Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

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Identification of Key Genes and Pathways for Enchondromas by Bioinformatics Analysis

Dose-Response ◽

10.1177/1559325820907536 ◽

2020 ◽

Vol 18 (1) ◽

pp. 155932582090753

Author(s):

Tianlong Wu ◽

Honghai Cao ◽

Lei Liu ◽

Kan Peng

Keyword(s):

Gene Expression ◽

Endoplasmic Reticulum ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Protein Processing ◽

Receptor Interaction ◽

Ppi Network ◽

Protein Protein Interaction ◽

Exact Etiology

Background: The risk of malignant transformation of enchondromas (EC) toward central chondrosarcoma is increased up to 35%, while the exact etiology of EC is unknown. The purpose of this research was to authenticate gene signatures during EC and reveal their potential mechanisms in occurrence and development of EC. Methods: The gene expression profiles was acquired from Gene Expression Omnibus database (no. GSE22855). The gene ontology (GO), protein–protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were utilized to identify differentially expressed genes (DEGs). Results: Finally, 242 DEGs were appraisal, containing 200 overregulated genes and 42 downregulated genes. The outcomes of GO analysis indicated that upregulated DEGs were mainly enriched in several biological processes containing response to hypoxia, calcium ion, and negative regulation extrinsic apoptotic signaling pathway. Furthermore, the upregulated DEGs were enriched in extracellular matrix (ECM)–receptor interaction, protein processing in endoplasmic reticulum and ribosome, which was analyzed by KEGG pathway. From the PPI network, the top 10 hub genes were identified, which were related to significant pathways containing ribosome, protein processing in endoplasmic reticulum, and ECM-receptor interaction. Conclusion: In conclusion, the present study may be helpful for understanding the diagnostic biomarkers of EC.

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Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

Disease Markers ◽

10.1155/2020/8816070 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23

Author(s):

Xiangchou Yang ◽

Liping Chen ◽

Yuting Mao ◽

Zijing Hu ◽

Muqing He

Keyword(s):

Gastric Cancer ◽

Extracellular Matrix ◽

Risk Score ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Receptor Interaction ◽

Clinicopathologic Features ◽

Normal Tissues ◽

Prognostic Application ◽

Score Model

The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t -test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081 ∗ log 2 CD 36 + 0.043 ∗ log 2 COL 5 A 2 + 0.001 ∗ log 2 ITGB 5 + 0.039 ∗ log 2 SDC 2 + 0.135 ∗ log 2 SV 2 B + 0.012 ∗ log 2 THBS 1 + 0.068 ∗ log 2 VTN + 0.023 ∗ log 2 VWF . The risk score model could well predict the outcome of patients with gastric cancer in both training ( n = 351 , HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046 ) and validation ( n = 300 , HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014 ) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures ( P < 0.0001 ). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001 ) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001 ) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

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Bioinformatics analysis of different candidate genes involved in hepatocellular carcinoma induced by HepG2 cells or tumor cells of patients

Journal of International Medical Research ◽

10.1177/0300060520932112 ◽

2020 ◽

Vol 48 (6) ◽

pp. 030006052093211

Author(s):

Xiang Zhang ◽

Songna Yin ◽

Ke Ma

Keyword(s):

Hepatocellular Carcinoma ◽

Hepg2 Cells ◽

Liver Parenchyma ◽

Preclinical Study ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Peroxisome Proliferator ◽

