Identification of potential core genes in Kawasaki disease using bioinformatics analysis

Objective The present study aimed to elucidate the underlying pathogenesis of Kawasaki disease (KD) and to identify potential biomarkers for KD. Methods Gene expression profiles for the GSE68004 dataset were downloaded from the Gene Expression Omnibus database. The pathways and functional annotations of differentially expressed genes (DEGs) in KD were examined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Protein–protein interactions of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING). Results Gene Ontology analysis revealed that DEGs in KD were significantly enriched in biological processes, including the inflammatory response, innate immune response, defense response to Gram-positive bacteria, and antibacterial humoral response. In addition, 10 hub genes with high connectivity were selected from among these DEGs ( ITGAM, MPO, MAPK14, SLC11A1, HIST2H2BE, ELANE, CAMP, MMP9, NTS, and HIST2H2AC). Conclusion The identification of several novel hub genes in KD enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with KD. ITGAM inhibitors in particular may be potential targets for KD therapy.

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SMC4, CCNB1 and CKS1B as potential targets and new critical biomarkers for the prognosis of human bladder cancer

10.21203/rs.3.rs-595148/v1 ◽

2021 ◽

Author(s):

Hongpeng Fang ◽

Zhansen Huang ◽

Xianzi Zeng ◽

Jiaming Wan ◽

Jieying Wu ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Transcriptional Level ◽

Hub Genes ◽

Kaplan Meier ◽

High Form

Abstract Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

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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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Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Comprehensive Bioinformatics Analysis Identifies POLR2I as a Key Gene in the Pathogenesis of Hypertensive Nephropathy

Frontiers in Genetics ◽

10.3389/fgene.2021.698570 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shilong You ◽

Jiaqi Xu ◽

Boquan Wu ◽

Shaojun Wu ◽

Ying Zhang ◽

...

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Chronic Hypertension ◽

Hub Genes ◽

Hypertensive Nephropathy ◽

Protein Levels

Hypertensive nephropathy (HN), mainly caused by chronic hypertension, is one of the major causes of end-stage renal disease. However, the pathogenesis of HN remains unclarified, and there is an urgent need for improved treatments. Gene expression profiles for HN and normal tissue were obtained from the Gene Expression Omnibus database. A total of 229 differentially co-expressed genes were identified by weighted gene co-expression network analysis and differential gene expression analysis. These genes were used to construct protein–protein interaction networks to search for hub genes. Following validation in an independent external dataset and in a clinical database, POLR2I, one of the hub genes, was identified as a key gene related to the pathogenesis of HN. The expression level of POLR2I is upregulated in HN, and the up-regulation of POLR2I is positively correlated with renal function in HN. Finally, we verified the protein levels of POLR2I in vivo to confirm the accuracy of our analysis. In conclusion, our study identified POLR2I as a key gene related to the pathogenesis of HN, providing new insights into the molecular mechanisms underlying HN.

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Microarray Data of Lacrimal Gland Implicates Dysregulated Protein Processing in Endoplasmic Reticulum in Graves’ Ophthalmopathy

Frontiers in Endocrinology ◽

10.3389/fendo.2020.571151 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wenling Tu ◽

Jia Yao ◽

Zhanjun Mei ◽

Xue Jiang ◽

Yuhong Shi

Keyword(s):

Gene Expression ◽

Endoplasmic Reticulum ◽

Gene Ontology ◽

Lacrimal Gland ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Protein Processing ◽

Potential Mechanism ◽

Hub Genes ◽

Graves Ophthalmopathy

Graves’ ophthalmopathy (GO) has become one of the most common orbital diseases. Although some evidences announced the potential mechanism of pathological changes in extraocular muscle and orbital adipose tissue, little is known about that in lacrimal enlargement of GO patients. Thus, gene expression profiles of lacrimal gland derived from GO patients and normal controls were investigated using the microarray datasets of GSE105149 and GSE58331. The raw data and annotation files of GSE105149 and GSE58331 were downloaded from Gene Expression Omnibus (GEO) database. Bioinformatics including differentially expressed genes (DEGs), Gene Ontology, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway, protein-protein interaction (PPI) network construction, hub gene identification, and gene set variation analysis (GSVA) were successively performed. A total of 173 overlapping DEGs in GSE105149 and GSE58331 were screened out, including 20 up-regulated and 153 down-regulated genes. Gene Ontology, KEGG and GSVA analyses of these DEGs showed that the most significant mechanism was closely associated with endoplasmic reticulum (ER). Moreover, we identified 40 module genes and 13 hub genes which were also enriched in the ER-associated terms and pathways. Among the hub genes, five genes including HSP90AA1, HSP90B1, DNAJC10, HSPA5, and CANX may be involved in the dysfunction of protein processing in ER. Taken together, our observations revealed a dysregulated gene network which is essential for protein processing in ER in GO patients. These findings provided a potential mechanism in the progression of lacrimal enlargement in GO patients, as a new insight into GO pathogenesis.

