Use of bioinformatic analyses in identifying characteristic genes and mechanisms active in the progression of idiopathic thrombocytopenic purpura in individuals with different phenotypes

Objective To explore the mechanism underlying the progression of newly diagnosed idiopathic thrombocytopenic purpura (ITP) to its chronic or remission state using bioinformatic methods. Methods GSE56232 and GSE46922 gene expression profile datasets were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes were identified and characteristic genes were screened by weighted gene co-expression network analysis. These genes were used for function enrichment analysis and construction of a protein–protein interaction network. Finally, characteristic genes were verified to determine potential molecular mechanisms underlying ITP progression. Results We found that characteristic genes in the chronic ITP group were mainly involved in intracellular processes and ion binding, while characteristic genes in the remission ITP group were involved in intracellular processes and nuclear physiological activities. We identified a sub-network of characteristic genes, LMNA, JUN, PRKACG, SMC3, which may indicate the mechanism by which newly diagnosed ITP progresses to chronic. Although no meaningful signaling pathways were found, the expression of NR3C1, TPR, SMC4, PANBP2, CHD1, and U2SURP may affect ITP progression from newly diagnosed to remission. Conclusion Our findings improve the understanding of the pathogenesis and progression of ITP, and may provide new directions for the development of treatment strategies.

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Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

BioMed Research International ◽

10.1155/2022/1776082 ◽

2022 ◽

Vol 2022 ◽

pp. 1-17

Author(s):

Md. Rakibul Islam ◽

Lway Faisal Abdulrazak ◽

Mohammad Khursheed Alam ◽

Bikash Kumar Paul ◽

Kawsar Ahmed ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Clinical Information ◽

Gene Expression Omnibus ◽

Hub Genes ◽

New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log fold change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

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FRI0584 SCREENING AND IDENTIFICATION OF BIOMARKERS IN MYOSITIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.1831 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 896-897

Author(s):

W. Liu ◽

X. Zhang

Keyword(s):

Gene Expression ◽

Interstitial Lung Disease ◽

Lung Disease ◽

Molecular Mechanisms ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network ◽

Geo Database

Background:Myositis, including dermatomyositis and polymyositis, is autoimmune disorders that is characterized by muscle degeneration in the proximal extremities, with the complications of weakness of muscles, interstitial lung disease and vascular lesions, even leading to death in an acute progressive process[1,2]. However, the molecular mechanisms of myositis are rarely understood.Objectives:Identify the candidate genes in myositis.Methods:Microarray datasets GSE128470, GSE48280 and GSE39454 were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and function enrichment analyses were conducted. The protein-protein interaction network and the analyses of hub genes were performed with STRING and Cytoscape.Results:There were 98 DEGs, of which the function and pathways enrichment analyses showed defense response, immune response, response to virus, inflammatory response, response to wounding, cell adhesion, cell proliferation, cell death and macromolecule metabolic process. 20 hub genes were identified, of which 7 including IRF9 TRIM22 MX2 IFITM1 IFI6 IFI44 IFI44L had not been reported in the literature, related to the response to virus, immune response, transcription from RNA polymerase II promoter, cell apoptosis, cell death. The verification analysis about the 7 genes in GSE128314 showed significant differences in myositis.Conclusion:In conclusion, DEGs and hub genes identified in our study showed the potential molecular mechanisms in myositis, providing the helpful targets for diagnosis and clinical strategy of myositis.References:[1] Wu H, Geng D, Xu J. An approach to the development of interstitial lung disease in dermatomyositis: a study of 230 cases in China[J]. Journal of International Medical Research. 2013;41(2):493–501.[2] Fathi M, Dastmalchi M, Rasmussen E, Lundberg IE, Tornling G. Interstitial lung disease, a common manifestation of newly diagnosed polymyositis and dermatomyositis[J]. Annals of the Rheumatic Diseases. 2004;63(3):297–301.Figure 1.The protein-protein interaction network of 20 hub genesFigure 2.7 genes in GSE128314 showed significant differences in myositisAcknowledgments:The authors acknowledge the efforts of the Gene Expression Omnibus (GEO) database. The interpretation and reporting of these data are the sole responsibility of the authors.Disclosure of Interests:None declared

