Identification Of Hub Genes Research Articles

Identification of hub genes regulating isoflavone accumulation in soybean seeds via GWAS and WGCNA approaches.

Isoflavones are the secondary metabolites synthesized by the phenylpropanoid biosynthesis pathway in soybean that benefits human and plant health. In this study, we have profiled seed isoflavone content by HPLC in 1551 soybean accessions grown in Beijing and Hainan for two consecutive years (2017 and 2018) and in Anhui for one year (2017). A broad range of phenotypic variations was observed for individual and total isoflavone (TIF) content. The TIF content ranged from 677.25 to 5823.29 µg g-1 in the soybean natural population. Using a genome-wide association study (GWAS) based on 6,149,599 single nucleotide polymorphisms (SNPs), we identified 11,704 SNPs significantly associated with isoflavone contents; 75% of them were located within previously reported QTL regions for isoflavone. Two significant regions on chromosomes 5 and 11 were associated with TIF and malonylglycitin across more than 3 environments. Furthermore, the WGCNA identified eight key modules: black, blue, brown, green, magenta, pink, purple, and turquoise. Of the eight co-expressed modules, brown (r = 0.68***), magenta (r = 0.64***), and green (r = 0.51**) showed a significant positive association with TIF, as well as with individual isoflavone contents. By combining the gene significance, functional annotation, and enrichment analysis information, four hub genes Glyma.11G108100, Glyma.11G107100, Glyma.11G106900, and Glyma.11G109100 encoding, basic-leucine zipper (bZIP) transcription factor, MYB4 transcription factor, early responsive to dehydration, and PLATZ transcription factor respectively were identified in brown and green modules. The allelic variation in Glyma.11G108100 significantly influenced individual and TIF accumulation. The present study demonstrated that the GWAS approach, combined with WGCNA, could efficiently identify isoflavone candidate genes in the natural soybean population.

Joint effect of THBS2 and VCAN accelerating the poor prognosis of gastric cancer.

Gastric cancer is the most common malignant tumor of the digestive system. The progression from gastritis to gastric cancer may be related to genetic factors, but the specific molecular mechanism remains unclear. Therefore, an in-depth study of the molecular mechanism of gastritis and gastric cancer is significant. We downloaded two gene profiles, GSE2669 and GSE116312, from the Gene Expression Omnibus (GEO) database. This study aims to apply bioinformatics technology to mine differentially expressed genes (DEGs), DEGs annotation, protein-protein interaction (PPI) network creation, and hub gene identification and expression between gastric cancer patients and gastritis patients. Overall survival analysis of hub genes, analysis by comparative toxicogenomics database for hub genes in gastric cancer, THBS2 and VCAN protein expression by immunohistochemistry for gastric cancer and gastritis as well as design of the biological process (BP) neural network was implemented. The MSLN, SPP1, THBS2, SPARC, FN1, IGFBP7, VCAN were up-regulated in gastric carcinoma samples, while FGA was down-regulated. The protein expression of THBS2 and VCAN in gastric cancer was significantly higher than that in gastritis. VCAN protein expression was positively associated with tumor invasion (P = 0.011) and HER2 overexpression (P = 0.031). Strong correlation among THBS2, VCAN, and gastric cancer based on the BP neural network. THBS2 and VCAN may be potential targets for improving gastric cancer patients' diagnosis and clinical efficacy.

Identification of Hub Genes Associated with Breast Cancer Using Integrated Gene Expression Data with Protein-Protein Interaction Network

Breast cancer (BC) is the most incident cancer type among women. BC is also ranked as the second leading cause of death among all cancer types. Therefore, early detection and prediction of BC are significant for prognosis and in determining the suitable targeted therapy. Early detection using morphological features poses a significant challenge for physicians. It is therefore important to develop computational techniques to help determine informative genes, and hence help diagnose cancer in its early stages. Eight common hub genes were identified using three methods: the maximal clique centrality (MCC), the maximum neighborhood component (MCN), and the node degree. The hub genes obtained were CDK1, KIF11, CCNA2, TOP2A, ASPM, AURKB, CCNB2, and CENPE. Enrichment analysis revealed that the differentially expressed genes (DEGs) influenced multiple pathways. The most significant identified pathways were focal adhesion, ECM-receptor interaction, melanoma, and prostate cancer pathways. Additionally, survival analysis using Kaplan–Meier was conducted, and the results showed that the obtained eight hub genes are promising candidate genes to serve as prognostic and diagnostic biomarkers for BC. Furthermore, a correlation study between the clinicopathological factors in BC and the eight hub genes was performed. The results showed that all eight hub genes are associated with the clinicopathological variables of BC. Using an integrated analysis of RNASeq and microarray data, a protein-protein interaction (PPI) network was developed. Eight hub genes were identified in this study, and they were validated using previous studies. Additionally, Kaplan-Meier was used to verify the prognostic value of the obtained hub genes.

