Screening for Key Pathways Associated with the Development of Osteoporosis by Bioinformatics Analysis

Objectives.We aimed to find the key pathways associated with the development of osteoporosis.Methods.We downloaded expression profile data of GSE35959 and analyzed the differentially expressed genes (DEGs) in 3 comparison groups (old_op versus middle, old_op versus old, and old_op versus senescent). KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses were carried out. Besides, Venn diagram analysis and gene functional interaction (FI) network analysis were performed.Results.Totally 520 DEGs, 966 DEGs, and 709 DEGs were obtained in old_op versus middle, old_op versus old, and old_op versus senescent groups, respectively. Lysosome pathway was the significantly enriched pathways enriched by intersection genes. The pathways enriched by subnetwork modules suggested that mitotic metaphase and anaphase and signaling by Rho GTPases in module 1 had more proteins from module.Conclusions.Lysosome pathway, mitotic metaphase and anaphase, and signaling by Rho GTPases may be involved in the development of osteoporosis. Furthermore, Rho GTPases may regulate the balance of bone resorption and bone formation via controlling osteoclast and osteoblast. These 3 pathways may be regarded as the treatment targets for osteoporosis.

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Metabolite Profiling of Different Sweet sorghum cultivars Seeds

10.21203/rs.2.10683/v1 ◽

2019 ◽

Author(s):

Yaxing Zhou ◽

Zhenguo Wang ◽

Yan Li ◽

Zhigang Li ◽

Hui Liu ◽

...

Keyword(s):

Sweet Sorghum ◽

Principal Component ◽

Venn Diagram ◽

Seed Color ◽

Benzoic Acid Derivatives ◽

Orthogonal Signal Correction ◽

Signal Correction ◽

Comparison Groups ◽

Different Color ◽

Diagram Analysis

Abstract Background Sorghum bicolor is one of the most important cereal crops in the world and is widely grown in arid or semi-arid areas. Results This study focused on the metabolic Diversity of three Sweet sorghum cultivars with white, red, and purple seeds to elucidate the factors responsible for the differences in Seed color. We found 651 metabolites were divided into 24 categories including Lipids_Fatty acids, Lipids_Glycerolipids, Flavonoid, Benzoic acid derivatives, Anthocyanins, Nucleotide and its derivates, etc. Through clustering analysis, principal component analysis (PCA), and orthogonal signal correction and partial least squares-discriminant analysis (OPLS-DA), different samples were clearly separated. It shows that contained metabolites of three groups are quite different. There are 217 significantly different metabolites between Z27 and HC4 (106 down-regulated, 111up-regulated), 240 between Z6 and HC4 (58 down-regulated, 182 up-regulated), 199 between Z6 and Z27 (54 down-regulated, 135 up-regulated). Venn diagram analysis indicated that 45 the differential metabolites were common to all three comparison groups. Conclusions This study provides new insights into the differences of metabolites between different color seeds and provides theoretical basis for the sufficient utilization of Sweet sorghum cultivars.

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Venn diagram analysis overestimates the extent of circadian rhythm reprogramming

FEBS Journal ◽

10.1111/febs.16095 ◽

2021 ◽

Author(s):

Anne Pelikan ◽

Hanspeter Herzel ◽

Achim Kramer ◽

Bharath Ananthasubramaniam

Keyword(s):

Circadian Rhythm ◽

Venn Diagram ◽

Diagram Analysis

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Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

BioMed Research International ◽

10.1155/2020/4505720 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Weishuang Xue ◽

Jinwei Li ◽

Kailei Fu ◽

Weiyu Teng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Peripheral Blood ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

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Identification of the key pathways and genes related to polycystic ovary syndrome using bioinformatics analysis

General Physiology and Biophysics ◽

10.4149/gpb_2018049 ◽

2019 ◽

Vol 38 (03) ◽

pp. 205-2014 ◽

Cited By ~ 1

Author(s):

X. Bi ◽

Z. Zhai ◽

S. Wang

Keyword(s):

Polycystic Ovary Syndrome ◽

Bioinformatics Analysis ◽

Polycystic Ovary ◽

Ovary Syndrome ◽

Key Pathways

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Gene Expression Profile of Active HE4 Stimulation in Epithelial Ovarian Cancer Cells: Microarray Study and Comprehensive Bioinformatics Analysis

10.21203/rs.3.rs-46014/v1 ◽

2020 ◽

Author(s):

Liancheng Zhu ◽

Mingzi Tan ◽

Haoya Xu ◽

Bei Lin

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Clinical Significance ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Coexpression Analysis ◽

