Co-expression Network Of Differentially Expressed Genes Research Articles

The identification of hub genes associated with pure ground glass nodules using weighted gene co-expression network analysis

BackgroundWhether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis.MethodsTo investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients’ tumor tissues compared to controls.ResultsA total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients’ tumor tissues compared to controls.ConclusionsTo summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.

Comparative Transcriptome Analysis Reveals Differential Gene Expression Pattern Associated with Heat Tolerance in Pepper (Capsicum annuum L.)

Pepper (Capsicum annuum L.) represents a highly significant agricultural commodity worldwide. Heat stress (HS) poses a severe threat to pepper productivity and quality. However, our understanding of the molecular alterations triggered by HS remains insufficient. This study focused on characterizing P19055 and P17087 as heat-tolerant and heat-sensitive pepper lines, respectively. Through RNA sequencing, we quantified transcript abundance in pepper fruit to investigate the impact of HS on gene expression. A total of 41,793 differentially expressed genes (DEGs) was identified, encompassing 33,703 known genes and 8090 novel genes. Additionally, we obtained 89,821 expressed transcripts, including 52,726 known transcripts and 37,095 new transcripts. By comparing gene expression levels between P17087 and P19055 fruit, we pinpointed 2324 genes exhibiting differential expression across three time points (0.5 h, 2 h, 6 h). Leveraging the weighted correlation network analysis (WGCNA) approach, we constructed a co-expression network of DEGs in P19055 and P17087 based on transcriptomic data acquired at these time points. Notably, nineteen modules displayed significant correlations with the time points following HS during the fruiting stage in both genotypes. Functional enrichment analysis revealed that the DEGs were primarily associated with metabolic and cellular processes in response to stress, particularly photosynthesis at the 2 h and 6 h time points. Moreover, we identified and categorized 26 families of transcription factors, including ERF (19), WRKY (12), MYB (15), NAC (7), bHLH (7), MIKC (7), GRAS (6), and 40 others, which may potentially regulate the expression of key genes in response to HS. Our findings contribute to a comprehensive understanding of the molecular mechanisms governing HS during the fruiting stage of pepper cultivation.

Transcriptome Analysis Revealed the Molecular Response Mechanism of High-Resistant and Low-Resistant Alfalfa Varieties to Verticillium Wilt.

Following infestation by Verticillium wilt, alfalfa (Medicago sativa L.) often shows symptoms such as disease spots, leaf loss, stem, and leaf yellowing, resulting in the decline of alfalfa yield and quality and causing significant losses to the alfalfa industry. The popularization and planting of disease-resistant varieties is the most effective method to prevent and control Verticillium wilt of alfalfa. Therefore, it is particularly important to reveal the resistance mechanism of Verticillium wilt resistant varieties of alfalfa. In this study, the physiological and biochemical indexes were measured on days 7, 14, 21, and 28 after inoculation with Verticillium alfalfae for investigating the response mechanisms of two alfalfa varieties, high-resistant WL343HQ, and low-resistant Dryland. Transcriptome sequencing of alfalfa samples infected with V. alfalfae and uninfected alfalfa samples was performed to analyze the potential functions and signaling pathways of differentially expressed genes (DEGs) by GO classification and KEGG enrichment analysis. Meanwhile, weighted gene co-correlation network analysis (WGCNA) algorithm was used to construct a co-expression network of DEGs. Inoculation with V. alfalfae significantly affected net photosynthetic rate, stomatal conductance, chlorophyll content, MDA content, JA and SA concentrations, and NO and H2O2 contents in both WL343HQ and Dryland inoculated with V. alfalfae. Most of the transcription factors in plants were classified in the WRKY, NAC, and bHLH families. WGCNA analysis showed that the number of transcription factors related to plant growth and disease resistance was higher in the corresponding modules of WL343HQ disease groups on days 7 and 28 (WVa) and (WVd) than in the corresponding modules of Dryland disease groups on days 7 and 21 (HVa) and (HVc). These findings provide data for further gene function validation and also provide a reference for in-depth studies on interactions between plants and pathogens.

The Identification and Validation of Hub Genes Associated with Acute Myocardial Infarction Using Weighted Gene Co-Expression Network Analysis.

