LnCeVar: a comprehensive database of genomic variations that disturb ceRNA network regulation

Abstract LnCeVar (http://www.bio-bigdata.net/LnCeVar/) is a comprehensive database that aims to provide genomic variations that disturb lncRNA-associated competing endogenous RNA (ceRNA) network regulation curated from the published literature and high-throughput data sets. LnCeVar curated 119 501 variation–ceRNA events from thousands of samples and cell lines, including: (i) more than 2000 experimentally supported circulating, drug-resistant and prognosis-related lncRNA biomarkers; (ii) 11 418 somatic mutation–ceRNA events from TCGA and COSMIC; (iii) 112 674 CNV–ceRNA events from TCGA; (iv) 67 066 SNP–ceRNA events from the 1000 Genomes Project. LnCeVar provides a user-friendly searching and browsing interface. In addition, as an important supplement of the database, several flexible tools have been developed to aid retrieval and analysis of the data. The LnCeVar–BLAST interface is a convenient way for users to search ceRNAs by interesting sequences. LnCeVar–Function is a tool for performing functional enrichment analysis. LnCeVar–Hallmark identifies dysregulated cancer hallmarks of variation–ceRNA events. LnCeVar–Survival performs COX regression analyses and produces survival curves for variation–ceRNA events. LnCeVar–Network identifies and creates a visualization of dysregulated variation–ceRNA networks. Collectively, LnCeVar will serve as an important resource for investigating the functions and mechanisms of personalized genomic variations that disturb ceRNA network regulation in human diseases.

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

BioMed Research International ◽

10.1155/2021/4093426 ◽

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.

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Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

BioMed Research International ◽

10.1155/2020/7136049 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Yuntao Shi ◽

Yingying Zhuang ◽

Jialing Zhang ◽

Mengxue Chen ◽

Shangnong Wu

Keyword(s):

Target Genes ◽

Cox Regression ◽

Expression Profiles ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Risk Groups ◽

Normal Mucosa ◽

Microrna Expression ◽

Functional Enrichment

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

BioMed Research International ◽

10.1155/2020/3813546 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Yun Zhong ◽

Zhe Liu ◽

Dangchi Li ◽

Qinyuan Liao ◽

Jingao Li

Keyword(s):

Gene Expression ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Test Set ◽

Cox Regression Analysis

Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

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

BMC Cancer ◽

10.1186/s12885-020-07470-4 ◽

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.

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Investigation of Candidate Genes and Mechanism Associated with Immune Cell during the Progression of Glioblastoma based on Bioinformatics

10.21203/rs.3.rs-117485/v1 ◽

2020 ◽

Author(s):

Hui Xie ◽

Xiao-hui Ding ◽

Ce Yuan ◽

Jin-jiang Li ◽

Zhao-yang Li ◽

...

Keyword(s):

Immune Cell ◽

Risk Model ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Normal Group ◽

Functional Enrichment ◽

M2 Macrophage ◽

Survival Prognosis ◽

Cerna Network ◽

Tumor Group

Abstract Background: This study aimed to investigate the molecular mechanism of immune cell infiltration during the development of glioblastoma multiforme (GBM), and explore the potential immune cell associated prognostic genes for GBM. Methods: Gene expression data and corresponding clinical data of GBM samples (tumor group) and normal samples (normal group) in TCGA-GBM and GTEx dataset were downloaded. The differentially expression analysis was performed on samples between two groups. Based on tumor immune microenvironment analysis, the immune-related RNAs (lncRNAs and mRNAs) were further explored. Then, functional enrichment analysis, ceRNA network, risk prediction model and prognosis investigation were performed. Finally, the results of survival prognosis of key genes were tested by additional datasets. Results: A total of 4989 up-regulated genes and 2349 down-regulated genes were revealed between tumor group and normal group. M2 macrophages accounted for the largest proportion of tumor infiltrates immune cells in GBM, and was related to the prognosis of GBM patients. Totally 168 mRNAs (KIF18B) and 5 lncRNAs were related to infiltration of M2 Macrophage, of which 25 mRNAs and 5 lncRNAs forms a ceRNA network through 37 miRNAs (eg., miR-6849-3p). These genes were mainly assembled in functions like signal release. A risk model based on 5 mRNAs (such as FOX4 and ELFN2) and lncRNA PR11-161H23.5 was constructed. Verification test showed that all 5 mRNAs were significantly associated with OS prognosis.Conclusions: M2 Macrophage infiltration might participate in tumorigenesis of GBM via RP11-161H23.5-miR6849-3p-KIF18B ceRNA interaction. Furthermore, mRNAs such as FOX4 and ELFN2 might be potential prognostic markers for GBM patients.

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Differentially Expressed Functional LncRNAs in Human Subjects With Metabolic Syndrome Reflect a Competing Endogenous RNA Network in Circulating Extracellular Vesicles

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.667056 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yongxin Li ◽

Yu Meng ◽

Yuanhang Liu ◽

Andre J. van Wijnen ◽

Alfonso Eirin ◽

...

