Dataset Information

7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis.

ABSTRACT: Background: Breast cancer is one of the deadliest malignant tumors worldwide. Due to its complex molecular and cellular heterogeneity, the efficacy of existing breast cancer risk prediction models is unsatisfactory. In this study, we developed a new lncRNA model to predict the prognosis of patients with BRCA. Methods: BRCA-related differentially-expressed long non-coding RNA were screened from the Cancer Genome Atlas database. A novel lncRNA model was developed by univariate and multivariate analyses to predict the prognosis of patients with BRCA. The efficacy of the model was verified by TCGA-based breast cancer samples. Identified lncRNA-related mRNA based on the co-expression method. Results: We constructed a 7-lncRNA breast cancer prediction model including LINC00377, LINC00536, LINC01224, LINC00668, LINC01234, LINC02037, and LINC01456. The breast cancer samples were divided into high-risk and low-risk groups based on the model, which verified the specificity and sensitivity of the model. The Area Under Curve (AUC) of the 3- and 5-year Receiver Operating Characteristic curve were 0.711 and 0.734, respectively, indicating that the model has good performance. Conclusion: We constructed a 7-lncRNA model to predict the prognosis of patients with BRCA, and suggest that these lncRNAs may play a specific role in the carcinogenesis of BRCA.

SUBMITTER: Li H

PROVIDER: S-EPMC6901675 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis.

Li Huayao H Gao Chundi C Liu Lijuan L Zhuang Jing J Yang Jing J Liu Cun C Zhou Chao C Feng Fubin F Sun Changgang C Sun Changgang C

Frontiers in oncology 20191203

<b>Background:</b> Breast cancer is one of the deadliest malignant tumors worldwide. Due to its complex molecular and cellular heterogeneity, the efficacy of existing breast cancer risk prediction models is unsatisfactory. In this study, we developed a new lncRNA model to predict the prognosis of patients with BRCA. <b>Methods:</b> BRCA-related differentially-expressed long non-coding RNA were screened from the Cancer Genome Atlas database. A novel lncRNA model was developed by univariate and mu ...[more]

PMID: 31850229

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Project description:Purpose:This study focused on identification of long non-coding RNAs (lncRNAs) for prognosis prediction of glioblastoma (GBM) through weighted gene co-expression network analysis (WGCNA) and L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model. Materials and methods:WGCNA was performed based on RNA expression profiles of GBM from Chinese Glioma Genome Atlas (CGGA), National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), and the European Bioinformatics Institute ArrayExpress for the identification of GBM-related modules. Subsequently, prognostic lncRNAs were determined using LASSO Cox PH model, followed by constructing a risk scoring model based on these lncRNAs. The risk score was used to divide patients into high- and low-risk groups. Difference in survival between groups was analyzed using Kaplan-Meier survival analysis. IncRNA-mRNA networks were built for the prognostic lncRNAs, followed by pathway enrichment analysis for these networks. Results:This study identified eight preserved GBM-related modules, including 188 lncRNAs. Consequently, C20orf166-AS1, LINC00645, LBX2-AS1, LINC00565, LINC00641, and PRRT3-AS1 were identified by LASSO Cox PH model. A risk scoring model based on the lncRNAs was constructed that could divide patients into different risk groups with significantly different survival rates. Prognostic value of this six-lncRNA signature was validated in two independent sets. C20orf166-AS1 was associated with antigen processing and presentation and cell adhesion molecule pathways, involving nine common genes. LBX2-AS1, LINC00641, PRRT3-AS1, and LINC00565 were related to focal adhesion, extracellular matrix receptor interaction, and mitogen-activated protein kinase signaling pathways, which shared 12 common genes. Conclusion:This prognostic six-lncRNA signature may improve prognosis prediction of GBM. This study reveals many pathways and genes involved in the mechanisms behind these lncRNAs.

Project description:BackgroundEsophageal cancer is one of the most deadly malignant tumors. Among the common malignant tumors in the world, esophageal cancer is ranked seventh, which has a high mortality rate. Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of various tumors. lncRNAs can competitively bind microRNAs (miRNAs) with mRNA, which can regulate the expression level of the encoded gene at the posttranscriptional level. This regulatory mechanism is called the competitive endogenous RNA (ceRNA) hypothesis, and ceRNA has important research value in tumor-related research. However, the regulation of lncRNAs is less studied in the study of esophageal cancer.MethodsThe Cancer Genome Atlas (TCGA) database was used to download transcriptome profiling data of esophageal cancer. Gene expression quantification data contains 160 cancer samples and 11 normal samples. These data were used to identify differentially expressed lncRNAs and mRNAs. miRNA expression data includes 185 cancer samples and 13 normal samples. The differentially expressed RNAs were identified using the edgeR package in R software. Then, the miRcode database was used to predict miRNAs that bind to lncRNAs. MiRTarBase, miRDB, and TargetScan databases were used to predict the target genes of miRNAs. Cytoscape software was used to draw ceRNA network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using DAVID 6.8. Finally, multifactor cox regression was used to screen lncRNAs related to prognosis.ResultsWe have screened 1331 DElncRNAs, 3193 DEmRNAs, and 162 DEmiRNAs. Among them, the ceRNA network contains 111 lncRNAs, 11 miRNAs, and 63 DEmRNAs. Finally, we established a prediction model containing three lncRNAs through multifactor Cox regression analysis.ConclusionsOur research screened out three independent prognostic lncRNAs from the ceRNA network and constructed a risk assessment model. This is helpful to understand the regulatory role of lncRNAs in esophageal cancer.

Dataset Information

7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis.

Publications

7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis.

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