scholarly journals Identification of differentially expressed genes in cervical cancer by bioinformatics analysis

2018 ◽  
Author(s):  
Yanshan Ge ◽  
Chaoyang Zhang ◽  
Songshu Xiao ◽  
Lin Liang ◽  
Shan Liao ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed

scholarly journals Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xiaoling Ma ◽  
Jinhui Liu ◽  
Hui Wang ◽  
Yi Jiang ◽  
Yicong Wan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Functional Analysis ◽  
Estrogen Receptor ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Venn Diagram ◽  
Hub Genes

Abstract Methylation functions in the pathogenesis of cervical cancer. In the present study, we applied an integrated bioinformatics analysis to identify the aberrantly methylated and differentially expressed genes (DEGS), and their related pathways in cervical cancer. Data of gene expression microarrays (GSE9750) and gene methylation microarrays (GSE46306) were gained from Gene Expression Omnibus (GEO) databases. Hub genes were identified by ‘limma’ packages and Venn diagram tool. Functional analysis was conducted by FunRich. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein–protein interaction (PPI) information. Gene Expression Profiling Interactive Analysis (GEPIA), immunohistochemistry staining, and ROC curve analysis were conducted for validation. Gene Set Enrichment Analysis (GSEA) was also performed to identify potential functions.We retrieved two upregulated-hypomethylated oncogenes and eight downregulated-hypermethylated tumor suppressor genes (TSGs) for functional analysis. Hypomethylated and highly expressed genes (Hypo-HGs) were significantly enriched in cell cycle and autophagy, and hypermethylated and lowly expressed genes (Hyper-LGs) in estrogen receptor pathway and Wnt/β-catenin signaling pathway. Estrogen receptor 1 (ESR1), Erythrocyte membrane protein band 4.1 like 3 (EPB41L3), Endothelin receptor B (EDNRB), Inhibitor of DNA binding 4 (ID4) and placenta-specific 8 (PLAC8) were hub genes. Kaplan–Meier method was used to evaluate survival data of each identified gene. Lower expression levels of ESR1 and EPB41L3 were correlated with a shorter survival time. GSEA results showed that ‘cell adhesion molecules’ was the most enriched item. This research inferred the candidate genes and pathways that might be used in the diagnosis, treatment, and prognosis of cervical cancer.

scholarly journals Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer

2021 ◽  
Vol 11 (5) ◽  
pp. 363
Author(s):  
Arafat Rahman Oany ◽  
Mamun Mia ◽  
Tahmina Pervin ◽  
Salem Ali Alyami ◽  
Mohammad Ali Moni
Keyword(s):  
Cervical Cancer ◽  
Systems Biology ◽  
Differentially Expressed Genes ◽  
Inflammatory Responses ◽  
Profile Analysis ◽  
Target Identification ◽  
Regulatory Genes ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Potential Biomarkers

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC retains its fatality rate of about half of the infected population globally. The major screening biomarkers and therapeutic target identification have now become a global concern. In the present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4643 differentially expressed genes. The up-regulatory genes mainly concentrate on immune-inflammatory responses, and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment, and we mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC and are verified by expression profile analysis. From our study, we have found that genes LILRB2 and CYBB play crucial roles in CC, as reported here for the first time. Furthermore, the survivability of the hub genes was also assessed, and among them, finally, CXCR4 has been identified as one of the most potential differentially expressed genes that might play a vital role in the survival of CC patients. Thus, CXCR4 could be used as a prognostic and/or diagnostic biomarker and a drug target for CC.

Bioinformatics analysis on differentially expressed genes of alveolar macrophage in IPF

2019 ◽  
Vol 45 (9-10) ◽  
pp. 288-296 ◽  
Author(s):  
Huaibin Wang ◽  
Miaomiao Wang ◽  
Kun Xiao ◽  
Xu Zhang ◽  
Peng Wang ◽  
...  
Keyword(s):  
Alveolar Macrophage ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed

scholarly journals Investigation of differentially-expressed microRNAs and genes in cervical cancer using an integrated bioinformatics analysis

2017 ◽  
Vol 13 (4) ◽  
pp. 2784-2790 ◽  
Author(s):  
Zhanzhan Xu ◽  
Yu Zhou ◽  
Fang Shi ◽  
Yexuan Cao ◽  
Thi Lan Anh Dinh ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Bioinformatics Analysis ◽  
Differentially Expressed

scholarly journals Bioinformatic gene analysis for potential therapeutic targets of Huntington’s disease in pre-symptomatic and symptomatic stage

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Chunchen Xiang ◽  
Shengri Cong ◽  
Bin Liang ◽  
Shuyan Cong
Keyword(s):  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Neurodegenerative Disorder ◽  
Psychiatric Symptoms ◽  
Therapeutic Targets ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Transcriptional Dysregulation

Abstract Background Huntington’s disease (HD) is a neurodegenerative disorder characterized by psychiatric symptoms, serious motor and cognitive deficits. Certain pathological changes can already be observed in pre-symptomatic HD (pre-HD) patients; however, the underlying molecular pathogenesis is still uncertain and no effective treatments are available until now. Here, we reanalyzed HD-related differentially expressed genes from the GEO database between symptomatic HD patients, pre-HD individuals, and healthy controls using bioinformatics analysis, hoping to get more insight in the pathogenesis of both pre-HD and HD, and shed a light in the potential therapeutic targets of the disease. Methods Pre-HD and symptomatic HD differentially expressed genes (DEGs) were screened by bioinformatics analysis Gene Expression Omnibus (GEO) dataset GSE1751. A protein–protein interaction (PPI) network was used to select hub genes. Subsequently, Gene Ontology (GO) enrichment analysis of DEGs and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of hub genes were applied. Dataset GSE24250 was downloaded to verify our hub genes by the Kaplan–Meier method using Graphpad Prism 5.0. Finally, target miRNAs of intersected hub genes involved in pre-HD and symptomatic HD were predicted. Results A total of 37 and 985 DEGs were identified in pre-HD and symptomatic HD, respectively. The hub genes, SIRT1, SUZ12, and PSMC6, may be implicated in pre-HD, and the hub genes, FIS1, SIRT1, CCNH, SUZ12, and 10 others, may be implicated in symptomatic HD. The intersected hub genes, SIRT1 and SUZ12, and their predicted target miRNAs, in particular miR-22-3p and miR-19b, may be significantly associated with pre-HD. Conclusion The PSMC6, SIRT1, and SUZ12 genes and their related ubiquitin-mediated proteolysis, transcriptional dysregulation, and histone metabolism are significantly associated with pre-HD. FIS1, CCNH, and their related mitochondrial disruption and transcriptional dysregulation processes are related to symptomatic HD, which might shed a light on the elucidation of potential therapeutic targets in HD.

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