Keyword(s):

Metabolic Syndrome ◽

Extracellular Vesicles ◽

Disease Risk ◽

Human Subjects ◽

Inflammatory Marker ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cerna Network

Metabolic syndrome (MetS), a collective cluster of disease risk factors that include dyslipidemia, obesity, inflammation, hypertension, and insulin resistance, affects numerous people worldwide. Accumulating studies have shown that long non-coding RNAs (lncRNAs) serve as competing endogenous RNAs (ceRNAs) to play essential roles in regulating gene expression in various diseases. To explore the role of lncRNAs as ceRNAs in MetS, we examined a MetS-associated network in circulating extracellular vesicles (EVs) collected from the systemic blood of MetS and control patients (n = 5 each). In total, 191 differentially expressed lncRNAs, 1,389 mRNAs, and 138 miRNAs were selected for further analysis. Biological processes and pathway functional enrichment analysis were performed based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The lncRNA/mRNA/miRNA ceRNA network was constructed by Cytoscape v3.8 based on the DE-RNAs and included 13 lncRNAs, 8 miRNAs, and 64 mRNAs. MetS patients showed elevated body weight, glucose, blood pressure, insulin, liver injury, and inflammatory marker levels. We found that lncRNAs reflect a ceRNA network that may regulate central cellular processes and complications of MetS, including cancer. These findings suggest that MetS alters the interactions among the ceRNA network components in circulating EVs and that this cargo of circulating EVs may have potential translational ramifications for MetS.

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

10.21203/rs.3.rs-36637/v3 ◽

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.

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Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

10.21203/rs.3.rs-56767/v1 ◽

2020 ◽

Author(s):

Yang Wang ◽

Chengping Hu

Keyword(s):

Lung Adenocarcinoma ◽

Cox Regression ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Protein Coding ◽

Cox Regression Analysis ◽

Protein Coding Genes

Abstract Background: Long non-coding RNAs (lncRNAs) have been reported to play essential roles in tumorigenesis and cancers prognosis, and they can be a potential cancer prognostic markers. However, in lung adenocarcinoma(LUAD), how lncRNA signatures predict the survival of patients is poorly understood. Our study aims to explore lncRNA signatures and prognostic function in LUAD.Methods: The expression and prognosis data of lncRNAs in LUAD patients was collected from the Cancer Genome Atlas (TCGA) data. All analyses were performed using the R package (version 3.6.2). Metascape, STRING and Cytoscape were used for enrichment analysis and function prediction of the lncRNA co-expressed protein-coding genes.Results: We have collected lncRNA expression data in 466 LUAD tumors, and a six-lncRNA signature(RP11-79H23.3, RP11-309M7.1, CTD-2357A8.3, RP11-108P20.4, U47924.29, LHFPL3-AS2) has been shown to be significantly related to LUAD patients’ overall survival. According to the lncRNA signatures, the high-risk and low-risk groups were divided in LUAD patients with different survival rates. Further multivariable cox regression analysis showed that the prognostic value of this signature was independent of clinical factors. The potential functional roles and hub co-expressed protein-coding genes in the six prognostic lncRNAs are shown in the functional enrichment analysis.Conclusions: These results showed that these six lncRNAs could be independent predicted prognostic biomarkers in LUAD patients.

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Development and Validation of a Prognostic Classifier Based on Metabolism-Related Genes for Breast Cancer

10.21203/rs.3.rs-1105292/v1 ◽

2021 ◽

Author(s):

Nan Wang ◽

Lin Li ◽

Jiangrui Chi ◽

Xinwei Liu ◽

Youyi Xiong ◽

...

Keyword(s):

Lipid Metabolism ◽

Cox Regression ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Marker Genes ◽

Tissue Samples ◽

Prognostic Gene

Abstract Background: Alterations in lipid metabolism have been implicated in the development of many tumors. However, the contribution of different lipid metabolism pathways to Breast invasive carcinoma (BRCA) remains to be fully established. Here, we attempted to ascertain the prognostic value of lipid metabolism-related genes in BRCA. Methods: We obtained RNA expression data and clinical information for BRCA and normal samples from public databases and downloaded a lipid metabolism-related gene set to harvest lipid metabolism-related genes. IPA was applied to identify the potential pathways and functions of DEGs related to lipid metabolism. Subsequently, univariate and multivariate Cox regression analyses were utilized to construct the prognostic gene signature and independent prognostic analyses. Thereafter, the differential expression of the selected marker genes SDC1 and SORBS1 in clinical tissue samples was verified by qRT-PCR, western blotting, and immunohistochemical experiments. Functional enrichment analysis of prognostic genes was achieved by the GO and KEGG databases. Moreover, Kaplan-Meier analysis, ROC curves, clinical immunohistochemistry conditions and follow-up results were employed to assess the prognostic potency. Potential compounds targeting prognostic genes were then screened by CMap database and a prognostic gene-drug interaction network was constructed using Comparative Toxicogenomics Database.Results: IPA demonstrated that the 162 lipid metabolism-related DEGs we obtained were involved in a variety of lipid metabolism and BRCA pathological signatures. Subsequent functional enrichment analysis of candidate prognostic lipid metabolism DEGs also revealed a similar outcome. The prognostic classifier we constructed comprising SDC1 and SORBS1 has a strong prognostic potency that was verified by the clinical conditions and follow-up results, it also can serve as an independent prognostic marker for BRCA. CMap filtered 37 potential compounds against prognostic genes. CTD indicated that the two prognostic genes had 16 drugs in common. Conclusion: Within this study, we identified a novel prognostic classifier based on two lipid metabolism-related genes: SDC1 and SORBS1. This classifier had accurately predicted the prognosis of our follow-up BRCA patients and this result highlighted a new perspective on the metabolic exploration of BRCA. In addition, SDC1 and SORBS1 could serve as a possible new target for the synthesis of BRCA drugs.

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