Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

Background:In osteosarcoma, the lung is the most common metastatic organ. Intensive work has been made to illuminate the pathogeny, but the specific metastatic mechanism remains unclear. Thus, we conducted the study to seek to find the key genes and critical functional pathways associated with progression and treatment in lung metastasis originating from osteosarcoma.Methods:Two independent datasets (GSE14359 and GSE85537) were screened out from the Gene Expression Omnibus (GEO) database and the overlapping differentially expressed genes (DEGs) were identified using GEO2R online platform. Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted using DAVID. Meanwhile, the protein-protein interaction (PPI) network constructed by STRING was visualized using Cytoscape. Afterwards, the key module and hub genes were extracted from the PPI network using the MCODE and cytoHubba plugin. Moreover, the raw data obtained from GSE73166 and GSE21257 were applied to verify the expression differences and conduct the survival analyses of hub genes, respectively. Finally, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape.Results:A total of 364 DEGs were identified, comprising 96 downregulated genes and 268 upregulated genes, which were mainly involved in cancer-associated pathways, adherens junction, ECM-receptor interaction, focal adhesion, MAPK signaling pathway. Subsequently, 10 hub genes were obtained and survival analysis demonstrated SKP2 and ASPM were closely related to poor prognosis of patients with osteosarcoma. Finally, hsa-miR-340-5p, has-miR-495-3p, and hsa-miR-96-5p were found to be most closely associated with these hub genes according to the interaction network of miRNAs and hub genes.Conclusion:The key genes and functional pathways identified in the study may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of lung metastasis originating from osteosarcoma, and provide potential diagnostic and therapeutic targets.     

Identification of potential biomarkers of peripheral blood mononuclear cell in hepatocellular carcinoma using bioinformatic analysis: A protocol for systematic review and meta-analysis

Background:Hepatocellular carcinoma (HCC) is the cause of an overwhelming number of cancer-related deaths across the world. Developing precise and noninvasive biomarkers is critical for diagnosing HCC. Our research was designed to explore potentially useful biomarkers of host peripheral blood mononuclear cell (PBMC) in HCC by integrating comprehensive bioinformatic analysis.Methods:Gene expression data of PBMC in both healthy individuals and patients with HCC were extracted from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to annotate the function of DEGs. Protein-protein interaction analysis was performed to screen the hub genes from DEGs. cBioportal database analysis was performed to assess the prognostic significance of hub genes. The Cancer Cell Line Encyclopedia (CCLE) and The Human Protein Atlas (HPA) database analyses were performed to confirm the expression levels of the hub genes in HCC cells and tissue.Results:A total of 95 DEGs were screened. Results of the GO analysis revealed that DEGs were primarily involved in platelet degranulation, cytoplasm, and protein binding. Results of the KEGG analysis indicated that DEGs were primarily enriched in focal adhesion. Five genes, namely, myosin light chain kinase (MYLK), interleukin 1 beta (IL1B), phospholipase D1 (PLD1), cortactin (CTTN), and moesin (MSN), were identified as hub genes. A search in the CCLE and HPA database showed that the expression levels of these hub genes were remarkably increased in the HCC samples. Survival analysis revealed that the overexpression of MYLK, IL1B, and PLD1 may have a significant effect on HCC survival. The aberrant high expression levels of MYLK, IL1B, and PLD1 strongly indicated worse prognosis in patients with HCC.Conclusions:The identified hub genes may be closely linked with HCC tumorigenicity and may act as potentially useful biomarkers for the prognostic prediction of HCC in PBMC samples.     

