The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis

Purpose. Heart failure (HF) is a clinical syndrome caused by ventricular insufficiency. In order to further explore the biomarkers related to HF, we apply the high-throughput database. Materials and Methods. The GSE21610 was applied for the differentially expressed gene (DEG) analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The Gene Set Enrichment Analysis (GSEA) was used for gene expression profile GSE21610. The Protein-Protein Interaction (PPI) network and modules were also constructed for research. These hub gene function pathways were estimated in HF progression. Result. We have identified 434 DEGs in total, including 304 downregulated DEGs and 130 upregulated DEGs. GO and KEGG illustrated that DEGs in HF were significantly enriched in G protein-coupled receptor binding, peroxisome, and cAMP signaling pathway. GSEA results showed gene set GSE21610 was gathered in lipid digestion, defense response to fungus, and intestinal lipid absorption. Finally, through analyzing the PPI network, we screened hub genes CDH1, TFRC, CCL2, BUB1B, and CD19 by the Cytoscape software. Conclusion. This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to HF. These analysis results provide us with new ideas for finding biomarkers and treatment methods for HF.

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Altered Genes and Biological Functions in Response to Severe Burns

BioMed Research International ◽

10.1155/2021/8836243 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Xinheng Liu ◽

Yongxian Rong ◽

Donglin Huang ◽

Zhijie Liang ◽

Xiaolin Yi ◽

...

Keyword(s):

Gene Expression ◽

Roc Curve ◽

Molecular Mechanisms ◽

Metabolic Response ◽

Heat Exposure ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Gene Set ◽

Severe Burns

Severe burns are acute wounds caused by local heat exposure, resulting in life-threatening systemic effects and poor survival. However, the specific molecular mechanisms remain unclear. First, we downloaded gene expression data related to severe burns from the GEO database (GSE19743, GSE37069, and GSE77791). Then, a gene expression analysis was performed to identify differentially expressed genes (DEGs) and construct protein-protein interaction (PPI) network. The molecular mechanism was identified by enrichment analysis and Gene Set Enrichment Analysis. In addition, STEM software was used to screen for genes persistently expressed during response to severe burns, and receiver operating characteristic (ROC) curve was used to identify key DEGs. A total of 2631 upregulated and 3451 downregulated DEGs were identified. PPI network analysis clustered these DEGs into 13 modules. Importantly, module genes mostly related with immune responses and metabolism. In addition, we identified genes persistently altered during the response to severe burns corresponding to survival and death status. Among the genes with high area under the ROC curve in the PPI network gene, CCL5 and LCK were identified as key DEGs, which may affect the prognosis of burn patients. Gene set variation analysis showed that the immune response was inhibited and several types of immune cells were decreased, while the metabolic response was enhanced. The results showed that persistent gene expression changes occur in response to severe burns, which may underlie chronic alterations in physiological pathways. Identifying the key altered genes may reveal potential therapeutic targets for mitigating the effects of severe burns.

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Identification and Functional Enrichment Analysis of Differentially Expressed Genes in Osteoarthritis

10.21203/rs.3.rs-31843/v1 ◽

2020 ◽

Author(s):

Chen Xu ◽

Ling-bing Meng ◽

Yu Xiao ◽

Yong Qiu ◽

Ying-jue Du ◽

...

Keyword(s):

Hydrogen Peroxide ◽

Differentially Expressed Genes ◽

Cellular Response ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Gene Set Enrichment ◽

Gene Set

Abstract Background Osteoarthritis (OA) is a chronic, progressive, inflammatory, degenerative disease, which has become an osteoarthropathy that seriously affects physical health and quality of life of elderly people. However, the etiology and pathogenesis of OA remains unclear. Therefore, the study purposed to utilize bioinformatics technology to perform identification and functional enrichment analysis of differentially expressed genes in osteoarthritis. Method The main methods of this study consist of access to microarray data (GSE82107 and GSE55235), identification of differently expressed genes (DEGs) by GEO2R between OA and normal synovium samples, enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) by Gene Set Enrichment Analysis (GSEA), construction and analysis of protein-protein interaction (PPI) network, significant module and hub genes. Result A total of 300 DEGs were identified, consisting of 64 up-regulated genes and 11 down-regulated genes in OA samples compared to normal synovium tissues. Gene set enrichment analysis of DEGs provided a comprehensive overview of some major pathophysiological mechanisms in OA: cellular response to hydrogen peroxide, P53 signaling pathway and so on. The study also built the PPI network, and a total of 10 key genes were identified: CYR61, PENK, GOLM1, DUSP1, ATF3, STC2, FOSB, PRSS23, TF, and TNC. Conclusion DEGs exists between OA patients and normal cartilage tissue, which may be involved in the related mechanism of OA development, especially cellular response to hydrogen peroxide and CYR61.

