Exploration and validation of hub genes and pathways in the progression of hypoplastic left heart syndrome via weighted gene co-expression network analysis

Background Despite significant progress in surgical treatment of hypoplastic left heart syndrome (HLHS), its mortality and morbidity are still high. Little is known about the molecular abnormalities of the syndrome. In this study, we aimed to probe into hub genes and key pathways in the progression of the syndrome. Methods Differentially expressed genes (DEGs) were identified in left ventricle (LV) or right ventricle (RV) tissues between HLHS and controls using the GSE77798 dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed and key modules were constructed for HLHS. Based on the genes in the key modules, protein–protein interaction networks were conducted, and hub genes and key pathways were screened. Finally, the GSE23959 dataset was used to validate hub genes between HLHS and controls. Results We identified 88 and 41 DEGs in LV and RV tissues between HLHS and controls, respectively. DEGs in LV tissues of HLHS were distinctly involved in heart development, apoptotic signaling pathway and ECM receptor interaction. DEGs in RV tissues of HLHS were mainly enriched in BMP signaling pathway, regulation of cell development and regulation of blood pressure. A total of 16 co-expression network were constructed. Among them, black module (r = 0.79 and p value = 2e−04) and pink module (r = 0.84 and p value = 4e−05) had the most significant correlation with HLHS, indicating that the two modules could be the most relevant for HLHS progression. We identified five hub genes in the black module (including Fbn1, Itga8, Itga11, Itgb5 and Thbs2), and five hub genes (including Cblb, Ccl2, Edn1, Itgb3 and Map2k1) in the pink module for HLHS. Their abnormal expression was verified in the GSE23959 dataset. Conclusions Our findings revealed hub genes and key pathways for HLHS through WGCNA, which could play key roles in the molecular mechanism of HLHS.

Bioinformatics Analysis of Differentially Expressed Genes Involved in Irritable Bowel Syndrome With Diarrhea

Purpose: Irritable bowel syndrome with diarrhea (IBS-D) is a common functional gastrointestinal disorder around the world. However, the molecular mechanisms of IBS-D are still not well understood. This study was designed to identify key biomarkers and immune infiltration in the rectal mucosa of IBS-D by bioinformatics analysis. Methods: The gene expression profiles of GSE36701 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified and functional enrichment and pathway analyses were performed. Using STRING and Cytoscape, protein-protein interaction (PPI) networks were constructed and core genes were identified. Subsequently, 22 immune cell types of IBS-D tissues were explored by the Cell type Identification by Estimating Relative Subsets of RNA Transcripts. Finally, the co-expression network of DEGs was estimated by the weigh gene co-expression network analysis method to identify IBS-D-related modules and deeply hub genes. Results: 224 up-regulated and 171 down-regulated genes in IBS-D patients: Our analysis indicated that several DEGs might play crucial roles in IBS-D, such as CDC20, UBE2C, AURKA, CDC26, CKS1B and PSMB3. Later, we found that immune infiltrating cells such as T cells CD4 memory resting, M2 macrophages are crucial in IBS-D progression. In the end, a total of 9 co-expression gene modules were calculated and the black module was found to have the highest correlation. 15 hub genes were identified both in DEGs and the black module. Conclusions: This study identified molecular mechanisms and a series of candidate genes as well as significant pathways from the bioinformatics network, which may provide a diagnostic method and therapeutic targets for IBS-D.

Transcriptome analysis reveals regulatory networks and hub genes in flavonoid metabolism of Rosa roxburghii