Weighted Gene Co-Expression Network Analysis (WGCNA) Discovered Novel Long Non-Coding RNAs for Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) affects reproductive-age women. This condition causes infertility, insulin resistance, obesity, and heart difficulties. The molecular basis and mechanism of PCOS might potentially generate effective treatments. Long non-coding RNAs (lncRNAs) show control over multifactorial disorders' growth and incidence. Numerous studies have emphasized its significance and alterations in PCOS. We used bioinformatic methods to find novel dysregulated lncRNAs in PCOS. To achieve this objective, the gene expression profile of GSE48301, comprising PCOS patients and normal control tissue samples, was evaluated using the R limma package with the following cut-off criterion: p-value < 0.05. Firstly, weighted gene co-expression network analysis (WGCNA) was used to determine the co-expression genes of lncRNAs; subsequently, hub gene identification and pathway enrichment analysis were used. With the defined criteria, nine novel dysregulated lncRNAs were identified. In WGCNA, different colors represent different modules. In the current study, WGCNA resulted in turquoise, gray, blue, and black co-expression modules with dysregulated lncRNAs. The pathway enrichment analysis of these co-expressed modules revealed enrichment in PCOS-associated pathways, including gene expression, signal transduction, metabolism, and apoptosis. In addition, CCT7, EFTUD2, ESR1, JUN, NDUFAB1, CTTNB1, GRB2, and CTNNB1 were identified as hub genes, and some of them have been investigated in PCOS. This study uncovered nine novel PCOS-related lncRNAs. To confirm how these lncRNAs control translational modification in PCOS, functional studies are required.

Exploration of the underlying comorbidity mechanism in psoriasis and periodontitis: a bioinformatics analysis

BackgroundIncreasing evidence indicates that psoriasis (PSO) and periodontitis (PD) are likely to occur together, however, the underlying mechanism remains unclear.Materials and methodsThe expression profiles of PSO (lesion vs non-lesion, GSE30999, GSE14905) and PD (affected vs unaffected gingival tissue, GSE16134, GSE10334) were downloaded from the GEO database. First, we investigated the common differentially expressed genes (DEGs) of PSO and PD. Then, GO and KEGG enrichment analysis, protein interaction network (PPI) construction, and hub gene identification analysis were carried out. Finally, GO and KEGG enrichment analysis, miRNA interaction analysis, and transcription factors (TFs) interaction analysis for hub genes were performed.ResultsEighteen DEGs were identified for further analysis, including 15 up-regulated genes and 3 down-regulated genes. 9 hub genes were then identified via Cytohubba, including IL1B, CXCL1, CXCL8, MMP12, CCL18, SELL, CXCL13, FCGR3B, and SELE. Their functions are mainly enriched in two aspects: neutrophil chemotaxis and migration, chemokine activation and interaction. The enriched signaling pathways includes three categories: host defense, inflammation-related signaling pathways, and disease-related pathways. 9 common miRNAs based on experimental evidence and 10 common TFs were further identified in both PSO and PD.ConclusionOur study revealed possible comorbidity mechanisms in PSO and PD from the perspective of bioinformatics tentatively. The data can present new insight for joint prevention and treatment of in PSO and PD, as well as provide data support for further prospective studies.

Identification and validation of hub genes and potential drugs involved in osteoarthritis through bioinformatics analysis.

