scholarly journals Screening and identification of key genes and pathways in metastatic uveal melanoma based on gene expression using bioinformatic analysis

Medicine ◽  
2020 ◽  
Vol 99 (43) ◽  
pp. e22974
Author(s):  
Jialu Xie ◽  
Zhenyu Wu ◽  
Xiaogang Xu ◽  
Guanlu Liang ◽  
Jiehui Xu
Keyword(s):  
Gene Expression ◽  
Uveal Melanoma ◽  
Bioinformatic Analysis ◽  
Screening And Identification ◽  
Key Genes

scholarly journals Integrated traditional Chinese medicine alleviates sciatica while regulating gene expression in peripheral blood

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yi Wang ◽  
Guogang Dai ◽  
Yan Xu ◽  
Ling Jiang ◽  
Zhibin Fu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Peripheral Blood ◽  
Bioinformatic Analysis ◽  
Expression Levels ◽  
Gene Dysregulation ◽  
Peripheral Blood Gene Expression ◽  
Key Genes ◽  
Upregulated Genes

Abstract Background Although integrated traditional Chinese medicine (TCM) has long been indicated to be effective in the treatment of sciatica and is widely used in the management of this condition, the mechanism by which integrated TCM alleviates sciatica has not yet been fully defined, and the effect of integrated TCM on gene expression in the peripheral blood of patients with sciatica is still unknown. We performed this study to investigate the effect of integrated TCM on peripheral blood gene expression in patients with sciatica and to explore new clues for studying the mechanism of integrated TCM in alleviating sciatica. Methods We used a microarray to identify differentially expressed genes (DEGs) in the peripheral blood of patients with sciatica and healthy controls (DEGs-baseline), bioinformatic analysis to reveal the characteristics of DEGs-baseline, and the key genes that contribute to the gene dysregulation. A microarray was also used to identify DEGs in the peripheral blood of patients with sciatica after integrated TCM treatment compared with those at baseline, and the expression levels of DEGs were validated by qRT-PCR. Results We identified 153 DEGs-baseline, which included 131 upregulated genes and 22 downregulated genes. Bioinformatic analysis revealed that most of the DEGs-baseline were related to immunity and the inflammatory response and that TLR4, MMP9, MPO, CAMP, RETN, TLR5, and IL1RN were key genes involved in the dysregulation of genes in the peripheral blood of patients with sciatica. The expression levels of TLR5, IL1RN, SLC8A1, RBM20, GPER1, IL27, SOCS1, and GRTP1-AS1 were decreased in the peripheral blood of patients after integrated TCM treatment compared with that at baseline, which was accompanied by relief of pain. Conclusion Integrated TCM treatment relieved pain while regulating the gene expression of TLR5, IL1RN, SLC8A1, RBM20, GPER1, IL27, SOCS1, and GRTP1-AS1 in the peripheral blood of patients with sciatica. Our study provides new clues for studying the mechanism of TCM in treating sciatica.

scholarly journals Identification of Key Genes and Pathways in Breast Cancer Based on Bioinformatic Analysis

2020 ◽  
Author(s):  
Chi Pan ◽  
Qingtao Ni
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Human Tumor ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Cancer Type ◽  
Hub Genes ◽  
Ampk Signaling ◽  
Key Genes

Abstract Breast cancer(BC) is the most frequent cancer type in women. However, the pathogenesis of BC is still not well understood. Thus, we aim to explore key genes and pathways in relation to BC. We used the Gene Expression Omnibus (GEO) database to identify the differently expression of genes in the carcinogenesis and progression of BC. Then, bioinformatics analysis was performed to determine the key genes targets and pathways associated with BC. The gene expression profile of the hub genes in human tumor was displayed using GEPIA. Finally, the expression of hub genes, correlation between genes and miRNA were screened via the miRDB, MirTarBase and DIANA Tools. We screened 159 downregulated genes and 55 upregulated genes in BC patients among the 4 datasets. The enriched functions of the DEGs involved in cell proliferation, positive regulation of Akt signaling and extracellular exosome, PPAR signaling pathway and AMPK signaling pathway. 3 hub genes were screened out by construction of PPI network. MELK were found to be upregulated, and CLU and NTRK2 were downregulated. Further verification showed that MELK displayed higher levels in almost all tumors. We found correlation between these hube genes and the miRNAs. All in all, three key genes closely related to the incidence of BC were identified, and the results could provide new potential molecular targets for the diagnosis and treatment of BC. In particular, MELK is regulated by multiple miRNAs and participate in the development of BC.

scholarly journals Bioinformatic Analysis Reveals Phosphodiesterase 4D-Interacting Protein as a Key Frontal Cortex Dementia Switch Gene

2020 ◽  
Vol 21 (11) ◽  
pp. 3787
Author(s):  
Judith A. Potashkin ◽  
Virginie Bottero ◽  
Jose A. Santiago ◽  
James P. Quinn
Keyword(s):  
Gene Expression ◽  
Chemical Interaction ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Molecular Pathways ◽  
Interacting Protein ◽  
Key Factor ◽  
Key Genes ◽  
Expression Pathways ◽  
Phosphodiesterase 4D