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Bioinformatics analysis of high-throughput data to validate potential novel biomarkers and small molecule drugs for glioblastoma multiforme

Journal of International Medical Research ◽

10.1177/0300060520924541 ◽

2020 ◽

Vol 48 (7) ◽

pp. 030006052092454

Author(s):

Fuwei Qi ◽

Qing Li ◽

Xiaojun Lu ◽

Zhihua Chen

Keyword(s):

Gene Expression ◽

Glioblastoma Multiforme ◽

Molecular Mechanisms ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Protein Protein Interaction ◽

Diagnosis And Prognosis ◽

Novel Biomarkers ◽

Patient Prognosis ◽

Microarray Datasets

Objective There have been no recent improvements in the glioblastoma multiforme (GBM) outcome, with median survival remaining 15 months. Consequently, the need to identify novel biomarkers for GBM diagnosis and prognosis, and to develop targeted therapies is high. This study aimed to establish biomarkers for GBM pathogenesis and prognosis. Methods In total, 220 overlapping differentially expressed genes (DEGs) were obtained by integrating four microarray datasets from the Gene Expression Omnibus database (GSE4290, GSE12657, GSE15824, and GSE68848). Then a 140-node protein–protein interaction network with 343 interactions was constructed. Results The immune response and cell adhesion molecules were the most significantly enriched functions and pathways, respectively, among DEGs. The designated hub genes ITGB5 and RGS4, which have a high degree of connectivity, were closely correlated with patient prognosis, and GEPIA database mining further confirmed their differential expression in GBM versus normal tissue. We also determined the 20 most appropriate small molecules that could potentially reverse GBM gene expression, Prestwick-1080 was the most promising and had the highest negative scores. Conclusions This study identified ITGB5 and RGS4 as potential biomarkers for GBM diagnosis and prognosis. Insights into molecular mechanisms governing GBM occurrence and progression will help identify alternative biomarkers for clinical practice.

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Bioinformatics Analysis of the Molecular Mechanism and Potential Treatment Target of Ankylosing Spondylitis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/7471291 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Fanyan Meng ◽

Ningna Du ◽

Daoming Xu ◽

Li Kuai ◽

Lanying Liu ◽

...

Keyword(s):

Gene Expression ◽

Ankylosing Spondylitis ◽

Molecular Mechanism ◽

Soft Tissues ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

R Package ◽

Gene Expression Omnibus ◽

Treatment Targets ◽

Protein Protein Interaction

Ankylosing spondylitis (AS) is an autoimmune disease that mainly affects the spinal joints, sacroiliac joints, and adjacent soft tissues. We conducted bioinformatics analysis to explore the molecular mechanism related to AS pathogenesis and uncover novel potential molecular targets for the treatment of AS. The profiles of GSE25101, containing gene expression data extracted from the blood of 16 AS patients and 16 matched controls, were acquired from the Gene Expression Omnibus (GEO) database. The background correction and standardization were carried out utilizing the transcript per million (TPM) method. After analysis of AS patients and the normal groups, we identified 199 differentially expressed genes (DEGs) with upregulation and 121 DEGs with downregulation by the limma R package. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) biological process enrichment analysis revealed that the DEGs with upregulation were mainly associated with spliceosome, ribosome, RNA-catabolic process, electron transport chain, etc. And the DEGs with downregulation primarily participated in T cell-associated pathways and processes. After analysis of the protein-protein interaction (PPI) network, our data revealed that the hub genes, comprising MRPL13, MRPL22, LSM3, COX7A2, COX7C, EP300, PTPRC, and CD4, could be the treatment targets in AS. Our data furnish new hints to uncover the features of AS and explore more promising treatment targets towards AS.

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Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

Bioscience Reports ◽

10.1042/bsr20201704 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Wei Han ◽

Biao Huang ◽

Xiao-Yu Zhao ◽

Guo-Liang Shen

Keyword(s):

Gene Expression ◽

Tumor Microenvironment ◽

Metastatic Melanoma ◽

Interaction Network ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

Subtype Identification ◽

Cancer Genome Atlas ◽

Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

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Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

International Journal of Molecular Sciences ◽

10.3390/ijms21031053 ◽

2020 ◽

Vol 21 (3) ◽

pp. 1053 ◽

Cited By ~ 4

Author(s):

Alejandro Cabrera-Andrade ◽

Andrés López-Cortés ◽

Gabriela Jaramillo-Koupermann ◽

César Paz-y-Miño ◽

Yunierkis Pérez-Castillo ◽

...

