Gene Co-expression Network Analysis Method Research Articles

Molecular Targets and Mechanisms of Hedyotis diffusa Willd. for Esophageal Adenocarcinoma Treatment Based on Network Pharmacology and Weighted Gene Co-expression Network Analysis.

Hedyotis diffusa Willd. (HDW) is a common anticancer herbal medicine in China, and its therapeutic effectiveness has been demonstrated in a range of cancer patients. There is no consensus about the therapeutic targets and molecular mechanisms of HDW, which contains many active ingredients. To clarify the mechanism of HDW for esophageal adenocarcinoma (EAC), we utilized network pharmacology and weighted gene co-expression network analysis methods (WGCNA). The gene modules that were linked with the clinical features of EAC were obtained through the WGCNA method. Then, the potential target genes were retrieved through the network pharmacology method in order to determine the targets of the active components. After enrichment analysis, a variety of signaling pathways with significant ratios of target genes were found, including regulation of trans-synaptic signaling, neuroactive ligand-receptor interaction and modulation of chemical synaptic transmission. By means of protein-protein interaction (PPI) network analysis, we have successfully identified the hub genes, which were AR, CNR1, GRIK1, MAPK10, MAPT, PGR and PIK3R1. Our study employed molecular docking simulations to evaluate the binding affinity of the active components with the hub gene. The identified active anticancer constituents in HDW are scopoletol, quercetin, ferulic acid, coumarin, and trans-4-methoxycinnamyl alcohol. Our findings shed light on the molecular underpinnings of HDW in the treatment of EAC and hold great promise for the identification of potential HDW compounds and biomarkers for EAC therapy.

Transcriptome analysis of the transition from primary to secondary growth of vertical stem in Eucalyptus grandis

Eucalyptus was one of the most cultivated hardwood species worldwide, with rapid growth, good wood properties and a wide range of adaptability. Eucalyptus stem undergoes primary growth (longitudinal growth) followed by secondary growth (radial growth), which produces biomass that is an important source of energy worldwide. In order to better understand the genetic regulation of secondary growth in Eucalyptus grandis, Transcriptome analyses in stem segments along a developmental gradient from the third internode to the eleventh internode of E. grandis that spanned primary to secondary growth were carried out. 5,149 genes that were differentially expressed during stem development were identified. Combining the trend analysis by the Mfuzz method and the module-trait correlation analysis by the Weighted Gene Co-expression Network Analysis method, a total of 70 differentially expressed genes (DEGs) selected from 868 DEGs with high connectivity were found to be closely correlated with secondary growth. Results revealed that the differential expression of these DEGs suggests that they may involve in the primary growth or secondary growth. AP1, YAB2 TFs and EXP genes are highly expressed in the IN3, whereas NAC, MYB TFs are likely to be important for secondary growth. These results will expand our understanding of the complex molecular and cellular events of secondary growth and provide a foundation for future studies on wood formation in Eucalyptus.

Identifying hub genes of sepsis-associated and hepatic encephalopathies based on bioinformatic analysis—focus on the two common encephalopathies of septic cirrhotic patients in ICU

