Identification of the 3-lncRNA Signature as a Prognostic Biomarker for Colorectal Cancer

Colorectal cancer (CRC) is one of the most common malignant carcinomas in the world, and metastasis is the main cause of CRC-related death. However, the molecular network involved in CRC metastasis remains poorly understood. Long noncoding RNA (lncRNA) plays a vital role in tumorigenesis and may act as a competing endogenous RNA (ceRNA) to affect the expression of mRNA by suppressing miRNA function. In this study, we identified 628 mRNAs, 144 lncRNAs, and 25 miRNAs that are differentially expressed (DE) in metastatic CRC patients compared with nonmetastatic CRC patients from the Cancer Genome Atlas (TCGA) database. Functional enrichment analyses confirmed that the identified DE mRNAs are extensively involved in CRC tumorigenesis and migration. By bioinformatics analysis, we constructed a metastasis-associated ceRNA network for CRC that includes 28 mRNAs, 12 lncRNAs, and 15 miRNAs. We then performed multivariate Cox regression analysis on the ceRNA-related DE lncRNAs and identified a 3-lncRNA signature (LINC00114, LINC00261, and HOTAIR) with the greatest prognostic value for CRC. Clinical feature analysis and functional enrichment analysis further proved that these three lncRNAs are involved in CRC tumorigenesis. Finally, we used Transwell, Cell Counting Kit (CCK)-8, and colony formation assays to clarify that the inhibition of LINC00114 promotes the migratory, invasive, and proliferative abilities of CRC cells. The results of the luciferase assay suggest that LINC00114 is the direct target of miR-135a, which also verified the ceRNA network. In summary, this study provides a metastasis-associated ceRNA network for CRC and suggests that the 3-lncRNA signature may be a useful candidate for the diagnosis and prognosis of CRC.

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Identification of the Prognostic LncRNA Biomarkers and Comprehensive Analysis of LncRNA-Mediated ceRNA Network for Uterine Corpus Endometrial Carcinoma

10.21203/rs.3.rs-989585/v1 ◽

2021 ◽

Author(s):

Yiran Cai ◽

Jin Cui ◽

Huiqun Wu

Keyword(s):

Regression Analysis ◽

Endometrial Carcinoma ◽

Noncoding Rna ◽

Molecular Mechanisms ◽

Cox Regression ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Uterine Corpus ◽

Cerna Network

Abstract Background Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through an lncRNA-mediated ceRNA network. Methods The transcriptome profiles and corresponding clinical profiles of UCEC dataset were retrieved from CPTAC and TCGA databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identified via “Edge R” package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor infiltrating immune cell(TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers. Results In the CPTAC dataset of UCEC, a ceRNA network comprised of 36 miRNAs, 123 lncRNAs and 124 targeted mRNAs was established, and 8 of 123 prognostic-related DElncRNAs(Differentially Expressed long noncoding RNA) were identified. While in the TCGA dataset, a ceRNA network comprised of 38 miRNAs, 83 lncRNAs and 110 targeted mRNAs was established, and 2 of 83 prognostic-related DElncRNAs were identified. After filtered by risk grouping and Cox regression analysis, 10 prognostic-related lncRNAs including LINC00443, LINC00483, C2orf48, TRBV11-2, MEG-8 were identified. In addition, 33 survival-related DEmRNAs(Differentially Expressed messager RNA) in two ceRNA networks were further validated in the HPA database. Finally, six lncRNA/miRNA/mRNA axes were established to elucidate prognostic regulatory roles in UCEC. Conclusion Several prognostic lncRNAs are identified and prognostic model of lncRNA-mediated ceRNA network is constructed, which promotes the understanding of UCEC development mechanisms and potential therapeutic targets.

