Bioinformatics Analysis of ZBTB16 as a Prognostic Marker for Ewing’s Sarcoma

Objective. The purpose of this study is to identify novel biomarkers for the prognosis of Ewing’s sarcoma based on bioinformatics analysis. Methods. The GSE63157 and GSE17679 datasets contain patient and healthy control microarray data that were downloaded from the Gene Expression Omnibus (GEO) database and analyzed through R language software to obtain differentially expressed genes (DEGs). Firstly, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment, protein-protein interaction (PPI) networks, and Cytoscape Molecular Complex Detection (MCODE) plug-in were then used to compute the highest scores of the module. After survival analysis, the hub genes were lastly obtained from the two module genes. Results. A total of 1181 DEGs were identified from the two GSEs. Through MCODE and survival analysis, we obtain 53 DEGs from the module and 29 overall survival- (OS-) related genes. ZBTB16 was the only downregulated gene after Venn diagrams. Survival analysis indicates that there was a significant correlation between the high expression of ZBTB16 and the OS of Ewing’s sarcoma (ES), and the low expression group had an unfavorable OS when compared to the high expression group. Conclusions. High expression of ZBTB16 may serve as a predictor biomarker of poor prognosis in ES patients.

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Identification of Common Oncogenic Genes and Pathways Both in Osteosarcoma and Ewing’s Sarcoma Using Bioinformatics Analysis

10.21203/rs.3.rs-358166/v1 ◽

2021 ◽

Author(s):

Jingwei Zhang ◽

Wenjun Liu ◽

Liang Ding ◽

Dongdong Cheng ◽

Haijun Xiao

Keyword(s):

Ewing’S Sarcoma ◽

Ewing's Sarcoma ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Receptor Interaction ◽

Hub Genes ◽

Ppi Networks

Abstract Objective: This study aimed to explore common oncogenic genes and pathways both in osteosarcoma and Ewing’s sarcoma. Methods: Microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were respectively identified using the limma package, followed by intersection of common DEGs. Then, protein-protein interaction (PPI) networks were constructed and hub genes were identified. Furthermore, functional enrichment analysis was analyzed. The expression of common oncogenic genes was validated in 38 osteosarcoma and 17 Ewing’s sarcoma tissues by RT-qPCR and western blot. Results: 201 genes were differentially expressed. There were 121 nodes and 232 edges in the PPI network. 12 genes were considered as hub genes. Functional enrichment analysis results showed that hub genes FN1, COL1A1 and COL1A2 were all involved in extracellular matrix, protease binding and ECM-receptor interaction, which could be involved in the development of osteosarcoma and Ewing’s sarcoma. Among common oncogenic genes, FN1, COL1A1 and COL1A2 were lowly expressed both in osteosarcoma and Ewing’ s sarcoma tissues at mRNA and protein levels. Conclusion: Our findings revealed that common oncogenic genes such as FN1, COL1A1 and COL1A2 and pathways were both in osteosarcoma and Ewing’ s sarcoma.

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Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

BioMed Research International ◽

10.1155/2020/4505720 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Weishuang Xue ◽

Jinwei Li ◽

Kailei Fu ◽

Weiyu Teng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Peripheral Blood ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

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miR-432 is a novel biomarker for PCNSL and is associated with cell adhesion: An integrated bioinformatics analysis

10.21203/rs.3.rs-16455/v1 ◽

2020 ◽

Author(s):

Qiang Ma

Keyword(s):