Ppi Network ◽

Peroxisome Proliferator Activated Receptor ◽

Protein Protein Interaction

Objective Hepatocellular carcinoma (HCC) is a common cancer with a high mortality rate; the molecular mechanism involved in HCC remain unclear. We aimed to provide insight into HCC induced with HepG2 cells and identify genes and pathways associated with HCC, as well as potential therapeutic targets. Methods Dataset GSE72581 was downloaded from the Gene Expression Omnibus, including samples from mice injected in liver parenchyma with HepG2 cells, and from mice injected with cells from patient tumor explants. Differentially expressed genes (DEGs) between the two groups of mice were analyzed. Then, gene ontology and Kyoto Encyclopedia of Gene and Genomes pathway enrichment analyses were performed. The MCODE plug-in in Cytoscape was applied to create a protein–protein interaction (PPI) network of DEGs. Results We identified 1,405 DEGs (479 upregulated and 926 downregulated genes), which were enriched in complement and coagulation cascades, peroxisome proliferator-activated receptor signaling pathway, and extracellular matrix–receptor interaction. The top 4 modules and top 20 hub genes were identified from the PPI network, and associations with overall survival were determined using Kaplan–Meier analysis. Conclusion This preclinical study provided data on molecular targets in HCC that could be useful in the clinical treatment of HCC.

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Bioinformatics Analysis of Potential Key Genes in Trastuzumab-Resistant Gastric Cancer

Disease Markers ◽

10.1155/2019/1372571 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Guangda Yang ◽

Liumeng Jian ◽

Xiangan Lin ◽

Aiyu Zhu ◽

Guohua Wen

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Prognostic Value ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Trastuzumab Resistance ◽

Hub Genes ◽

Her2 Positive ◽

Protein Protein Interaction ◽

Stem Cell Pluripotency

Background. This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods. The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results. A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions. Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.

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Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

BioMed Research International ◽

10.1155/2020/7658230 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Xinyu Chong ◽

Rui Peng ◽

Yan Sun ◽

Luyu Zhang ◽

Zheng Zhang

Keyword(s):

Gastric Cancer ◽

Topological Analysis ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Protein Protein Interaction ◽

Kaplan Meier ◽

Key Genes ◽

Secreted Phosphoprotein 1 ◽

Centrality Analysis

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

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Integrated analysis of lncRNA–miRNA–mRNA ceRNA network and the potential prognosis indicators in sarcomas

BMC Medical Genomics ◽

10.1186/s12920-021-00918-x ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Lu Gao ◽

Yu Zhao ◽

Xuelei Ma ◽

Ling Zhang

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

Gene Prediction ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Integrated Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Cerna Network

Abstract Background Competitive endogenous RNA (ceRNA) networks have revealed a new mechanism of interaction between RNAs, and play crucial roles in multiple biological processes and development of neoplasms. They might serve as diagnostic and prognosis markers as well as therapeutic targets. Methods In this work, we identified differentially expressed mRNAs (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcomas by comparing the gene expression profiles between sarcoma and normal muscle samples in Gene Expression Omnibus (GEO) datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were applied to investigate the primary functions of the overlapped DEGs. Then, lncRNA-miRNA and miRNA-mRNA interactions were predicted, and the ceRNA regulatory network was constructed using Cytoscape software. In addition, the protein–protein interaction (PPI) network and survival analysis were performed. Results A total of 1296 DEGs were identified in sarcoma samples by combining the GO and KEGG enrichment analyses, 338 DELs were discovered after the probes were reannotated, and 36 DEMs were ascertained through intersecting two different expression miRNAs sets. Further, through target gene prediction, a lncRNA–miRNA–mRNA ceRNA network that contained 113 mRNAs, 69 lncRNAs and 29 miRNAs was constructed. The PPI network identified the six most significant hub proteins. Survival analysis revealed that seven mRNAs, four miRNAs and one lncRNA were associated with overall survival of sarcoma patients. Conclusions Overall, we constructed a ceRNA network in sarcomas, which might provide insights for further research on the molecular mechanism and potential prognosis biomarkers.

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Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

Iranian Journal of Public Health ◽

10.18502/ijph.v50i11.7578 ◽

2021 ◽

Author(s):

Congcong Wang ◽

Jianping Guo ◽

Xiaoyang Zhao ◽

Jia Jia ◽

Wenting Xu ◽

...

Keyword(s):

Bioinformatics Analysis ◽

Expression Profiles ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Analysis Tool ◽

Hub Genes ◽

Protein Protein Interaction ◽

Kegg Analysis ◽

Prognostic Assessment ◽

Scheme Selection

Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis. Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA. Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1. Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.

