scholarly journals Construction of a ceRNA network in glioma and analysis of its clinical significance

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Guangdong Liu ◽  
Haihong Li ◽  
Wenyang Ji ◽  
Haidong Gong ◽  
Yan Jiang ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Normal Brain ◽  
Central Nervous System Tumor ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background Glioma is the most common central nervous system tumor with a poor survival rate and prognosis. Previous studies have found that long non-coding RNA (lncRNA) and competitive endogenous RNA (ceRNA) play important roles in regulating various tumor mechanisms. We obtained RNA-Seq data of glioma and normal brain tissue samples from TCGA and GTEx databases and extracted the lncRNA and mRNA expression data. Further, we analyzed these data using weighted gene co-expression network analysis and differential expression analysis, respectively. Differential expression analysis was also carried out on the mRNA data from the GEO database. Further, we predicted the interactions between lncRNA, miRNA, and targeted mRNA. Using the CGGA data to perform univariate and multivariate Cox regression analysis on mRNA. Results We constructed a Cox proportional hazard regression model containing four mRNAs and performed immune infiltration analysis. Moreover, we also constructed a ceRNA network including 21 lncRNAs, two miRNAs, and four mRNAs, and identified seven lncRNAs related to survival that have not been previously studied in gliomas. Through the gene set enrichment analysis, we found four lncRNAs that may have a significant role in tumors and should be explored further in the context of gliomas. Conclusions In short, we identified four lncRNAs with research value for gliomas, constructed a ceRNA network in gliomas, and developed a prognostic prediction model. Our research enhances our understanding of the molecular mechanisms underlying gliomas, providing new insights for developing targeted therapies and efficiently evaluating the prognosis of gliomas.

scholarly journals Comprehensive analysis of ceRNA network related to lincRNA in glioblastoma and prediction of clinical prognosis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guangdong Liu ◽  
Danian Liu ◽  
Jingjing Huang ◽  
Jianxin Li ◽  
Chuang Wang ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Model ◽  
Differential Expression Analysis ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
Normal Brain ◽  
Tissue Samples ◽  
Clinical Prognosis ◽  
Cerna Network

Abstract Background Long intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Here, we aimed to study the ceRNA network of lincRNA in glioblastoma (GBM). Methods We obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. The ceRNA network was further screened by the DEmiRNA and mRNA of Cox model. Results A prognostic prediction model was constructed for patients with GBM. We assembled a ceRNA network consisting of 18 lincRNAs, 6 miRNAs, and 8 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM. Conclusion We identified four lincRNAs that have research value for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.

scholarly journals Comprehensive Analysis of lincRNA-Related ceRNA Network in Glioblastoma

2020 ◽  
Author(s):  
Guangdong Liu ◽  
Danian Liu ◽  
Jingjing Huang ◽  
Jianxin Li ◽  
Chuang Wang ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Model ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
Normal Brain ◽  
Tissue Samples ◽  
Cerna Network

Abstract BackgroundLong intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Currently, there is a dearth of studies on the ceRNA network of lincRNAs in glioblastoma (GBM), which we aimed to assess in the present study. MethodsWe obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. ResultsAccording to the Cox model, we assembled a ceRNA network consisting of 23 lincRNAs, 14 miRNAs, and 13 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM. ConclusionWe identified four lincRNAs that have the most research values for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.

scholarly journals Identification of Prognostic RBPs in Osteosarcoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110049
Author(s):  
Bei Li ◽  
Long Fang ◽  
Baolong Wang ◽  
Zengkun Yang ◽  
Tingbao Zhao
Keyword(s):  
Regression Analysis ◽  
Prognostic Factors ◽  
Differential Expression ◽  
Ribosome Biogenesis ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Malignant Tumors ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Construction and Validation of a Prognostic Risk Model Based on Autophagy-Related Genes in Hepatocellular Carcinoma Cells

2021 ◽  
Author(s):  
Rui Feng ◽  
Jian Li ◽  
Weiling Xuan ◽  
Hanbo Liu ◽  
Dexin Cheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Differential Expression ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis ◽  
Dismal Prognosis

Abstract Background Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer and the main cause of cancer mortality. Its high complexity and dismal prognosis bring dramatic difficulty to treatment. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into seeking novel biomarkers for HCC. Methods Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. GO and KEGG pathway analyses were conducted on these genes. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by ICGC validation set. Results Altogether, 42 autophagy-related differential genes were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model was constituted by 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, FKBP1A. Validation confirmed the accuracy and independence of this model in predicting HCC patient’s prognosis. Conclusion A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

scholarly journals Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guomin Wu ◽  
Qihao Wang ◽  
Ting Zhu ◽  
Linhai Fu ◽  
Zhupeng Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Clinical Features ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

Abstract 545: Differential mRNA Expression is Influenced by Apolipoprotein A-I in Order to Promote Foam Cell Regression