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Construction of a Pseudogene‐associated ceRNA Network and Identification of Prognostic Pseudogene Biomarkers for Colorectal Cancer

10.21203/rs.3.rs-620279/v1 ◽

2021 ◽

Author(s):

Zhuoqi Li ◽

Jing Zhou ◽

Liankun Gu ◽

Baozhen Zhang

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Cox Regression ◽

Expression Profiles ◽

Colon Adenocarcinoma ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier

Abstract Colorectal cancer (CRC) is one of the most common and deadly malignant carcinomas. Many long noncoding RNAs (lncRNA) have been reported to play an important role in the tumorigenesis of CRC by interacting with miRNAs and influencing the expression of some mRNAs through a competing endogenous RNA (ceRNA) network. Pseudogenes are one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks, but there are few studies about pseudogenes in CRC. In this study, total of 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma (COAD) obtained from The Cancer Genome Atlas (TCGA). And a ceRNA network was constructed based on these RNAs. Kaplan–Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis on the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. What’s more, the results were validated by the Gene Expression Omnibus (GEO) database, and quantitative real‐time PCR (qRT‐PCR) in 113 pairs of CRC tissues. In conclusion, this study provides a pseudogene-associated ceRNA network and 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful to predict the survival of CRC patients.

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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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Fatty Acid Metabolism-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Patients With Colorectal Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.704038 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yurui Peng ◽

Chenxin Xu ◽

Jun Wen ◽

Yuanchuan Zhang ◽

Meng Wang ◽

...

Keyword(s):

Colorectal Cancer ◽

Fatty Acid ◽

Fatty Acid Metabolism ◽

Fatty Acid Synthase ◽

Acid Metabolism ◽

Cox Regression ◽

Clinical Stage ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Cerna Network

Abnormal metabolism, including abnormal fatty acid metabolism, is an emerging hallmark of cancer. The current study sought to investigate the potential prognostic value of fatty acid metabolism-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC). To this end, we obtained the gene expression data and clinical data of patients with CRC from The Cancer Genome Atlas (TCGA) database. Through gene set variation analysis (GSVA), we found that the fatty acid metabolism pathway was related to the clinical stage and prognosis of patients with CRC. After screening differentially expressed RNAs, we constructed a fatty acid metabolism-related competing endogenous RNA (ceRNA) network based on the miRTarBase, miRDB, TargetScan, and StarBase databases. Next, eight fatty acid metabolism-related lncRNAs included in the ceRNA network were identified to build a prognostic signature with Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, and a nomogram was established based on the lncRNA signature and clinical variables. The signature and nomogram were further validated by Kaplan–Meier survival analysis, Cox regression analysis, calibration plots, receiver operating characteristic (ROC) curves, decision curve analysis (DCA). Besides, the TCGA internal and the quantitative real-time polymerase chain reaction (qRT-PCR) external cohorts were applied to successfully validate the robustness of the signature and nomogram. Finally, in vitro assays showed that knockdown of prognostic lncRNA TSPEAR-AS2 decreased the triglyceride (TG) content and the expressions of fatty acid synthase (FASN) and acetyl-CoA carboxylase 1 (ACC1) in CRC cells, which indicated the important role of lncRNA TSPEAR-AS2 in modulating fatty acid metabolism of CRC. The result of Oil Red O staining showed that the lipid content in lncRNA TSPEAR-AS2 high expression group was higher than that in lncRNA TSPEAR-AS2 low expression group. Our study may provide helpful information for fatty acid metabolism targeting therapies in CRC.

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Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

Technology in Cancer Research & Treatment ◽

10.1177/1533033820984177 ◽

2020 ◽

Vol 19 ◽

pp. 153303382098417

Author(s):

Ting-ting Liu ◽

Shu-min Liu

Keyword(s):

Colorectal Cancer ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Small Gtpase ◽

Functional Enrichment ◽

Clinical Correlation ◽

Cox Regression Analysis ◽

Prognostic Analysis ◽

Key Genes

Objective: The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC). Methods: This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes. Results: Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation. Conclusions: In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

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Development and Validation of a Unique Gignature of Cancer Stemness-related Genes for Predicting the Overall survival of Colorectal Cancer Patients

10.21203/rs.3.rs-125323/v1 ◽

2020 ◽

Author(s):

Ran Wei ◽

Jichuan Quan ◽

Shuofeng Li ◽

Zhao Lu ◽

Xu Guan ◽

...

