Significant Module Genes Research Articles

Identification of molecular targets of Hypericumperforatum in blood for major depressive disorder: a machine-learning pharmacological study

BackgroundMajor depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Hypericumperforatum (HP) is a traditional herb that has been shown to have antidepressant effects, but its mechanism is unclear. This study aims to identify the molecular targets of HP for the treatment of MDD.MethodsWe performed differential analysis and weighted gene co-expression network analysis (WGCNA) with blood mRNA expression cohort of MDD and healthy control to identify DEGs and significant module genes (gene list 1). Three databases, CTD, DisGeNET, and GeneCards, were used to retrieve MDD-related gene intersections to obtain MDD-predicted targets (gene list 2). The validated targets were retrieved from the TCMSP database (gene list 3). Based on these three gene lists, 13 key pathways were identified. The PPI network was constructed by extracting the intersection of genes and HP-validated targets on all key pathways. Key therapeutic targets were obtained using MCODE and machine learning (LASSO, SVM-RFE). Clinical diagnostic assessments (Nomogram, Correlation, Intergroup expression), and gene set enrichment analysis (GSEA) were performed for the key targets. In addition, immune cell analysis was performed on the blood mRNA expression cohort of MDD to explore the association between the key targets and immune cells. Finally, molecular docking prediction was performed for the targets of HP active ingredients on MDD.ResultsDifferential expression analysis and WGCNA module analysis yielded 933 potential targets for MDD. Three disease databases were intersected with 982 MDD-predicted targets. The TCMSP retrieved 275 valid targets for HP. Separate enrichment analysis intersected 13 key pathways. Five key targets (AKT1, MAPK1, MYC, EGF, HSP90AA1) were finally screened based on all enriched genes and HP valid targets. Combined with the signaling pathway and immune cell analysis suggested the effect of peripheral immunity on MDD and the important role of neutrophils in immune inflammation. Finally, the binding of HP active ingredients (quercetin, kaempferol, and luteolin) and all 5 key targets were predicted based on molecular docking.ConclusionsThe active constituents of Hypericumperforatum can act on MDD and key targets and pathways of this action were identified.

Identification of hub glutamine metabolism-associated genes and immune characteristics in pre-eclampsia.

Preeclampsia (PE) is a severe complication of unclear pathogenesis associated with pregnancy. This research aimed to elucidate the properties of immune cell infiltration and potential biomarkers of PE based on bioinformatics analysis. Two PE datasets were imported from the Gene ExpressioOmnibus (GEO) and screened to identify differentially expressed genes (DEGs). Significant module genes were identified by weighted gene co-expression network analysis (WGCNA). DEGs that interacted with key module genes (GLu-DEGs) were analyzed further by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. The diagnostic value of the genes was assessed using receiver operating characteristic (ROC) curves and protein-protein interaction (PPI) networks were constructed using GeneMANIA, and GSVA analysis was performed using the MSigDB database. Immune cell infiltration was analyzed using the TISIDB database, and StarBase and Cytoscape were used to construct an RBP-mRNA network. The identified hub genes were validated in two independent datasets. For further confirmation, placental tissue from healthy pregnant women and women with PE were collected and analyzed using both RT-qPCR and immunohistochemistry. A total of seven GLu-DEGs were obtained and were found to be involved in pathways associated with the transport of sulfur compounds, PPAR signaling, and energy metabolism, shown by GO and KEGG analyses. GSVA indicated significant increases in adipocytokine signaling. Furthermore, single-sample Gene Set Enrichment Analysis (ssGSEA) indicated that the levels of activated B cells and T follicular helper cells were significantly increased in the PE group and were negatively correlated with GLu-DEGs, suggesting their potential importance. In summary, the results showed a correlation between glutamine metabolism and immune cells, providing new insights into the understandingPE pathogenesis and furnishing evidence for future advances in the treatment of this disease.

Downregulated annexin A1 expression correlates with poor prognosis, metastasis, and immunosuppressive microenvironment in Ewing's sarcoma.

