Identification of Potential Key Genes and Pathways in Enzalutamide-Resistant Prostate Cancer Cell Lines: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database

Enzalutamide (ENZ) has been approved for the treatment of advanced prostate cancer (PCa), but some patients develop ENZ resistance initially or after long-term administration. Although a few key genes have been discovered by previous efforts, the complete mechanisms of ENZ resistance remain unsolved. To further identify more potential key genes and pathways in the development of ENZ resistance, we employed the GSE104935 dataset, including 5 ENZ-resistant (ENZ-R) and 5 ENZ-sensitive (ENZ-S) PCa cell lines, from the Gene Expression Omnibus (GEO) database. Integrated bioinformatics analyses were conducted, such as analysis of differentially expressed genes (DEGs), Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) analysis, gene set enrichment analysis (GSEA), and survival analysis. From these, we identified 201 DEGs (93 upregulated and 108 downregulated) and 12 hub genes (AR, ACKR3, GPER1, CCR7, NMU, NDRG1, FKBP5, NKX3-1, GAL, LPAR3, F2RL1, and PTGFR) that are potentially associated with ENZ resistance. One upregulated pathway (hedgehog pathway) and seven downregulated pathways (pathways related to androgen response, p53, estrogen response, TNF-α, TGF-β, complement, and pancreas β cells) were identified as potential key pathways involved in the occurrence of ENZ resistance. Our findings may contribute to further understanding the molecular mechanisms of ENZ resistance and provide some clues for the prevention and treatment of ENZ resistance.

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Bioinformatics analysis of key genes and pathways in Hashimoto thyroiditis tissues

Bioscience Reports ◽

10.1042/bsr20200759 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Long Zheng ◽

Xiaojie Dou ◽

Huijia Song ◽

Pengwei Wang ◽

Wei Qu ◽

...

Keyword(s):

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Interaction Analysis ◽

Differentially Expressed Gene ◽

Enrichment Analysis ◽

Hashimoto Thyroiditis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Key Genes

Abstract Hashimoto thyroiditis (HT) is one of the most common autoimmune diseases, and the incidence of HT continues to increase. Long-term, uncontrollable HT results in thyroid dysfunction and even increases carcinogenesis risks. Since the origin and development of HT involve many complex immune processes, there is no effective therapy for HT on a pathogenesis level. Although bioinformatics analysis has been utilized to seek key genes and pathways of thyroid cancer, only a few bioinformatics studies that focus on HT pathogenesis and mechanisms have been reported. In the present study, the Gene Expression Omnibus dataset (GSE29315) containing 6 HT and 8 thyroid physiological hyperplasia samples was downloaded, and differentially expressed gene (DEG) analysis, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, protein–protein interaction analysis, and gene set enrichment analysis were performed. In total, 85 DEGs, containing 76 up-regulated and 9 down-regulated DEGS, were identified. The DEGs were mainly enriched in immune and inflammatory response, and the signaling pathways were involved in cytokine interaction and cytotoxicity. Moreover, ten hub genes were identified, and IFN-γ, IFN-α, IL6/JAK/STAT3, and inflammatory pathways may promote the origin and progression of HT. The present study indicated that exploring DEGs and pathways by bioinformatics analysis has important significance in understanding the molecular mechanisms of HT and providing potential targets for the prevention and treatment of HT.

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mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Genes ◽

10.3390/genes11030257 ◽

2020 ◽

Vol 11 (3) ◽

pp. 257 ◽

Cited By ~ 6

Author(s):

Yitong Zhang ◽

Joseph Ta-Chien Tseng ◽

I-Chia Lien ◽

Fenglan Li ◽

Wei Wu ◽

...

Keyword(s):

Gene Expression ◽

Stem Cell ◽

Lung Adenocarcinoma ◽

Tumor Necrosis Factor Receptor ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Clinical Stages ◽

Key Genes ◽

Geo Database

Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

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Expression Profile Analysis Identifies Key Genes as Prognostic Markers for Metastasis of Osteosarcoma

10.21203/rs.2.20786/v1 ◽

2020 ◽

Author(s):

Xiaoqing Guan ◽

Zhiyuan Guan ◽

Jiafu Ji ◽

Chunli Song

Keyword(s):