BackgroundIn the ICU ward, septic cirrhotic patients are susceptible to suffering from sepsis-associated encephalopathy and/or hepatic encephalopathy, which are two common neurological complications in such patients. However, the mutual pathogenesis between sepsis-associated and hepatic encephalopathies remains unclear. We aimed to identify the mutual hub genes, explore effective diagnostic biomarkers and therapeutic targets for the two common encephalopathies and provide novel, promising insights into the clinical management of such septic cirrhotic patients.MethodsThe precious human post-mortem cerebral tissues were deprived of the GSE135838, GSE57193, and GSE41919 datasets, downloaded from the Gene Expression Omnibus database. Furthermore, we identified differentially expressed genes and screened hub genes with weighted gene co-expression network analysis. The hub genes were then subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway functional enrichment analyses, and protein-protein interaction networks were constructed. Receiver operating characteristic curves and correlation analyses were set up for the hub genes. Finally, we explored principal and common signaling pathways by using Gene Set Enrichment Analysis and the association between the hub genes and immune cell subtype distribution by using CIBERSORT algorithm.ResultsWe identified seven hub genes—GPR4, SOCS3, BAG3, ZFP36, CDKN1A, ADAMTS9, and GADD45B—by using differentially expressed gene analysis and weighted gene co-expression network analysis method. The AUCs of these genes were all greater than 0.7 in the receiver operating characteristic curves analysis. The Gene Set Enrichment Analysis results demonstrated that mutual signaling pathways were mainly enriched in hypoxia and inflammatory response. CIBERSORT indicated that these seven hub genes were closely related to innate and adaptive immune cells.ConclusionsWe identified seven hub genes with promising diagnostic value and therapeutic targets in septic cirrhotic patients with sepsis-associated encephalopathy and/or hepatic encephalopathy. Hypoxia, inflammatory, and immunoreaction responses may share the common downstream pathways of the two common encephalopathies, for which earlier recognition and timely intervention are crucial for management of such septic cirrhotic patients in ICU.

A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis

Ferroptosis is a regulated form of cell death that involves iron-dependent lipid peroxidation. Ferroptosis-related genes (FRGs) play an essential role in the tumorigenesis of gastric cancer (GC), which is one of the most common and lethal cancers worldwide. Understanding the prognostic significance of FRGs in GC can shed light on GC treatment and diagnosis. In this study, we proposed a new gene co-expression network analysis method, namely EP-WGCNA. This method used Euclidean and Pearson weighted distance (EP_dis) to construct a weighted gene co-expression network instead of the Pearson’s correlation coefficient used in the original WGCNA method. The aim was to better capture the interactions and functional associations among genes. We used EP-WGCNA to identify the FRGs related to GC phenotype and applied bioinformatics methods to select the FRGs associated with the prognosis (P-FRGs) of GC patients. Firstly, we screened the FRGs that were differentially expressed based on the TCGA and GTEx databases. Then, we selected the P-FRGs using EP-WGCNA, Cox regression, and Kaplan–Meier analysis. The prognostic model based on P-FRGs-Cox (ALB, BNIP3, DPEP1, GLS2, MEG3, PDK4, TF, and TSC22D3) was constructed on the TCGA-GTEx dataset. According to the median risk score, all patients in the TCGA training dataset and GSE84426 testing dataset were classified into a high- or low-risk group. GC patients in the low-risk group showed higher survival probability than those in the high-risk group. The time-dependent receiver operating characteristic (timeROC) showed that EP-WGCNA-Cox predicted 0.77 in the training set and 0.64 in the testing set for the 5-year survival rate of GC patients, which was better than traditional WGCNA-Cox (P-WGCNA-Cox). In addition, we validated that the P-FRGs were significantly differentially expressed in the adjacent non-tumor gastric tissues and tumor tissues by immunohistochemical staining from the Human Protein Atlas (HPA) database. We also found that the P-FRGs were enriched in tumorigenic pathways by enrichment analysis. In conclusion, our study demonstrated that EP-WGCNA can mine the key FRGs related to the phenotype of GC and is superior to the P-WGCNA. The EP-WGCNA-Cox model based on P-FRGs is reliable in predicting the survival rate of GC patients and can provide potential biomarkers and therapeutic targets for GC.