基于GEO数据分析参与MERS-CoV感染致病的信号传导通路和关键分子

目的 基于基因表达谱芯片数据,筛选和分析中东呼吸综合症病毒(Middle East respiratory syndrome coronavirus, MERS-CoV)感染人类呼吸道上皮细胞后的差异基因,确定参与致病的信号传导通路和关键分子。方法 在美国国立生物技术信息中心的Gene Expression Omnibus(GEO)数据库搜选MERS-CoV病毒感染支气管上皮细胞表达谱数据,使用在线分析工具GEOR分析MERS-CoV感染支气管上皮细胞正常组和感染组各种基因的表达情况,以具有统计学意义的在表达量上升或下降2倍以上的差异基因为研究对象,使用基因功能分析注释工具DAVID对差异基因进行基因本体分析和信号传导通路分析,使用STRING构建基因间相互作用网络,利用cytoscape筛选相互作用网络的关键基因。结果 对比感染与未感染组的基因表达,筛选到了差异基因1 553个,其中上调基因850个,下调基因703个。基因功能富集分析结果提示这些差异基因涉及免疫反应,炎症反应,凋亡过程,支气管纤毛形成和运动等多条信号传导通路。结论 研究MERS-CoV感染肺部支气管细胞基因表达谱,发现在细胞中起重要作用的多条信号通路的异常,筛选到多个关键基因,这些信号通路可能在病毒致病过程中起到重要作用。本研究将为揭示MERS-CoV 致病的分子机制提供帮助,为确定新的治疗靶标和策略提供数据。     

Tumor-associated macrophages based signaling pathway analysis and hub genes identification in glioma

Tumor-associated macrophages (TAMs) play a crucial role in the immune response to many malignancies, but the signaling pathways by which the glioma microenvironment cross-talk with TAMs are poorly understood. The aim of this study was to uncover the potential signaling pathways of the regulation of TAMs and identify candidate targets for therapeutic intervention of glioma through bioinformatics analysis.Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets were used to download RNA-Seq data and microarray data of human glioma specimen. Differentially expressed genes (DEGs) between CD68-high samples and CD68-low samples were sorted. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs was conducted. Protein-protein interaction (PPI) network were formed to identify the hub genes.The prognostic value of TAMs in glioma patients was confirmed. A total of 477 specific DEGs were sorted. The signaling pathway was identified in pathway enrichment and the DEGs showed prominent representations of immune response networks in glioma. The hub genes including C3, IL6, ITGB2, PTAFR, TIMP1 and VAMP8 were identified form the PPI network and they were all correlated positively with the expression of CD68 and showed the excellent prognostic value in glioma patients.TAMs can be used as a good prognostic indicator in glioma patients. By analyzing comprehensive bioinformatics data, we uncovered the underlying signaling pathway of the DEGs between glioma patients with high and low expression level of CD68. Furthermore, the 6 hub genes identified were closely associated with TAMs in glioma microenvironment and need further investigation.     

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

Background:Hepatocellular 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.Methods:Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein–protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan–Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.Results:A total of 197 DEGs were identified. The protein–protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.Conclusion:Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.     

Key factors and potential drug combinations of nonalcoholic steatohepatitis: Bioinformatic analysis and experimental validation-based study

BackgroundNonalcoholic fatty liver disease and its advanced stage, nonalcoholic steatohepatitis (NASH), are the major cause of hepatocellular carcinoma (HCC) and other end-stage liver disease. However, the potential mechanism and therapeutic strategies have not been clarified. This study aimed to identify potential roles of miRNA/mRNA axis in the pathogenesis and drug combinations in the treatment of NASH.MethodsMicroarray GSE59045 and GSE48452 were downloaded from the Gene Expression Omnibus and analyzed using R. Then we obtained differentially expressed genes (DE-genes). DAVID database was used for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis. Protein-protein interaction (PPI) networks were used for the identification of hub genes. We found upstream regulators of hub genes using miRTarBase. The expression and correlation of key miRNA and its targets were detected by qPCR. Drug Pair Seeker was employed to predict drug combinations against NASH. The expression of miRNA and hub genes in HCC was identified in the Cancer Genome Atlas database and Human Protein Atlas database.ResultsNinety-four DE-genes were accessed. GO and KEGG analysis showed that these predicted genes were linked to lipid metabolism. Eleven genes were identified as hub genes in PPI networks, and they were highly expressed in cells with vigorous lipid metabolism. hsa-miR-335-5p was the upstream regulator of 9 genes in the 11 hub genes, and it was identified as a key miRNA. The hub genes were highly expressed in NASH models, while hsa-miR-335-5p was lowly expressed. The correlation of miRNA-mRNA was established by qPCR. Functional verification indicated that hsa-miR-335-5p had inhibitory effect on the development of NASH. Finally, drug combinations were predicted and the expression of miRNA and hub genes in HCC was identified.ConclusionsIn the study, potential miRNA-mRNA pathways related to NASH were identified. Targeting these pathways may be novel strategies against NASH.     