Ewing's sarcoma (ES) is a common bone malignancy in children and adolescents that severely affects the prognosis of patients. The aim of this study was to identify novel biomarkers and potential therapeutic targets for ES. Highly prognosis-related hub genes were identified by independent prognostic analysis in the GSE17679 dataset. We then performed survival analysis, Cox regression analysis and clinical correlation analysis on the key gene and validated them with the GSE63157, GSE45544 and GSE73166 datasets. Differentially expressed genes (DEGs) were screened based on the high and low expression of key gene, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to explore the underlying mechanisms of ES, and significant module genes were established based on protein-protein interaction (PPI) networks. Furthermore, the correlations between module genes and the immune microenvironment were analyzed and the correlations between the key gene and immune infiltration levels in sarcoma were investigated using TIMER and TISIDB. Finally, the expression levels of these key genes in ES cell lines (RD-ES and A673 cells) were further validated by real-time quantitative PCR (RT-qPCR). CCK-8 and EdU assays were performed to assess the effect of ANXA1 knockdown on RD-ES cell proliferation. ANXA1 was identified as a key gene for ES prognosis. The overall survival (OS) time of patients with low ANXA1 expression was shorter, and the expression level of ANXA1 in the metastatic group was significantly lower than that in the primary group (P<0.01). Additionally, the abundance of 12 immune cells in the ANXA1 low-expression group was significantly lower than that in the high-expression group (all P<0.05), which may be related to the inhibition of the immune microenvironment. A PPI network was constructed based on 96 DEGs to further identify the five ANXA1-related module genes (COL1A2, MMP9, VIM, S100A11 and S100A4). The expression levels of ANXA1, COL1A2, MMP9, VIM, S100A11 and S100A4 were significantly different between ES cell lines and mesenchymal stem cells after validation in two ES cell lines (all P<0.01). Among these genes, ANXA1, COL1A2, MMP9, VIM and S100A4 were significantly associated with the prognosis of ES patients (all P<0.05). Importantly, ANXA1 knockdown significantly promoted the proliferation of RD-ES cells, which may explain the susceptibility to ES metastasis in the ANXA1 low-expression group. ANXA1 may serve as an independent prognostic biomarker for ES patients and is associated with metastasis and the immunosuppressive microenvironment in ES, which needs to be validated in further studies.

Diagnostic model constructed by nine inflammation-related genes for diagnosing ischemic stroke and reflecting the condition of immune-related cells.

Ischemic cerebral infarction is the most common type of stroke with high rates of mortality, disability, and recurrence. However, the known diagnostic biomarkers and therapeutic targets for ischemic stroke (IS) are limited. In the current study, we aimed to identify novel inflammation-related biomarkers for IS using machine learning analysis and to explore their relationship with the levels of immune-related cells in whole blood samples. Gene expression profiles of healthy controls and patients with IS were download from the Gene Expression Omnibus. Analysis of differentially expressed genes (DEGs) was performed in healthy controls and patients with IS. Single-sample gene set enrichment analysis was performed to calculate inflammation scores, and weighted gene co-expression network analysis was used to analyze genes in significant modules associated with inflammation scores. Key DEGs in significant modules were then analyzed using LASSO regression analysis for constructing a diagnostic model. The effectiveness and specificity of the diagnostic model was verified in healthy controls and patients with IS and with cerebral hemorrhage (CH) using qRT-PCR. The relationship between diagnostic score and the levels of immune-related cells in whole blood were analyzed using Pearson correlations. A total of 831 DEGs were identified. Both chronic and acute inflammation scores were higher in patients with IS, while 54 DEGs were also clustered in the gene modules associated with chronic and acute inflammation scores. Among them, a total of 9 genes were selected to construct a diagnostic model. Interestingly, RT-qPCR showed that the diagnostic model had better diagnostic value for IS but not for CH. The levels of lymphocytes were lower in blood of patients with IS, while the levels of monocytes and neutrophils were increased. The diagnostic score of the model was negatively associated with the levels of lymphocytes and positively associated with levels of monocytes and neutrophils. Taken together, the diagnostic model constructed using the inflammation-related genes TNFSF10, ID1, PAQR8, OSR2, PDK4, PEX11B, TNIP1, FFAR2, and JUN exhibited high and specific diagnostic value for IS and reflected the condition of lymphocytes, monocytes, and neutrophils in the blood. The diagnostic model may contribute to the diagnosis of IS.