Gene Expression ◽

Target Genes ◽

Prognostic Markers ◽

Profile Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Key Genes

Abstract Background : Osteosarcoma (OS) is the most common malignant tumor of bone which was featured with osteoid or immature bone produced by the malignant cells, and biomarkers are urgently needed to identify patients with this aggressive disease. Methods : We downloaded gene expression profiles from Gene Expression Omnibus (GEO) and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets for OS, respectively, and performed weighted gene co-expression network analysis (WGCNA) to identify the key module. Whereafter, functional annotation and Gene Set Enrichment Analysis (GSEA) demonstrated the relationships between target genes and OS. Results : In this study, we discovered four key genes – ALOX5AP, HLA-DMB, HLA-DRA and SPINT2 as new prognostic markers and confirmed their relationship with OS metastasis in the validation set. Conclusions : Overall, our work may shed light on the roles of ALOX5AP, HLA-DMB, HLA-DRA and SPINT2, thus providing valuable clues to investigate the metastasis of OS and corroborating the potential clinical application value of the 4-gene signature to some extent.

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Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

International Journal of Molecular Sciences ◽

10.3390/ijms22126505 ◽

2021 ◽

Vol 22 (12) ◽

pp. 6505

Author(s):

Jishizhan Chen ◽

Jia Hua ◽

Wenhui Song

Keyword(s):

Stem Cells ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Negative Control ◽

Gene Set Enrichment Analysis ◽

Venn Diagram ◽

Hub Genes ◽

Protein Protein Interaction ◽

Two Samples ◽

Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

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AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.1773 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1037.2-1038

Author(s):

X. Sun ◽

S. X. Zhang ◽

S. Song ◽

T. Kong ◽

C. Zheng ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Shanxi Province ◽

Receptor Interaction ◽

Term Therapy ◽

Pathway Enrichment Analysis ◽

Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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Analysis of MCM Proteins’ Role as a Potential Target of Statins in Patients with Acute Type A Aortic Dissection through Bioinformatics

Genes ◽

10.3390/genes12030387 ◽

2021 ◽

Vol 12 (3) ◽

pp. 387

Author(s):

Zheyong Liang ◽

Yongjian Zhang ◽

Qiang Chen ◽

Junjun Hao ◽

Haichen Wang ◽

...

Keyword(s):

Aortic Dissection ◽

Molecular Mechanisms ◽

Vascular Diseases ◽

Enrichment Analysis ◽

Type A ◽

Protective Role ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Acute Type ◽

Type A Aortic Dissection

Acute aortic dissection is one of the most severe vascular diseases. The molecular mechanisms of aortic expansion and dissection are unclear. Clinical studies have found that statins play a protective role in aortic dissection development and therapy; however, the mechanism of statins’ effects on the aorta is unknown. The Gene Expression Omnibus (GEO) dataset GSE52093, GSE2450and GSE8686 were analyzed, and genes expressed differentially between aortic dissection samples and normal samples were determined using the Networkanalyst and iDEP tools. Weight gene correlation network analysis (WGCNA), functional annotation, pathway enrichment analysis, and the analysis of the regional variations of genomic features were then performed. We found that the minichromosome maintenance proteins (MCMs), a family of proteins targeted by statins, were upregulated in dissected aortic wall tissues and play a central role in cell-cycle and mitosis regulation in aortic dissection patients. Our results indicate a potential molecular target and mechanism for statins’ effects in patients with acute type A aortic dissection.

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Identification of key genes associated with lung adenocarcinoma by bioinformatics analysis

Science Progress ◽

10.1177/0036850421997276 ◽

2021 ◽

Vol 104 (1) ◽

pp. 003685042199727

Author(s):

Xinyu Wang ◽

Jiaojiao Yang ◽

Xueren Gao

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Enrichment Analysis ◽

Tumor Stage ◽

Gene Expression Omnibus ◽

Poor Overall Survival ◽

Key Genes ◽

Low Expression

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, comprising around 40% of all lung cancer. Until now, the pathogenesis of LUAD has not been fully elucidated. In the current study, we comprehensively analyzed the dysregulated genes in lung adenocarcinoma by mining public datasets. Two sets of gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. The dysregulated genes were identified by using the GEO2R online tool, and analyzed by R packages, Cytoscape software, STRING, and GPEIA online tools. A total of 275 common dysregulated genes were identified in two independent datasets, including 54 common up-regulated and 221 common down-regulated genes in LUAD. Gene Ontology (GO) enrichment analysis showed that these dysregulated genes were significantly enriched in 258 biological processes (BPs), 27 cellular components (CCs), and 21 molecular functions (MFs). Furthermore, protein-protein interaction (PPI) network analysis showed that PECAM1, ENG, KLF4, CDH5, and VWF were key genes. Survival analysis indicated that the low expression of ENG was associated with poor overall survival (OS) of LUAD patients. The low expression of PECAM1 was associated with poor OS and recurrence-free survival of LUAD patients. The cox regression model developed based on age, tumor stage, ENG, PECAM1 could effectively predict 5-year survival of LUAD patients. This study revealed some key genes, BPs, CCs, and MFs involved in LUAD, which would provide new insights into understanding the pathogenesis of LUAD. In addition, ENG and PECAM1 might serve as promising prognostic markers in LUAD.