Purpose: Osteoarthritis (OA) is a common degenerative disease, which still lacks specific therapeutic drugs. Synovitis is one of the most important pathological process in OA. Therefore, we aim to identify and analyze the hub genes and their related networks of OA synovium with bioinformatics tools to provide theoretical basis for potential drugs. Materials and methods: Two datasets were obtained from GEO. DEGs and hub genes of OA synovial tissue were screened through Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment as well as protein-protein interaction (PPI) network analysis. Subsequently, the correlation between expression of hub genes and ferroptosis or pyroptosis was analyzed. CeRNA regulatory network was constructed after predicting the upstream miRNAs and lncRNAs. The validation of hub genes was undertook through RT-qPCR and ELISA. Finally, potential drugs targeting pathways and hub genes were identified, followed by the validation of the effect of two potential drugs on OA. Results: A total of 161 commom DEGs were obtained, of which 8 genes were finally identified as hub genes through GO and KEGG enrichment analysis as well as PPI network analysis. Eight genes related to ferroptosis and pyroptosis respectively were significantly correlated to the expression of hub genes. 24 miRNAs and 69 lncRNAs were identified to construct the ceRNA regulatory network. The validation of EGR1, JUN, MYC, FOSL1, and FOSL2 met the trend of bioinformatics analysis. Etanercept and Iguratimod reduced the secretion of MMP-13 and ADAMTS5 of fibroblast-like synoviocyte. Conclusion: EGR1, JUN, MYC, FOSL1, and FOSL2 were identified as hub genes in the development of OA after series of bioinformatics analysis and validation. Etanercept and Iguratimod seemed to have opportunities to be novel drugs for OA.

Identification of Hub Genes for Colorectal Cancer with Liver Metastasis Using miRNA-mRNA Network.

Liver metastasis is an important cause of death in patients with colorectal cancer (CRC). Increasing evidence indicates that microRNAs (miRNAs) are involved in the pathogenesis of colorectal cancer liver metastasis (CRLM). This study is aimed at exploring the potential miRNA-mRNA regulatory network. From the GEO database, we downloaded the microarray datasets GSE56350 and GSE73178. GEO2R was used to conduct differentially expressed miRNAs (DEMs) between CRC and CRLM using the GEO2R tool. Then, GO and KEGG pathway analysis for differentially expressed genes (DEGs) performed via DAVID. A protein-protein interaction (PPI) network was constructed by the STRING and identified by Cytoscape. Hub genes were identified by miRNA-mRNA network. Finally, the expression of the hub gene expression was assessed in the GSE81558. The four DEMs (hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p) were identified as common DEMs in GSE56350 and GSE73178 datasets. The SP1 was likely to adjust the upregulated DEMs; however, the YY1 could regulate both the upregulated and downregulated DEMs. A total of 3925 genes (3447 upregulated DEM genes and 478 downregulated DEM genes) were screened. These predicted genes were mainly linked to Platinum drug resistance, Cellular senescence, and ErbB signaling pathway. Through the gene network construction, most of the hub genes were found to be modulated by hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p. Among the top 20 hub genes, the expression of CREB1, RHOA, and EGFR was significantly different in the GSE81558 dataset. In this study, miRNA-mRNA networks in CRLM were screened between CRC patients and CRLM patients to provide a new method to predict for the pathogenesis and development of CRC.

A bioinformatics approach to the identification of hub genes of Huo Xin Pill (HXP) for the treatment of acute myocardial infarction

Purpose: To apply bioinformatics for the identification of potential genes associated with Huo Xin Pill (HXP), a traditional Chinese medicine (TCM) used for the treatment of acute myocardial infarction AMI).Methods: Mouse AMI expression profile dataset GSE153485 and HXP-treated mouse AMI expression profile dataset GSE147365 were downloaded from GEO database. Then, R software was used to screen differentially-expressed genes in AMI and differentially-expressed genes in HXP-treated AMI. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, Venn diagrams, and protein-protein interaction (PPI) analysis were carried out on the hub genes linked to the effect of HXP on AMI.Results: Six hub genes were identified. Based on the differential analysis of the sham and AMI groups, GSE153485 and GSE147365 had 840 and 2116 differentially-expressed genes, respectively (p &lt; 0.05). The GO and KEGG analyses revealed enrichments in actin filament organization, membrane repolarization, and regulation of the actin cytoskeleton. Differential analysis of the use of HXP on AMI showed that GSE147365 had 380 differentially-expressed genes, comprising 96 up-regulated genes and 284 down-regulated genes (p &lt; 0.05). Thirteen potential acting target genes were obtained using a enn diagram, while 6 key acting genes were obtained via final screening.Conclusion: Six (6) hub genes linked to HXP and AMI have been identified using bioinformatics: Egr2, Tubb2a, Col4a2, Cnn2, Lmna, and Col4a1. This study provides a partial experimental basis for the use of HXP in the treatment of AMI. In addition, it provides new potential targets for the treatment of AMI.