Comprehensive analysis reveals COPB2 and RYK associated with tumor stages of larynx squamous cell carcinoma

BackgroundLaryngeal squamous cell carcinoma (LSCC) is one of the highly aggressive malignancy types of head and neck squamous cell carcinomas; genes involved in the development of LSCC still need exploration.MethodsWe downloaded expression profiles of 96 (85 in advanced stage and 11 in early stage) LSCC patients from TCGA-HNSC. Function enrichment and protein-protein interactions of genes in significant modules were conducted. Univariate and multivariate Cox regression analyses were performed to explore potential prognostic biomarkers for LSCC. The expression levels of genes at different stages were compared and visualized via boxplots. Immune infiltration was examined by the CIBERSORTx web-based tool and depicted with ggplot2. Gene set enrichment analysis (GSEA) was utilized to analyze functional enrichment terms and pathways. Immunohistochemical staining (IHC) was used to verify the expression of genes in the LSCC samples.ResultsWe identified 25 modules, including 3 modules significantly related to tumor stages of LSCC via weighted gene co-expression network analysis (WGCNA). UIMC1, NPM1, and DCTN4 in the module ‘cyan’, TARS in the module ‘darkorange’, and COPB2 and RYK in the module ‘lightyellow’ showed statistically significant relation to overall survival. The expression of COPB2, DCTN4, RYK, TARS, and UIMC1 indicated association with the change of fraction of immune cells in LSCC patients; two genes, COPB2 and RYK, indicated different expression in various tumor stages of LSCC. Finally, COPB2 and RYK showed high-expression in tumor tissues of advanced LSCC patients.ConclusionsOur study provided a potential perceptive in analyzing progression of LSCC cells and exploring prognostic genes.

Integrative bioinformatics analysis of potential therapeutic targets and immune infiltration characteristics in dilated cardiomyopathy.

BackgroundDilated cardiomyopathy (DCM) is currently the major cause of systolic heart failure. This study explored potential therapeutic targets and investigated the role of immune cell infiltration in DCM.MethodsThree DCM datasets (GSE3585, GSE9800, and GSE84796) from the Gene Expression Omnibus (GEO) database were merged into an integrated dataset, and batch effects were removed. Differentially expressed genes (DEGs) were screened and the associations between gene co-expression modules and clinical traits were assessed by weighted gene co-expression network analysis (WGCNA) in R software. Any DEGs from the integrated dataset overlapped with the significant module genes were defined as common genes (CGs). Enrichment analysis of the CGs was performed. The protein-protein interaction (PPI) network of the CGs was visualized and the hub gene was identified by using Cytoscape 3.8.2 software. The miRNA-transcription factor-mRNA (miRNA-TF-mRNA) network was constructed using Cytoscape to unveil the regulatory relationships in DCM. Finally, the CIBERSORT method (https://cibersort.stanford.edu/) was used to investigate immune cell infiltration in DCM.ResultsA total of 53 DEGs were identified, and 5 gene co-expression modules were detected by WGCNA of the DCM and control group samples of cardiac tissue. Genes such as FRZB, ASPN, and PHLDA1 were significantly upregulated, whereas IDH2 and ENDOG were significantly downregulated. Functional enrichment analysis showed that CGs were mainly enriched in the extracellular matrix (ECM) signaling pathway. ASPN was the hub gene in the PPI network. The miRNA-TF-mRNA network revealed that FRZB and ASPN were targeted by paired related homeobox 2 (Prrx2). We also found that miR-129-5p could regulate ASPN, PHLDA1, and IDH2 simultaneously. The immune infiltration analysis revealed higher levels of M1 macrophages in DCM samples than in the control samples.ConclusionsIn conclusion, we speculate that miR-129-5p might target ASPN in regulating DCM via the ECM signaling pathway. Macrophage infiltration may be involved in ECM remodeling and eventually lead to DCM.

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.

Identification of Key Genes Involved in Pancreatic Ductal Adenocarcinoma with Diabetes Mellitus Based on Gene Expression Profiling Analysis.