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Transcriptomic Analysis of the Differential Nephrotoxicity of Diverse Brominated Flame Retardants in Rat and Human Renal Cells

International Journal of Molecular Sciences ◽

10.3390/ijms221810044 ◽

2021 ◽

Vol 22 (18) ◽

pp. 10044

Author(s):

Lillie Marie A. Barnett ◽

Naomi E. Kramer ◽

Amanda N. Buerger ◽

Deirdre H. Love ◽

Joseph H. Bisesi ◽

...

Keyword(s):

Cell Lines ◽

Flame Retardants ◽

Molecular Mechanisms ◽

Annexin V ◽

Enrichment Analysis ◽

Brominated Flame Retardants ◽

Human Cells ◽

Gene Set Enrichment Analysis ◽

Coagulation Cascade ◽

Transcriptional Changes

Brominated flame retardants (BFRs) are environmentally persistent, are detected in humans, and some have been banned due to their potential toxicity. BFRs are developmental neurotoxicants and endocrine disruptors; however, few studies have explored their potential nephrotoxicity. We addressed this gap in the literature by determining the toxicity of three different BFRs (tetrabromobisphenol A (TBBPA), hexabromocyclododecane (HBCD), and tetrabromodiphenyl ether (BDE-47)) in rat (NRK 52E) and human (HK-2 and RPTEC) tubular epithelial cells. All compounds induced time- and concentration-dependent toxicity based on decreases in MTT staining and changes in cell and nuclear morphology. The toxicity of BFRs was chemical- and cell-dependent, and human cells were more susceptible to all three BFRs based on IC50s after 48 h exposure. BFRs also had chemical- and cell-dependent effects on apoptosis as measured by increases in annexin V and PI staining. The molecular mechanisms mediating this toxicity were investigated using RNA sequencing. Principal components analysis supported the hypothesis that BFRs induce different transcriptional changes in rat and human cells. Furthermore, BFRs only shared nine differentially expressed genes in rat cells and five in human cells. Gene set enrichment analysis demonstrated chemical- and cell-dependent effects; however, some commonalities were also observed. Namely, gene sets associated with extracellular matrix turnover, the coagulation cascade, and the SNS-related adrenal cortex response were enriched across all cell lines and BFR treatments. Taken together, these data support the hypothesis that BFRs induce differential toxicity in rat and human renal cell lines that is mediated by differential changes in gene expression.

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Integrative analyses of biomarkers and pathways for heart failure

10.21203/rs.3.rs-1031511/v1 ◽

2021 ◽

Author(s):

Shaowei Fan ◽

Yuanhui Hu

Keyword(s):

Heart Failure ◽

Extracellular Matrix ◽

Microarray Data ◽

Enrichment Analysis ◽

Signal Pathway ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Pathway Enrichment Analysis ◽

Ppi Networks ◽

Key Genes

Abstract Background: Heart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated. Methods: The microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF. Results: We identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine / threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules. Conclusions: the enrichment pathway of DEGs and go ontology may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

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Comparative pathway enrichment analysis in gastrointestinal cell lines Caco-2, HT-29, HEPG2, and colon fibroblasts using a custom expression panel for tight-junction and cytoskeletal regulatory genes

10.1101/747105 ◽

2019 ◽

Cited By ~ 1

Author(s):

JM Robinson

Keyword(s):

Gene Expression ◽

Comparative Analysis ◽

Tight Junction ◽

Cell Lines ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Intestinal Epithelial ◽

Expression Data ◽

Pathway Enrichment ◽

Ht 29

AbstractThis brief report details results from a comparative analysis of Nanostring expression data between cell lines HEPG2, Caco-2, HT-29, and colon fibroblasts. Raw and normalized data are available publicly in the NCBI GEO/Bioproject databases. Results identify cell-line specific variations in gene expression relevant to intestinal epithelial function.

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