Identification of hub genes and pathways of ferroptosis in Fusarium keratitis by bioinformatics methods.

Fungal keratitis is a common blinding eye disease, and Fusarium is one of the main species that cause fungal keratitis. As is well known, oxidative stress plays an important role in Fusarium keratitis and it is also a significant initiating factor of ferroptosis. But the relationship between Fusarium keratitis and ferroptosis is currently unclear. This study aimed to speculate and validate potential ferroptosis-related genes in Fusarium keratitis using bioinformatics analysis, which provided ideas for further research on its specific mechanism and new targets for its treatment. The microarray expression profiling dataset (GSE58291) came from Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were obtained by the limma package of the R software. The DEGs were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the DEGs were intersected with the genes in the ferroptosis database. The top 5 hub genes were obtained by the protein-protein interaction (PPI) network analysis and the cytoHubba plug-in of Cytoscape software. The hub genes were subjected to GSEA analysis. Then we analyzed the immune infiltration of the samples by CIBERSORT and ssGSEA algorithm. Finally, we validated the mRNA of hub genes by qPCR. A total of 1,368 DEGs were identified and 26 ferroptosis-related DEGs were obtained. At the same time, ferroptosis-related pathways were enriched by GO and KEGG using DEGs. HMOX1, CYBB, GPX2, ALOX5 and SRC were obtained by the PPI network analysis and the cytoHubba plug-in of Cytoscape software. The iron metabolism and immune response related pathways were enriched using GSEA. They included hematopoietic cell lineage, lysosome and FC gamma R mediated phagocytosis. T cells follicular helper, monocytes, macrophages and mast cells might play an important role in Fusarium keratitis using analysis of immune infiltration. Finally, qPCR confirmed that the expression of HMOX1, CYBB, ALOX5 mRNA in the DON group was significantly elevated, while the expression of GPX2 were significantly decreased. Ferroptosis may play an important role in Fusarium keratitis. HMOX1, CYBB, ALOX5 and GPX2 may be key ferroptosis-related genes in the pathogenesis of Fusarium keratitis.

Identification of hub genes involved in cisplatin resistance in head and neck cancer

BackgroundCisplatin resistance is one of the major contributors to the poor survival rate among head and neck cancer (HNC) patients. Focusing on the protein–protein interaction rather than a single protein could provide a better understanding of drug resistance. Thus, this study aimed to identify hub genes in a complex network of cisplatin resistance associated genes in HNC chemotherapy via a series of bioinformatic tools. MethodsThe genes involved in cisplatin resistance were retrieved from the NCBI gene database using “head and neck cancer” and “cisplatin resistance” as key words. The human genes retrieved were analyzed for their interactions and enriched using the STRING database. The interaction between KEGG pathways and genes was visualized in Cytoscape 3.7.2. Further, the hub gene was identified using the Cytohubba plugin of Cytoscape and validated using UALCAN and Human Protein Atlas database. Validated genes were investigated for the drug–gene interaction using the DGIbd database. ResultsOut of 137 genes obtained using key words, 133 were associated with cisplatin resistance in the human species. A total of 150 KEGG pathways, 82 cellular components, 123 molecular functions, and 1752 biological processes were modulated on enrichment analysis. Out of 37 hub genes, CCND1, AXL, CDKN2A, TERT, and EXH2 genes were found to have significant (p < 0.05) mRNA expression and effect on overall survival whereas protein expression was found to be positive for all the significant genes except TERT. Thus, they can be targeted with palbociclib, methotrexate, bortezomib and fluorouracil, sorafenib, dasatinib, carboplatin, paclitaxel, gemcitabine, imatinib, doxorubicin, and vorinostat. ConclusionAs the pathogenesis of head and neck cancer is complex, targeting hub genes and associated pathways involved in cisplatin resistance could bring a milestone change in the drug discovery and management of drug resistance which might uplift overall survival among HNC patients.