This study aimed to identify key genes involved in the progression of diabetic pancreatic ductal adenocarcinoma (PDAC). Two gene expression datasets (GSE74629 and GSE15932) were obtained from Gene Expression Omnibus. Then, differentially expressed genes (DEGs) between diabetic PDAC and non-diabetic PDAC were identified, followed by a functional analysis. Subsequently, gene modules related to DM were extracted by weighed gene co-expression network analysis. The protein-protein interaction (PPI) network for genes in significant modules was constructed and functional analyses were also performed. After that, the optimal feature genes were screened by support vector machine (SVM) recursive feature elimination and SVM classification model was built. Finally, survival analysis was conducted to identify prognostic genes. The correlations between prognostic genes and other clinical factors were also analyzed. Totally, 1546 DEGs with consistent change tendencies were identified and functional analyses showed they were strongly correlated with metabolic pathways. Furthermore, there were two significant gene modules, in which RPS27A and UBA52 were key genes. Functional analysis of genes in two gene modules revealed that these genes primarily participated in oxidative phosphorylation pathway. Additionally, 21 feature genes were closely related with diabetic PDAC and the corresponding SVM classifier markedly distinguished diabetic PDAC from non-diabetic PDAC patients. Finally, decreased KIF22 and PYGL levels had good survival outcomes for PDAC. Four genes (RPS27A, UBA52, KIF22 and PYGL) might be involved in the pathogenesis of diabetic PDAC. Furthermore, KIF22 and PYGL acted as prognostic biomarkers for diabetic PDAC.

Identification of monocyte-associated genes as predictive biomarkers of heart failure after acute myocardial infarction

BackgroundAcute myocardial infarction (AMI) is a major contributor of heart failure (HF). Peripheral blood mononuclear cells (PBMCs), mainly monocytes, are the essential initiators of AMI-induced HF. The powerful biomarkers for early identification of AMI patients at risk of HF remain elusive. We aimed to identify monocyte-related critical genes as predictive biomarkers for post-AMI HF.MethodsWe performed weighted gene co-expression network analysis (WGCNA) on transcriptomics of PBMCs from AMI patients who developed HF or did not. Functional enrichment analysis of genes in significant modules was performed via Metascape. Then we obtained the single-cell RNA-sequencing data of recruited monocytes/macrophages from AMI and control mice using the Scanpy and screened 381 differentially expressed genes (DEGs) between the two groups. We validated the expression changes of the 25 genes in cardiac macrophages from AMI mice based on bulk RNA-sequencing data and PBMCs data mentioned above.ResultsIn our study, the results of WGCNA showed that two modules containing 827 hub genes were most significantly associated with post-AMI HF, which mainly participated in cell migration, inflammation, immunity, and apoptosis. There were 25 common genes between DEGs and hub genes, showing close relationship with inflammation and collagen metabolism. CUX1, CTSD and ADD3 exhibited consistent changes in three independent studies. Receiver operating characteristic curve analysis showed that each of the three genes had excellent performance in recognizing post-AMI HF patients.ConclusionOur findings provided a set of three monocyte-related biomarkers for the early prediction of HF development after AMI as well as potential therapeutic targets of post-AMI HF.

Identification of potential biomarkers and available drugs for oral squamous cell carcinoma.

BackgroundOral squamous cell carcinoma (OSCC) is the most common oral tumor globally. However, optimal therapeutic targets for OSCC have not been identified. This study aimed to identify the potential gene markers and available drugs for OSCC.MethodsTwo transcriptional datasets containing OSCC gene expression data (GSE30784 and GSE23558) were selected from the Gene Expression Omnibus database. The interactive web tool GEO2R was then used to analyze the differentially expressed genes (DEGs) analysis. A Venn diagram was used to integrate the DEGs screened out by the two microarrays. Subsequently, a protein-protein interaction (PPI) network analysis of DEGs was performed using the Cytoscape, Database for Annotation, Visualization and Intergrated Discovery, and STRING databases. In addition to constructing the PPI networks among these DEGs, we chose several significant gene modules to conduct further gene-drug interaction analyses. Lastly, the existing drugs that target these module genes were selected to explore their therapeutic efficacy in treating OSCC.ResultsA total of 199 DEGs were screened out by the two microarrays. They were found to be associated with several processes, including the epoxygenase P450 pathway and the organelle membrane. The significant module genes in the PPI networks were CYP2E1, SCEL, KRT4, and KRT19. One existing drug, etoposide, which targets the CYP2E1 gene, was acquired.ConclusionsFour potential biomarkers (CYP2E1, SCEL, KRT4, and KRT19) and one existing drug (etoposide) were obtained for gene expression prediction through a series of bioinformatics methods.