Cell Adhesion ◽

Operating Characteristic ◽

Target Genes ◽

Bioinformatics Analysis ◽

Central Nervous System Lymphoma ◽

Gene Expression Omnibus ◽

Serum Samples ◽

Protein Protein Interaction ◽

Candidate Mirnas ◽

Ppi Networks

Abstract Background: Primary central nervous system lymphoma (PCNSL), a rare form of the non-Hodgkin's lymphoma (NHL), usually has a poor prognosis, and molecular pathogenesis of PCNSL has not been fully elucidated. Here, potential miRNA biomarkers were investigated in patients with PCNSL using an integrated bioinformatics analysis. Methods: Expression profile arrays (GSE122011, GSE139031, and GSE25297) were obtained from the Gene Expression Omnibus (GEO). Free-scale miRNA co-expression networks were constructed with 27 PCNSL patients from GSE122011 by the weighted gene co-expression network analysis (WGCNA) in order to identify candidate biomarkers. Subsequently, miRNA-miRNA networks were visualized with the Cytoscape. Expression of candidate miRNAs was assessed in serum samples from GSE139031, including 42 PCNSL patients and 77 non-cancer individuals, and the sensitivity and the specificity were assessed by the receiver operating characteristic (ROC) curve. From GSE25297, differentially expressed genes (DEGs) from the PCNSL tissues (n = 7) and the normal lymph nodes (n = 7) were compared, target genes of candidate miRNAs were downloaded from TargetScan database, and target genes that were also down-regulated in GSE25297 were used to construct the protein-protein interaction (PPI) networks and for the gene ontology (GO) analysis. Results: miRNAs were clustered into two groups with 8 modules in 27 patients with PCNSL. One group consisted of the yellow and the turquoise modules, and the second group consisted of the other six modules. In the miRNA-miRNA network, the highest nodes were observed between miR-432 and miR-330-3p, which were from the yellow and the turquoise modules, and only miR-432 was closely associated with both the yellow (0.977, P = 2.88E -18 ) and the turquoise modules (0.525, P = 0.005). Additionally, patients with PCNSL had higher serum miR-432 expression compared with that in the non-cancer controls in GSE139031, and miR-432 has a higher accuracy for discriminating between PCNSL and non-cancer samples (AUC: 0.77; 95% CI: 0.6923 to 0.8550). For target genes of miR-432, RASGRF , DGKG , SMIM22 , SPOCD1 , NRCAM , CNTN2 , PTPRD , POTED , IGSF3 , SLC24A2 , CTNND2 , AIF1L , TMEM229A , GLDN , and MOBP were down-regulated in the PCNSL tissues. Among them, CTNND2 , GLDN , NRCAM , and PTPRD were associated with cell adhesion. Conclusion: Up-regulated miR-432 expression is a novel biomarker for patients with PCNSL and may be associated with cell adhesion.

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Analysis of differential gene expression between right-sided and left-sided colon cancer by bioinformatics analysis

10.21203/rs.3.rs-23894/v1 ◽

2020 ◽

Author(s):

Qiangwei Chi ◽

Shizuan Chen ◽

Shaotang Li

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Bioinformatics Analysis ◽

Disease Free Survival ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks

Abstract Background Colon cancer is a common tumor of the digestive tract worldwide. Recent researches have revealed that colon cancer exhibits distinct differences in clinical and biological characteristics depending on the location of the tumor. However, the underlying genetic and molecular mechanism of the differences between right-sided colon cancer (RCC) and left-sided colon cancer (LCC) are not fully understood. This study aimed to identify molecular potential biomarkers and therapeutic targets for precise treatment of right-sided and left-sided colon cancer using bioinformatics analysis. Methods The gene microarray profile, named GSE44076, from the Gene Expression Omnibus (GEO) public database was downloaded and processed to then select differentially expressed genes (DEGs) on the base of two sample groups of RCC and LCC. Also, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein–protein interaction (PPI) network construction, module analysis, validation of hub genes, and survival analysis. Results Finally, we obtained 2259 DEGs between RCC and LCC, 1300 of which were upregulated in RCC and 945 of which were upregulated in LCC. The results of GO and KEGG analysis of the DEGs indicated that the biological functions of DEGs in RCC and LCC were significantly different. CTLA4, IL10, IL2RB, IFNG, NCAM1, EGFR, MYC, SRC, CUL3, and NCBP2 were identified from the PPI networks as the hub genes of RCC and LCC. Among the hub genes, the log-rank tests for overall survival (OS) and disease free survival (DFS) were applied. Moreover, all hub genes, except CUL3, had differential expression levels of miRNA between tumor group and normal group. Conclusion These hub genes and pathways identified based on bioinformatics analysis might conduce to explain the differences between RCC and LCC, and most of the hub genes were specific to the malignant tissues. Notably, these hub genes, especially the genes associated with immunotherapy such as CTLA4, might be potential specific targets or prognostic markers for precise treatment of colon cancer.

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975. Why Sex Make a Difference in HIV Clinical Course? Bioinformatics Analysis of Differential Expressed Gene in Females and Males with HIV Disease

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1161 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S516-S517

Author(s):

Kulachanya Suwanwongse ◽

Nehad Shabarek

Keyword(s):

Gene Expression ◽

Bioinformatics Analysis ◽

Underlying Mechanism ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Gender Inequalities ◽

