Integrated Bioinformatics Analysis Exhibits Pivotal Exercise-Induced Genes and Corresponding Pathways in Malignant Melanoma

Malignant melanoma represents a sort of neoplasm deriving from melanocytes or cells developing from melanocytes. The balance of energy and energy-associated body composition and body mass index could be altered by exercise, thereby directly affecting the microenvironment of neoplasm. However, few studies have examined the mechanism of genes induced by exercise and the pathways involved in melanoma. This study used three separate datasets to perform comprehensive bioinformatics analysis and then screened the probable genes and pathways in the process of exercise-promoted melanoma. In total, 1,627 differentially expressed genes (DEGs) induced by exercise were recognized. All selected genes were largely enriched in NF-kappa B, Chemokine signaling pathways, and the immune response after gene set enrichment analysis. The protein-protein interaction network was applied to excavate DEGs and identified the most relevant and pivotal genes. The top 6 hub genes (Itgb2, Wdfy4, Itgam, Cybb, Mmp2, and Parp14) were identified, and importantly, 5 hub genes (Itgb2, Wdfy4, Itgam, Cybb, and Parp14) were related to weak disease-free survival and overall survival (OS). In conclusion, our findings demonstrate the prognostic value of exercise-induced genes and uncovered the pathways of these genes in melanoma, implying that these genes might act as prognostic biomarkers for melanoma.

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Identification of Key Genes and Pathways in Osteosarcoma by Bioinformatics Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2022/7549894 ◽

2022 ◽

Vol 2022 ◽

pp. 1-16

Author(s):

Xiujin Chen ◽

Nan Zhang ◽

Yuanyuan Zheng ◽

Zhichao Tong ◽

Tuanmin Yang ◽

...

Keyword(s):

Gene Expression ◽

Cell Adhesion ◽

Adhesion Molecules ◽

Expression Profile ◽

Cell Adhesion Molecules ◽

Bioinformatics Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Hub Genes

Purpose. Osteosarcoma (OS) is the most primary bone malignant tumor in adolescents. Although the treatment of OS has made great progress, patients’ prognosis remains poor due to tumor invasion and metastasis. Materials and Methods. We downloaded the expression profile GSE12865 from the Gene Expression Omnibus database. We screened differential expressed genes (DEGs) by making use of the R limma software package. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, we performed the function and pathway enrichment analyses. Then, we constructed a Protein-Protein Interaction network and screened hub genes through the Search Tool for the Retrieval of Interacting Genes. Result. By analyzing the gene expression profile GSE12865, we obtained 703 OS-related DEGs, which contained 166 genes upregulated and 537 genes downregulated. The DEGs were primarily abundant in ribosome, cell adhesion molecules, ubiquitin-ubiquitin ligase activity, and p53 signaling pathway. The hub genes of OS were KDR, CDH5, CD34, CDC42, RBX1, POLR2C, PPP2CA, and RPS2 through PPI network analysis. Finally, GSEA analysis showed that cell adhesion molecules, chemokine signal pathway, transendothelial migration, and focal adhesion were associated with OS. Conclusion. In this study, through analyzing microarray technology and bioinformatics analysis, the hub genes and pathways about OS are identified, and the new molecular mechanism of OS is clarified.

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Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

Natural Product Communications ◽

10.1177/1934578x20982138 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1934578X2098213

Author(s):

Xiaodong Deng ◽

Yuhua Liang ◽

Jianmei Hu ◽

Yuhui Yang

Keyword(s):

Diabetes Mellitus ◽

Molecular Docking ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Network Pharmacology ◽

Hub Genes ◽

Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

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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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Combining Bioinformatics and Experiments to Identify and Verify Gene Prognostic Markers in Hepatocellular Carcinoma

Clinical Oncology and Research ◽

10.31487/j.cor.2021.04.02 ◽

2021 ◽

pp. 1-12

Author(s):

Li Luo ◽

Rong Wang ◽

Liaoyun Zhang ◽

Piao Zhang ◽

Dongmei Tian ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Proportional Hazards ◽