Abstract 4382: Bioinformatics analysis for hub genes and pathways in hepatocellular carcinoma

Abstract Hepatocellular carcinoma (HCC) is the most common primary liver cancer with a high recurrence rate and mortality. There is no effective treatment for HCC and the molecular mechanisms of HCC are still not fully understood. To identify the potential biomarkers in the carcinogenesis and progression of HCC, microarray datasets GSE101685, GSE121248, GSE38941, GSE62232 and GSE87630 were downloaded from Gene Expression Omnibus (GEO) database. Screening of differential expressed genes (DEGs), and functional enrichment analyses were performed. The protein-protein interaction network (PPI) were constructed and significant module genes and hub genes were identified using STRING and Cytoscape. 149 DEGs were screened, including 114 down-regulated genes and 35 up-regulated genes. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell cycle, oocyte meiosis and protein binding. Survival analysis and co-expression network analysis showed that TOP2A, TPX2, RACGAP1 and PTTG1 may be involved in the carcinogenesis, invasion or recurrence of HCC. In conclusion, the present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of HCC, and provide candidate targets for diagnosis and treatment of HCC. Key words: Hepatocellular carcinoma; Differentially expressed genes; Protein-protein interaction; Bioinformatics; Microarray Citation Format: Xianzhang Zeng, Lu Ao, Xinjian Lin, Xu Lin, Wannan Chen. Bioinformatics analysis for hub genes and pathways in hepatocellular carcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4382.

Identification of Immune-Related Genes Contributing to the Development of Glioblastoma Using Weighted Gene Co-expression Network Analysis.

Background: The tumor microenvironment (TME) of human glioblastoma (GBM) exhibits considerable immune cell infiltration, and such cell types have been shown to be widely involved in the development of GBM. Here, weighted correlation network analysis (WGCNA) was performed on publicly available datasets to identify immune-related molecules that may contribute to the progression of GBM and thus be exploited as potential therapeutic targets.Methods: WGCNA was used to identify highly correlated gene clusters in Chinese Glioma Genome Atlas glioma dataset. Immune-related genes in significant modules were subsequently validated in the Cancer Genome Atlas (TCGA) and Rembrandt databases, and impact on GBM development was examined in migration and vascular mimicry assays in vitro and in an orthotopic xenograft model (GL261 luciferase-GFP cells) in mice.Results: WGCNA yielded 14 significant modules, one of which (black) contained genes involved in immune response and extracellular matrix formation. The intersection of these genes with a GO immune-related gene set yielded 47 immune-related genes, five of which exhibited increased expression and association with worse prognosis in GBM. One of these genes, TREM1, was highly expressed in areas of pseudopalisading cells around necrosis and associated with other proteins induced in angiogenesis/hypoxia. In macrophages induced from THP1 cells, TREM1 expression levels were increased under hypoxic conditions and associated with markers of macrophage M2 polarization. TREM1 siRNA knockdown in induced macrophages reduced their ability to promote migration and vascular mimicry in GBM cells in vitro, and treatment of mice with LP-17 peptide, which blocks TREM1, inhibited growth of GL261 orthotopic xenografts. Finally, blocking the cytokine receptor for CSF1 in induced macrophages also impeded their potential to promote tumor migration and vascular mimicry in GBM cells.Conclusions: Our results demonstrated that TREM1 could be used as a novel immunotherapy target for glioma patients.

Comprehensive analysis reveals a six-gene signature and associated drugs in mimic inguinal hernia model.