Acute myocardial infarction (AMI), one of the most severe and fatal cardiovascular diseases, remains the main cause of mortality and morbidity worldwide. The objective of this study is to investigate the potential biomarkers for AMI based on bioinformatics analysis. A total of 2102 differentially expressed genes (DEGs) were screened out from the data obtained from the gene expression omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) explored the co-expression network of DEGs and determined the key module. The brown module was selected as the key one correlated with AMI. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses demonstrated that genes in the brown module were mainly enriched in ‘ribosomal subunit’ and ‘Ribosome’. Gene Set Enrichment Analysis revealed that ‘TNFA_SIGNALING_VIA_NFKB’ was remarkably enriched in AMI. Based on the protein–protein interaction network, ribosomal protein L9 (RPL9) and ribosomal protein L26 (RPL26) were identified as the hub genes. Additionally, the polymerase chain reaction (PCR) results indicated that the expression levels of RPL9 and RPL26 were both downregulated in AMI patients compared with controls, in accordance with the bioinformatics analysis. In summary, the identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of AMI.

Potential biomarkers of acute myocardial infarction based on co-expression network analysis.

Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in ‘RAGE receptor binding’ and ‘negative regulation of T cell cytokine production’. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in ‘cardiovascular diseases’, of AKR1C3 in ‘cardenolide biosynthesis’, of MGAM in ‘glycogenolysis’, of B3GNT5 in ‘glycosphingolipid biosynthesis’ and of ACSL1 in ‘icosapentaenoate biosynthesis II’. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI.

Identification of Hub Genes Correlated With Poor Prognosis for Patients With Uterine Corpus Endometrial Carcinoma by Integrated Bioinformatics Analysis and Experimental Validation.

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC.

Immune response gene coexpression network analysis of Arachis hypogaea infected with Aspergillus flavus

Aspergillus flavus (A. flavus) infection and aflatoxin contamination is a major bottleneck for peanut cultivation and value chain industry. In this study, a transcriptomic network study was conducted by retrieving publically available RNA-seq datasets of resistant and susceptible peanut varieties infected by A. flavus separately to understand the peanut defense mechanism against A. flavus. The gene expression analysis revealed differentially expressed genes (DEGs) in response to the different levels of infection and coexpression network of DEGs deciphered hub genes involved in the immune process in resistant and susceptible varieties. The interplay of resistance conferring genes and cell wall related genes was observed through functional enrichment analysis in response to pathogen infection and identified few key genes such as Protein P21, R genes, Pattern Recognition Receptor genes, Pectinesterases, Laccase and Thaumatin-like protein 1b as candidate genes in imparting immune response against A. flavus.

Identification of Molecular Regulatory Features and Markers for Acute Type A Aortic Dissection.

Background Acute type A aortic dissection (ATAAD) is one of the most lethal cardiovascular diseases, and its molecular mechanism remains unclear. Methods Differentially expressed genes (DEGs) between ATAAD and control were detected by limma R package in GSE52093, GSE153434, GSE98770, and GSE84827, respectively. The coexpression network of DEGs was identified by the WGCNA package. Enrichment analysis was performed for module genes that were positively correlated with ATAAD using clusterProfiler R package. In addition, differentially methylated markers between aortic dissection and control were identified by ChAMP package. After comparing with ATAAD-related genes, a protein-protein interaction (PPI) network was established based on the STRING database. The genes with the highest connectivity were identified as hub genes. Finally, differential immune cell infiltration between ATAAD and control was identified by ssGSEA. Results From GSE52093 and GSE153434, 268 module genes were obtained with consistent direction of differential expression and high correlation with ATAAD. They were significantly enriched in T cell activation, HIF-1 signaling pathway, and cell cycle. In addition, 2060 differentially methylated markers were obtained from GSE84827. Among them, 77 methylation markers were ATAAD-related DEGs. Using the PPI network, we identified MYC, ITGA2, RND3, BCL2, and PHLPP2 as hub genes. Finally, we identified significantly differentially infiltrated immune cells in ATAAD. Conclusion The hub genes we identified may be regulated by methylation and participate in the development of ATAAD through immune inflammation and oxidative stress response. The findings may provide new insights into the molecular mechanisms and therapeutic targets for ATAAD.

Immune-associated biomarkers for early diagnosis of Parkinson's disease based on hematological lncRNA-mRNA co-expression.

Early stage diagnosis of Parkinson’s disease (PD) is challenging without significant motor symptoms. The identification of effective molecular biomarkers as a hematological indication of PD may help improve the diagnostic timelines and accuracy. In the present paper, we analyzed and compared the blood samples of PD and control (CTR) patients to identify the disease-related changes and determine the putative biomarkers for PD diagnosis. Based on the RNA sequencing analysis, differentially expressed genes (DEGs) were identified, and the co-expression network of DEGs was constructed using the weighted gene correlation network analysis (WGCNA). The analysis leads to the identification of 87 genes that were exclusively regulated in the PD group, whereas 66 genes were significantly increased and 21 genes were significantly decreased in contrast with the control group. The results indicate that the core lncRNA–mRNA co-expression network greatly changes the immune response in PD patients. Specifically, the results showed that Prader Willi Angelman Region RNA6 (PWAR6), LINC00861, AC83843.1, IRF family, IFIT family and calcium/calmodulin-dependent protein kinase IV (CaMK4) may play important roles in the immune system of PD. Based on the findings from the present study, future research aims at identifying novel therapeutic strategies for PD.