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Screening and identification of key biomarkers and related transcription factors in ovarian cancer by integrated bioinformatics analysis

10.21203/rs.3.rs-20327/v1 ◽

2020 ◽

Author(s):

Wenqiong Qin ◽

Qiang Yuan ◽

Yi Liu ◽

Ying Zeng ◽

Dandan Ke ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Molecular Mechanisms ◽

Malignant Tumors ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Transcriptional Factor ◽

Ppi Network ◽

Hub Genes

Abstract Background Ovarian tumors are the most malignant tumors of all gynecological tumors, and although multiple efforts have been made to elucidate the pathogenesis, the molecular mechanisms of ovarian cancer remain unclear. Methods In this study, we used bioinformatics to identify genes involved in the carcinogenesis and progression of ovarian cancer. Three microarray datasets (GSE14407, GSE29450, and GSE54388) were downloaded from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. For a more in-depth understanding of the DEGs, functional and pathway enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The associated transcriptional factor (TFs) regulation network of the DEGs was also constructed. Kaplan Meier-plotter, Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas (HPA) database and the Oncomine database were implemented to validated hub genes. Results A total of 514 DEGs were detected after the analysis of the three gene expression profiles, including 171 upregulated and 343 downregulated genes. Nine hub genes ( CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, TTK) were obtained from the PPI network. Survival analysis showed that high expression levels of seven hub genes ( CCNB1, BUB1, BUB1B, CCNA2, AURKA, CDK1, and RRM2) were associated with worse overall survival (OS). All of seven hub genes were discovered highly expressed in ovarian cancer samples compared to normal ovary samples in GEPIA. Immunostaining results from the HPA database suggested that the expressions of CCNB1, CCNA2, AURKA, and CDK1 proteins were increased in ovarian cancer tissues, and Oncomine analysis indicated that the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 have associated with patient clinicopathological information. From the gene-transcriptional factor network, key transcriptional factors, such as POLR2A, ZBTB11, KLF9, and ELF1, were identified with close interactions with these hub genes. Conclusion We identified six significant DEGs with poor prognosis in ovarian cancer, which could be of potential biomarkers for ovarian cancer patients.

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Modulation of the Immune Response by Deferasirox in Myelodysplastic Syndrome Patients

Pharmaceuticals ◽

10.3390/ph14010041 ◽

2021 ◽

Vol 14 (1) ◽

pp. 41

Author(s):

Hana Votavova ◽

Zuzana Urbanova ◽

David Kundrat ◽

Michaela Dostalova Merkerova ◽

Martin Vostry ◽

...

Keyword(s):

Gene Expression ◽

Immune Response ◽

Blood Cell ◽

Myelodysplastic Syndrome ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Adaptive Immune Response ◽

Gene Expression Profiles ◽

Iron Chelator ◽

Multiple Cancer

Deferasirox (DFX) is an oral iron chelator used to reduce iron overload (IO) caused by frequent blood cell transfusions in anemic myelodysplastic syndrome (MDS) patients. To study the molecular mechanisms by which DFX improves outcome in MDS, we analyzed the global gene expression in untreated MDS patients and those who were given DFX treatment. The gene expression profiles of bone marrow CD34+ cells were assessed by whole-genome microarrays. Initially, differentially expressed genes (DEGs) were determined between patients with normal ferritin levels and those with IO to address the effect of excessive iron on cellular pathways. These DEGs were annotated to Gene Ontology terms associated with cell cycle, apoptosis, adaptive immune response and protein folding and were enriched in cancer-related pathways. The deregulation of multiple cancer pathways in iron-overloaded patients suggests that IO is a cofactor favoring the progression of MDS. The DEGs between patients with IO and those treated with DFX were involved predominantly in biological processes related to the immune response and inflammation. These data indicate DFX modulates the immune response mainly via neutrophil-related genes. Suppression of negative regulators of blood cell differentiation essential for cell maturation and upregulation of heme metabolism observed in DFX-treated patients may contribute to the hematopoietic improvement.

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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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Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

Journal of Diabetes Research ◽

10.1155/2021/5546199 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Bojun Xu ◽

Lei Wang ◽

Huakui Zhan ◽

Liangbin Zhao ◽

Yuehan Wang ◽

...

Keyword(s):

Gene Expression ◽

Diabetic Nephropathy ◽

Protein Interactions ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Topological Analysis ◽

Gene Expression Omnibus ◽

Complement Cascade ◽

Hub Genes ◽

Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

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Transcriptome-wide gene expression plasticity in Stipa grandis in response to grazing intensity differences

10.22541/au.160655409.95797246/v1 ◽

2020 ◽

Author(s):

Zhenhua Dang ◽

Yuanyuan Jia ◽

Yunyun Tian ◽

Jiabin Li ◽

Yanan Zhang ◽

...

Keyword(s):

Gene Expression ◽

Metabolic Pathways ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Grazing Intensity ◽

Glycolate Oxidase ◽

Bisphosphate Carboxylase ◽

Grazing Intensities

Organisms have evolved effective and distinct adaptive strategies to survive. Stipa grandis is one of the widespread dominant species on the typical steppe of the Inner Mongolian Plateau, and is regarded as a suitable species for studying the effects of grazing in this region. Although phenotypic (morphological and physiological) variations in S. grandis in response to long-term grazing have been identified, the molecular mechanisms underlying adaptations and plastic responses remain largely unknown. Accordingly, we performed a transcriptomic analysis to investigate changes in gene expression of S. grandis under four different grazing intensities. A total of 2,357 differentially expressed genes (DEGs) were identified among the tested grazing intensities, suggesting long-term grazing resulted in gene expression plasticity that affected diverse biological processes and metabolic pathways in S. grandis. DEGs were identified that indicated modulation of Calvin–Benson cycle and photorespiration metabolic pathways. The key gene´expression profiles encoding various proteins (e.g., Ribulose-1,5-bisphosphate carboxylase/oxygenase, fructose-1,6-bisphosphate aldolase, glycolate oxidase etc.) involved in these pathways suggest that they may synergistically respond to grazing to increase the resilience and stress tolerance of S. grandis. Our findings provide scientific clues for improving grassland use and protection, and identify important questions to address in future transcriptome studies.

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