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Identification of IL-6 as a potential mediator of the myocardial fibrosis that occurs in response to surgery with cardiopulmonary bypass in children with Tetralogy of Fallot

Cardiology in the Young ◽

10.1017/s1047951121001803 ◽

2021 ◽

pp. 1-7

Author(s):

Hongtao Liu ◽

Yun Zhang ◽

Zhenhai Wu ◽

Liangqing Zhang

Keyword(s):

Heart Failure ◽

Network Analysis ◽

Myocardial Fibrosis ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Gene Correlation ◽

Cell Expression

Abstract Background: Tetralogy of Fallot is a common CHD. Studies have shown a close link between heart failure and myocardial fibrosis. Interleukin-6 has been suggested to be a post-independent factor of heart failure. This study aimed to explore the relationship between IL-6 and myocardial fibrosis during cardiopulmonary bypass. Material and Methods: We downloaded the expression profile dataset GSE132176 from Gene Expression Omnibus. After normalising the raw data, Gene Set Enrichment Analysis and differential gene expression analysis were performed using R. Further, a weighted gene correlation network analysis and a protein–protein interaction network analysis were used to identify HUB genes. Finally, we downloaded single-cell expression data for HUB genes using PanglaoDB. Results: There were 119 differentially expressed genes in right atrium tissues comparing the post-CPB group with the pre-CPB group. IL-6 was found to be significantly up-regulated in the post-CPB group. Six genes (JUN, FOS, ATF3, EGR1, IL-6, and PTGS2) were identified as HUB genes by a weighted gene correlation network analysis and a protein–protein interaction network analysis. Gene Set Enrichment Analysis showed that IL-6 affects the myocardium during CPB mainly through the JAK/STAT signalling pathway. Finally, we used PanglaoDB data to analyse the single-cell expression of the HUB genes. Conclusion: Our findings suggest that high expression of IL-6 and the activation of the JAK/STAT signalling pathway during CPB maybe the potential mechanism of myocardial fibrosis. We speculate that the high expression of IL-6 might be an important factor leading to heart failure after ToF surgery. We expect that these findings will provide a basis for the development of targeted drugs.

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Prognostic Biomarker SYK and its Correlation with Immune Infiltrates in Glioma

10.21203/rs.3.rs-839283/v1 ◽

2021 ◽

Author(s):

Pei Liu ◽

Jiamin Guo ◽

Xiaoxiao Xu ◽

Haixin Sun ◽

Zheng Gong

Keyword(s):

Development Process ◽

Prognostic Biomarker ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Ppi Network ◽

Glioma Patient ◽

Gene Set Enrichment ◽

Protein Protein Interaction ◽

Cancer Genome Atlas

Abstract Background: Tumor microenvironment (TME) has great effects on the development process of glioma, and we sought to identify effective prognostic factors by analyzing data from patients with glioma. In this paper, CIBERSORT and ESTIMATE calculations were employed to figure up the ratio of tumor-infiltrating immune cells (TICs) and the quantity of immune and stromal components in 698 glioma dates from The Cancer Genome Atlas (TCGA) database. In addition, differentially expressed genes (DEGs) were studied by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single genes associated with prognosis were identified by PPI network and COX combined analysis. Results: Immune and stromal scores of TME were significantly correlated with glioma patient survival. Through protein–protein interaction (PPI) network and regression analysis of COX, we finally determined that SYK was the best prognostic factor for patients with glioma. Gene Set Enrichment Analysis (GSEA) and CIBERSORT analysis were also employed, with the former showed that high-expression SYK group’s genes are principally enriched immune-related activities and the latter revealed that SYK expression was positively associated with T cells CD4 memory resting and Monocytes. All the above experimental analyses provided the theoretical basis for the biological prediction of SYK.Conclusions: SYK contributes to immune predictors in glioma patients by facilitating the shift of TME from immune dominance to metabolic activity, which provides promising insights into the treatment of glioma.

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Mining the TCGA Database for Microenvironment‐Related Genes with Prognostic Value in Gastric Cancer

10.21203/rs.3.rs-175261/v1 ◽

2021 ◽

Author(s):

Ting Liu ◽

Zheng Gong ◽

Hong Zhang ◽

Yi Wan ◽

Ming-Han Ren ◽

...