Identification of Potential Hub Genes Related to Aflatoxin B1, Liver Fibrosis and Hepatocellular Carcinoma via Integrated Bioinformatics Analysis

The molecular mechanism of the hepatotoxicant aflatoxin B1 to induce liver fibrosis and hepatocellular carcinoma (HCC) remains unclear, to offer fresh perspectives on the molecular mechanisms underlying the onset and progression of AFB1-Fibrosis-HCC, which may offer novel targets for the detection and therapy of HCC caused by AFB1. In this study, expression profiles of AFB1, liver fibrosis and liver cancer-related datasets were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by the GEO2R tool. The STRING database, CytoHubba, and Cytoscape software were used to create the protein-protein interaction and hub genes of the combined genes, and the ssGSEA score for inflammatory cells related gene sets, the signaling pathway, and immunotherapy were identified using R software and the GSEA database. The findings revealed that AFB1-associated liver fibrosis and HCC combined genes were linked to cell process disruptions, the BUB1B and RRM2 genes were identified as hub genes, and the BUB1B gene was significantly increased in JAK-STAT signaling gene sets pathways as well as having an immunotherapy-related impact. In conclusion, BUB1B and RRM2 were identified as potential biomarkers for AFB1-induced fibrosis and HCC progression.

Identification and Validation of Hub Genes in the Stenosis of Arteriovenous Fistula.

Arteriovenous fistula (AVF) is the most widely used hemodialysis vascular access in China. However, stenosis of the AVF limits its use. The mechanism of AVF stenosis is currently unknown. Therefore, the purpose of our study was to explore the mechanisms of AVF stenosis. In this study, we identified the differentially expressed genes (DEGs) based on the Gene Expression Omnibus (GEO) dataset (GSE39488) between venous segments of AVF and normal veins. A protein-protein interaction (PPI) network was constructed to identify hub genes of AVF stenosis. Finally, six hub genes (FOS, NR4A2, EGR2, CXCR4, ATF3, and SERPINE1) were found. Combined with the results of the PPI network analysis and literature search, FOS and NR4A2 were selected as the target genes for further investigation. We validated the bioinformatic results via reverse transcription PCR (RT-PCR) and Western blot analyses on human and rat samples. The expression levels of the mRNA and protein of FOS and NR4A2 were upregulated in both human and rat samples. In summary, we found that FOS may play an important role in AVF stenosis, which could be a potential therapeutic target of AVF stenosis.

Identification of major hub genes involved in high-fat diet-induced obese visceral adipose tissue based on bioinformatics approach

ABSTRACT High-fat diet (HFD) can cause obesity, inducing dysregulation of the visceral adipose tissue (VAT). This study aimed to explore potential biological pathways and hub genes involved in obese VAT, and for that, bioinformatic analysis of multiple datasets was performed. The expression profiles (GSE30247, GSE167311 and GSE79434) were downloaded from Gene Expression Omnibus. Overlapping differentially expressed genes (ODEGs) between normal diet and HFD groups in GSE30247 and GSE167311 were selected to run protein–protein interaction network, GO and KEGG analysis. The hub genes in ODEGs were screened by Cytoscape software and further verified in GSE79434 and obese mouse model. A total of 747 ODEGs (599 up-regulated and 148 down-regulated) were screened, and the GO and KEGG analysis showed that the up-regulated ODEGs were significantly enriched in inflammatory response and extracellular matrix receptor interaction pathways. On the other hand, the down-regulated ODEGs were involved in metabolic pathways; however, there were no significant KEGG pathways. Furthermore, six hub genes, Mki67, Rac2, Itgb2, Emr1, Tyrobp and Csf1r were acquired. These pathways and genes were verified in GSE79434 and VAT of obese mice. This study revealed that HFD induced VAT expansion, inflammation and fibrosis, and the hub genes could be used as therapeutic biomarkers in obesity.

Identification of hub genes significantly linked to subarachnoid hemorrhage and epilepsy via bioinformatics analysis.