Comparative transcriptome and co-expression analysis reveal key genes involved in leaf margin serration in Perilla frutescens

ObjectiveIn this study, we aimed to identify the genes involved in leaf margin serration in Perilla frutescens. P. frutescens (Family: Lamiaceae) is widely grown in Asian countries. Perilla leaf is the medicinal part stipulated in the Chinese Pharmacopoeia. There are mainly two types of perilla leaves: one with serrated leaf margin which is the phenotype described in the pharmacopoeia and the other with smooth leaf margin. MethodsTranscriptome sequencing, co-expression analysis, and qRT-PCR analysis of six perilla tissues sampled from two different phenotypes (serrated and smooth leaves) were performed. ResultsForty-three differentially expressed genes (DEGs), which may potentially regulate leaf shape, were identified through de novo transcriptome sequencing between the two groups. Genes involved in leaf shape regulation were identified. Simultaneously, we validated five DEGs by qRT-PCR, and the results were consistent with the transcriptome data. In addition, 1186 transcription factors (TFs) belonging to 45 TF families were identified. Moreover, the co-expression network of DEGs was constructed. ConclusionThe study identified the key genes that control leaf shape by comparing the transcriptomes. Our findings also provide basic data for further exploring P. frutescens, which can help study the mechanism of leaf shape development and molecular breeding.

Identification of key miRNAs in the progression of hepatocellular carcinoma using an integrated bioinformatics approach.

BackgroudIt has been shown that aberrant expression of microRNAs (miRNAs) and transcriptional factors (TFs) is tightly associated with the development of HCC. Therefore, in order to further understand the pathogenesis of HCC, it is necessary to systematically study the relationship between the expression of miRNAs, TF and genes. In this study, we aim to identify the potential transcriptomic markers of HCC through analyzing common microarray datasets, and further establish the differential co-expression network of miRNAs–TF–mRNA to screen for key miRNAs as candidate diagnostic markers for HCC.MethodWe first downloaded the mRNA and miRNA expression profiles of liver cancer from the GEO database. After pretreatment, we used a linear model to screen for differentially expressed genes (DEGs) and miRNAs. Further, we used weighed gene co-expression network analysis (WGCNA) to construct the differential gene co-expression network for these DEGs. Next, we identified mRNA modules significantly related to tumorigenesis in this network, and evaluated the relationship between mRNAs and TFs by TFBtools. Finally, the key miRNA was screened out in the mRNA–TF–miRNA ternary network constructed based on the target TF of differentially expressed miRNAs, and was further verified with external data set.ResultsA total of 465 DEGs and 215 differentially expressed miRNAs were identified through differential genes expression analysis, and WGCNA was used to establish a co-expression network of DEGs. One module that closely related to tumorigenesis was obtained, including 33 genes. Next, a ternary network was constructed by selecting 256 pairs of mRNA–TF pairs and 100 pairs of miRNA–TF pairs. Network mining revealed that there were significant interactions between 18 mRNAs and 25 miRNAs. Finally, we used another independent data set to verify that miRNA hsa-mir-106b and hsa-mir-195 are good classifiers of HCC and might play key roles in the progression of HCC.ConclusionOur data indicated that two miRNAs—hsa-mir-106b and hsa-mir-195—are identified as good classifiers of HCC.

Molecular mechanisms associated with breast cancer based on integrated gene expression profiling by bioinformatics analysis

In this study, we aimed to gain more insights into the underlying molecular mechanisms responsible for breast cancer (BC) progression. Three gene expression profiles of human BC were integrated and used to screen the differentially expressed genes (DEGs) between healthy breast samples and BC samples. Protein–protein interaction (PPI) network of DEGs was constructed by mapping DEGs into the Search Tool for the Retrieval of Interacting Genes (STRING) database; then the subnetworks of PPI were constructed with plug-in, MCODE and DEGs in Subnetwork 1 were analysed based on Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway database (http://www.genome.jp/kegg/). In addition, co-expression network of DEGs was established using the Cytoscape. Totalally 931 DEGs were selected, including 340 up-regulated genes and 591 down-regulated genes. KEGG pathway analysis for DEGs in Subnetwork 1 showed that the pathogenesis of BC was associated with cell cycle, oocyte meiosis, progesterone-mediated oocyte maturation and p53 signalling pathways. Meanwhile, the most significant-related DEGs were found by co-expression network analysis of DEGs. In conclusion, CCNG1 might be involved in the progression of BC via inhibiting cell proliferation, and ADAMTS1 might play a crucial role in BC development through the regulation of angiogenesis.