Keyword(s):

Gastric Cancer ◽

Distant Metastasis ◽

Clinical Stage ◽

Enrichment Analysis ◽

Clinicopathological Characteristics ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Gene Set Enrichment ◽

Gene Set ◽

Protein Protein Interaction

Abstract BackgroundGastric cancer (GC) is the fifth most common cancer worldwide. Previous studies have suggested that the tumor microenvironment (TME) plays an important role in the development and prognosis of GC. In this study, we aimed to identify genes in tumor-infiltrating immune cells (TICs) that influence the progression and prognosis of GC. MethodsWe used the ESTIMATE algorithm to calculate the scores of the stromal and immune components of the TME in 407 GC samples collected from The Cancer Genome Atlas (TCGA) database.The differentially expressed genes (DEGs) were intersected by a protein-protein interaction (PPI) network and analyzed by univariate Cox regression.Further analysis showed the correlation between MCEMP1 and the clinicopathological characteristics of GC patients (clinical stage, distant metastasis) and survival.Then we used Gene set enrichment analysis (GSEA) and CIBERSORT analysis to examine the relationship between MCEMP1 and the TME.ResultsThe analysis revealed that the expression of MCEMP1 was positively correlated with the clinicopathological characteristics of GC patients (clinical stage, distant metastasis) and negatively correlated with survival. Gene set enrichment analysis (GSEA) indicated that gene sets in the MCEMP1 high expression group were concentrated mainly in immune-related pathways. CIBERSORT analysis of the proportion of TICs revealed that neutrophils and M2 macrophages were positively correlated with MCEMP1 expression, suggesting that MCEMP1 is responsible for preservation of the immune-dominant status of the TME. ConclusionHigh MCEMP1 expression might be a biomarker of a poor prognosis in GC patients and provide a clue regarding the different statuses of the TME, offering additional insight into therapy for GC.

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Expression profiling of LncRNA and miRNA in SDF-1-induced articular chondrocyte degeneration

10.21203/rs.3.rs-36488/v1 ◽

2020 ◽

Author(s):

Guoliang Wang ◽

Jiali Zheng ◽

Lu He ◽

YaoYu Xiang ◽

Yanlin Li

Keyword(s):

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Articular Chondrocyte ◽

Gene Regulation Network ◽

Protein Protein Interaction ◽

Regulation Network ◽

The Difference ◽

Core Genes

Abstract Background With the in-depth exploration of the gene regulation network associated with the pathogenesis of osteoarthritis (OA), lncRNA has been found to play a major role in regulating the development of osteoarthritis. In this study, the expressions of miRNAs and lncRNAs in chondrocytes (2 days) of SDF-1-induced articular chondrocyte degeneration model and in normal chondrocytes were detected and the difference between them was visualized. The bioinformatics analysis was performed in parallel to elucidate the interactions between miRNAs and protein molecules. Results It was found that 186 lncRNA changes had significant statistical differences, of which 88 lncRNA were up-regulated and 98 lncRNA were down-regulated. A total of 684 miRNA had significant statistical differences in their expression changes. Gene Ontology and Kyoto Encyclopedia of Genes were performed for the gene set enrichment analysis to determine the key biological processes and pathways. The protein-protein interaction (PPI) network indicated that CXCL10, ISG15, MYC, MX1, OASL, FIICT1, RSAD2, MX2, IFI44, and LBST2 are the ten core genes. The PPI network identified the most important functional modules to elucidate the differential expression of miRNA. Conclusions These data may provide new insights into the molecular mechanisms of osteoarthritis chondrocyte degeneration, and the identification of lncRNA and miRNA can provide potential therapeutic targets for the diagnosis and differential diagnosis of osteoarthritis.

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Identification of Key Claudin Genes Associated With Survival Prognosis and Diagnosis in the Colon Cancer Through Integrated Bioinformatic Analysis‏.

10.21203/rs.3.rs-1007190/v1 ◽

2021 ◽

Author(s):

Rana Alghamdi ◽

Maryam Al-Zahrani

Keyword(s):

Colon Cancer ◽

Differential Expression Analysis ◽

Colon Adenocarcinoma ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Survival Prognosis ◽

Diagnostic Efficacy ◽

Gene Set ◽

Protein Protein Interaction

Abstract Background: Claudin’s gene are associated with various aberrant physiological and cellular signaling. However, the association of claudins with survival prognosis, signaling pathways, and diagnostic efficacy in colon cancer remain lacking. Methods: We used various bioinformatics methods, including differential expression analysis, gene set enrichment analysis (GSEA), protein-protein interaction (PPI), survival analysis, single sample gene set enrichment analysis (ssGSEA), mutation analysis, and identifying receiver operating characteristic (ROC) curve of claudins in the TCGA colon adenocarcinoma (COAD). Results: We found that: CLDN2, CLDN1, CLDN14, CLDN16, CLDN18, CLDN9, CLDN12, and CLDN6 are elevated in COAD. In contrast, the CLDN8, CLDN23, CLDN5, CLDN11, CLDN7, and CLDN15 are downregulated in COAD. Various claudin’s genes are mutated and associated with diagnostic efficacy in the COAD. Conclusions: Claudin’s genes are associated with prognosis, immune regulation, signaling pathway regulations, and diagnosis. These findings may provide new molecular insight into the treatment of colon cancer.