Although epilepsy has been linked to subarachnoid hemorrhage (SAH), the underlying mechanism has not been fully elucidated. This study aimed to further explore the potential mechanisms in epilepsy and SAH through genes. Gene expression profiles for subarachnoid hemorrhage (GSE36791) and epilepsy (GSE143272) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify the common differentially expressed genes (DEGs) to epilepsy and SAH, which were further analyzed by functional enrichment analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted correlation network analysis (WGCNA) were used to identify common module genes related to the infiltration of immune cells in epilepsy and SAH. Hub module genes were identified using a protein-protein interaction (PPI) network. Finally, the most relevant genes were obtained by taking the intersection points between the DEGs and hub module genes. We performed validation by retrospectively analyzing the RT-PCR levels of the most relevant genes in patients with pure SAH and patients with SAH complicated with epilepsy. Our experiments verified that the SAH and SAH+epilepsy groups were significantly different from the normal control group. In addition, significant differences were observed between the SAH and SAH+epilepsy groups. In total, 159 common DEGs-85 downregulated genes and 74 upregulated genes-were identified. Functional analysis emphasized that the immune response was a common feature to epilepsy and SAH. The results of ssGSEA and WGCNA revealed changes in immunocyte recruitment and the related module genes. Finally, MMP9 and C3aR1 were identified as hub genes, and RT-PCR confirmed that the expression levels of the hub genes were higher in epilepsy and SAH samples than in normal samples. Our study revealed the pathogenesis of SAH complicated with epilepsy and identified hub genes that might provide new ideas for further mechanistic studies.

Identification and validation of oxidative stress and immune-related hub genes in Alzheimer’s disease through bioinformatics analysis

Alzheimer’s disease (AD) is the leading cause of dementia in aged population. Oxidative stress and neuroinflammation play important roles in the pathogenesis of AD. Investigation of hub genes for the development of potential therapeutic targets and candidate biomarkers is warranted. The differentially expressed genes (DEGs) in AD were screened in GSE48350 dataset. The differentially expressed oxidative stress genes (DEOSGs) were analyzed by intersection of DEGs and oxidative stress-related genes. The immune-related DEOSGs and hub genes were identified by weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) analysis, respectively. Enrichment analysis was performed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The diagnostic value of hub genes was assessed by receiver operating characteristic analysis and validated in GSE1297. The mRNA expression of diagnostic genes was determined by qRT-PCR analysis. Finally, we constructed the drug, transcription factors (TFs), and microRNA network of the diagnostic genes. A total of 1160 DEGs (259 up-regulated and 901 down-regulated) were screened in GSE48350. Among them 111 DEOSGs were identified in AD. Thereafter, we identified significant difference of infiltrated immune cells (effector memory CD8 T cell, activated B cell, memory B cell, natural killer cell, CD56 bright natural killer cell, natural killer T cell, plasmacytoid dendritic cell, and neutrophil) between AD and control samples. 27 gene modules were obtained through WGCNA and turquoise module was the most relevant module. We obtained 66 immune-related DEOSGs by intersecting turquoise module with the DEOSGs and identified 15 hub genes through PPI analysis. Among them, 9 hub genes (CCK, CNR1, GAD1, GAP43, NEFL, NPY, PENK, SST, and TAC1) were identified with good diagnostic values and verified in GSE1297. qRT-PCR analysis revealed the downregulation of SST, NPY, GAP43, CCK, and PENK and upregulation of NEFL in AD. Finally, we identified 76 therapeutic agents, 152 miRNAs targets, and 91 TFs regulatory networks. Our study identified 9 key genes associated with oxidative stress and immune reaction in AD pathogenesis. The findings may help to provide promising candidate biomarkers and therapeutic targets for AD.

