scholarly journals S1159 Identification of Three Up-Regulated Hub Genes as Potential Diagnostic Markers, Prognostic Markers, and Therapeutic Targets in Hepatocellular Carcinoma by Integrated Bioinformatic Analysis

2021 ◽  
Vol 116 (1) ◽  
pp. S541-S541
Author(s):  
Chenyu Sun ◽  
Yue Chen ◽  
Tianming Zhao ◽  
Shaodi Ma ◽  
Na Hyun Kim ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Markers ◽  
Therapeutic Targets ◽  
Bioinformatic Analysis ◽  
Diagnostic Markers ◽  
Hub Genes

scholarly journals CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Wan-Xia Yang ◽  
Yun-Yan Pan ◽  
Chong-Ge You
Keyword(s):  
Hepatocellular Carcinoma ◽  
Age Of Onset ◽  
Function Analysis ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Pathway Enrichment Analysis ◽  
Poor Survival ◽  
Hub Genes ◽  
Normal Tissues

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein–protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

scholarly journals Bioinformatic Analysis Combined With Experimental Validation Reveals Novel Hub Genes and Pathways Associated With Focal Segmental Glomerulosclerosis

2022 ◽  
Vol 8 ◽  
Author(s):  
Yan-Pei Hou ◽  
Tian-Tian Diao ◽  
Zhi-Hui Xu ◽  
Xin-Yue Mao ◽  
Chang Wang ◽  
...  
Keyword(s):  
Focal Segmental Glomerulosclerosis ◽  
Experimental Validation ◽  
Therapeutic Targets ◽  
Bioinformatic Analysis ◽  
Pcr Analysis ◽  
Diagnostic Efficiency ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Qrt Pcr ◽  
Segmental Glomerulosclerosis

Background: Focal segmental glomerulosclerosis (FSGS) is a type of nephrotic syndrome leading to end-stage renal disease, and this study aimed to explore the hub genes and pathways associated with FSGS to identify potential diagnostic and therapeutic targets.Methods: We downloaded the microarray datasets GSE121233 and GSE129973 from the Gene Expression Omnibus (GEO) database. The datasets comprise 25 FSGS samples and 25 normal samples. The differential expression genes (DEGs) were identified using the R package “limma”. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the database for Annotation, Visualization and Integrated Discovery (DAVID) to identify the pathways and functional annotation of the DEGs. The protein–protein interaction (PPI) was constructed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. The hub genes of the DEGs were then evaluated using the cytoHubba plugin of Cytoscape. The expression of the hub genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) using the FSGS rat model, and receiver operating characteristic (ROC) curve analysis was performed to validate the accuracy of these hub genes.Results: A total of 45 DEGs including 18 upregulated and 27 downregulated DEGs, were identified in the two GSE datasets (GSE121233 and GSE129973). Among them, five hub genes with a high degree of connectivity were selected. From the PPI network, of the top five hub genes, FN1 was upregulated, while ALB, EGF, TTR, and KNG1 were downregulated. The qRT-PCR analysis of FSGS rats confirmed that the expression of FN1 was upregulated and that of EGF and TTR was downregulated. The ROC analysis indicated that FN1, EGF, and TTR showed considerable diagnostic efficiency for FSGS.Conclusion: Three novel FSGS-specific genes were identified through bioinformatic analysis combined with experimental validation, which may promote our understanding of the molecular underpinning of FSGS and provide potential therapeutic targets for the clinical management.

scholarly journals Identification of hub genes in hepatocellular carcinoma using integrated bioinformatic analysis

Aging ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 5439-5468 ◽  
Author(s):  
Shengni Hua ◽  
Zhonghua Ji ◽  
Yingyao Quan ◽  
Meixiao Zhan ◽  
Hao Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Bioinformatic Analysis ◽  
Hub Genes

scholarly journals Systematic Analysis of the Transcriptome Profiles and Co-Expression Networks of Tumour Endothelial Cells Identifies Several Tumour-Associated Modules and Potential Therapeutic Targets in Hepatocellular Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1768
Author(s):  
Thomas Mohr ◽  
Sonja Katz ◽  
Verena Paulitschke ◽  
Nadim Aizarani ◽  
Alexander Tolios
Keyword(s):  
Hepatocellular Carcinoma ◽  
Endothelial Cells ◽  
Cell Growth ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Gene Expression Profiles ◽  
Gene Interaction ◽  
Immune Functions ◽  
Hub Genes ◽  
Systematic Analysis