Comprehensive analysis of hypoxia-related genes for prognosis, immune features, and drugs treatment strategy in gastric cancer using bulk and single-cell RNA-sequencing

Hypoxia is one of the malignant characteristics of solid tumors and is related to the multiple malignant characteristics of the tumor. No study has not yet reported a systematical analysis of the characteristics of hypoxia from single-cell resolution in gastric cancer. In our research, we investigated the hypoxia features of various types of cells in single-cell resolution, identified hypoxia-related genes by the weighted gene co-expression network analysis method. Through the hypoxia-related genes from single-cell levels, we screened out 13 genes and established a prognostic model. This model performs well in the training dataset and multiple independent verification data sets. We thought that tumor hypoxia might affect the DNA methylation of cells and promote the transcription of genes associated with malignant features, thereby promoting tumor progression. We found that the more tumor associated genes in the high-risk group showed hypomethylation and high hypoxia-risk score group have more tumor-related genes, more immunosuppressive immune cells and more enrichment of cancer -related pathways. The lower risk group is more sensitive to three chemotherapy drugs for gastric cancer. Our study illustrates the crucial role of hypoxia in gastric cancer. Hypoxia-related gene prognostic model has been established and has good performance. Hypoxia-related risk score can also be used to guide a patient’s drug treatment strategy.

Identification of prognostic genes for early basal-like breast cancer with weighted gene co-expression network analysis.

Background:Breast cancer (BC) has become the leading cause of death for women’s malignancies and increasingly threatens the health of women worldwide. However, there is a lack of effective targeted drugs for basal-like BC. Therefore, biomarkers related to the prognosis of early BC need to be identified.Methods:The RNA-seq data of 87 cases of early basal-like BC and 111 cases of normal breast tissue from The Cancer Genome Atlas were explored by the weighted gene co-expression network analysis method and Limma package. Then, intersected genes were identified, and hub genes were selected by the maximal clique centrality method. The prognostic effect of the hub genes was also evaluated in early basal-like BC.Results:In total, 601 IGs were identified in this study. An APPI network was constructed, and the top 10 hub genes were selected, namely, cyclin B1, cyclin A2, cyclin-dependent kinase 1, cell division cycle 20, DNA topoisomerase II alpha, BUB1 mitotic checkpoint serine/threonine kinase, aurora kinase B (AURKB), cyclin B2, kinesin family member 11, and assembly factor for spindle microtubules. Only AURKB was found to be significantly associated with the overall prognosis of early basal-like BC. The immune cell infiltration analysis showed that the infiltration numbers of CD4 + T cells and naïve CD8 + T cells were positively correlated with the AURKB expression level, while those of naïve B cells and macrophage M2 cells were negatively correlated with the AURKB expression level in basal-like BC.Conclusion:AURKB might be a potential prognostic indicator in early basal-like BC.

Bioinformatics Analysis of Common Genetic and Molecular Traits and Association of Portal Hypertension with Pulmonary Hypertension.

Portal hypertension (PH) is an important cause of pulmonary arterial hypertension(PAH), but its mechanism is still unclear. We used genetic data analysis to explore the shared genes and molecular mechanisms of PH and PAH. We downloaded the PH and PAH data from the GEO database, and used the weighted gene coexpression network analysis method (WGCNA) to analyze the coexpression modules of idiopathic noncirrhotic portal hypertension (INCPH) and cirrhotic portal hypertension (CPH) and pulmonary hypertension, respectively. Enrichment analysis was performed on the common genes, and differential gene expressions (DEGs) were used for verification. The target genes of INCPH and PAH were obtained by string and cytoscape software, and the miRNAs of target genes were predicted by miRwalk, miRDB, and TargetScan and their biological functions were analyzed; finally, we used PanglaoDB to predict the expression of target genes in cells. In WGCNA, gene modules significantly related to PAH, CPH, and INCPH were identified, and enrichment function analysis showed that the common pathway of PAH and CPH were “P53 signaling pathway,” “synthesis of neutral lipids”; PAH and INCPH are “terminal,” “Maintenance Regulation of Granules,” and “Toxin Transport.” DEGs confirmed the results of WGCNA; the common miRNA functions of PAH and cirrhosis were enriched for “P53 signaling pathway,” “TGF-β signaling pathway,” “TNF signaling pathway,” and “fatty acid metabolism,” and the miRNAs-mRNAs network suggested that hsa-miR-22a-3p regulates MDM2 and hsa-miR-34a-5p regulates PRDX4; the target genes of PAH and INCPH are EIF5B, HSPA4, GNL3, RARS, UTP20, HNRNPA2B1, HSP90B1, METAP2, NARS, SACM1L, and their target miRNA function enrichment showed EIF5B, HNRNPA2B1, HSP90B1, METAP2, NARS, SACM1L, and HSPA4 are associated with telomeres and inflammation, panglaoDB showed that target genes are located in endothelial cells, smooth muscle cells, etc. In conclusion, the mechanism of pulmonary hypertension induced by portal hypertension may be related to telomere dysfunction and P53 overactivation, and lipid metabolism and intestinal inflammation are also involved in this process.

