scholarly journals Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of Pancreatic cancer

2020 ◽  
Author(s):  
Fuqiang Zu ◽  
Peng Liu ◽  
Huaitao Wang ◽  
Ting Zhu ◽  
Jian Sun ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Tumor Formation ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Cerna Network ◽  
Competing Endogenous Rna Network

Abstract Background: It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression.Methods: We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC.Results: A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b-/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC.Conclusion: Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b-/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

scholarly journals Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of pancreatic cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuqiang Zu ◽  
Peng Liu ◽  
Huaitao Wang ◽  
Ting Zhu ◽  
Jian Sun ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Tumor Formation ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Cerna Network ◽  
Competing Endogenous Rna Network

Abstract Background It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression. Methods We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC. Results A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC. Conclusion Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

scholarly journals Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of Pancreatic cancer

2020 ◽  
Author(s):  
Fuqiang Zu ◽  
Peng Liu ◽  
Huaitao Wang ◽  
Ting Zhu ◽  
Jian Sun ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Tumor Formation ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Cerna Network ◽  
Competing Endogenous Rna Network

Abstract Background: It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression.Methods: We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC.Results: A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b-/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC.Conclusion: Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b-/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

scholarly journals Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of Pancreatic cancer

2020 ◽  
Author(s):  
Xiaodong Tan ◽  
Fuqiang Zu ◽  
Peng Liu ◽  
Huaitao Wang ◽  
Ting Zhu ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Tumor Formation ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Cerna Network ◽  
Competing Endogenous Rna Network

Abstract Background It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing multi-omics analysis, we aim to identify the pathway-related ceRNA network associated with PC progression. Methods We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC. Results A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b-/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC. Conclusion Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b-/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

scholarly journals Building a Competing Endogenous RNA Network to Find Potential Long Non-Coding RNA Biomarkers for Pheochromocytoma

2018 ◽  
Vol 51 (6) ◽  
pp. 2916-2924 ◽  
Author(s):  
Ying-Chun Liang ◽  
Yu-Peng Wu ◽  
Dong-Ning Chen ◽  
Shao-Hao Chen ◽  
Xiao-Dong Li ◽  
...  
Keyword(s):  
Digital Gene Expression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Competing Endogenous Rna Network ◽  
Patient Prognosis

Background/Aims: Accumulating evidence has shown that long non-coding RNAs (lncRNAs) in competing endogenous RNA (ceRNA) networks play crucial roles in tumor survival and patient prognosis; however, studies investigating ceRNA networks in pheochromocytoma (PCC) are lacking. In this study, we investigated the pathogenesis of PCC and whether lncRNAs acting through ceRNAs networks were associated with prognosis. Methods: A total of 183 PCC samples and 3 control samples from The Cancer Genome Atlas database were analyzed. The Empirical Analysis of Digital Gene Expression Data package in R (edgeR) was used to analyze differentially expressed RNAs. Biological processes and pathways functional enrichment analysis were performed based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. LncRNA/mRNA/miRNA ceRNA network was constructed by Cytoscape v3.0 software based on the differentially expressed RNAs Survival package in R was used to perform survival analysis. Results: In total, 554 differentially expressed lncRNAs, 1775 mRNAs and 40 miRNAs were selected for further analysis. Subsequently, 23 lncRNAs, 22 mRNAs, and 6 miRNAs were included in the constructed ceRNA network. Meanwhile, two of the 23 lncRNAs (C9orf147 and BSN-AS2) were identified as independent predictors of overall survival in PCC patients (P< 0.05). Conclusion: This study improves the understanding of lncRNA-related ceRNA networks in PCC and suggests that the lncRNAs C9orf147 and BSN-AS2 could be independent prognostic biomarkers and potential therapeutic targets for PCC.

scholarly journals Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Lixian Chen ◽  
Zhonglu Ren ◽  
Yongming Cai
Keyword(s):  
Prognostic Factors ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Cerna Network

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

scholarly journals Identification of potential prognostic markers for osteosarcoma by integrated analysis

2020 ◽  
Author(s):  
Yuxiang Ge ◽  
Wang Ding ◽  
Chong Bian ◽  
Huijie Gu ◽  
Jun Xu ◽  
...  
Keyword(s):  
Prognostic Markers ◽  
Bone Development ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Modules

Abstract Background: Osteosarcoma (OS), one of the utmost common and malignant cancer, accounts for over 30% among skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. Here, we intend to identify gene modules and candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to build a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.