Malignant Tumors ◽

Expression Profiles ◽

Disease Free Survival ◽

Enrichment Analysis ◽

Cox Proportional Hazards ◽

Gene Set Enrichment Analysis ◽

Hub Genes

Background: Hepatocellular Carcinoma (HCC) is one of the highly malignant tumors threatening human health. The current research aimed to identify potential prognostic gene biomarkers for HCC. Materials and Methods: Microarray data of gene expression profiles of HCC from GEO were downloaded. After screening overlapping differentially expressed genes (DEGs) by R software. The STRING database and Cytoscape were used to identify hub genes. Cox proportional hazards regression was performed to screen the potential prognostic genes. Moreover, quantitative real-time PCR analyses were performed to detect the expression of ANLN in liver cancer cells and tissues. Finally, its possible pathways and functions were predicted using gene set enrichment analysis (GSEA). Result: A total of 566 DEGs were obtained from the overlapping analysis of three mRNA microarray dataset. Six key hub genes including RACGAP1, KIF20, DLGAP5, CDK1, BUB1B and ANLN, were associated with poor prognosis of patients with HCC. Higher expression of ANLN was associated with reduced overall survival and disease-free survival in patients with HCC. Multivariate analysis revealed that ANLN expression was an independent risk factor affecting overall survival. RT-PCR and Western blot analysis further demonstrated that ANLN expression was increased in HCC compared with patient-matched adjacent normal tissues. Notably, Gene enrichment analysis revealed that DEGs in ANLN-high patients were enriched in cell cycle, DNA duplication and p53 signaling pathway. Conclusion: The high expression of RACGAP1, KIF20, DLGAP5, CDK1, BUB1B and ANLN might be poor prognostic biomarkers in HCC patients, and may help to individualize the management of HCC.

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Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.580190 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jingni Wu ◽

Xiaomeng Xia ◽

Ye Hu ◽

Xiaoling Fang ◽

Sandra Orsulic

Keyword(s):

Network Analysis ◽

Topological Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Messenger Rnas ◽

Hub Genes ◽

Protein Protein Interaction ◽

Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

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Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-019-53471-0 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Chengzhang Li ◽

Jiucheng Xu

Keyword(s):

Hepatocellular Carcinoma ◽

Random Forest ◽

Interaction Network ◽

Enrichment Analysis ◽

Maximal Clique ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Fisher Score ◽

Protein Protein Interaction ◽

Kaplan Meier

AbstractThis study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

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Identification of CDC25C as a Potential Biomarker in Hepatocellular Carcinoma Using Bioinformatics Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820967474 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096747

Author(s):

Ruifeng Xun ◽

Hougen Lu ◽

Xianwang Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Bioinformatics Analysis ◽

Disease Free Survival ◽

Interaction Network ◽

Enrichment Analysis ◽

Normal Liver ◽

High Rate ◽

Specific Gene ◽

Ppi Networks ◽

Potential Biomarker

Hepatocellular carcinoma (HCC) is the most aggressive type of gastrointestinal tumor, with a high rate of mortality. However, identifying biomarkers for the treatment of HCC remains to be developed. We aimed to determine whether cell division cycle 25C (CDC25C) could be used as a novel diagnostic and therapeutic biomarker in HCC. Expression of CDC25C in HCC was analyzed by using GEPIA (Gene Expression Profiling Interactive Analysis) and UALCAN databases. GEPIA and CBioPortal databases were applied to analyze patients’survival and CDC25C mutations, respectively. PPI (Protein-Protein Interaction) network was further built by STRING (Search Tool for the Retrieval of Interacting Genes) and Metascape Web portals. To the best of our knowledge, the novel observations identified in the present study reveal that the expression of CDC25C in HCC was significantly enhanced when compare to that in normal liver tissues (P < 0.001). A higher CDC25C expression resulted in a remarkably shorter disease free survival as well as overall survival. Moreover, the expression of CDC25C in HCC was related to HCC patients’grade and race, but not gender. The expression levels of CDC25C elevated gradually from stage 1 to 3 but decreased in stage 4. The specific gene mutations V41A, L87 H, N222 K and X309-splice of CDC25C occurred in HCC samples and these unique mutations were not detected in any other tumor tissues. Finally, PPI networks and GO enrichment analysis suggested that CDC25C might be associated with cell cycle and p53 signaling pathway. Taken together, bioinformatics analysis revealed that CDC25C might be a potential diagnostic predictor for HCC.