Research on a Weighted Gene Co-expression Network Analysis method for mining pathogenic genes in thyroid cancer.

Thyroid cancer (TC) is one of the most common thyroid malignancies occurring worldwide, and accounts for about 1% of all the malignant tumors. It is one of the fastest growing tumor and can occur at any age, but it is more common in women. It is important to find the pathogenesis and treatment targets of TC. In this pursuit, the present study was envisaged to investigate the effective carcinogenic biological macromolecules, so as to provide a better understanding of the occurrence and development of TC. The clinical and gene expression data were collected from The Cancer Genome Atlas (TCGA). We clustered mRNA and long non-coding RNA (lncRNA) into different modules by Weighted Gene Co-expression Network Analysis (WGCNA), and calculated the correlation coefficient between the genes and clinical phenotypes. Using WGCNA, we identified the module with the highest correlation coefficient. Subsequently, by using the differential genes expression analysis to screen the differential micro-RNA (miRNA), the univariate Cox proportional hazard regression was employed to screen the hub genes related to overall survival (OS), with P < 0.05 as the statistical significance threshold. Finally, we designed a hub competitive endogenous RNA(ceRNA) network of disease-associated lncRNAs, miRNAs, and mRNAs. From the results of enrichment analysis, the association of these genes could be related to the occurrence and development of TC, and these hub RNAs can be valuable prognostic markers and therapeutic targets in TC.

Integrated Physiochemical, Hormonal, and Transcriptomic Analysis Revealed the Underlying Mechanisms for Granulation in Huyou (Citrus changshanensis) Fruit.

Juice sac granulation is a common internal physiological disorder of citrus fruit. In the present study, we compared the physiochemical characteristics and transcriptome profiles of juice sacs in different granulation levels from Huyou fruit (Citrus changshanensis). The accumulation of cell wall components, including the water-soluble pectin, protopectin, cellulose, and lignin, were significantly correlated with the granulation process, resulting in the firmness increase of the juice sac. The in situ labeling of the cell wall components indicated the early accumulation of cellulose and high-methylesterified pectin in the outer layer cells, as well as the late accumulation of lignin in the inner layer cells of the juice sac. Several phytohormones, including auxins, abscisic acids, cytokinins, jasmonic acid, salicylic acid, and/or their metabolites, were positively correlated to the granulation level, indicating an active and complex phytohormones metabolism in the granulation process. Combining the trend analysis by the Mfuzz method and the module-trait correlation analysis by the Weighted Gene Co-expression Network Analysis method, a total of 2940 differentially expressed genes (DEGs) were found to be positively correlated with the granulation level. Gene Ontology (GO) enrichment indicated that the selected DEGs were mainly involved in the cell wall organization and biogenesis, cell wall macromolecule metabolic process, carbohydrate metabolic process, and polysaccharide metabolic process. Among these selected genes, those encoding β-1,4-xylosyltransferase IRX9, cellulose synthase, xyloglucan: xyloglucosyl transferase, xyloglucan galactosyltransferase MUR3, α-1,4-galacturonosyltransferase, expansin, polygalacturonase, pectinesterase, β-glucosidase, β-galactosidase, endo-1,3(4)-β-glucanase, endoglucanase and pectate lyase that required for the biosynthesis or structural modification of cell wall were identified. In addition, NAC, MYB, bHLH, and MADS were the top abundant transcription factors (TFs) families positively correlated with the granulation level, while the LOB was the top abundant TFs family negatively correlated with the granulation level.