The mechanisms that initiate dementia are poorly understood and there are currently no treatments that can slow their progression. The identification of key genes and molecular pathways that may trigger dementia should help reveal potential therapeutic reagents. In this study, SWItch Miner software was used to identify phosphodiesterase 4D-interacting protein as a key factor that may lead to the development of Alzheimer’s disease, vascular dementia, and frontotemporal dementia. Inflammation, PI3K-AKT, and ubiquitin-mediated proteolysis were identified as the main pathways that are dysregulated in these dementias. All of these dementias are regulated by 12 shared transcription factors. Protein–chemical interaction network analysis of dementia switch genes revealed that valproic acid may be neuroprotective for these dementias. Collectively, we identified shared and unique dysregulated gene expression, pathways and regulatory factors among dementias. New key mechanisms that lead to the development of dementia were revealed and it is expected that these data will advance personalized medicine for patients.

scholarly journals Screening and Identification of Key Genes, Pathways, and Drugs Associated with Neuropathic Pain in Dorsal Horn: Evidence from Bioinformatic Analysis

2021 ◽  
Vol Volume 14 ◽  
pp. 1813-1826
Author(s):  
Xiao Yang ◽  
Lin Zhu ◽  
Bingcheng Zhao ◽  
Jingjuan Hu ◽  
Fan Deng ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Dorsal Horn ◽  
Bioinformatic Analysis ◽  
Screening And Identification ◽  
Key Genes

scholarly journals Activation of the S100A7/RAGE Pathway by IGF-1 Contributes to Angiogenesis in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 621
Author(s):  
Maria Grazia Muoio ◽  
Marianna Talia ◽  
Rosamaria Lappano ◽  
Andrew H. Sims ◽  
Veronica Vella ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Endothelial Cells ◽  
Vascular Endothelial Cells ◽  
Bioinformatic Analysis ◽  
Tube Formation ◽  
Endothelial Cell Proliferation ◽  
Diabetic Patients ◽  
Obesity And Diabetes ◽  
Er Positive

Background: Breast cancer (BC) mortality is increased among obese and diabetic patients. Both obesity and diabetes are associated with dysregulation of both the IGF-1R and the RAGE (Receptor for Advanced Glycation End Products) pathways, which contribute to complications of these disorders. The alarmin S100A7, signaling through the receptor RAGE, prompts angiogenesis, inflammation, and BC progression. Methods: We performed bioinformatic analysis of BC gene expression datasets from published studies. We then used Estrogen Receptor (ER)-positive BC cells, CRISPR-mediated IGF-1R KO BC cells, and isogenic S100A7-transduced BC cells to investigate the role of IGF-1/IGF-1R in the regulation of S100A7 expression and tumor angiogenesis. To this aim, we also used gene silencing and pharmacological inhibitors, and we performed gene expression and promoter studies, western blotting analysis, ChIP and ELISA assays, endothelial cell proliferation and tube formation assay. Results: S100A7 expression correlates with worse prognostic outcomes in human BCs. In BC cells, the IGF-1/IGF-1R signaling engages STAT3 activation and its recruitment to the S100A7 promoter toward S100A7 increase. In human vascular endothelial cells, S100A7 activates RAGE signaling and prompts angiogenic effects. Conclusions: In ER-positive BCs the IGF-1 dependent activation of the S100A7/RAGE signaling in adjacent endothelial cells may serve as a previously unidentified angiocrine effector. Targeting S100A7 may pave the way for a better control of BC, particularly in conditions of unopposed activation of the IGF-1/IGF-1R axis.

scholarly journals The Detection and Bioinformatic Analysis of Alternative 3′ UTR Isoforms as Potential Cancer Biomarkers

2021 ◽  
Vol 22 (10) ◽  
pp. 5322
Author(s):  
Nitika Kandhari ◽  
Calvin A. Kraupner-Taylor ◽  
Paul F. Harrison ◽  
David R. Powell ◽  
Traude H. Beilharz
Keyword(s):  
Gene Expression ◽  
Cell Transformation ◽  
Alternative Polyadenylation ◽  
Cancer Biomarkers ◽  
Bioinformatic Analysis ◽  
Alternative Transcript ◽  
Clinical Parameters ◽  
Transcript Cleavage ◽  
Cleavage And Polyadenylation ◽  
Potential Cancer

Alternative transcript cleavage and polyadenylation is linked to cancer cell transformation, proliferation and outcome. This has led researchers to develop methods to detect and bioinformatically analyse alternative polyadenylation as potential cancer biomarkers. If incorporated into standard prognostic measures such as gene expression and clinical parameters, these could advance cancer prognostic testing and possibly guide therapy. In this review, we focus on the existing methodologies, both experimental and computational, that have been applied to support the use of alternative polyadenylation as cancer biomarkers.

scholarly journals Study on potential differentially expressed genes in stroke by bioinformatics analysis

2021 ◽  
Author(s):  
Xitong Yang ◽  
Pengyu Wang ◽  
Shanquan Yan ◽  
Guangming Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Normal Control ◽  
Enrichment Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

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