Potential Role and Clinical Value of PPP2CA in Hepatocellular Carcinoma

Background and AimsProtein phosphatase 2A (PP2A) is associated with many cancers. This study aimed to clarify whether PPP2CA, which encodes the alpha isoform of the catalytic subunit of PP2A, plays a role in hepatocellular carcinoma (HCC) and to identify the potential underlying molecular pathways.MethodsBased on bioinformatics, public databases and our in-house RNA-Seq database, we analyzed the clinical value and molecular mechanism of PPP2CA in HCC.ResultsData were analyzed from 2,545 patients with HCC and 1,993 controls without HCC indexed in The Cancer Genome Atlas database, the Gene Expression Omnibus database and our in-house RNA-Seq database. PPP2CA expression was significantly higher in HCC tissue than in non-cancerous tissues (standardized mean difference: 0.69, 95% confidence interval [CI]: 0.50–0.89). PPP2CA expression was able to differentiate HCC from non-HCC, with an area under the summary receiver operator characteristic curve of 0.79 (95% CI: 0.75–0.83). Immunohistochemistry of tissue sections confirmed that PPP2CA protein was up-regulated in HCC tissues. High PPP2CA expression in HCC patients was associated with shorter overall, progression-free and disease-free survival. Potential molecular pathways through which PPP2CA may be involved in HCC were determined using miRWalk 2.0 as well as analysis of Gene Ontology categories, Kyoto Encyclopedia of Genes and Genomes pathways, and protein-protein interaction networks.ConclusionsPPP2CA is up-regulated in HCC and higher expression correlates with worse prognosis. PPP2CA shows potential as a diagnostic marker for HCC. Future studies should examine whether PPP2CA contributes to HCC through the candidate microRNAs, pathways and hub genes identified in this study.     

The potential mechanism of Chebulae Fructus in the treatment of hepatocellular carcinoma on the basis of network pharmacology

Introduction and objectivesHepatocellular carcinoma (HCC) ranks third on the list of the leading cause for cancer death globally. The treatment of HCC patients is unsatisfactory. However, the traditional Chinese medicine Chebulae Fructus has potential efficacy in the treatment of HCC.Materials and methodsWe mined the active ingredients of Chebulae Fructus and its main targets from the Traditional Chinese Medicine Systems Pharmacology database. HCC-related datasets were downloaded from The Cancer Genome Atlas database and differentially expressed genes (DEGs) in HCC were obtained by differential expression analysis. Top10 small molecule compounds capable of reversing HCC pathology were screened by the Connectivity Map database based on DEGs. Ellipticine, an extract of Chebulae Fructus, had the potential to reverse HCC pathology. Protein-Protein Interaction (PPI) networks of DEGs in HCC were constructed using STRING. Eighteen potential targets of Chebulae Fructus for the treatment of HCC were obtained by taking intersection of DEGs in HCC with targets corresponding to the active constituents of Chebulae Fructus. In addition, MTT assay was also employed to examine the effect of ellipticine on HCC cell viability.ResultsIt has been shown that ellipticine and ellagic acid have antitumor activity. Random Walk with Restart analysis of PPI networks was performed using potential targets as seeds, and the genes with the top 50 affinity coefficients were selected to construct a drug-active constituent-gene interaction network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of key genes involved in the treatment of HCC with Chebulae Fructus demonstrated that these genes were mainly enriched in signaling pathways related to tumor metabolism such as cAMP signaling pathway and Ras signaling pathway. Finally, it was verified by MTT assay that proliferation of HCC cells could be remarkably hindered.ConclusionsWe excavated ellipticine, a key active constituent of Chebulae Fructus, by network pharmacology, and elucidated the signaling pathways involved in Chebulae Fructus, providing a theoretical basis for the use of Chebulae Fructus for HCC clinical application.     

