scholarly journals Integrated Transcriptomic Analysis Revealed Hub Genes and Pathways Involved in Sorafenib Resistance in Hepatocellular Carcinoma

2021 ◽  
Vol 27 ◽  
Author(s):  
Xili Jiang ◽  
Wei Zhang ◽  
Lifeng Li ◽  
Shucai Xie
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Pearson Correlation ◽  
Acquired Resistance ◽  
Rank Aggregation ◽  
Health Concern ◽  
Hub Genes ◽  
Multikinase Inhibitor

Hepatocellular carcinoma (HCC), a high mortality malignancy, has become a worldwide public health concern. Acquired resistance to the multikinase inhibitor sorafenib challenges its clinical efficacy and the survival benefits it provides to patients with advanced HCC. This study aimed to identify critical genes and pathways associated with sorafenib resistance in HCC using integrated bioinformatics analysis. Differentially expressed genes (DEGs) were identified using four HCC gene expression profiles (including 34 sorafenib-resistant and 29 sorafenib-sensitive samples) based on the robust rank aggregation method and R software. Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING), and small molecules reversing sorafenib resistance were searched for using the connectivity map (CMAP) database. Pearson correlation and survival analyses of hub genes were performed using cBioPortal and Gene Expression Profiling and Interactive Analysis (GEPIA). Finally, the expression levels of hub genes in sorafenib-resistant HCC cells were verified using quantitative polymerase chain reaction (q-PCR). A total of 165 integrated DEGs (66 upregulated and 99 downregulated in sorafenib resistant samples compared sorafenib sensitive ones) primarily enriched in negative regulation of endopeptidase activity, extracellular exosome, and protease binding were identified. Some pathways were commonly shared between the integrated DEGs. Seven promising therapeutic agents and 13 hub genes were identified. These findings provide a strategy and theoretical basis for overcoming sorafenib resistance in HCC patients.

scholarly journals Hub gene identification and prognostic model establishment for patients with HBV-related hepatocellular carcinoma

2021 ◽  
Author(s):  
Lianmei Wang ◽  
Jing Meng ◽  
Shasha Qin ◽  
Aihua Liang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Cyclin B1 ◽  
Mitotic Checkpoint ◽  
Cancer Genome ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Model Based

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~50% of HCC cases. Identification of biomarkers is pivotal for the therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. Cyclin-dependent kinase 1 (CDK1), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Aurora kinase A (AURKA), Cyclin B2 (CCNB2), Enhancer of zeste homolog 2 (EZH2), Cell division cycle 20 (CDC20), DNA topoisomerase II alpha (TOP2A), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and ZW10 interactor (ZWINT), were identified as the top-ten hub genes. The expression of hub-genes was verified in the liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP), the cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. A four-gene prognostic related model based on the expression of ZWINT, EZH2, FOXM1 and CDK1 were established through Cox regression analysis in ICGC-LIRI-JP project, and verified in TCGA-HCC cohort. Furthermore, a nomogram model based on pathology stage, gender and four-genes prognostic model was built to predict the prognosis for HBV–HCC patients. In conclusion, ZWINT, EZH2, FOXM1 and CDK1 play a pivotal role in HBV-HCC, and are potential therapeutic targets of HBV HCC.

scholarly journals Distinct Molecular Mechanisms Analysis of Obesity Based on Gene Expression Profiles

2020 ◽  
Author(s):  
Harish Joshi ◽  
Basavaraj Mallikarjunayya Vastrad ◽  
Nidhi Joshi ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Rank Aggregation ◽  
Receiver Operating Curve ◽  
Progression Rate ◽  
Hub Genes

Abstract Background Obesity is the most common metabolic disorder worldwide. Its progression rate has remained high in recent years. ObjectivesTherefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. MethodsThe gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Pathway enrichment analyses and Gene Ontology (GO) were performed by online tool ToppCluster. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then, the mentha, miRNet and NetworkAnalyst databases were used to construct the protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network. The module analysis was performed by the PEWCC1 plug‐in of Cytoscape based on the whole PPI network.Results We finally filtered out HSPA8, ESR1, YWHAH, RPL14, SOD2, BTG2, LYZ and EFNA1 hub genes. Hub genes were validated by ICH analysis, Receiver operating curve (ROC) analysis and RT-PCR. The robust DEGs linked with the development of obesity were screened through the ArrayExpress database, and integrated bioinformatics analysis was conducted. ConclusionsOur study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

scholarly journals Hub Gene Identification and Prognostic Model Establishment for Patients with HBV-related Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Lianmei Wang ◽  
Jing Liu ◽  
Zhong Xian ◽  
Jingzhuo Tian ◽  
Chunying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cyclin B1 ◽  
Mitotic Checkpoint ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Hub Genes

