Comprehensive Analysis of lncRNAs Related to the Prognosis of Esophageal Cancer Based on ceRNA Network and Cox Regression Model

Background. Esophageal 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. Methods. The 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. Results. We 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. Conclusions. Our 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.

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Identification of a novel lncRNA‑miRNA‑mRNA competing endogenous RNA network associated with prognosis of breast cancer

10.21203/rs.2.24821/v1 ◽

2020 ◽

Author(s):

Xiaohui Wan ◽

Shuhong Hao ◽

Chunmei Hu ◽

Rongfeng Qu

Keyword(s):

Breast Cancer ◽

Cancer Biology ◽

Cox Regression ◽

Malignant Tumors ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Competing Endogenous Rna ◽

Cox Regression Analysis ◽

Target Mrna ◽

Cerna Network

Abstract Background: Breast cancer is one of the most common malignant tumors. Recently, the effects of competing endogenous RNA (ceRNA) on molecular biological mechanism of cancer has aroused great interext. However, research on the pathogenesis and biomarkers of breast cancer is still limited. Thus, this study is aimed to identify the competing endogenous RNA (ceRNA) network related to prognosis of patients with breast cancer. Methods: The RNA SEQ data and corresponding clinical information were downloaded from the Cancer Genome Atlas (TCGA) database, and the difference analysis was performed after data quality control. The similarity between two groups of genes with traits in the network was analyzed by weighted correlation network analysis (WGCNA) . Next, the interaction among lncRNA, miRNA, and mRNA was predicted using miRcode, TargetScan, miRDB, and miRTarBase database, and the lncRNA-miRNA-mRNA ceRNA network was constructed. Finally, the survival model of target mRNA was established by Cox regression analysis. Results: In total 5056 differentially expressed lncRNAs, 712 differentially expressed miRNAs, and 9878 differentially expressed mRNAs were identified. WGCNA predicted that 823 lncRNAs and 1813 mRNAs were closely related to the breast cancer. The lncRNA-miRNA-mRNA ceRNA network involved in breast cancer was constructed with 27 lncRNA, 14 miRNAs and 4 mRNAs. The AUC of four survival models of target mRNA (ZC3H12B + HRH1 + TMEM132C + PAG1) was 0.609, which suggested the sensitivity and specificity of prognosis prediction of breast cancer. Conclusion: This study provides insight into the ceRNA network involved in breast cancer biology, which significantly associated with gene regulation and prognosis of breast cancer.

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Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

10.21203/rs.3.rs-16348/v1 ◽

2020 ◽

Author(s):

Ze-bing Song ◽

Guo-pei Zhang ◽

shaoqiang li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

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Construction of a lncRNA-Associated ceRNA Network and Identification of A Potential Six-lncRNA Prognostic Signature for Esophageal Cancer

10.21203/rs.3.rs-31505/v1 ◽

2020 ◽

Author(s):

Jun Hu ◽

Fang Wang ◽

Logen Liu ◽

Wenfeng Ning

Keyword(s):

Esophageal Cancer ◽

Roc Curve ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Roc Curve Analysis ◽