Inguinal hernia often occurs in elderly men, and more than one in five men will undergo inguinal hernia repair during their lifetime. Nevertheless, the underlying molecular mechanisms of the pathogenesis behind hernia formation is still unclear. The aims in this study are finding out the potential gene markers and available drugs. Firstly, we re-analyzed the GSE92748 datasets, including four high and four low expressions of humanized aromatase transgenic mice, which refers to mimic humanized hernia, to identify differentially expressed genes (DEGs) in AromhumH group compared with AromhumL group by the criteria: fold change ≥ 1.4 and adjust P value < 0.05. Secondly, the gene ontology and signaling pathway enrichment analyses of these DEGs were performed through online databases. In addition to the protein and protein interaction networks among these DEGs were constructed and the significant gene modules were chosen for further gene-drug interaction analysis. Lastly, the existing drugs target to these module genes were screen to explore the therapeutic effect for treatment of hernia. We have identified 64 DEGs, which were associated with muscle system process, actomyosin structure organization etc. Moreover, the significant module genes in PPI networks were Cmya1, Casq2, Cmya5, Ttn, Csrp3 and Actc1, and one existing drug, DEXAMETHASONE, have targeted to Actc1 gene. In the paper, we identified 6 potential genes and one existing drug for inguinal hernia, which might be used as targets and drugs for the study of inguinal hernia.

Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BackgroundThe underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA). Material and methodsWe obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the “limma” package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples. ResultsThe preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples. ConclusionsWe identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.

Weighted gene co-expression network analysis to explore the mechanism of heroin addiction in human nucleus accumbens.

Heroin dependence is a complex behavioral disease, and a chronic encephalopathy with theimportant feature of relapse. The purpose of the study was to identify the regulatory mechanism of the nucleus accumbens (NAc) in heroin dependence. We used weighted gene co-expression network analysis to analyze the GSE87823 data package, which included 27 heroin users and 22 controls of human NAc tissue. Modules were correlated with basic information of samples and enrichment analyses used to identify biological function and transcription factors and online tools were used to perform the gene ontology of significant genes. We identified one gene module from the total data (blue) and the male data (turquoise), respectively. The overlap genes of top 10 hub genes in significant modules (PRR11, SLC35E1, LPP, ZNF721, ZNF611, LRRFIP1) were selected to identify as candidate genes in the regulation mechanism of NAc in heroin dependence. Then, we accorded the results to further explorethatmiRNA-hsa-miR-155-5p in male and total may be apotential marker. The candidate genes may serve as novel prognostic markers and treatment targets. Hsa-miR-155-5p may be a promising regulatory point for the treatment of heroin addiction.

Co-expression network analysis identified CDH11 in association with progression and prognosis in gastric cancer.

Background and aimsGastric cancer (GC) is one of the most common cancers worldwide, and its pathogenesis is related to a complex network of gene interactions. The aims of our study were to find hub genes associated with the progression and prognosis of GC and illustrate the underlying mechanisms.MethodsWeighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of GC patients from Gene Expression Omnibus (GEO) database to identify significant gene modules and hub genes associated with TNM stage in GC. Functional enrichment analysis and protein–protein interaction network analysis were performed using the significant module genes. We regarded the common hub genes in the co-expression network and protein–protein interaction (PPI) network as “real” hub genes for further analysis. Hub gene was validated in another independent dataset and The Cancer Genome Atlas (TCGA) dataset.ResultsIn the significant purple module (R2=0.35), a total of 12 network hub genes were identified, among which six were also hub nodes in the PPI network of the module genes. Functional annotation revealed that the genes in the purple module focused on the biological processes of system development, biological adhesion, extracellular structure organization and metabolic process. In terms of validation, CDH11 had a higher correlation with the TNM stage than other hub genes and was strongly correlated with biological adhesion based on GO functional enrichment analysis. Data obtained from the Gene Expression Profiling Interactive Analysis (GEPIA) showed that CDH11 expression had a strong positive correlation with GC stages (P<0.0001). In the testing set and Oncomine dataset, CDH11 was highly expressed in GC tissues (P<0.0001). Survival analysis indicated that samples with a high CDH11 expression showed a poor prognosis. Cox regression analysis demonstrated an independent predictor of CDH11 expression in GC prognosis (HR=1.482, 95% CI: 1.015–2.164). Furthermore, gene set enrichment analysis (GSEA) demonstrated that multiple tumor-related pathways, especially focal adhesion, were enriched in CDH11 highly expressed samples.ConclusionCDH11 was identified and validated in association with progression and prognosis in GC, probably by regulating biological adhesion and focal adhesion-related pathways.