Distance correlation application to gene co-expression network analysis

BackgroundTo construct gene co-expression networks, it is necessary to evaluate the correlation between different gene expression profiles. However, commonly used correlation metrics, including both linear (such as Pearson’s correlation) and monotonic (such as Spearman’s correlation) dependence metrics, are not enough to observe the nature of real biological systems. Hence, introducing a more informative correlation metric when constructing gene co-expression networks is still an interesting topic.ResultsIn this paper, we test distance correlation, a correlation metric integrating both linear and non-linear dependence, with other three typical metrics (Pearson’s correlation, Spearman’s correlation, and maximal information coefficient) on four different arrays (macrophage and liver) and RNA-seq (cervical cancer and pancreatic cancer) datasets. Among all the metrics, distance correlation is distribution free and can provide better performance on complex relationships and anti-outlier. Furthermore, distance correlation is applied to Weighted Gene Co-expression Network Analysis (WGCNA) for constructing a gene co-expression network analysis method which we named Distance Correlation-based Weighted Gene Co-expression Network Analysis (DC-WGCNA). Compared with traditional WGCNA, DC-WGCNA can enhance the result of enrichment analysis and improve the module stability.ConclusionsDistance correlation is better at revealing complex biological relationships between gene profiles compared with other correlation metrics, which contribute to more meaningful modules when analyzing gene co-expression networks. However, due to the high time complexity of distance correlation, the implementation requires more computer memory.

Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach.

Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.

Identification of Hub Genes of Keloid Fibroblasts by Coexpression Network Analysis and Degree Algorithm.

Background Keloid is a benign dermal tumor characterized by abnormal proliferation and invasion of fibroblasts. The establishment of biomarkers is essential for the diagnosis and treatment of keloids. Methods We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Differential expressed genes (DEGs) in GSE145725 and genes in significant modules were integrated to identify overlapping key genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed, as well as protein-protein interaction (PPI) network construction for hub gene screening. Results Using the R package of WGCNA, 22 coexpression modules consisting of different genes were identified from the top 5,000 genes with maximum mean absolute deviation in 19 human fibroblast samples. Blue-green and yellow modules were identified as the most important modules, where genes overlapping with DEGs were identified as key genes. We identified the most critical functions and pathways as extracellular structure organization, vascular smooth muscle contraction, and the cGMP-PKG signaling pathway. Hub genes from key genes as BMP4, MSX1, HAND2, TBX2, SIX1, IRX1, EDN1, DLX5, MEF2C, and DLX2 were identified. Conclusion The blue-green and yellow modules may play an important role in the pathogenesis of keloid. 10 hub genes were identified as potential biomarkers and therapeutic targets for keloid.

Hub Genes of Recurrent Laryngeal Cancer Identified by Weighted Gene Co-Expression Network Analysis

Objective in order to discover correlated gene modules and hub genes for recurrent laryngeal squamous cell carcinoma through weighted gene co-expression network analysis method. The microarray dataset of recurrent laryngeal cancer, namely GSE27020, were obtained from the gene expression Omnibus database. Weighted gene co-expression network analysis was introduced to establish a gene co-expression network, mining key clinical trait correlated hub genes. Gene ontology enrichment analyses were performed for the genes in modules related to recurrent laryngeal squamous cell carcinoma. Then, we build up a proteinprotein interaction network with the genes in interest gene module and identified hub genes through analyze such protein-protein interaction network. The hub genes were mined using cytohubba plus-in. Finally, we analyzed these hub genes overall survival using gene expression profiling interactive analysis database. Forty four gene co-expression gene modules were achieved via weighted gene co-expression network analysis analysis. We found that the orangered4 module was the most correlated module with recurrence in laryngeal squamous cell carcinoma patients. Genes in the orangered4 module were related to organelle organization, response to chemical, regulation of catalytic activity and regulation of cell differentiation. Two genes were discovered as hub genes which were related to poor prognosis, namely annexin A2 and S100 calcium binding protein A10. Here, we found several hub genes that played important roles in recurrent laryngeal squamous cell carcinoma, which may improve our understanding of the mechanisms underlying recurrence laryngeal squamous cell carcinoma.

Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

BackgroundGestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.MethodsMetabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.ResultsThis study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.ConclusionsMetabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Construction and Validation of a Novel Eight-Gene Risk Signature to Predict the Progression and Prognosis of Bladder Cancer.

The progression from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) increases the risk of death. It is therefore important to find new relevant molecular models that will allow for effective prediction of the progression and prognosis of bladder cancer (BC). Using RNA-Sequence data of 49 BC patients in Shanghai tenth people’s hospital (STPH) and weighted gene co-expression network analysis methods, a co-expression network of genes was developed and three key modules associated with malignant progression were selected. Based on the genes in three key modules, an eight-gene risk signature was established using univariate Cox regression and the Least absolute shrinkage and selection operator Cox model in The Cancer Genome Atlas Program (TCGA) and validated in validation sets. Subsequently, a nomogram based on the risk signature was constructed for prognostic prediction. The mRNA and protein expression levels of eight genes in cell lines and tissues were further investigated. The novel eight-gene risk signature was closely related to the malignant clinical features of BC and could predict the prognosis of patients in the training dataset (TCGA) and four validation sets (GSE32894, GSE13507, IMvigor210 trial, and STPH). The nomogram showed good prognostic prediction and calibration. The mRNA and protein expression levels of the eight genes were differentially expressed in cell lines and tissues. In our study, we established a novel eight-gene risk signature that could predict the progression and prognoses of BC patients.

An edge-based statistical analysis of long non-coding RNA expression profiles reveals a negative association between Parkinson\u2019s disease and colon cancer

BackgroundColon cancer (CC) is one of the most common malignant tumors, while Parkinson’s disease (PD) is the second most common neurodegenerative disorder. Recent accumulating evidence indicates that these two diseases are associated with each other. Also, from the perspective of long non-coding RNAs, some well-known genes such as H19 and PVT1 can link these two diseases together. Several studies have shown that patients with PD had a decreased risk of developing CC compared with patients without PD. However, controversies surround the relationship between PD and CC, and to date, no concordant conclusion has been drawn.MethodsIn this study, we aimed to assess the association between these two diseases based on lncRNA-to-lncRNA interactions. Motivated by the weighted gene co-expression network analysis method, a customized procedure was proposed and used to identify differentially correlated edges (DCEs) in the respective interaction networks for PD and CC and explore how these two diseases are linked.ResultsOf the two sets of DCEs for PD and CC, 16 pairs overlapped. Among them, 15 edges had opposite signs, with positive signs for CC indicating a gain of connectivity, whereas negative signs for PD indicating a loss of connectivity.ConclusionsBy using the lncRNA expression profiles, and a customized procedure, an answer to the question about how PD and CC are associated is provided.

Bioinformatics Analysis of Microarray Datasets to Identify Prognostic Factors in Lung Adenocarcinoma.