Cerna Network ◽

Diagnostic Potential ◽

Robust Likelihood

Abstract BACKGROUND: Mounting evidence has shown that long noncoding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) which participate in the initiation and progression of cancers. In the ceRNA network, lncRNAs, microRNAs (miRNAs) and mRNAs, communicate with and co-regulate each other. Rarely there is a systematic lncRNA-mediated ceRNA network and potential specific ceRNA pairs or triples of esophageal cancer (EC). In this study, we investigate the lncRNA-mediated ceRNA network in EC and screen the potential prognostic lncRNA biomarkers.METHODS: We obtained mRNA, miRNA, and lncRNA expression data and relevant clinical features on patients with EC from The Cancer Genome Atlas (TCGA), and used the edgR package to identify differentially expressed mRNAs, lncRNAs and miRNAs between EC samples and normal samples. The EC ceRNA network was constructed based on miRNA target prediction through the databases of miRcode, miRDB, miRTarBase and TargetScan. And then Pearson’s correlation analysis was adopted to identify co-expression mRNA-lncRNA pairs. Finally, the robust likelihood-based survival analysis and Cox regression models were used to identify prognosis-related lncRNAs, which was evaluated by Kaplan-Meier and receiver operating characteristic (ROC) curve analysis.RESULTS: A total of 3,200 mRNAs, 131 miRNAs and 1,338 lncRNAs were identified as significantly differentially expressed in EC, of which, 30 mRNAs, 15 lncRNAs, and 8 miRNAs were incorporated in the ceRNA network. According to the ceRNA network node degrees, lncRNA MAGI2-AS3, hsa-mir-93 and TGFBR2 were the key genes. Also, the ceRNA network revealed some important ceRNA pairs and triples, such as SNX29P2-TGFBR2 and MAGI2-AS-hsa-mir-143-COL1A1. Finally, we developed a six-lncRNA signature (ZNF341-AS1, AC130324.2, AC027271.1, AL591212.1, AL732314.4 and LOC105372352), with improved diagnostic potential for EC with the area under the ROC curve of 0.93.CONCLUSIONS: our present work sheds new light on the tumorigenesis roles of lncRNA-mediated ceRNA network in EC and identifies a six‐lncRNA model that could be used as candidate prognostic signature.

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Genome-Wide Analysis of Prognostic lncRNAs, miRNAs, and mRNAs Forming a Competing Endogenous RNA Network in Hepatocellular Carcinoma

Cellular Physiology and Biochemistry ◽

10.1159/000492519 ◽

2018 ◽

Vol 48 (5) ◽

pp. 1953-1967 ◽

Cited By ~ 31

Author(s):

Peng Lin ◽

Dong-yue Wen ◽

Qing Li ◽

Yun He ◽

Hong Yang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Models ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Primary Liver Tumor ◽

Cerna Network ◽

Dismal Prognosis

Background/Aims: Hepatocellular carcinoma (HCC) is the most prevalent subtype of primary liver tumor worldwide. Growing evidence has led to a consensus that long non-coding RNAs (lncRNAs) have considerable influence on tumorigenesis and tumor progression of HCC via the mechanism of competing endogenous RNAs (ceRNAs). Methods: Here, we systematically investigated the expression landscape and clinical prognostic value of lncRNAs, micorRNAs (miRNAs), and mRNAs from The Cancer Genome Atlas. Differentially expressed RNAs were submitted to Cox regression analysis and the construction of prognostic indexes. A lncRNA-miRNA-mRNA regulatory network was then constructed based on interaction information derived from miRcode, TargetScan, miRTarBase, and miRDB. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to reveal and determine the functional roles of the ceRNA network in the prognosis of HCC. Results: We detected 77 differentially expressed lncRNAs, 29 differentially expressed miRNAs, and 1014 differentially expressed mRNAs in HCC, which were significantly associated with the overall survival of patients with HCC. We developed three prognostic prediction models that showed moderate predicting prognosis performance and were highly correlated with tumor burden, histological grade and pathological stage. Additionally, 10 survival-related lncRNAs, 6 survival-related miRNAs, and 31 survival-related mRNAs were included to develop a ceRNA network. Further functional enrichment analysis suggested that the ceRNA network was associated with a dismal prognosis for patients with HCC by disturbing the homeostasis of the cell cycle. Conclusion: Together, our study highlights the significant roles of lncRNAs in the development and implementation of monitoring surveillance and prognosis of HCC and provides a deeper understanding of the lncRNA-related ceRNA regulatory mechanism in the pathogenesis of HCC.

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Prognostic lncRNAs, miRNAs, and mRNAs Form a Competing Endogenous RNA Network in CESC

10.21203/rs.3.rs-352332/v1 ◽

2021 ◽

Author(s):

Lang Li ◽

Qiusheng Guo ◽

Gaochen Lan ◽

Fei Liu ◽

Wenwu Wang ◽

...

