Gene expression profiles and pathway enrichment analysis of human osteosarcoma cells exposed to sorafenib

Abstract Background The purpose of present study was to identify the differentially expressed genes (DEGs) associated with BMP-9-induced osteogenic differentiation of mesenchymal stem cells (MSCs) by using bioinformatics methods. Methods Gene expression profiles of BMP-9-induced MSCs were compared between with GFP-induced MSCs and BMP-9-induced MSCs. GSE48882 containing two groups of gene expression profiles, 3 GFP-induced MSC samples and 3 from BMP-9-induced MSCs, was downloaded from the Gene Expression Omnibus (GEO) database. Then, DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that the identified DEGs were potentially involved in cytoplasm, nucleus, and extracellular exosome signaling pathway. Results A total of 1967 DEGs (1029 upregulated and 938 downregulated) were identified from GSE48882 datasets. R/Bioconductor package limma was used to identify the DEGs. Further analysis revealed that there were 35 common DEGs observed between the samples. GO function and KEGG pathway enrichment analysis, among which endoplasmic reticulum, protein export, RNA transport, and apoptosis was the most significant dysregulated pathway. The result of protein-protein interaction (PPI) network modules demonstrated that the Hspa5, P4hb, Sec61a1, Smarca2, Pdia3, Dnajc3, Hyou1, Smad7, Derl1, and Surf4 were the high-degree hub nodes. Conclusion Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in BMP-9 induced MSCs, which could improve our understanding of the key genes and pathways for BMP-9-induced osteogenic of MSCs.

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Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

Genomics ◽

10.1016/j.ygeno.2021.11.031 ◽

2021 ◽

Author(s):

A. Siavoshi ◽

M. Taghizadeh ◽

E. Dookhe ◽

M. Piran

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

High Throughput ◽

Expression Profiles ◽

Differentially Expressed Gene ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Rna Seq ◽

Pathway Enrichment

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In Silico Discovery of Candidate Drugs against Covid-19

Viruses ◽

10.3390/v12040404 ◽

2020 ◽

Vol 12 (4) ◽

pp. 404 ◽

Cited By ~ 42

Author(s):

Claudia Cava ◽

Gloria Bertoli ◽

Isabella Castiglioni

Keyword(s):

Gene Expression ◽

In Silico ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Basic Mechanism ◽

Cell Receptor ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis

Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.

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Analyzing gene expression profiles with preliminary validations in cardiac hypertrophy induced by pressure overload

Canadian Journal of Physiology and Pharmacology ◽

10.1139/cjpp-2017-0585 ◽

2018 ◽

Vol 96 (8) ◽

pp. 701-709 ◽

Cited By ~ 1

Author(s):

Jing Gao ◽

Yuhong Li ◽

Tongmei Wang ◽

Zhuo Shi ◽

Yiqi Zhang ◽

...

Keyword(s):

Gene Expression ◽

Cardiac Hypertrophy ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Pressure Overload ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Pathway Enrichment Analysis ◽

Ontology Term ◽

The Common

The aim of this study was to identify the key genes involved in the cardiac hypertrophy (CH) induced by pressure overload. mRNA microarray data sets GSE5500 and GSE18801 were downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were screened using the Limma package; then, functional and pathway enrichment analysis were performed for common DEGs using the Database for Annotation, Visualization and Integrated Discovery database. Furthermore, the top DEGs were further validated using quantitative PCR in the hypertrophic heart tissue induced by isoprenaline. A total of 113 common DEGs with absolute fold change > 0.5, including 60 significantly upregulated DEGs and 53 downregulated DEGs, were obtained. Gene ontology term enrichment analysis suggested that common upregulated DEG were mainly enriched in neutrophil chemotaxis, extracellular fibril organization, and cell proliferation; and the common downregulated genes were significantly enriched in ion transport, endoplasmic reticulum, and dendritic spine. Kyoto Encyclopedia of Genes and Genomes pathway analysis found that the common DEGs were mainly enriched in extracellular matrix receptor interaction, phagosome, and focal adhesion. Additionally, the expression of Mfap4, Ltbp2, Aspn, Serpina3n, and Cnksr1 were upregulated in the model of CH, while the expression of Anp32a was downregulated. The current study identified the key deregulated genes and pathways involved in the CH, which could shed new light to understand the mechanism of CH.

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Identification and Verification of Core Genes in Colorectal Cancer

BioMed Research International ◽

10.1155/2020/8082697 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Houxi Xu ◽

Yuzhu Ma ◽

Jinzhi Zhang ◽

Jialin Gu ◽

Xinyue Jing ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Malignant Neoplasm ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Core Genes

Colorectal cancer, a malignant neoplasm that occurs in the colorectal mucosa, is one of the most common types of gastrointestinal cancer. Colorectal cancer has been studied extensively, but the molecular mechanisms of this malignancy have not been characterized. This study identified and verified core genes associated with colorectal cancer using integrated bioinformatics analysis. Three gene expression profiles (GSE15781, GSE110223, and GSE110224) were downloaded from the Gene Expression Omnibus (GEO) databases. A total of 87 common differentially expressed genes (DEGs) among GSE15781, GSE110223, and GSE110224 were identified, including 19 upregulated genes and 68 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for common DEGs using clusterProfiler. These common DEGs were significantly involved in cancer-associated functions and signaling pathways. Then, we constructed protein-protein interaction networks of these common DEGs using Cytoscape software, which resulted in the identification of the following 10 core genes: SST, PYY, CXCL1, CXCL8, CXCL3, ZG16, AQP8, CLCA4, MS4A12, and GUCA2A. Analysis using qRT-PCR has shown that SST, CXCL8, and MS4A12 were significant differentially expressed between colorectal cancer tissues and normal colorectal tissues (P<0.05). Gene Expression Profiling Interactive Analysis (GEPIA) overall survival (OS) has shown that low expressions of AQP8, ZG16, CXCL3, and CXCL8 may predict poor survival outcome in colorectal cancer. In conclusion, the core genes identified in this study contributed to the understanding of the molecular mechanisms involved in colorectal cancer development and may be targets for early diagnosis, prevention, and treatment of colorectal cancer.