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Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/4530180 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Yanzhe Wang ◽

Wenjuan Cai ◽

Liya Gu ◽

Xuefeng Ji ◽

Qiusheng Shen

Keyword(s):

Atrial Fibrillation ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Cell Counting ◽

Ppi Network ◽

Hub Genes ◽

Bioinformatics Analyses ◽

Protein Protein Interaction ◽

Endothelial Growth Factor

Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result. 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion. Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment.

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Identification of Key Genes and Pathways in Gefitinib-Resistant Lung Adenocarcinoma using Bioinformatics Analysis

Evolutionary Bioinformatics ◽

10.1177/11769343211023767 ◽

2021 ◽

Vol 17 ◽

pp. 117693432110237

Author(s):

Kailin Mao ◽

Fang Lin ◽

Yingai Zhang ◽

Hailong Zhou

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Signaling Pathways ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Underlying Mechanisms ◽

Gefitinib Resistance

Gefitinib resistance is a serious threat in the treatment of patients with non-small cell lung cancer (NSCLC). Elucidating the underlying mechanisms and developing effective therapies to overcome gefitinib resistance is urgently needed. The differentially expressed genes (DEGs) were screened from the gene expression profile GSE122005 between gefitinib-sensitive and resistant samples. GO and KEGG analyses were performed with DAVID. The protein-protein interaction (PPI) network was established to visualize DEGs and screen hub genes. The functional roles of CCL20 in lung adenocarcinoma (LUAD) were examined using gene set enrichment analysis (GSEA). Functional analysis revealed that the DEGs were mainly concentrated in inflammatory, cell chemotaxis, and PI3K signal regulation. Ten hub genes were identified based on the PPI network. The survival analysis of the hub genes showed that CCL20 had a significant effect on the prognosis of LUAD patients. GSEA analysis showed that CCL20 high expression group was mainly enriched in cytokine-related signaling pathways. In conclusion, our analysis suggests that changes in inflammation and cytokine-related signaling pathways are closely related to gefitinib resistance in patients with lung cancer. The CCL20 gene may promote the formation of gefitinib resistance, which may serve as a new biomarker for predicting gefitinib resistance in patients with lung cancer.

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Screening and identification of MSX1 for predicting progestin resistance in endometrial cancer

10.21203/rs.3.rs-25541/v1 ◽

2020 ◽

Author(s):

linlin yang ◽

Yunxia Cui ◽

Ting Huang ◽

Xiao Sun ◽

Yudong Wang

Keyword(s):

Overall Survival ◽

Endometrial Cancer ◽

Methylation Status ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Genomic Alteration ◽

Hub Genes ◽

Gene Set ◽

Protein Protein Interaction ◽

Screening And Identification

Abstract Background Progestin resistance is a critical obstacle for endometrial conservative therapy. Therefore, the studies to acquire a more comprehensive understanding of the mechanisms are very important. However, the pivotal roles of essential molecules are still unexplored.Methods We downloaded GSE121367 from the GEO database. The “limma” R language package was applied to identify differentially expressed genes (DEGs). We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) analysis. Protein–protein interaction was constructed by STRING and visualized in Cytoscape. The tumor immune microenvironment was explored by TISIDB database. Methylation validation and overall survival analysis was conducted by TCGA database. In addition, the upstream modulators of hub genes were predicted by miRTarBase and Network Analyst database.Results A total of 3282 DEGs were identified and they were mostly enriched in cell adhesion pathway. We screened out ten hub genes including CDH1, JAG1, PTGES, EPCAM, CNTNAP2, TBX1, MSX1, KRT19, OAS1 and DAB2 among different groups, whose genomic alteration rates were low based on the current endometrial carcinoma sample sets. Has-miR-335-5p, has-miR-124-3p, MAZ and TFDP1 were the most prominent upstream regulators. The methylation status of CDH1, JAG1, EPCAM and MSX1 were decreased, corresponding to their high protein expression, which also predicted better overall survival. The homeobox protein of MSX1 showed significantly tissue specificity and better prognostic value.Conclusions Our study identified the gene of MSX1 promised to be the specific indicator. This would shed new light on the underlying biological mechanism to overcome progestin resistance of endometrial cancer.

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