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Rank Test ◽

Prognostic Signature ◽

Competing Endogenous Rna ◽

Genetic Characteristics ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumorigenesis involves a combination of multiple genetic alteration processes. Constructing a survival-associated competing endogenous RNA (ceRNA) network and a multi-mRNA-based prognostic signature model can help us better understand the complexity and genetic characteristics of CESC.Methods: The RNA-seq data and clinical information of CESC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs, lncRNAs and miRNAs were identified by edgeR package. Constructing prognostic model used the differentially expressed RNAs. The Kaplan-Meier method and log-rank test were performed to assess survival rates. The relationships between overall survival (OS) and clinical parameters were evaluated by Cox regression analysis. A survival-associated ceRNA network was constructed by multiMiR package and miRcode database. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) were used to identify the functional role of the ceRNA network in the prognosis of CESC.Results: Differentially expressed 298 mRNAs, 8 miRNAs, and 29 lncRNAs were significantly associated with the prognosis of CESC. The prognostic signature model based on 4 mRNAs (OPN3, DAAM2, HENMT1, and CAVIN3) was constructed. The prognostic ability was 0.726 for this model. Patients in the high-risk group were significantly associated with worse OS. The KEGG pathways were significantly enriched in the TGF-β and Cell adhesion molecules signaling pathways.Conclusion: This study identified several potential prognostic biomarkers to construct a multi-mRNA-based prognostic model for CESC.

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Identification and analysis of long non-coding RNA related miRNA sponge regulatory network in bladder urothelial carcinoma

Cancer Cell International ◽

10.1186/s12935-019-1052-2 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 6

Author(s):

Jiawu Wang ◽

Chengyao Zhang ◽

Yan Wu ◽

Weiyang He ◽

Xin Gou

Keyword(s):

Urothelial Carcinoma ◽

Regulatory Network ◽

Cox Regression ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Cerna Network ◽

Ppi Networks ◽

Bladder Urothelial Carcinoma

Abstract Background The aim of this study was to investigate the regulatory network of lncRNAs as competing endogenous RNAs (ceRNA) in bladder urothelial carcinoma (BUC) based on gene expression data derived from The Cancer Genome Atlas (TCGA). Materials and methods RNA sequence profiles and clinical information from 414 BUC tissues and 19 non-tumor adjacent tissues were downloaded from TCGA. Differentially expressed RNAs derived from BUC and non-tumor adjacent samples were identified using the R package “edgeR”. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the “clusterProfiler” package. Gene ontology and protein–protein interaction (PPI) networks were analyzed for the differentially expressed mRNAs using the “STRING” database. The network for the dysregulated lncRNA associated ceRNAs was then constructed for BUC using miRcode, miRTarBase, miRDB, and TargetScan. Cox regression analysis was performed to identify independent prognostic RNAs associated with BUC overall survival (OS). Survival analysis for the independent prognostic RNAs within the ceRNA network was calculated using Kaplan–Meier curves. Results Based on our analysis, a total of 666, 1819 and 157 differentially expressed lncRNAs, mRNAs and miRNAs were identified respectively. The ceRNA network was then constructed and contained 59 lncRNAs, 23 DEmiRNAs, and 52 DEmRNAs. In total, 5 lncRNAs (HCG22, ADAMTS9-AS1, ADAMTS9-AS2, AC078778.1, and AC112721.1), 2 miRNAs (hsa-mir-145 and hsa-mir-141) and 6 mRNAs (ZEB1, TMEM100, MAP1B, DUSP2, JUN, and AIFM3) were found to be related to OS. Two lncRNAs (ADAMTS9-AS1 and ADAMTS9-AS2) and 4 mRNA (DUSP2, JUN, MAP1B, and TMEM100) were validated using GEPIA. Thirty key hub genes were identified using the ranking method of degree. KEGG analysis demonstrated that the majority of the DEmRNAs were involved in pathways associated with cancer. Conclusion Our findings provide an understanding of the important role of lncRNA–related ceRNAs in BUC. Additional experimental and clinical validations are required to support our findings.