Rosa roxburghii Tratt, the most popular fruit that blooms in the southwest of China, is rich in flavonoids. However, the regulatory network and critical genes involved in the metabolism of flavonoid compounds in R. roxburghii are still unknown. In this study, we revealed that flavonoid, anthocyanin and catechin accumulated at different levels in various tissues of R. roxburghii . We further obtained and analyzed differentially expressed genes (DEGs) involved in flavonoid metabolism from five samples of R. roxburghii by transcriptome sequencing. A total of 1 130 DEGs were identified, including 166 flavonoid pathway biosynthesis genes, 622 transcription factors, 301 transporters, and 221 cytochrome P450 proteins. A weighted gene co-expression network analysis (WGCNA) of the DEGs uncovered different co-expression networks. In terms of biosynthesis enzymes, cytochrome P450 CYP749A22 and CYP72A219 were highlighted in regulation flavonoids content. Anthocyanin 3-O-glucosyltransferase and F3’H were the top two critical enzymes for anthocyanin content. In contrast, caffeic acid 3-O-methyltransferase, 4-coumarate-CoA ligase, and shikimate O-hydroxycinnamoyl transferase were essential for catechin accumulation. Additionally, the eigengene network of the “black” module had high correlations with total flavonoid (r= 0.9, p=5e-06). There were 26 eigengenes in the “black” module, including six flavonoid biosynthesis, 14 TFs and six transporters. Among them, besides cytochrome P450 proteins ( DN136557_c0_g1 , DN135573_c0_g1 and DN145971_c4_g1 ), isoflavone-hydroxylase ( DN143321_c3_g1 ) was crucial for total flavonoids content based on the high degree of connectivity. The transcription factors RrWRKY45 ( DN142829_c1_g5 ), RrTCP20 ( DN146443_c1_g1) and RrERF118 ( DN141507_c3_g2) were significantly correlated with flavonoids in R. roxburghii . The present transcriptomic and biochemical data on metabolites should encourage further investigation on functional genomics and breeding of R. roxburghii with strong pharmaceutical potential.

Identifying Conserved Generic Aspergillus spp. Co-Expressed Gene Modules Associated with Germination Using Cross-Platform and Cross-Species Transcriptomics

Aspergillus spp. is an opportunistic human pathogen that may cause a spectrum of pulmonary diseases. In order to establish infection, inhaled conidia must germinate, whereby they break dormancy, start to swell, and initiate a highly polarized growth process. To identify critical biological processes during germination, we performed a cross-platform, cross-species comparative analysis of germinating A. fumigatus and A. niger conidia using transcriptional data from published RNA-Seq and Affymetrix studies. A consensus co-expression network analysis identified four gene modules associated with stages of germination. These modules showed numerous shared biological processes between A. niger and A. fumigatus during conidial germination. Specifically, the turquoise module was enriched with secondary metabolism, the black module was highly enriched with protein synthesis, the darkgreen module was enriched with protein fate, and the blue module was highly enriched with polarized growth. More specifically, enriched functional categories identified in the blue module were vesicle formation, vesicular transport, tubulin dependent transport, actin-dependent transport, exocytosis, and endocytosis. Genes important for these biological processes showed similar expression patterns in A. fumigatus and A. niger, therefore, they could be potential antifungal targets. Through cross-platform, cross-species comparative analysis, we were able to identify biologically meaningful modules shared by A. fumigatus and A. niger, which underscores the potential of this approach.

Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis

Background: Endometrial cancer (EC) is one of the most lethal gynecological cancers around the world. The aim of this study is to identify the potential immune microenvironment-related biomarkers associated with the prognosis for EC.Methods: RNA-seq data and clinical information of EC patients were derived from The Cancer Genome Atlas (TCGA). The immune score of each EC sample was obtained by ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify the interesting module and potential key genes concerning the immune score. The expression patterns of the key genes were then verified via the GEPIA database. Finally, CIBERSORT was applied to evaluate the relative abundances of 22 immune cell types in EC.Results: Immune scores were significantly associated with tumor grade and histology of EC, and high immune scores may exert a protective influence on the survival outcome for EC. WGCNA indicated that the black module was significantly correlated with the immune score. Function analysis revealed it mainly involved in those terms related to immune regulation and inflammatory response. Moreover, 11 key genes (APOL3, C10orf54, CLEC2B, GIMAP1, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GYPC, IFFO1, TAGAP) were identified from the black module, validated by the GEPIA database, and revealed strong correlations with infiltration levels of multiple immune cell types, as was the prognosis of EC.Conclusion: In this study, 11 key genes showed abnormal expressions and strong correlations with immune infiltration in EC, most of which were significantly associated with the prognosis of EC. These findings made them promising therapeutic targets for the treatment of EC.