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High Expression of UBB, RAC1, and ITGB1 Predicts Worse Prognosis among Nonsmoking Patients with Lung Adenocarcinoma through Bioinformatics Analysis

BioMed Research International ◽

10.1155/2020/2071593 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Huan Deng ◽

Yichao Huang ◽

Li Wang ◽

Ming Chen

Keyword(s):

Extracellular Matrix ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Hub Genes ◽

Go Enrichment

Purpose. The molecular mechanism underlying the tumorigenesis and progression of lung adenocarcinoma (LUAD) in nonsmoking patients remains unclear. This study was conducted to select crucial therapeutic and prognostic biomarkers for nonsmoking patients with LUAD. Methods. Microarray datasets from the Gene Expression Omnibus (GSE32863 and GSE75037) were analyzed for differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis of DEGs was performed, and protein-protein interaction network was then constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. Hub genes were then identified by the rank of degree. Overall survival (OS) analyses of hub genes were performed among nonsmoking patients with LUAD in Kaplan-Meier plotter. The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (THPA) databases were applied to verify hub genes. In addition, we performed Gene Set Enrichment Analysis (GSEA) of hub genes. Results. We identified 1283 DEGs, including 743 downregulated and 540 upregulated genes. GO enrichment analyses showed that DEGs were significantly enriched in collagen-containing extracellular matrix and extracellular matrix organization. Moreover, 19 hub genes were identified, and 12 hub genes were closely associated with OS. Although no obvious difference was detected in ITGB1, the downregulation of UBB and upregulation of RAC1 were observed in LUAD tissues of nonsmoking patients. Immunohistochemistry in THPA database confirmed that UBB and ITGB1 were downregulated, while RAC1 was upregulated in LUAD. GSEA suggested that ribosome, B cell receptor signaling pathway, and cell cycle were associated with UBB, RAC1, and ITGB1 expression, respectively. Conclusions. Our study provides insights into the underlying molecular mechanisms of the carcinogenesis and progression of LUAD in nonsmoking patients and demonstrated UBB, RAC1, and ITGB1 as therapeutic and prognostic indicators for nonsmoking LUAD. This is the first study to report the crucial role of UBB in nonsmoking LUAD.

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Bioinformatics Analysis and Identification of Potential Biomarkers in Gastric Cancer

10.21203/rs.3.rs-111288/v1 ◽

2020 ◽

Author(s):

Jingdi Yang ◽

Bo Peng ◽

Xianzheng Qin ◽

Tian Zhou

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Differentially Expressed Genes ◽

Bioinformatics Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer.Methods: Three gene expression profiles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter were used to verify the identified hub genes.Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were significantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed differentially expressed genes were enriched in the secretion of gastric acid and collecting duct acid, leukocyte transendothelial migration and ECM-receptor interaction. According to protein-protein interaction network, 10 hub genes were identified by Maximal Clique Centrality method.Conclusion: By using bioinformatics analysis, COL1A1, BGN, THY1, TFF2 and SST were identified as the potential biomarkers for early detection of gastric cancer.

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Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

10.21203/rs.3.rs-88832/v1 ◽

2020 ◽

Author(s):

Xiaolong Chen ◽

Zhixiong Xia ◽

Yafeng Wan ◽

Ping Huang

Keyword(s):

Hepatocellular Carcinoma ◽

Disease Free Survival ◽

Interaction Network ◽

Enrichment Analysis ◽

Hub Genes ◽

Free Survival ◽

Protein Protein Interaction ◽

Kaplan Meier ◽

Human Protein Atlas ◽

Kaplan Meier Plotter

Abstract BackgroundHepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven-genes and potential drugs in HCC. MethodsThree mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, GO terms analysis and KEGG enrichment analysis, construction of protein–protein interaction network. The expression levels of hub genes were validated based on TCGA, GEPIA and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of the hub genes were further conducted by Kaplan-Meier plotter and the GEPIA. DGIdb database was performed to search the candidate drugs for HCC. ResultsFinally, 197 DEGs were identified. The PPI network was constructed using STRING software. Then ten genes were selected and considered as the hub genes. The ten genes were all closely related to the survival of HCC patients. DGIdb database predicted 39 small molecules as the possible drugs for treating HCC. ConclusionsOur study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in future.

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