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elisa C Maruko ◽  
Hao Xu ◽  
Sushma Kaul ◽  
Brian J Capaldo ◽  
Nathalie Pamir ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Immune Cell ◽  
Foam Cell ◽  
Differential Expression Analysis ◽  
Ldl Receptor ◽  
Gene Set Enrichment Analysis ◽  
Control Group ◽  
Apolipoprotein A

Atherosclerosis is a disease of both lipids and inflammatory immune cells. More specifically, elevated plasma levels of low-density lipoproteins (LDL) leads to migration of circulating monocytes into the artery wall. Lipid loaded monocyte cells subsequently proliferate in the arterial walls becoming macrophage foam cells; a hallmark of atherosclerotic lesions. A proposed mechanism of the protective effects of high-density lipoprotein (HDL) is apolipoprotein A-I (apo A-I) acting as a mediator of cholesterol efflux and subsequent foam cell regression. To better understand the biological changes stimulated by apo A-I treatment, differential expression analysis of microarray data was performed on spleen cells from apo A-I treated mice. LDL receptor null (LDLr -/- ) and LDL receptor and apo A-I null (LDLr -/- , apoA-I -/- ) mice were fed a western diet consisting of 0.2% cholesterol and 42% of calories as fat for 12 weeks. After 6 weeks of diet, a subset of mice for each genotype was subcutaneously injected with 200 micrograms of apo A-I 3 times a week for the remaining 6 weeks. The control group mice were subcutaneously injected with 200 micrograms of saline or BSA. Spleen cell RNA was isolated, purified, and analyzed for differential expression analysis using Illumina BeadArray Microarray Technology Analysis. Individual gene expression analysis for LDLr -/- , apoA-I -/- apo A-I treated mice showed 281 significantly differentially expressed genes compared to BSA treated mice. LDLr -/- A-I treated mice had 1502. Of the significant genes, 189 intersected across both genotypes. LDLr -/- , apoA-I -/- A-I mice showed 73 up-regulated and 116 down-regulated genes. Similarly, LDLr -/- A-I mice had 71 up-regulated and 118 down-regulated. One-directional Gene Set Enrichment Analysis (GSEA) of LDLr -/- , apoA-I -/- A-I mice revealed 49 significant pathways while a total of 63 were found for LDLr -/- . Of these pathways, 21 were up-regulated and 13 were down-regulated in both genotypes. Eight of the top 10 most significant up-regulated pathways in both genotypes were immune cell related. Their functions involve receptor, adhesion, and chemokine signaling. Overall, preliminary analysis suggests A-I treatment induces similar gene expression changes across different genotypes.

scholarly journals Easy and efficient ensemble gene set testing with EGSEA

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2010 ◽  
Author(s):  
Monther Alhamdoosh ◽  
Charity W. Law ◽  
Luyi Tian ◽  
Julie M. Sheridan ◽  
Milica Ng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
P Gene ◽  
Wide Range ◽  
Gene Set Testing

Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.

scholarly journals N6-Methyladenosine-Related Long Non-coding RNA Signature Associated With Prognosis and Immunotherapeutic Efficacy of Clear-Cell Renal Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Tianming Ma ◽  
Xiaonan Wang ◽  
Jiawen Wang ◽  
Xiaodong Liu ◽  
Shicong Lai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differential Expression ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

Increasing evidence suggests that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in cancer progression and immunotherapeutic efficacy in clear-cell renal cell carcinoma (ccRCC). In this study, we conducted a comprehensive ccRCC RNA-seq analysis using The Cancer Genome Atlas data to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for ccRCC. Forty-four prognostic m6A-related lncRNAs (m6A-RLs) were screened using Pearson correlation analysis (|R| > 0.7, p < 0.001) and univariable Cox regression analysis (p < 0.01). Using consensus clustering, the patients were divided into two clusters with different overall survival (OS) rates and immune status according to the differential expression of the lncRNAs. Gene set enrichment analysis corroborated that the clusters were enriched in immune-related activities. Twelve prognostic m6A-RLs were selected and used to construct the m6A-RLPS through least absolute shrinkage and selection operator Cox regression. We validated the differential expression of the 12 lncRNAs between tumor and non-cancerous samples, and the expression levels of four m6A-RLs were further validated using Gene Expression Omnibus data and Lnc2Cancer 3.0 database. The m6A-RLPS was verified to be an independent and robust predictor of ccRCC prognosis using univariable and multivariable Cox regression analyses. A nomogram based on age, tumor grade, clinical stage, and m6A-RLPS was generated and showed high accuracy and reliability at predicting the OS of patients with ccRCC. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression. In conclusion, we established a novel m6A-RLPS with a favorable prognostic value for patients with ccRCC. The 12 m6A-RLs included in the signature may provide new insights into the tumorigenesis and allow the prediction of the treatment response of ccRCC.

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