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Cancer Stemness ◽

Poor Overall Survival ◽

Tissue Samples ◽

Cox Regression Analysis ◽

Rna Methylation ◽

M6a Rna Methylation

Abstract Background: Cancer stem cells (CSCs), which are characterized by self-renewal and plasticity, are highly correlated with tumor metastasis and drug resistance. To fully understand the role of CSCs in colorectal cancer (CRC), we evaluated the stemness traits and prognostic value of stemness-related genes in CRC.Methods: In this study, the data from 616 CRC patients from The Cancer Genome Atlas (TCGA) were assessed and subtyped based on the mRNA expression-based stemness index (mRNAsi). The correlations of cancer stemness with the immune microenvironment, tumor mutational burden (TMB) and N6-methyladenosine (m6A) RNA methylation regulators were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify the crucial stemness-related genes and modules. Furthermore, a prognostic expression signature was constructed using Lasso-penalized Cox regression analysis. The signature was validated via multiplex immunofluorescence staining of tissue samples in an independent cohort of 48 CRC patients.Results: This study suggests that high mRNAsi scores are associated with poor overall survival in stage Ⅳ CRC patients. Moreover, the levels of TMB and m6A RNA methylation regulators were positively correlated with mRNAsi scores, and low mRNAsi scores were characterized by increased immune activity in CRC. The analysis identified 2 key modules and 34 key genes as prognosis-related candidate biomarkers. Finally, a 3-gene prognostic signature (PARPBP, KNSTRN and KIF2C) was explored together with specific clinical features to construct a nomogram, which was successfully validated in an external cohort. Conclusions: There is a unique correlation between CSCs and the prognosis of CRC patients, and the novel biomarkers related to cell stemness could accurately predict the clinical outcomes of these patients.

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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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Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

BioMed Research International ◽

10.1155/2020/2719739 ◽

2020 ◽

Vol 2020 ◽

pp. 1-43

Author(s):

Beilei Wu ◽

Lijun Tao ◽

Daqing Yang ◽

Wei Li ◽

Hongbo Xu ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Score ◽

Immune Cells ◽

Clinical Characteristics ◽

Cox Regression ◽

Roc Curves ◽

Functional Enrichment ◽

Cox Regression Analysis ◽

Immune Genes ◽

Score Model

Objective. Stromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores. Methods. A cohort of CRC patients (n=433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset. Results. Immune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients’ prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration. Conclusion. Our study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

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Up-Regulation of Long Noncoding RNA CCAL Predicts Poor Patient Prognosis and Promotes Tumor Metastasis in Osteosarcoma

The International Journal of Biological Markers ◽

10.5301/jbm.5000240 ◽

2017 ◽

Vol 32 (1) ◽

pp. 108-112 ◽

Cited By ~ 11

Author(s):

Da-Kai Zhou ◽

Xi-Wang Yang ◽

Huining Li ◽

Yongbo Yang ◽

Zhen-Jun Zhu ◽

...

Keyword(s):

Colorectal Cancer ◽

Cell Proliferation ◽

Noncoding Rna ◽

Tumor Metastasis ◽

Cox Regression ◽

Migration And Invasion ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Patient Prognosis ◽

Invasion Assays

Background Long noncoding RNAs (IncRNAs) play essential roles in tumor progression. Aberrant colorectal cancer-associated IncRNA (CCAL) has been found in colorectal cancer. However, the function of IncRNA CCAL in osteosarcoma (OS) remains unclear. Methods Quantitative real-time PCR (qRT-PCR) was performed to measure CCAL expression in OS tissues and adjacent nontumor tissues. The correlation betweent CCAL expression and clinicopathological features and prognosis was also analyzed. In addition, the function of CCAL was further evaluated by cell proliferation, migration and invasion assays. Results We showed that CCAL was significantly up-regulated in OS tissues compared with adjacent nontumor tissues. Increased expression of CCAL was correlated with advanced TNM stage and metastasis. Kaplan-Meier analysis demonstrated that patients with high CCAL expression had lower overall survival than those with low CCAL expression. Multivariate Cox regression analysis indicated that CCAL expression might be an independent prognostic factor for OS patients. In addition, functional assays showed that decreased CCAL expression could inhibit OS cell proliferation, migration and invasion ability. Conclusions Our findings suggested that CCAL plays critical roles in OS progression and could act as a therapeutic target in the treatment of OS.

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

BioMed Research International ◽

10.1155/2020/3075729 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Chao Li ◽

Wu Yao ◽

Congcong Zhao ◽

Guo Yang ◽

Jingjing Wei ◽

...

Keyword(s):

Esophageal Cancer ◽

Target Genes ◽

Cox Regression ◽

Malignant Tumors ◽

Transcriptome Profiling ◽

Assessment Model ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Cerna Network

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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