Keyword(s):

Molecular Mechanisms ◽

Bone Cancer ◽

Interaction Network ◽

Enrichment Analysis ◽

Protein Protein Interaction ◽

Pathogenic Genes ◽

Total List ◽

Consensus Strategy ◽

Primary Bone ◽

Molecular Etiology

Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.

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Uncovering potential lncRNAs and nearby mRNAs in systemic lupus erythematosus from the Gene Expression Omnibus dataset

Epigenomics ◽

10.2217/epi-2019-0145 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1795-1809 ◽

Cited By ~ 2

Author(s):

Haiyu Cao ◽

Dong Li ◽

Huixiu Lu ◽

Jing Sun ◽

Haibin Li

Keyword(s):

Gene Expression ◽

Systemic Lupus Erythematosus ◽

Lupus Erythematosus ◽

Noncoding Rnas ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Systemic Lupus ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

Aim: The aim of this study was to find potential differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in systemic lupus erythematosus. Materials & methods: Differentially expressed lncRNAs and mRNAs were obtained in the Gene Expression Omnibus dataset. Functional annotation of differentially expressed mRNAs was performed, followed by protein–protein interaction network analysis. Then, the interaction network of lncRNA-nearby targeted mRNA was built. Results: Several interaction pairs of lncRNA-nearby targeted mRNA including NRIR-RSAD2, RP11-153M7.5-TLR2, RP4-758J18.2-CCNL2, RP11-69E11.4-PABPC4 and RP11-496I9.1-IRF7/ HRAS/ PHRF1 were identified. Measles and MAPK were significantly enriched signaling pathways of differentially expressed mRNAs. Conclusion: Our study identified several differentially expressed lncRNAs and mRNAs. And their interactions may play a crucial role in the process of systemic lupus erythematosus.

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A Network Pharmacology Approach to Uncover the Mechanisms of Shen-Qi-Di-Huang Decoction against Diabetic Nephropathy

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2018/7043402 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 9

Author(s):

Sha Di ◽

Lin Han ◽

Qing Wang ◽

Xinkui Liu ◽

Yingying Yang ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Signaling Pathway ◽

Molecular Mechanisms ◽

Interaction Network ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Protein Protein Interaction ◽

Regulation Of Blood Pressure ◽

Protein Protein Interaction Network

Shen-Qi-Di-Huang decoction (SQDHD), a well-known herbal formula from China, has been widely used in the treatment of diabetic nephropathy (DN). However, the pharmacological mechanisms of SQDHD have not been entirely elucidated. At first, we conducted a comprehensive literature search to identify the active constituents of SQDHD, determined their corresponding targets, and obtained known DN targets from several databases. A protein-protein interaction network was then built to explore the complex relations between SQDHD targets and those known to treat DN. Following the topological feature screening of each node in the network, 400 major targets of SQDHD were obtained. The pathway enrichment analysis results acquired from DAVID showed that the significant bioprocesses and pathways include oxidative stress, response to glucose, regulation of blood pressure, regulation of cell proliferation, cytokine-mediated signaling pathway, and the apoptotic signaling pathway. More interestingly, five key targets of SQDHD, named AKT1, AR, CTNNB1, EGFR, and ESR1, were significant in the regulation of the above bioprocesses and pathways. This study partially verified and predicted the pharmacological and molecular mechanisms of SQDHD on DN from a holistic perspective. This has laid the foundation for further experimental research and has expanded the rational application of SQDHD in clinical practice.