scholarly journals Identification of a Competing Endogenous RNA Network Related to Immune Signature in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Ting Zhu ◽  
Yong Yu ◽  
Jun Liu ◽  
Kaiming Ren
Keyword(s):  
Immune Responses ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
New Era ◽  
Immune Signature ◽  
Cerna Network ◽  
Competing Endogenous Rna Network ◽  
Immune Related Genes

BackgroundThe establishment of immunotherapy has led to a new era in oncotherapy. But the signature of immune-related genes (IRGs) in LUAD remains to be elucidated. Here we use integrated analysis to identify IRGs roles in immune signature and detect their relationship with competing endogenous RNA (ceRNA) networks in LUAD progression.MethodsBy analyzing the RNA-seq data from different platforms, we recognized the differentially expressed genes (DEGs) of each platform and screened out the top 20 hub IRGs related to immune responses. Then, we applied the CIBERSORT algorithm to explore the landscape of tumor-infiltrating immune cells (TILs) in LUAD and their connection with hub genes. Next, we predicted and validated the upstream miRNAs and lncRNAs according to their expression and prognostic roles. Finally, we constructed and validated an immune-related ceRNA network by co-expression analysis.ResultsA total of 71 IRGs were identified among 248 DEGs, which play key roles in immune responses. CIBERSORT analysis showed that six hub genes were closely related to TILs, such as SPP1 and naive B cells (R = −0.17), TEK and resting mast cells (R = 0.37). Stepwise prediction and validation from mRNA to lncRNA, including 6 hub genes, 5 miRNAs, and 9 lncRNAs, were applied to construct a ceRNA network. Ultimately, we confirmed the TMPO-AS1/miR-126-5p/SPP1 and CARD8-AS1/miR-21-5p/TEK as immune-related ceRNA networks in LUAD progression.ConclusionWe elucidated two immune-related ceRNA networks in LUAD progression, which can be considered as immunotherapy targets for this disease.

scholarly journals Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in human aortic dissection

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hao Zhang ◽  
Ce Bian ◽  
Simei Tu ◽  
Fanxing Yin ◽  
Panpan Guo ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Down Regulation ◽  
Tissue Samples ◽  
Cerna Network

Abstract Background Many studies on long chain non-coding RNAs (lncRNAs) are published in recent years. But the roles of lncRNAs in aortic dissection (AD) are still unclear and should be further examined. The present work focused on determining the molecular mechanisms underlying lncRNAs regulation in aortic dissection on the basis of the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network. Methods This study collected the lncRNAs (GSE52093), mRNAs (GSE52093) and miRNAs (GSE92427) expression data within human tissue samples with aortic dissection group and normal group based on Gene Expression Omnibus (GEO) database. Results This study identified three differentially expressed lncRNAs (DELs), 19 differentially expressed miRNAs (DEmiRs) and 1046 differentially expressed mRNAs (DEGs) identified regarding aortic dissection. Furthermore, we constructed a lncRNA-miRNA-mRNA network through three lncRNAs (including two with up-regulation and one with down-regulation), five miRNAs (five with up-regulation), as well as 211 mRNAs (including 103 with up-regulation and 108 with down-regulation). Simultaneously, we conducted functional enrichment and pathway analyses on genes within the as-constructed ceRNA network. According to our PPI/ceRNA network and functional enrichment analysis results, four critical genes were found (E2F2, IGF1R, BDNF and PPP2R1B). In addition, E2F2 level was possibly modulated via lncRNA FAM87A-hsa-miR-31-5p/hsa-miR-7-5p or lncRNA C9orf106-hsa-miR-7-5p. The expression of IGF1R may be regulated by lncRNA FAM87A-hsa-miR-16-5p/hsa-miR-7-5p or lncRNA C9orf106-hsa-miR-7-5p. Conclusion In conclusion, the ceRNA interaction axis we identified is a potentially critical target for treating AD. Our results shed more lights on the possible pathogenic mechanism in AD using a lncRNA-associated ceRNA network.

scholarly journals Identification of novel biomarkers and prognostic value of the location (head, body, or tail) of pancreatic cancer

2019 ◽  
Author(s):  
Shuwen Han ◽  
Xi Yang ◽  
Jiamin Xu ◽  
Wei Wu ◽  
Jin Liu
Keyword(s):  
Pancreatic Cancer ◽  
Risk Model ◽  
Pancreatic Head ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Multivariate Regression Analysis ◽  
Functional Enrichment ◽  
Expression Data ◽  
Pancreatic Head Cancer ◽  
Cerna Network