Pathway Enrichment

Abstract Background.Human Epididymis Protein 4 (HE4) is a novel serum biomarker for diagnosis of epithelial ovarian cancer (EOC) with high specificity and sensitivity compared with CA125, and the increasing researches have been carried out on its roles in promoting carcinogenesis and chemoresistance in EOC in recent years, however, its underlying molecular mechanisms remain poorly understood. The aim of this study was to elucidate the molecular mechanisms of HE4 stimulation and to identify the key genes and pathways mediating carcinogenesis in EOC using microarray and bioinformatics analysis.Methods. We established a stable HE4-silence ES-2 ovarian cancer cell line labeled as “S”, and its active HE4 protein stimulated cells labeled as “S4”. Human whole genome microarray analysis was used to identify deferentially expressed genes (DEGs) from triplicate samples of S4 and S cells. “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis (GSEA) were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal for WFDC2 coexpression analysis. GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction (qRT-PCR) was applied for validation. The protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape. Results.In total, 713 DEGs were found (164 up regulated and 549 down regulated) and further analyzed by GO, pathway enrichment and PPI analyses. We found that MAPK pathway accounted for a significant portion of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2 coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) that were also dramatically changed in S4 cells and validated by dataset GSE51088. Kaplan–Meier survival statistics revealed clinical significance for all of the 10 target genes. Finally, PPI was constructed, sixteen hub genes and eight molecular complex detections (MCODEs) were identified, the seeds of five most significant MCODEs were subjected to GO and KEGG enrichment analysis and their clinical significance was evaluated.Conclusions.By applying microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of active HE4 stimulation in EOC cells. We offered several possible mechanisms and identified therapeutic and prognostic targets of HE4 in EOC.

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Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

10.21203/rs.3.rs-100248/v1 ◽

2020 ◽

Author(s):

Liancheng Zhu ◽

Mingzi Tan ◽

Haoya Xu ◽

Bei Lin

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Coexpression Analysis ◽

Pathway Enrichment

Abstract Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.

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Identification of key pathways and genes in nonalcoholic fatty liver disease using bioinformatics analysis

Archives of Medical Science ◽

10.5114/aoms.2020.93343 ◽

2020 ◽

Vol 16 (2) ◽

pp. 374-385 ◽

Cited By ~ 1

Author(s):

Jingqi Liu ◽

Bogeng Lin ◽

Zhiqing Chen ◽

Manxiang Deng ◽

Ye Wang ◽

...

Keyword(s):

Liver Disease ◽

Fatty Liver ◽

Nonalcoholic Fatty Liver Disease ◽

Fatty Liver Disease ◽

Bioinformatics Analysis ◽

Nonalcoholic Fatty Liver ◽

Key Pathways

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Bioinformatic Analysis of Neuroimmune Mechanism of Neuropathic Pain

BioMed Research International ◽

10.1155/2020/4516349 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Hao Yu ◽

Yang Liu ◽

Chao Li ◽

Jianhao Wang ◽

Bo Yu ◽

...

Keyword(s):

Neuropathic Pain ◽

Inflammatory Responses ◽

Interaction Network ◽

Gene Interaction ◽

Bioinformatic Analysis ◽

Venn Diagram ◽

Hub Genes ◽

Gene Interaction Network ◽

Pathway Enrichment ◽

Protein Protein Interaction

Background. Neuropathic pain (NP) is a devastating complication following nerve injury, and it can be alleviated by regulating neuroimmune direction. We aimed to explore the neuroimmune mechanism and identify some new diagnostic or therapeutic targets for NP treatment via bioinformatic analysis. Methods. The microarray GSE18803 was downloaded and analyzed using R. The Venn diagram was drawn to find neuroimmune-related differentially expressed genes (DEGs) in neuropathic pain. Gene Ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network were used to analyze DEGs, respectively. Besides, the identified hub genes were submitted to the DGIdb database to find relevant therapeutic drugs. Results. A total of 91 neuroimmune-related DEGs were identified. The results of GO and pathway enrichment analyses were closely related to immune and inflammatory responses. PPI analysis showed two important modules and 8 hub genes: PTPRC, CD68, CTSS, RAC2, LAPTM5, FCGR3A, CD53, and HCK. The drug-hub gene interaction network was constructed by Cytoscape, and it included 24 candidate drugs and 3 hub genes. Conclusion. The present study helps us better understand the neuroimmune mechanism of neuropathic pain and provides some novel insights on NP treatment, such as modulation of microglia polarization and targeting bone resorption. Besides, CD68, CTSS, LAPTM5, FCGR3A, and CD53 may be used as early diagnostic biomarkers and the gene HCK can be a therapeutic target.

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