Most patients with lung adenocarcinoma (LUAD) present high recurrence rate and poor prognosis after therapy. Therefore, the purpose of this study was to identify prognostic factors involved in LUAD. Five microarray datasets (including GSE75037, GSE63459, GSE43458, GSE32863, and GSE10072) were downloaded. After data preprocessing and quality control, meta-analysis was performed to screen differentially expressed genes (DEGs) using the MetaDE.ES method in MetaDE package. Subsequently, network construction and module identification were conducted by the Weighted Gene Co-expression Network Analysis method. Moreover, survival-associated genes were identified using the univariate and multivariate Cox regression method in survival package. The risk score model was constructed by prognosis associated genes, followed by the Kaplan-Meier survival analysis. Oncomine expressions analysis of several prognosis associated genes was conducted. The expression levels of key genes were detected using quantitative real-time PCR experiments. A total of 1434 DEGs between LUAD and normal samples were identified. Nine disease-associated modules were identified, in which M8 module was most correlated with LAUD phenotype. A total of 89 indicators (including T stage, M stage, and ADIPOR2) were significantly associated with LAUD prognosis, while only T stage and 9 DEGs (e.g., ARHGEF3, GTSE1, RBM15 and CD52) were retained as the potential prognostic factors following multivariate COX regression analysis. The upregulated adiponectin receptor 2 (ADIPOR2), rho guanine nucleotide exchange factor 3 (ARHGEF3), and CD52 molecule (CD52), and downregulated GTSE1 were validated in LAUD samples of Oncomine database. Importantly, ADIPOR2 and ARHGEF3 were confirmed to be down-regulated in LUAD tissues. ADIPOR2, ARHGEF3, G2 and S-phase expressed 1 (GTSE1) and CD52 might be promising prognostic factors in LUAD.

Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice

BackgroundmRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features.ResultsIn this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay.ConclusionsWe present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis.

PurposeMantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma that is incurable with standard therapies. The use of gene expression analysis has been of interest, recently, to detect biomarkers for cancer. There is a great need for systemic coexpression network analysis of MCL and this study aims to establish a gene coexpression network to forecast key genes related to the pathogenesis and prognosis of MCL.MethodsThe microarray dataset GSE93291 was downloaded from the Gene Expression Omnibus database. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on the modules deemed important. The protein–protein interaction networks were constructed and visualized using Cytoscape software on the basis of the STRING website; the hub genes in the top weighted network were identified. Survival data were analyzed using the Kaplan–Meier method and were compared using the log-rank test.ResultsSeven coexpression modules consisting of different genes were applied to 5,000 genes in the 121 human MCL samples using WGCNA software. GO and KEGG enrichment analysis identified the blue module as one of the most important modules; the most critical pathways identified were the ribosome, oxidative phosphorylation and proteasome pathways. The hub genes in the top weighted network were regarded as real hub genes (IL2RB, CD3D, RPL26L1, POLR2K, KIF11, CDC20, CCNB1, CCNA2, PUF60, SNRNP70, AKT1 and PRPF40A). Survival analysis revealed that seven genes (KIF11, CDC20, CCNB1, CCNA2, PRPF40A, CD3D and PUF60) were associated with overall survival time (p < 0.05).ConclusionsThe blue module may play a vital role in the pathogenesis of MCL. Five real hub genes (KIF11, CDC20, CCNB1, CCNA2 and PUF60) were identified as potential prognostic biomarkers as well as therapeutic targets with clinical utility for MCL.

Comprehensive Understanding of the Interaction Among Stress Hormones Signalling Pathways by Gene Co-expression Network

Background: Plants respond to various stresses at the same time. Recent studies show that interactions of various phytohormones can play important roles in response to stresses. Objective: Although many studies have been done about the effects of the individual hormones, little information exists about the crosstalk among the hormone signalling pathways in plants. Methods: In this work, the weighted gene co-expression network analysis method was used to define modules containing genes with highly correlated expression patterns in response to abscisic acid, jasmonic acid, and salicylic acid in Arabidopsis. Results: Results indicate that plant hormones cause major changes the expression profile and control diverse cell functions, including response to environmental stresses and external factors, cell cycle, and antioxidant activity. In addition, AtbHLH15 and HY5 transcription factors can participate in phytochrome pathways in response to the phytohormones. It is probable that some Type III WRKY transcription factors control the response to bacterium separately from the other stresses. The E2Fa/DPa transcription factor also regulates the cell cycle. Conclusion: In general, many processes and pathways in plants may be regulated using a combination of abscisic acid, jasmonic acid, and salicylic acid.