Abstract Objective : This study was designed to identify the differentially expressed mRNA, microRNA (miRNA), and long non-coding RNA (lncRNA) and their functions in pancreatic cancer (PC). Methods: The expression data of PC and normal samples were downloaded from the GEO database. The expression data of pancreatic head (H), body (B), and tail (T) were downloaded from the TCGA database. After data preprocessing, the differential analyses between PC vs. Normal, H vs. B, H vs. T, and T vs. B were performed. Overlapping genes between PC vs. Normal and the different locations (the union of genes among T vs. B, T vs. H, and B vs. H) were selected. The competing endogenous RNAs (ceRNA) network was constructed based on co-expression analysis and prediction of targets, followed by functional enrichment analysis. Construction of an mRNA prognosis risk model and screening of prognostic factors were performed using Cox univariate/multivariate regression analysis, followed by Nomogram model construction. Finally, the gene-drug interactions were predicted for the DE-mRNA. Results: A five-mRNA prognostic model (GRHL2+CACNA1A+GRM1+UPK1B+PKHD1) was constructed, and the risk score was relatively increased with the increased expression of the GRHL2, PKHD1, and UPK1B, and the decreased expression of CACNA1A and GRM1. Compared with pancreatic body/tail cancer, the expression of GRHL2 was increased, while the expression of CACNA1A and GRM1 was decreased in pancreatic head cancer. LncRNA AC006369.2-miR-146a-5p-CACNA1A/GRM1 was a regulatory axis in the ceRNA network. Verapamil was predicted to be an antagonist of CACNA1A. Conclusion: Our results provide a new direction for the accurate diagnosis and treatment of PC and for investigating the mechanism of PC.

scholarly journals Identification of mRNA-miRNA-lncRNA Competing Endogenous RNA Network in Adrenocortical Carcinoma Using Bioinformatics Analysis 

2020 ◽  
Author(s):  
Senlin Ye ◽  
Haohui Wang ◽  
Wei Li ◽  
Lu Yi
Keyword(s):  
Adrenocortical Carcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Node Degree ◽  
Pathway Enrichment Analysis ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Key Genes ◽  
Worse Prognosis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare malignant tumor originating from the adrenal cortex. However, there are no effective therapies to treat patients with ACC. LncRNA participates in a variety of biological processes of cancers. We constructed ceRNA network and identify key competing endogenous RNAs (ceRNAs) in adrenocortical carcinoma (ACC) using bioinformatic processing tools. Methods: Firstly, the differentially expressed genes (DEGs) were identified by analyzing GSE12368 and GSE19750 datasets. SangerBox was used to generate volcano maps. DAVID database was used for functional enrichment analysis. STRING database was used to conduct Protein-protein interaction (PPI) network, and hub genes were identified by Cytoscape plug-in CytoHubba. UCSC database was used to construct hierarchical clustering of hub genes. Upstream miRNAs of mRNAs were predicted by miRTarBase and upstream lncRNAs of miRNA by miRNet. Expression analysis for lncRNAs were performed via GEPIA. Prognostic analysis for genes, miRNAs and lncRNAs were performed via cBioPortal, OncomiR and GEPIA, respectively. Results: In this study, 49 and 276 upregulated and downregulated significant DEGs were identified. KEGG pathway enrichment analysis showed that they were significantly enriched in cancer-associated pathways. According to node degree, the top 10 upregulated genes and downregulated genes were classfied as hub genes. However, only 9 hub genes were defined as key genes because alteration was significantly associated with worse prognosis and all the 9 key genes were upregulated hub genes. Then, 15 miRNAs were predicted to target the 7 out of 9 key genes. But only 4 miRNAs were defined as key miRNAs because alteration significantly influenced prognosis in cancer. 185 lncRNAs were predicted to potentially interaction with the 4 miRNAs. Only 3 lncRNAs(XIST, HOXA11-AS and TMPO-AS1) were up-regulated and only 1 lncRNA (HOXA11-AS ) indicated alteration was significantly associated with worse prognosis in adrenocortical carcinoma. HOXA11-AS were finally identified as key lncRNA. Finally, RRM2-miR-24-3p/let-7a-5p-HOXA11-AS, CDK1/MCM4-miR-24-3P-HOXA11-AS competing endogenous RNA (ceRNA) sub-networks were constructed in adrenocortical carcinoma. Conclution：This study has constructed RRM2-miR-24-3p/let-7a-5p-HOXA11-AS, CDK1/MCM4-miR-24-3p-HOXA11-AS competing endogenous RNA (ceRNA) sub-networks. Our results suggested that these sub-networks might be potential therapeutic targets or prognostic biomarkers in ACC.

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