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Regulatory T Cell-Related Gene Biomarkers in the Deterioration of Atherosclerosis

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.661709 ◽

2021 ◽

Vol 8 ◽

Author(s):

Meng Xia ◽

Qingmeng Wu ◽

Pengfei Chen ◽

Cheng Qian

Keyword(s):

Gene Expression ◽

Cardiovascular Events ◽

Molecular Mechanisms ◽

Inflammatory Responses ◽

Expression Patterns ◽

Interaction Network ◽

Gene Signature ◽

Smooth Muscle Contraction ◽

Gene Expression Omnibus ◽

Scoring Method

Background: Regulatory T cells (Tregs) have shown to be protective against the development of atherosclerosis, a major pathological cause for cardiovascular events. Here, we aim to explore the roles of Tregs-related genes in atherosclerosis deterioration.Methods and Results: We downloaded the gene expression profile of 29 atherosclerotic samples from the Gene Expression Omnibus database with an accession number of GSE28829. The abundance of Tregs estimated by the CIBERSORT algorithm was negatively correlated with the atherosclerotic stage. Using the limma test and correlation analysis, a total of 159 differentially expressed Tregs-related genes (DETregRGs) between early and advanced atherosclerotic plaques were documented. Functional annotation analysis using the DAVID tool indicated that the DETregRGs were mainly enriched in inflammatory responses, immune-related mechanisms, and pathways such as complement and coagulation cascades, platelet activation, leukocyte trans-endothelial migration, vascular smooth muscle contraction, and so on. A protein-protein interaction network of the DETregRGs was then constructed, and five hub genes (PTPRC, C3AR1, CD53, TLR2, and CCR1) were derived from the network with node degrees ≥20. The expression patterns of these hub DETregRGs were further validated in several independent datasets. Finally, a single sample scoring method was used to build a gene signature for the five DETregRGs, which could distinguish patients with myocardial infarction from those with stable coronary disease.Conclusion: The results of this study will improve our understanding about the Tregs-associated molecular mechanisms in the progression of atherosclerosis and facilitate the discovery of novel biomarkers for acute cardiovascular events.

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Integrated aqueous humor ceRNA and miRNA-TF-mRNA network analysis reveals potential molecular mechanisms governing primary open-angle glaucoma pathogenesis

10.1101/2020.07.17.208397 ◽

2020 ◽

Author(s):

Xiaoqin Wang ◽

Ming Chen ◽

Liuzhi Zeng ◽

Longqian Liu

Keyword(s):

Gene Expression ◽

Aqueous Humor ◽

Molecular Mechanisms ◽

Primary Open Angle Glaucoma ◽

Open Angle Glaucoma ◽

Gene Expression Omnibus ◽

Open Angle ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Non Coding Rnas

AbstractPrimary open-angle glaucoma (POAG) is the leading cause of blindness globally, which develops through complex and poorly understood biological mechanisms. Herein, we conducted an integrated bioinformatics analysis of extant aqueous humor (AH) gene expression datasets in order to identify key genes and regulatory mechanisms governing POAG progression. We downloaded AH gene expression datasets (GSE101727 and GSE105269) corresponding to healthy controls and POAG patients from the Gene Expression Omnibus. We then identified mRNAs, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) that were differentially expressed (DE) between control and POAG patients. DEmRNAs and DElncRNAs were then subjected to pathway enrichment analyses, after which a protein-protein interaction (PPI) network was generated. This network was then expanded to establish lncRNA-miRNA-mRNA and miRNA-transcription factor(TF)-mRNA networks. In total, the GSE101727 dataset was used to identify 2746 DElncRNAs and 2208 DEmRNAs, while the GSE105269 dataset was used to identify 45 DEmiRNAs. We ultimately constructed a competing endogenous RNA (ceRNA) network incorporating 37, 5, and 14 of these lncRNAs, miRNAs and mRNAs, respectively. The proteins encoded by these 14 hub mRNAs were found to be significantly enriched for activities that may be linked to POAG pathogenesis. In addition, we generated a miRNA-TF-mRNA regulatory network containing 2 miRNAs (miR-135a-5p and miR-139-5p), 5 TFs (TGIF2, TBX5, HNF1A, TCF3, and FOS) and 5 mRNAs (SHISA7, ST6GAC2, TXNIP, FOS, and DCBLD2). The SHISA7, ST6GAC2, TXNIP, FOS, and DCBLD2 genes that may be viable therapeutic targets for the prevention or treatment of POAG, and regulated by the TFs (TGIF2, HNF1A, TCF3, and FOS).

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