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Construction of a lncRNA-associated ceRNA network and identification of a potential six-lncRNA prognostic signature for esophageal cancer

10.21203/rs.2.14727/v1 ◽

2019 ◽

Author(s):

Jun Hu ◽

Fang Wang ◽

Logen Liu ◽

Ning Wenfeng

Keyword(s):

Esophageal Cancer ◽

Roc Curve ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Roc Curve Analysis ◽

Cerna Network ◽

Diagnostic Potential ◽

Robust Likelihood

Abstract BACKGROUND: Mounting evidence has shown that long noncoding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) which participate in the initiation and progression of cancers. In the ceRNA network, lncRNAs, microRNAs (miRNAs) and mRNAs, communicate with and co-regulate each other. Rarely there is a systematic lncRNA-mediated ceRNA network and potential specific ceRNA pairs or triples of esophageal cancer (EC). In this study, we investigate the lncRNA-mediated ceRNA network in EC and screen the potential prognostic lncRNA biomarkers. METHODS: We obtained mRNA, miRNA, and lncRNA expression data and relevant clinical features on patients with EC from The Cancer Genome Atlas (TCGA), and used the edgR package to identify differentially expressed mRNAs, lncRNAs and miRNAs between EC samples and normal samples. The EC ceRNA network was constructed based on miRNA target prediction through the databases of miRcode, miRDB, miRTarBase and TargetScan. And then Pearson’s correlation analysis was adopted to identify co-expression mRNA-lncRNA pairs. Finally, the robust likelihood-based survival analysis and Cox regression models were used to identify prognosis-related lncRNAs, which was evaluated by Kaplan-Meier and receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 3,200 mRNAs, 131 miRNAs and 1,338 lncRNAs were identified as significantly differentially expressed in EC, of which, 30 mRNAs, 15 lncRNAs, and 8 miRNAs were incorporated in the ceRNA network. According to the ceRNA network node degrees, lncRNA MAGI2-AS3, hsa-mir-93 and TGFBR2 were the key genes. Also, the ceRNA network revealed some important ceRNA pairs and triples, such as SNX29P2-TGFBR2 and MAGI2-AS-hsa-mir-143-COL1A1. Finally, we developed a six-lncRNA signature (ZNF341-AS1, AC130324.2, AC027271.1, AL591212.1, AL732314.4 and LOC105372352), with improved diagnostic potential for EC with the area under the ROC curve of 0.93. CONCLUSIONS: our present work sheds new light on the tumorigenesis roles of lncRNA-mediated ceRNA network in EC and identifies a six‐lncRNA model that could be used as candidate prognostic signature.

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Development of A Prognostic Index and Screening of Prognosis Related Genes Based on an Immunogenomic Landscape Analysis of Bladder Cancer

10.21203/rs.3.rs-135671/v1 ◽

2021 ◽

Author(s):

GenYi Qu ◽

Guang Yang ◽

Yong Xu ◽

Maolin Xiang ◽

Cheng Tang

Keyword(s):

Bladder Cancer ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

Prognostic Index ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Cox Analysis

Abstract Background: Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore the use of immunotherapy for BLCA.Methods: Gene expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal. Differentially-expressed and survival-associated IRGs in patients with BLCA were identified using computational algorithms and Cox regression analysis. We also performed functional enrichment analysis. Based on IRGs, we employed multivariate Cox analysis to develop a new prognostic index.Results: We identified 261 IRGs that were differentially expressed between BLCA tissue and adjacent tissue, 30 of which were significantly associated with the overall survival (all P<0.01). According to multivariate Cox analysis, nine survival-related IRGs (MMP9, PDGFRA, AHNAK, OAS1, OLR1, RAC3, IGF1, PGF, and SH3BP2) were high-risk genes. We developed a prognostic index based on these IRGs and found it accurately predicted BLCA outcomes associated with the TNM stage. Intriguingly, the IRG-based prognostic index reflected infiltration of macrophages.Conclusions: An independent IRG-based prognostic index provides a practical approach for assessing patients' immune status and prognosis with BLCA. This index independently predicted outcomes of BLCA.

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Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

BMC Gastroenterology ◽

10.1186/s12876-021-01734-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sheng Zheng ◽

Zizhen Zhang ◽

Ning Ding ◽

Jiawei Sun ◽

Yifeng Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

High Efficiency ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Lasso Regression ◽

Sequencing Data ◽

Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

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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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