Identification of key module and hub genes in pulpitis using weighted gene co-expression network analysis

BackgroundPulpitis is a common disease mainly caused by bacteria. Conventional approaches of diagnosing the state of dental pulp are mainly based on clinical symptoms, thereby harbor deficiencies. The accurate and rapid diagnosis of pulpitis is important for choosing the suitable therapy. The study aimed to identify pulpits related key genes by integrating micro-array data analysis and systems biology network-based methods such as weighted gene co-expression network analysis (WGCNA).MethodsThe micro-array data of 13 inflamed pulp and 11 normal pulp were acquired from Gene Expression Omnibus (GEO). WGCNA was utilized to establish a genetic network and categorize genes into diverse modules. Hub genes in the most associated module to pulpitis were screened out using high module group members (MM) methods. Pulpitis model in rat was constructed and iRoot BP plus was applied to cap pulp. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for validation of hub genes.ResultsWGCNA was established and genes were categorized into 22 modules. The darkgrey module had the highest correlation with pulpitis among them. A total of 5 hub genes (HMOX1, LOX, ACTG1, STAT3, GNB5) were identified. RT-qPCR proved the differences in expression levels of HMOX1, LOX, ACTG1, STAT3, GNB5 in inflamed dental pulp. Pulp capping reversed the expression level of HMOX1, LOX, ACTG1.ConclusionThe study was the first to produce a holistic view of pulpitis, screen out and validate hub genes involved in pulpitis using WGCNA method. Pulp capping using iRoot BP plus could reverse partial hub genes.

Identification of hub genes in the fructose-1, 6 bisphosphate 1-protein interaction network based on differential expression, and survival impact on hepatocellular carcinoma patients

Identification of hub genes in the fructose-1, 6 bisphosphate 1-protein interaction network based on differential expression, and survival impact on hepatocellular carcinoma patients

Molecular mechanisms investigation for liver metastasis of colorectal cancer by combined bioinformatic gene expression profile analysis

Background: Colorectal cancer (CRC) is one of the most common cancers worldwide. As the molecular mechanism for liver metastasis of CRC has not yet been completely discovered, identification of hub genes and pathways of this disease is of importance for revealing potential molecular mechanism of colorectal cancer progression. This study aimed to identify potential biomarkers and survival analysis of hub genes for CRC treatment. Methods: The differentially expressed genes (DEGs) between colorectal cancer liver metastasis and primary tumor were screened using microarray data from two datasets GSE179979, GSE144259 obtained from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) and KEGG pathway enrichment analyses were performed for DEGs using DAVID database, the protein-protein interaction (PPI) network was constructed using the Cytoscape software, and module analysis was performed using MCODE. Then, overall survival (OS), progression free interval (PFI) and disease specific survival (DSS) analysis of hub genes was performed by using TCGA database. The correlations between hub genes and clinical values were validated through CRN and immunohistochemistry (IHC) stain. Results: A total of 64 DEGs were obtained, KEGG pathway analysis showed that the significant pathways included PPAR signaling pathway, Complement and coagulation cascades. Four hub genes (ITIH2, ALB, CPB2, HGFAC) and two biomarkers (CPB2, HGFAC) with significantly prognostic values were verified by Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) cohort. Conclusions: CPB2 and HGFAC may serve as new biomarkers in diagnosing liver metastasis of CRC or potential drug target.

Identification of Hub Genes in Prostate Cancer by Bioinformatics Analysis

Identification of Hub Genes in Prostate Cancer by Bioinformatics Analysis

Identification of Hub Genes and Biomarkers Between Hyperandrogen and Normoandrogen Polycystic Ovary Syndrome by Bioinformatics Analysis.

The common and divergent genetic mechanisms of hyperandrogen (HA) and normoandrogen (NA) polycystic ovary syndrome (PCOS) are currently unknown. To explore the hub genes and potential mechanisms of HA and NA PCOS through bioinformatics analysis. The GSE137684 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The co-expressed genes and differentially expressed genes (DEGs) between HA and NA PCOS samples were functionally annotated by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The protein-protein interaction (PPI) network of the DEGs was constructed and visualized using STRING and Cytoscape respectively, and the hub genes were screened using the Cytohubba plug-in. The transcription factors (TFs) of these hub genes were identified with JASPAR database, and the hub gene-TF regulatory network was constructed. A total of 327 DEGs, including 191 upregulated and 136 downregulated genes, were identified in HA PCOS relative to NA PCOS. Ten hub genes were screened, of which MYC, CAV1 and HGF were mainly enriched in the Proteoglycans in cancer pathway. In addition, 47 TFs were identified that are involved in the regulation of hub genes. MYC, CAV1 and HGF are potential diagnostic biomarkers and therapeutic targets for HA PCOS.