Protein-protein interaction network and mechanism analysis of hepatitis C.

We predicted potential genes and identified pathways associated with hepatitis C. The gene expression profiles of GSE40184 from blood samples and GSE38597 from liver biopsy samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were recognized using the Limma Package. The Pearson correlation test was used to construct the co-expression network of DEGs. Gene set enrichment analysis was used to define significant functions and pathways for DEGs. A total of 165 DEGs in blood samples and 523 DEGs in liver biopsy samples were identified. Eight DEGs were common between these samples. Gene Ontology enrichment analysis showed that 165 DEGs in blood samples were significantly enriched regarding the response to protein binding, receptor binding, G-protein coupled receptor binding, cytokine receptor binding, and cytokine activity. The most significant term of the Kyoto Encyclopedia of Genes and Genomes pathway was the cytokine-cytokine receptor interaction. Protein-protein interaction network analysis indicated that three subnetworks with more nodes and edges were involved in these interactions. We used robust biomarkers that were differentially expressed in hepatitis C and determined their relevance in the biological function, signal pathways, protein-protein interaction network, and co-expression network of hepatitis C.

Ranking candidate genes of esophageal squamous cell carcinomas based on differentially expressed genes and the topological properties of the co-expression network.

BackgroundThe aim of this study was to identify the candidate genes of esophageal squamous cell carcinoma (ESCC).MethodsGene expression profiling of 17 ESCC samples and 17 adjacent normal samples, GSE20347, was downloaded from Gene Expression Omnibus database. The raw data were preprocessed, and the differentially expressed genes (DEGs) between ESCC and normal samples were identified by using SAM software (false discovery rate <0.001). Then, the co-expression network of DEGs was constructed based on Pearson’s correlation test (r-value ≥0.8). Furthermore, the topological properties of the co-expression network were analyzed through NetworkAnalyzer (default settings) of Cytoscape. The expression fold changes of DEGs and topological properties were utilized to identify the candidate genes of ESCC (Crin score >4), which were further analyzed based on DAVID functional enrichment analysis (P-value <0.05).ResultsA total of 1,063 DEGs were identified, including 490 up-regulated and 573 down-regulated DEGs. Then, the co-expression network of DEGs was constructed, containing 999 nodes and 46,323 edges. Based on the expression fold changes of DEGs and the topological properties of the co-expression network, DEGs were ranked, and the top 24 genes were candidate genes of ESCC, such as CRISP3, EREG, CXCR2, and CRNN. Furthermore, the 24 genes were significantly enriched in bio-functions regarding cell differentiation, glucan biosynthetic process and immune response.ConclusionThe present study suggested that CRISP3, EREG, CXCR2, and CRNN might be causative genes of ESCC, and play vital roles in the development of ESCC. However, further experimental studies are needed to confirm our results.Electronic supplementary materialThe online version of this article (doi:10.1186/s40001-014-0052-x) contains supplementary material, which is available to authorized users.

Gene expression analysis of lung adenocarcinoma and matched adjacent non-tumor lung tissue.

The aim of this study was to find disease-associated genes and gene functions in lung adenocarcinoma and matched adjacent non-tumor lung tissues with DNA microarray. We downloaded the gene expression profile GSE32863 from the Gene Expression Omnibus database including 58 lung adenocarcinoma and 58 adjacent non-tumor lung tissue samples. Data were preprocessed and the differentially expressed genes (DEGs) were identified using packages in the R computing language. The selected DEGs were further analyzed with bioinformatics methods. After the coexpression network of DEGs was constructed by STRING (Search Tool for the Retrieval of Interacting Genes/Proteins), we analyzed gene functions with DAVID (The Database for Annotation, Visualization and Integrated Discovery) and WebGestalt (WEB-based Gene Set Analysis Toolkit). A total of 1429 genes were filtered as DEGs, including 873 downregulated genes and 556 upregulated genes, and the DEGs including CDC45, CCNB2, CDC20, MCM2, PTTG1, MCM4 and FEN1 were most significantly related to cell cycle and DNA replication. The discovery of featured genes which were significantly related to cell cycle and DNA replication has potential for use in the clinic for the diagnosis of lung adenocarcinoma in the future. However, further experiments will be needed to confirm our result.