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~ 50% of HCC cases. Establishment of a prognostic model is pivotal for clinical therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. DNA topoisomerase II alpha (TOP2A), Disks large-associated protein 5 (DLGAP5), RAD51 associated protein 1 (RAD51AP1), ZW10 interactor (ZWINT), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Cyclin B2 (CCNB2), Aurora kinase A (AURKA), and Cyclin-dependent kinase 1 (CDK1) were identified as the top-ten hub genes. These hub-genes were verified by the Liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP) project, The Cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. FOXM1 and CDK1 were found to be prognostic-related molecules for HBV-HCC patients. The expression patterns of FOXM1 and CDK1were consistently in human and mouse. Furthermore, a nomogram model based on histology grade, pathology stage, sex and, expression of FOXM1 and CDK1 was built to predict the prognosis for HBV–HCC patients. The nomogram model could be used to predict the prognosis of HBV-HCC cases.

scholarly journals Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

2019 ◽  
Vol 28 (1_suppl) ◽  
pp. 76S-86S ◽  
Author(s):  
Zengyuan Zhou ◽  
Yuzheng Li ◽  
Haiyue Hao ◽  
Yuanyuan Wang ◽  
Zihao Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Prognostic Biomarkers ◽  
Ppi Network ◽  
Hub Genes ◽  
P53 Signaling

Hepatocellular carcinoma (HCC) is a widespread, common type of cancer in Asian countries, and the need for biomarker-matched molecularly targeted therapy for HCC has been increasingly recognized. However, the effective treatment for HCC is unclear. Therefore, identifying additional hub genes and pathways as novel prognostic biomarkers for HCC is necessary. In this study, the expression profiles of GSE121248, GSE45267 and GSE84402 were obtained from the Gene Expression Omnibus (GEO), including 132 HCC and 90 noncancerous liver tissues. Differentially expressed genes (DEGs) between HCC and noncancerous samples were identified by GEO2 R and Venn diagrams. In total, 109 DEGs were identified in these datasets, including 24 upregulated genes and 85 downregulated genes. Subsequently, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) preliminary analyses of the DEGs were performed using DAVID. The protein–protein interaction (PPI) network of the DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized in Cytoscape. Module analysis of the PPI network was performed using MCODE to get hub genes. Moreover, the influence of the hub genes on overall survival was determined with Kaplan–Meier plotter. All hub genes were analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and KEGG. Overall, the hub genes DTL, CDK1, CCNB1, RACGAP1, ECT2, NEK2, BUB1B, PBK, TOP2A, ASPM, HMMR, RRM2, CDKN3, PRC1, and ANLN were upregulated in HCC, and the survival rate was lower for HCC with increased expression of these hub genes. CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway, and CCNB1, CDK1, and BUB1B were enriched in the cell cycle. In brief, we screened 15 hub genes and pathways to identify potential prognostic markers for HCC treatment. However, the specific occurrence and development of HCC with expression of the hub genes should be verified in vivo and in vitro.

scholarly journals Genome-wide gene expression changes in postpartum depression point towards an altered immune landscape

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Divya Mehta ◽  
Karen Grewen ◽  
Brenda Pearson ◽  
Shivangi Wani ◽  
Leanne Wallace ◽  
...  
Keyword(s):  
Gene Expression ◽  
Depressive Symptoms ◽  
Postpartum Depression ◽  
Expression Profiles ◽  
Depression Scale ◽  
Well Being ◽  
Health Concern ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Gene Expression

AbstractMaternal postpartum depression (PPD) is a significant public health concern due to the severe negative impact on maternal and child health and well-being. In this study, we aimed to identify genes associated with PPD. To do this, we investigated genome-wide gene expression profiles of pregnant women during their third trimester of pregnancy and tested the association of gene expression with perinatal depressive symptoms. A total of 137 women from a cohort from the University of North Carolina, USA were assessed. The main phenotypes analysed were Edinburgh Postnatal Depression Scale (EPDS) scores at 2 months postpartum and PPD (binary yes/no) based on an EPDS cutoff of 10. Illumina NextSeq500/550 transcriptomic sequencing from whole blood was analysed using the edgeR package. We identified 71 genes significantly associated with postpartum depression scores at 2 months, after correction for multiple testing at 5% FDR. These included several interesting candidates including TNFRSF17, previously reported to be significantly upregulated in women with PPD and MMP8, a matrix metalloproteinase gene, associated with depression in a genome-wide association study. Functional annotation of differentially expressed genes revealed an enrichment of immune response-related biological processes. Additional analysis of genes associated with changes in depressive symptoms from recruitment to 2 months postpartum identified 66 genes significant at an FDR of 5%. Of these genes, 33 genes were also associated with depressive symptoms at 2 months postpartum. Comparing the results with previous studies, we observed that 15.4% of genes associated with PPD in this study overlapped with 700 core maternal genes that showed significant gene expression changes across multiple brain regions (P = 7.9e-05) and 29–53% of the genes were also associated with estradiol changes in a pharmacological model of depression (P values range = 1.2e-4–2.1e-14). In conclusion, we identified novel genes and validated genes previously associated with oestrogen sensitivity in PPD. These results point towards the role of an altered immune transcriptomic landscape as a vulnerability factor for PPD.