Analysis of susceptibility genes and myocardial infarction risk correlation of ischemic cardiomyopathy based on bioinformatics

BackgroundThe present study was to investigated differential expressed genes (GEGs) in ischemic cardiomyopathy (ICM), and to construct regulation networks, and to study the correlation between myocardial infarction risk.MethodsData sets were downloaded from the Gene Expression Omnibus (GEO) to screen out messenger RNA (mRNA) and long non-coding RNA (lncRNA) differentially expressed between ICM samples and normal samples. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Differentially expressed mRNA and lncRNA were analyzed, and bioinformatics methods were used to predict and analyze microRNA (miRNA), and a competing endogenous RNA (Hub gene) regulatory network was constructed. Using the Limma software package in R language, DEGs of ICM were screened with non-heart failure donors as the control group under the conditions that the differential expression ratio was not less than 2 times, and the corrected P value was <0.05. The ClusterProfiler software package was used for GO enrichment analysis and KEGG enrichment analysis. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 11.0 online database was used to screen key genes for protein-protein interaction (PPI) network analysis.ResultsThe GO function analysis and KEGG pathway analysis showed that the DEGs were significantly enriched in metabolic pathways, oxidative phosphorylation, extracellular matrix receptor interactions, and other pathways, and were closely related to fibrosis, collagen catabolic process, and inflammatory response function, and a Hub gene regulatory network related to ICM lncRNA was constructed. Bioinformatics methods were used to effectively analyze the DEGs of ICM, and the Hub gene regulatory network of ICM was successfully constructed.ConclusionsThis study identified a certain risk correlation between ICM susceptibility genes and myocardial infarction.     

乙型肝炎病毒相关肝细胞癌核心差异表达基因生物信息学分析

目的 探索与乙型肝炎病毒（HBV）相关肝细胞癌（HCC）发生发展相关的核心基因,为进一步揭示HBV相关HCC发病机制提供参考。方法 从高通量基因表达数据库（GEO）中下载GSE55092、GSE121248两个数据集,采用R语言筛选HCC组织和癌旁组织间差异表达基因（DEGs）,并绘制可视化火山图。对DEGs基因进行基因本体论（GO）和京都基因和基因组百科全书（KEGG）富集分析,构建蛋白质相互作用（PPI）网络,并用Cytoscape 3.9.0开源平台中分子复合物检测（MCODE）和cytoHubba插件筛选核心DEGs。利用UALCAN和Kaplan Meier-plotter数据库中临床样本数据对筛选出的核心DEGs进行差异表达和生存分析验证。结果 从GSE55092数据集和GSE121248数据集中分别筛选出1 148个和686个DEGs,其中下调表达基因分别为703个和477个、上调表达基因分别为445个和209个;两个数据集共筛选出557个共同表达的DEGs,其中下调表达基因384个、上调表达基因173个。GO富集分析显示,DEGs主要参与细胞分裂、细胞增殖、氧化还原、免...     

Dermatomyositis: immunological landscape,biomarkers, and potential candidate drugs

Introduction

Dermatomyositis (DM) is a rare inflammatory disease characterized by the invasion of the skin and muscles. Environmental, genetic, and immunological factors contribute to disease pathology. To date, no bioinformatics studies have been conducted on the potential pathogenic genes and immune cell infiltration in DM. Therefore, we aimed to identify differentially expressed genes (DEGs) and immune cells, as well as potential pathogenic genes and immune characteristics, which may be useful for the diagnosis and treatment of DM.

Method

GSE1551, GSE5370, GSE39454, and GSE48280 from Gene Expression Omnibus were included in our study. Limma, ClusterProfiler, and Kyoto Encyclopedia of Genes and Genomes were used to identify DEGs, Gene Ontology (GO), and perform pathway analyses, respectively. Cytoscape was used to construct the protein-protein interaction (PPI) network. Small-molecule drugs were identified using a connectivity map (CMap), and the TIMER database was used to identify infiltrating cells.

Results

DEG analysis identified 12 downregulated and 163 upregulated genes. GO analysis showed that DEGs were enriched in immune-related pathways. Ten hub genes were identified from the PPI network. Additionally, CMap analysis showed that caffeic acid, sulfaphenazole, molindone, tiabendazole, and bacitracin were potential small-molecule drugs with therapeutic significance. We identified eight immune cells with differential infiltration in patients with DM and controls. Finally, we constructed a powerful diagnostic model based on memory B cells, M1, and M2 macrophages.

Conclusions

This study explored the potential molecular mechanism and immunological landscape of DM and may guide future research and treatment of DM.

Key Points

? We explored the molecular mechanism and immunological landscape of dermatomyositis.

? GO analysis showed that DEGs were enriched in immune-related pathways.

? We predicted small-molecular drugs with potential therapeutic significance based on bioanalytical techniques.

? We identified six immune cells with differential infiltration in patients with DM and controls.