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Integrated analysis of the miRNA-mRNA network associated with LMP1 gene in nasopharyngeal carcinoma

10.1101/615237 ◽

2019 ◽

Author(s):

Yang Yang ◽

Wen Liu ◽

Yan Zhang ◽

Shuo Wu ◽

Bing Luo

Keyword(s):

Nasopharyngeal Carcinoma ◽

Expression Profiles ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Integrated Analysis ◽

Protein Protein Interaction ◽

Barr Virus ◽

Lmp1 Gene ◽

Epstein Barr

AbstractEpstein-Barr virus oncogenic latent membrane protein 1 (LMP1) has been known to play important roles in nasopharyngeal carcinoma (NPC). LMP1 gene also induced a variety of microRNAs (miRNAs) which bear pivotal roles in regulation of mRNAs expression. However, little was known about the global change of mRNAs and miRNAs induced by LMP1 gene in NPC. In our study, one NPC tissue microarray profile and two LMP1-associated microarray expression profiles data were downloaded from the Gene Expression Omnibus database. A protein-protein interaction network was constructed by using bioinformatics platform Gene-Cloud of Biotechnology Information (GCBI). 78 differentially expressed miRNAs and 3322 differentially expressed genes were identified in order to generate a macroscopic network between miRNAs and mRNAs associated with LMP1 gene. In addition, two significant models were generated to illustrate the expression tendency. Our study provided a way to reveal the interaction between miRNAs and mRNAs in LMP1 axis, bringing insights into the pathogenesis of NPC.

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Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

10.21203/rs.3.rs-24106/v1 ◽

2020 ◽

Author(s):

Sheng Chang ◽

Yang Cao

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Osteogenic Sarcoma ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Potential Biomarkers

Abstract Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

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Identification of Differentially Expressed miRNAs and mRNAs in Vestibular Schwannoma by Integrated Analysis

BioMed Research International ◽

10.1155/2019/7267816 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Yanhua Lei ◽

Ping Guo ◽

Xiuguo Li ◽

Yuanyuan Zhang ◽

Ting Du

Keyword(s):

Vestibular Schwannoma ◽

Functional Annotation ◽

Expression Profiles ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Integrated Analysis ◽

Protein Protein Interaction ◽

Mirna Expression Profiles ◽

Differentially Expressed Mirnas

Background.Vestibular schwannoma (VS) is benign, slow-growing brain tumor that negatively impacts patient quality of life, which may cause even death. This study aimed to explore key genes and microRNAs (miRNAs) associated with VS.Methods.The mRNA and miRNA expression profiles of VS downloaded from Gene Expression Omnibus (GEO) database were included in this study to perform an integrated analysis. The differentially expressed mRNAs (DEmRNAs) and miRNAs (DEmiRNAs) were identified. Then, functional annotation and protein-protein interaction networks (PPI) of DEmRNAs were constructed. DEmiRNA-target DEmRNAs analysis and functional annotation of DEmiRNA-target DEmRNAs were performed.Results.A total of 2627 DEmRNAs (1194 upregulated and 1433 downregulated DEmRNAs) and 21 DEmiRNAs (12 upregulated and 9 downregulated DEmiRNAs) were identified. ISG15, TLE1, and XPC were three hub proteins of VS-specific PPI network. A total of 2970 DEmiRNAs-DEmRNAs pairs were obtained. Among which, hsa-miR-181a-5p, hsa-miR-106-5p, and hsa-miR-34a-5p were the top three DEmiRNAs that covered most DEmRNAs. The functional annotation of DEmiRNA-target DEmRNAs revealed that the DEmiRNA-target DEmRNAs were significantly enriched in cGMP-PKG signaling pathway, adrenergic signaling in cardiomyocytes, and pathways in cancer.Conclusion.The results of this present study may provide a comprehensive understanding for the roles of DEmRNAs and DEmiRNAs in the pathogenesis of VS and developing potential biomarkers of VS.

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