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Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysis

Carcinogenesis ◽

10.1093/carcin/bgz194 ◽

2019 ◽

Vol 41 (6) ◽

pp. 743-750 ◽

Cited By ~ 1

Author(s):

Ting-Yu Chen ◽

Yang Liu ◽

Liang Chen ◽

Jie Luo ◽

Chao Zhang ◽

...

Keyword(s):

Gene Expression ◽

Current Knowledge ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Module Preservation ◽

New Biomarkers

Abstract Glioma is the most common brain tumor with high mortality. However, there are still challenges for the timely and accurate diagnosis and effective treatment of the tumor. One hundred and twenty-one samples with grades II, III and IV from the Gene Expression Omnibus database were used to construct gene co-expression networks to identify hub modules closely related to glioma grade, and performed pathway enrichment analysis on genes from significant modules. In gene co-expression network constructed by 2345 differentially expressed genes from 121 gene expression profiles for glioma, we identified the black and blue modules that associated with grading. The module preservation analysis based on 118 samples indicates that the two modules were replicable. Enrichment analysis showed that the extracellular matrix genes were enriched for blue module, while cell division genes were enriched for black module. According to survival analysis, 21 hub genes were significantly up-regulated and one gene was significantly down-regulated. What’s more, IKBIP, SEC24D, and FAM46A are the genes with little attention among the 22 hub genes. In this study, IKBIP, SEC24D, and FAM46A related to glioma were mentioned for the first time to the current knowledge, which might provide a new idea for us to study the disease in the future. IKBIP, SEC24D and FAM46A among the 22 hub genes identified that are related to the malignancy degree of glioma might be used as new biomarkers to improve the diagnosis, treatment and prognosis of glioma.

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Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage

Bone and Joint Research ◽

10.1302/2046-3758.75.bjr-2017-0284.r1 ◽

2018 ◽

Vol 7 (5) ◽

pp. 343-350 ◽

Cited By ~ 6

Author(s):

A. He ◽

Y. Ning ◽

Y. Wen ◽

Y. Cai ◽

K. Xu ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Mrna Expression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Signalling Pathway ◽

Pathway Enrichment Analysis ◽

Osteoarthritic Cartilage ◽

Pathway Enrichment ◽

Genome Wide

Aim Osteoarthritis (OA) is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control the expression of genes and are likely to regulate the OA transcriptome. We performed integrative genomic analyses to define methylation-gene expression relationships in osteoarthritic cartilage. Patients and Methods Genome-wide DNA methylation profiling of articular cartilage from five patients with OA of the knee and five healthy controls was conducted using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, California). Other independent genome-wide mRNA expression profiles of articular cartilage from three patients with OA and three healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Integrative pathway enrichment analysis of DNA methylation and mRNA expression profiles was performed using integrated analysis of cross-platform microarray and pathway software. Gene ontology (GO) analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Results We identified 1265 differentially methylated genes, of which 145 are associated with significant changes in gene expression, such as DLX5, NCOR2 and AXIN2 (all p-values of both DNA methylation and mRNA expression < 0.05). Pathway enrichment analysis identified 26 OA-associated pathways, such as mitogen-activated protein kinase (MAPK) signalling pathway (p = 6.25 × 10-4), phosphatidylinositol (PI) signalling system (p = 4.38 × 10-3), hypoxia-inducible factor 1 (HIF-1) signalling pathway (p = 8.63 × 10-3 pantothenate and coenzyme A (CoA) biosynthesis (p = 0.017), ErbB signalling pathway (p = 0.024), inositol phosphate (IP) metabolism (p = 0.025), and calcium signalling pathway (p = 0.032). Conclusion We identified a group of genes and biological pathwayswhich were significantly different in both DNA methylation and mRNA expression profiles between patients with OA and controls. These results may provide new clues for clarifying the mechanisms involved in the development of OA. Cite this article: A. He, Y. Ning, Y. Wen, Y. Cai, K. Xu, Y. Cai, J. Han, L. Liu, Y. Du, X. Liang, P. Li, Q. Fan, J. Hao, X. Wang, X. Guo, T. Ma, F. Zhang. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018;7:343–350. DOI: 10.1302/2046-3758.75.BJR-2017-0284.R1.

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A Method of Pathway Enrichment Analysis Based Gene Expression Variability

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1206.2012.00410 ◽

2013 ◽

Vol 40 (12) ◽

pp. 1256

Author(s):

XiaoDong JIA ◽

XiuJie CHEN ◽

Xin WU ◽

JianKai XU ◽

FuJian TAN ◽

...

Keyword(s):

Gene Expression ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Expression Variability ◽

Pathway Enrichment

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

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

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.

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

10.21203/rs.3.rs-1023446/v1 ◽

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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