survival. This study identified potential novel plasma circRNA biomarkers and provides insights into the underlying mechanisms of gastric cancer pathogenesis.