     

Identification of Key Genes for Hepatitis Delta Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis

Background: It has been proposed that hepatitis delta virus (HDV) induces hepatic carcinogenesis by distinct molecular events compared with hepatocellular carcinoma (HCC) that is commonly induced by other hepatitis viruses. This study aimed to explore the underlying mechanism by identifying the key genes for HDV-HCC using bioinformatics analysis.Methods: The {"type":"entrez-geo","attrs":{"text":"GSE107170","term_id":"107170"}}GSE107170 dataset was downloaded and the differentially expressed genes (DEGs) were obtained by the online tool GEO2R. Gene otology (GO) functional analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R packages. The protein-protein interaction (PPI) network was constructed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Hub genes were selected by Cytoscape software according to degree algorithm. The hub genes were further validated in terms of expression and survival analysis based on public databases.Results: A total of 93 commonly upregulated genes and 36 commonly downregulated genes were found. The top 5 upregulated hub genes were TFRC, ACTR2, ARPC1A, ARPC3, and ARPC2. The top 5 downregulated hub genes were CTNNB1, CCND1, CDKN1B, CDK4, and CDKN1A. In the validation analysis, the expressions of ARPC1A, ARPC3, and CDK4 were promoted in general liver cancer samples. Higher expressions of ARPC2 and CDK4 and lower expressions of CDKN1A, CCND1, and CDKN1B were associated with worse prognosis in general HCC patients.Conclusion: The present study identifies a series of key genes that may be involved in the carcinogenesis of HDV-HCC and used as prognostic factors.     

Molecular Mechanisms for Alcoholic Hepatitis Based on Analysis of Gene Expression Profile

Background:

Alcoholic hepatitis (AH) is an acute manifestation of alcoholic liver disease with high mortality rates.

Objectives:

Our aim was to study the molecular mechanisms of AH.

Materials and Methods:

The differentially expressed genes (DEGs) in liver between AH and control cases were identified by analyzing the {"type":"entrez-geo","attrs":{"text":"GSE28619","term_id":"28619"}}GSE28619 microarray data using t-test. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) enrichment analyses were performed using DAVID online tool. The protein-protein interaction (PPI) network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and the subnetwork was identified by BioNet. Both PPI network and subnetwork were visualized using the Cytoscape software.

Results:

Total 908 DEGs (551 up- and 357 down-regulated DEGs) were obtained. The up-regulated DEGs were significantly enriched in 15 pathways and 112 GO biological processes. The down-regulated DEGs were significantly enriched in 22 pathways and 84 GO biological processes. The PPI network with 608 nodes and 2878 interactions was constructed and the subnetwork with 53 nodes and 131 interactions was also identified. The hub DEGs (TSPO, PPIB, NME1 and NME2) were extracted in this subnetwork.

Conclusions:

TSPO might contribute to the liver damage and AH progression induced by mitochondrial dysfunction through oxidative stress of liver. TSPO interacted with PPIB might play important roles in liver damage in AH. The interaction between NME1 and NME2 might contribute to the transformation from AH to hepatocellular carcinoma.     

Bioinformatics study on different gene expression profiles of fibroblasts and vascular endothelial cells in keloids

Keloid is a benign fibroproliferative skin tumor. The respective functions of fibroblasts and vascular endothelial cells in keloid have not been fully studied. The purpose of this study is to identify the respective roles and key genes of fibroblasts and vascular endothelial cells in keloids, which can be used as new targets for diagnosis or treatment.The microarray datasets of keloid fibroblasts and vascular endothelial cells were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis. The search tool for retrieval of interacting genes and Cytoscape were used to construct protein-protein interaction (PPI) networks and analyze gene modules. The hub genes were screened out, and the relevant interaction networks and biological process analysis were carried out.In fibroblasts, the DEGs were significantly enriched in collagen fibril organization, extracellular matrix organization and ECM-receptor interaction. The PPI network was constructed, and the most significant module was selected, which is mainly enriched in ECM-receptor interaction. In vascular endothelial cells, the DEGs were significantly enriched in cytokine activity, growth factor activity and transforming growth factor-β (TGF-β) signaling pathway. Module analysis was mainly enriched in TGF-β signaling pathway. Hub genes were screened out separately.In summary, the DEGs and hub genes discovered in this study may help us understand the molecular mechanisms of keloid, and provide potential targets for diagnosis and treatment.