Hiv Disease ◽

Go Analysis ◽

Hiv Progression ◽

Pathway Analyses

Abstract Background Human immunodeficiency virus (HIV) disease progression are different among genders, in which women usually progress to acquired immunodeficiency syndrome (AIDS) faster than men. The mechanisms resulting in the gender biases of HIV progression are unclear. We conducted a bioinformatics analysis of differentially expressed genes (DEGs) in women and men with HIV disease to understand the sex-based differences in HIV pathogenesis. Methods We obtained microarray data from the Gene Expression Omnibus (GEO) database using our pre-defined search strategy and analyzed data using the GEO2R platform. The t-test was done to compare DEGs between females and males with HIV diseases. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was implemented to systematically extract biological features and processes of retrieving DEGs via gene ontology (GO) analysis. A Systemic search was performed to evaluate each DEG function and its possible association with HIV. Results One gene expression profiling data were retrieved: GSE 140713, composed of 40 males and 10 females with HIV1 infected samples. A GEO2R analysis yielded 19 DEGs (Table 1). The GO analysis result was demonstrated in Tables 2 and 3. Following a systemic search, we found two DEGs, which have previous studies reported an association with HIV: DDX3X (20 studies) and PDS5 (1 study). We proposed DDX3X (t 5.3, p 0.0037) is responsible for gender inequalities of HIV progression because of: 1. DDX3X is needed in the HIV1 life cycle. 2. Several studies confirmed a positive correlation between DDX3X expression and HIV1 replication. 3. Our study found an up-regulated DDX3X expression in women corresponded to the fact that women progress to AIDS faster than men. 4. Our GO analysis showed female up-regulated genes were enriched in positive regulation of the gene expression pathway, which can be explained by DDX3X and its underlying mechanism. Table 1: DEGs in women and men with HIV1 disease Table 2: GO functional enrichment pathway analyses of overall retrieving DEGs Table 3: GO functional enrichment pathway analyses of down- and up-regulated clusters of DEGs Conclusion Aberrant DDX3X expression may contribute to sex-based differences in HIV disease. Drugs modifying DDX3X gene expression will be beneficial in the treatment of HIV especially resolving the HIV drug resistance problem because current anti-HIV drugs target viral components posed the risk of viral mutation. Disclosures All Authors: No reported disclosures

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Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

BioMed Research International ◽

10.1155/2018/4761064 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Binbin Xie ◽

Yiran Li ◽

Rongjie Zhao ◽

Yuzi Xu ◽

Yuhui Wu ◽

...

Keyword(s):

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Regulation Of Transcription ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Search Tool ◽

Poor Outcomes

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

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Title: Weighted Gene Co-Expression Network Analysis Identifies Five Hub Genes Associated with Metastasis in Synovial Sarcoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210628112429 ◽

2021 ◽

Vol 24 ◽

Author(s):

Hongzeng Wu ◽

Benzheng Zhang ◽

Jiazheng Zhao ◽

Yi Zhao ◽

Xiaowei Ma ◽

...

Keyword(s):

Synovial Sarcoma ◽

Tumor Metastasis ◽

Gene Expression Omnibus ◽

Survival Ratio ◽

Hub Genes ◽

Protein Protein Interaction ◽

Metastatic Lesions ◽

Ppi Networks ◽

Malignant Soft Tissue ◽

Geo Database

Background: Synovial sarcoma (SS) refers to a malignant soft tissue sarcoma (STS) which often occurs in children and adults and has a poor prognosis in elderly patients. Patients with local lesions can be treated with extensive surgical resection combined with adjuvant or radiotherapy, whereas about half of the cases have recurrent diseases and metastatic lesions, and five-year survival ratio is assessed within the range of 27% - 55% only. Method: We downloaded a set of expression profile data (GSE40021) related to SS metastasis based on the Gene Expression Omnibus (GEO) database, and selected distinctly represented genes (DEGs) related to tumor metastasis. WGCNA was used to emphasize the DEGs related to tumor metastasis and obtain co-expression modules. Then, the module most related to SS metastasis was screened out. The genes enriched in this module were analyzed by Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway improvement analysis. Cytoscape software was used for constructing protein-protein interaction (PPI) networks, and hub genes were screened in Oncomine analysis. Result: We selected 514 DEGs, consisting of 210 up-regulated genes and 304 down-regulated genes. Through WGCAN, we got seven co-expression modules and the module most related to SS metastasis was the turquoise module, which contained 66 genes. Finally, we screened out five hub genes (HJURP, NCAPG, TPX2, CENPA, NDC80) through CytoHubba and Oncomine analysis. Conclusion: In this study, we screened five hub genes that may help in clinical diagnosis and serve as the latent purpose of SS treatment.

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/7471291 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Fanyan Meng ◽

Ningna Du ◽

Daoming Xu ◽

Li Kuai ◽

Lanying Liu ◽

...

Keyword(s):

Gene Expression ◽

Ankylosing Spondylitis ◽

Molecular Mechanism ◽

Soft Tissues ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

R Package ◽

Gene Expression Omnibus ◽

Treatment Targets ◽

Protein Protein Interaction

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

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Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

European Journal of Medical Research ◽

10.1186/s40001-020-00441-2 ◽

2020 ◽

Vol 25 (1) ◽

Author(s):

Xue Jiang ◽

Zhijie Xu ◽

Yuanyuan Du ◽

Hongyu Chen

Keyword(s):

Gene Ontology ◽

Inflammatory Response ◽

Molecular Mechanism ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Control Group ◽

Integrin Subunit ◽

Ppi Network ◽

Hub Genes ◽

Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN. Results In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN. Conclusions We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

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