scholarly journals Microarray analysis identification of key pathways and interaction network of differential gene expressions during osteogenic differentiation

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Fatemeh Khodabandehloo ◽  
Sara Taleahmad ◽  
Reza Aflatoonian ◽  
Farzad Rajaei ◽  
Zahra Zandieh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Wnt Signaling Pathway ◽  
Hub Genes ◽  
Gene Expressions ◽  
Cell Based Therapy ◽  
Wnt Pathways ◽  
Key Pathways

Abstract Background Adult bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stem cells that can differentiate into three lineages. They are suitable sources for cell-based therapy and regenerative medicine applications. This study aims to evaluate the hub genes and key pathways of differentially expressed genes (DEGs) related to osteogenesis by bioinformatics analysis in three different days. The DEGs were derived from the three different days compared with day 0. Results Gene expression profiles of GSE37558 were obtained from the Gene Expression Omnibus (GEO) database. A total of 4076 DEGs were acquired on days 8, 12, and 25. Gene ontology (GO) enrichment analysis showed that the non-canonical Wnt signaling pathway and lipopolysaccharide (LPS)-mediated signaling pathway were commonly upregulated DEGs for all 3 days. KEGG pathway analysis indicated that the PI3K-Akt and focal adhesion were also commonly upregulated DEGs for all 3 days. Ten hub genes were identified by CytoHubba on days 8, 12, and 25. Then, we focused on the association of these hub genes with the Wnt pathways that had been enriched from the protein-protein interaction (PPI) by the Cytoscape plugin MCODE. Conclusions These findings suggested further insights into the roles of the PI3K/AKT and Wnt pathways and their association with osteogenesis. In addition, the stem cell microenvironment via growth factors, extracellular matrix (ECM), IGF1, IGF2, LPS, and Wnt most likely affect osteogenesis by PI3K/AKT.

scholarly journals Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

scholarly journals Peripheral Blood Gene Expression and IgG Glycosylation Profiles as Markers of Tocilizumab Treatment in Rheumatoid Arthritis

2012 ◽  
Vol 39 (5) ◽  
pp. 916-928 ◽  
Author(s):  
BERTALAN MESKO ◽  
SZILARD POLISKA ◽  
SZILVIA SZAMOSI ◽  
ZOLTAN SZEKANECZ ◽  
JANOS PODANI ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Peripheral Blood ◽  
Multiple Testing ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Response To Treatment ◽  
Peripheral Blood Mononuclear ◽  
Gene Sets

Objective.Tocilizumab, a humanized anti-interleukin-6 receptor monoclonal antibody, has recently been approved as a biological therapy for rheumatoid arthritis (RA) and other diseases. It is not known if there are characteristic changes in gene expression and immunoglobulin G glycosylation during therapy or in response to treatment.Methods.Global gene expression profiles from peripheral blood mononuclear cells of 13 patients with RA and active disease at Week 0 (baseline) and Week 4 following treatment were obtained together with clinical measures, serum cytokine levels using ELISA, and the degree of galactosylation of the IgG N-glycan chains. Gene sets separating responders and nonresponders were tested using canonical variates analysis. This approach also revealed important gene groups and pathways that differentiate responders from nonresponders.Results.Fifty-nine genes showed significant differences between baseline and Week 4 and thus correlated with treatment. Significantly, 4 genes determined responders after correction for multiple testing. Ten of the 12 genes with the most significant changes were validated using real-time quantitative polymerase chain reaction. An increase in the terminal galactose content of N-linked glycans of IgG was observed in responders versus nonresponders, as well as in treated samples versus samples obtained at baseline.Conclusion.As a preliminary report, gene expression changes as a result of tocilizumab therapy in RA were examined, and gene sets discriminating between responders and nonresponders were found and validated. A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA treated with tocilizumab was documented.

scholarly journals Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

scholarly journals Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

2021 ◽  
Author(s):  
Li Guoquan ◽  
Du Junwei ◽  
He Qi ◽  
Fu Xinghao ◽  
Ji Feihong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Treatment Options ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Chronic Lymphocytic Thyroiditis ◽  
Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

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