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related death, with tumour associated liver endothelial cells being thought to be major drivers in HCC progression. This study aims to compare the gene expression profiles of tumour endothelial cells from the liver with endothelial cells from non-tumour liver tissue, to identify perturbed biologic functions, co-expression modules, and potentially drugable hub genes that could give rise to novel therapeutic targets and strategies. Gene Set Variation Analysis (GSVA) showed that cell growth-related pathways were upregulated, whereas apoptosis induction, immune and inflammatory-related pathways were downregulated in tumour endothelial cells. Weighted Gene Co-expression Network Analysis (WGCNA) identified several modules strongly associated to tumour endothelial cells or angiogenic activated endothelial cells with high endoglin (ENG) expression. In tumour cells, upregulated modules were associated with cell growth, cell proliferation, and DNA-replication, whereas downregulated modules were involved in immune functions, particularly complement activation. In ENG+ cells, upregulated modules were associated with cell adhesion and endothelial functions. One downregulated module was associated with immune system-related functions. Querying the STRING database revealed known functional-interaction networks underlying the modules. Several possible hub genes were identified, of which some (for example FEN1, BIRC5, NEK2, CDKN3, and TTK) are potentially druggable as determined by querying the Drug Gene Interaction database. In summary, our study provides a detailed picture of the transcriptomic differences between tumour and non-tumour endothelium in the liver on a co-expression network level, indicates several potential therapeutic targets and presents an analysis workflow that can be easily adapted to other projects.

Prognostic markers and putative therapeutic targets for hepatocellular carcinoma

2010 ◽  
Vol 31 (2) ◽  
pp. 179-193 ◽  
Author(s):  
Maddalena Frau ◽  
Fiorella Biasi ◽  
Francesco Feo ◽  
Rosa M. Pascale
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Markers ◽  
Therapeutic Targets

scholarly journals Identification of Key Functional Modules and Immunomodulatory Regulators of Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Ding Luo ◽  
Xiang Zhang ◽  
Xiao-Kai Li ◽  
Gang Chen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Therapeutic Targets ◽  
Treatment Resistance ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Functional Modules ◽  
Hub Genes ◽  
Gene Set

Despite the advances in the treatment of hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfactory due to postsurgical recurrence and treatment resistance. Therefore, it is important to reveal the mechanisms underlying HCC and identify potential therapeutic targets against HCC, which could facilitate the development of novel therapies. Based on 12 HCC samples and 12 paired paracancerous normal tissues, we identified differentially expressed mRNAs and lncRNAs using the “limma” package in R software. Moreover, we used the weighted gene coexpression network analysis (WGCNA) to analyze the expression data and screened hub genes. Furthermore, we performed pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. In addition, the relative abundance of a given gene set was estimated by single-sample Gene Set Enrichment Analysis. We identified 687 differentially expressed mRNAs and 260 differentially expressed lncRNAs. A total of 6 modules were revealed by WGCNA, and MT1M and MT1E genes from the red module were identified as hub genes. Moreover, pathway analysis revealed the top 10 enriched KEGG pathways of upregulated or downregulated genes. Additionally, we also found that CD58 might act as an immune checkpoint gene in HCC via PD1/CTLA4 pathways and regulate the levels of tumor-infiltrating immune cells in HCC tissues, which might be an immunotherapeutic target in HCC. Our research identified key functional modules and immunomodulatory regulators for HCC, which might offer novel diagnostic biomarkers and/or therapeutic targets for cancer immunotherapy.

scholarly journals Identification of Key Genes with Clinical Outcome in Benign Meningioma Using Bioinformatic Analysis

2020 ◽  
Author(s):  
Ming Cao ◽  
Chen Shen ◽  
Jie Zhu ◽  
YuHai Wang
Keyword(s):  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Microarray Dataset ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Benign Meningioma ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Key Genes

Abstract Background: Meningioma is the second most common type of brain neoplasms.However,the underlying molecular mechanisms are still not clear,and the main treatment is mainly surgery plus radiotherapy. Material and method: To explore the key genes in benign meningioma,we downloaded microarray dataset GSE43290 from Gene Expression Omnibus(GEO) database.The differential genes (DEGs) between benign meningioma and normal meninges were identified by GEO2R.The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway were performed by the Database for Annotation,Visualization and Integrated Discovery (DAVID).The protein-protein interaction (PPI) network and module analysis were performed and visualized by the Search Tool for the Retrieval of Interacting Gene database (STRING) and Cytoscape.The hub genes were evaluated by the Cytohubba and further explored by MCODE plugin of Cytoscape and Enrichr.The relationship between hub genes and clinical factors were further explored by GSE16581 through R software. Result: A total of 358 DEGs were identified,including 15 upregulated genes and 343 downregulated genes.The main enriched functions were extracellular matrix organization、inflammatory response、cell adhesion、extracellular space and integrin binding.The main KEGG pathways were Malaria and focal adhesion.Among these DEGs,5 overlapping genes(CXCL8、AGT、CXCL2、CXCL12、CXCR4) were selected as hub genes.CXCL2 and CXCL8 were correlated with age and tumor recurrence,which could be clinical therapeutic targets. Conclusion: This study indicates the key genes in benign meningioma which may help us understand the molecular mechanisms and provide the candidate therapeutic targets.

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