Key Genes and Pathways in Human Left Atrial Appendage Identified by Co-expression Analysis

Background and Objective Left atrial appendage (LAA) closure (LAAC) is a technique that has shown potential for the prevention of thrombo-embolic events in atrial fibrillation (AF) patients. The short and long-term effects of LAAC on neuro-hormonal changes have been highlighted in several recent studies. In addition, metabolic and hemodynamic profile changes were identified after LAAC, which may be attributed to the potential influence of specific LAA genetic profiling. However, only a few studies have deciphered the specific LAA genetic profiling. Therefore, we sought to conduct a weighted gene co-expression network analysis (WGCNA) to identify highly correlated gene modules in the LAA and to identify the hub genes with the highest degree of connectivity in selected modules. Functional enrichment analysis was performed to investigate the pivotal biological processes and pathways of defined gene modules in the LAA. Material and Methods Genes exhibiting the highest expression levels (top 25%) of variation in the microarray samples from the combined GSE41177 and GSE79768 dataset were identified. These datasets were obtained from the Gene Expression Omnibus (GEO) database. The combined dataset, which was used to conduct the WGCNA, included 38 paired samples that compared LAA (n = 19) with left atrium-pulmonary vein junction (LA-PV, n = 19) specimens. Gene ontology and functional enrichment analyses were performed to define genes belonging to the key modules of LAA. Hub genes were screened out from the key modules by algorithms and interactions analysis and which were visualized using Cytoscape software. Results Two modules with 397 (pink) and 419 (black) probes were identified to be specifically related to LAA (pink: r = 0.22, p = 9.7×10 − 6; black: r = 0.27, p = 2×10 − 8). In the functional analyses, the pink module showed an association with serine-type endopeptidase activity, cell-cell adhesion, synapse and axon guidance and protein processing. The black module was primarily associated with metabolic processes, such as the triglyceride metabolic process, triglyceride biosynthetic process, fatty acid oxidation, carbohydrate biosynthetic process, insulin signaling pathway, regulation of lipid storage, PPAR signaling pathway, regulation of lipolysis adipocytes, response to peptide hormone and amino acid biosynthesis. A total of five genes, including LRRN4 (leucine-rich repeat neuronal protein 4) and KLK11 (kallikrein related peptidase 11) in the pink module, as well as GYG2 (glycogenin 2), GPD1 (glycerol-3-phosphate dehydrogenase 1) and DGAT2 (diacylglycerol O-acyltransferase 2) from the black module, were identified as hub genes. Conclusions Using WGCNA bioinformatic approach, we defined key genes and pathways with specific biological characteristics in the human LAA, and our results thus are helpful to understand the underlying mechanisms responsible for the neuro-hormonal changes following LAA closure procedures.

Weighted Gene Co-expression Network Analysis Identifies Crucial Genes Mediating Progression of Carotid Plaque

BackgroundSurface rupture of carotid plaque can cause severe cerebrovascular disease, including transient ischemic attack and stroke. The aim of this study was to elucidate the molecular mechanism governing carotid plaque progression and to provide candidate treatment targets for carotid atherosclerosis.MethodsThe microarray dataset GSE28829 and the RNA-seq dataset GSE104140, which contain advanced plaque and early plaque samples, were utilized in our analysis. Differentially expressed genes (DEGs) were screened using the “limma” R package. Gene modules for both early and advanced plaques were identified based on co-expression networks constructed by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in each module. In addition, hub genes for each module were identified. Crucial genes were identified by molecular complex detection (MCODE) based on the DEG co-expression network and were validated by the GSE43292 dataset. Gene set enrichment analysis (GSEA) for crucial genes was performed. Sensitivity analysis was performed to evaluate the robustness of the networks that we constructed.ResultsA total of 436 DEGs were screened, of which 335 were up-regulated and 81 were down-regulated. The pathways related to inflammation and immune response were determined to be concentrated in the black module of the advanced plaques. The hub gene of the black module was ARHGAP18 (Rho GTPase activating protein 18). NCF2 (neutrophil cytosolic factor 2), IQGAP2 (IQ motif containing GTPase activating protein 2) and CD86 (CD86 molecule) had the highest connectivity among the crucial genes. All crucial genes were validated successfully, and sensitivity analysis demonstrated that our results were reliable.ConclusionTo the best of our knowledge, this study is the first to combine DEGs and WGCNA to establish a DEG co-expression network in carotid plaques, and it proposes potential therapeutic targets for carotid atherosclerosis.

Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial Carcinoma by Immune Score and Weighted Correlation Network Analysis

Background: Endometrial cancer (EC) is one of the most lethal gynecological cancer in women. It is imperative to identify the potential immune microenvironment-related biomarkers associated with the prognosis for EC. Methods: RNA-seq data and related clinical information of EC patients were derived from The Cancer Genome Atlas (TCGA). The immune score of each EC sample was obtained by ESTIMATE algorithm. Weighted gene co-expression network analysis (WCGNA) was used to identify the interesting module and potential key genes concerning the immune score. Further, the expression patterns of the key genes were verified via the GEPIA database. Last, CIBERSORT was used to evaluate the relative abundances of 22 immune cell types in EC. Results: Immune scores were significantly associated with tumor grade and histology of EC, and high immune scores may exert a protective influence on the survival outcome for EC. WCGNA indicated that the black module was significantly correlated with the immune score in EC. Function analysis revealed it mainly involved in those terms related to immune regulation and inflammatory response. Moreover, 11 key genes were identified from the black module, validated by the GEPIA database, and revealed strong correlations with infiltration levels of multiple immune cell types, as was the prognosis of EC. Conclusion: In our study, 11 key genes showed abnormal expressions and strong correlations with immune cell infiltration in EC, most of which were significantly associated with the prognosis of EC. These findings made them promising therapeutic targets for the treatment of EC.

Exploration and Validation of Hub Genes and Pathways in the Progression of Hypoplastic Left Heart Syndrome via Weighted Gene Co-Expression Network Analysis

Objective: Despite significant progress in surgical treatment of hypoplastic left heart syndrome (HLHS), its mortality and morbidity are still high. Little is known about the molecular abnormalities of the syndrome. In this study, we aimed to probe into hub genes and key pathways in the progression of the syndrome.Methods: Differentially expressed genes (DEGs) were identified in left ventricle (LV) or right ventricle (RV) tissues between HLHS and controls using the GSE77798 dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed and key modules were constructed for HLHS. Based on the genes in the key modules, protein-protein interaction (PPI) networks were conducted, and hub genes and key pathways were screened. Finally, the GSE23959 dataset was used to validate hub genes between HLHS and controls.Results: 88 and 41 DEGs were identified for LV and RV tissues between HLHS and controls, respectively. DEGs in LV tissues of HLHS were distinctly involved in heart development, apoptotic signaling pathway and ECM receptor interaction. DEGs in RV tissues of HLHS were mainly enriched in BMP signaling pathway, regulation of cell development and regulation of blood pressure. A total of 16 co-expression network were constructed. Among them, black module (r=0.79 and p-value=2e-04) and pink module (r=0.84 and p-value=4e-05) had the most significant correlation with HLHS, indicating that the two modules could be the most relevant for HLHS progression. We identified five hub genes in the black module (including Fbn1, Itga8, Itga11, Itgb5 and Thbs2), and five hub genes (including Cblb, Ccl2, Edn1, Itgb3 and Map2k1) in the pink module for HLHS. Their abnormal expression was verified in the GSE23959 dataset. Conclusion: Our findings revealed hub genes and key pathways for HLHS through WGCNA, which could play key roles in the molecular mechanism of HLHS.

Identification of key genes and pathways associated with Crohn’s disease by bioinformatics analysis

Crohn’s disease is a type of inflammatory bowel disease posing a significant threat to human health all over the world. Genome-wide gene expression profiles of mucosal colonic biopsies have provided some insight into the molecular mechanisms of Crohn’s disease. However, the exact pathogenesis is unclear. This study aimed to identify key genes and significant signaling pathways associated with Crohn’s disease by bioinformatics analysis. To identify key genes, an integrated analysis of gene expression signature was conducted using a robust rank aggregation approach. A total of 179 Crohn’s disease patients and 94 healthy controls from nine public microarray datasets were included. MMP1 and CLDN8 were two key genes screened from the differentially expressed genes. Connectivity Map predicted several small molecules as possible adjuvant drugs to treat CD. Besides, we used weighted gene co-expression network analysis to explore the co-expression modules associated with Crohn’s disease pathogenesis. Seven main functional modules were identified, of which black module showed the highest correlation with Crohn’s disease. The genes in black module mainly enriched in Interferon Signaling and defense response to virus. Blue module was another important module and enriched in several signaling pathways, including extracellular matrix organization, inflammatory response and blood vessel development. There were also several other meaningful functional modules which enriched in many biological processes. The present study identified a number of key genes and pathways correlated with Crohn’s disease and potential drugs to combat it, which might offer insights into Crohn’s disease pathogenesis and provide a clue to potential treatments.