scholarly journals Explore prognostic biomarker of bladder cancer based on competing endogenous network

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Faping Li ◽  
Hui Guo ◽  
Bin Liu ◽  
Nian Liu ◽  
Zhixiang Xu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regulation Of Transcription ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Competing Endogenous Rna Network ◽  
Risk Patients ◽  
Sequence Profiles

Abstract Bladder cancer (BC) is the most common tumor of the urinary tract. Increasing evidence showed that long non-coding RNA (lncRNA) is a critical regulator in cancer development and progression. However, the functions of lncRNAs in the development of BC remain mostly undefined. In the present study, based on RNA sequence profiles from The Cancer Genome Atlas database, we identified 723 lncRNAs, 157 miRNAs, and 1816 mRNAs aberrantly expressed in BC tissues. A competing endogenous RNA network, including 49 lncRNAs, 17 miRNAs, and 36 mRNAs, was then established. The functional enrichment analyses showed that the mRNAs in the ceRNA network mainly participated in ‘regulation of transcription’ and ‘pathways in cancer’. Moreover, the Cox regression analyses demonstrated that three lncRNAs (AC112721.1, TMPRSS11GP, and ADAMTS9-AS1) could serve as independent risk factors. We established a risk prediction model with these lncRNAs. Kaplan–Meier curve analysis showed that high-risk patients’ prognosis was lower than that of low-risk patients (P=0.001). The present study provides novel insights into the lncRNA-mediated ceRNA network and the potential of lncRNAs to be candidate prognostic biomarkers in BC, which could help better understand the pathological changes and pathogenesis of BC and be useful for clinical studies in the future.

scholarly journals Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Jian-Rong Sun ◽  
Chen-Fan Kong ◽  
Kun-Min Xiao ◽  
Jia-Lu Yang ◽  
Xiang-Ke Qu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Risk Patients

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

The Analysis of lncRNA-miRNA-mRNA Competitive Endogenous RNA Network to Identify 4-lncRNA Signature Related to the Prognosis of Bladder Cancer

2021 ◽  
Author(s):  
Huili Zhu ◽  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Yuping Wang ◽  
Yongli Yang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Normal Bladder ◽  
Cancer Tissues ◽  
Network Server ◽  
Competitive Endogenous Rna

Abstract BackgroundBladder cancer (BLCA) is one of the leading causes of cancer deaths in the world, and the molecular mechanism of its pathogenesis is very complicated. Long non-coding RNA (lncRNA) can interact with microRNA (miRNA) through the mechanism of competitive endogenous RNA (ceRNA), and affect the expression of Messenger RNA (mRNA), and affect the pathogenesis of bladder cancer. This study aims to construct the ceRNA-regulated bladder cancer network related to lncRNA and identify a novel lncRNA signature related to the survival prognosis of patients with bladder cancer. It was validated in GEPIA's online bioinformatics network server assists. MethodsThe RNA sequencing data of normal and adjacent bladder cancer tissues are from the Cancer Genome Atlas (TCGA). We identify differentially expressed (DE) genes by comparing gene expression between normal tissues and tumors in the TCGA dataset. Construct a ceRNA network and explore potential biological markers. Based on the ceRNA network, univariate regression analysis and multivariate regression analysis were used to screen out the lncRNA related to the overall survival (OS) of bladder cancer. It was validated in GEPIA's online bioinformatics network server assists. Receiver operating characteristic curve (ROC) analysis was used to evaluate the prognostic value of the risk score.ResultsWe screened out 666 lncRNAs, 160 microRNAs (miRNAs), and 1,820 Messenger RNAs (mRNAs) by comparing normal bladder cancer tissues and adjacent tissues (P<0.05). Then, we constructed a ceRNA regulatory network containing 44 DElncRNA, 22 DEmiRNA, and 52 DEmRNA. The survival analysis of differential genes in the ceRNA network identified 9 lncRNAs, 8 miRNAs, and 12 mRNAs that are associated with the prognosis of BLCA. Cox regression analysis of 9 LncRNAs related to the prognosis of bladder cancer showed that 4 lncRNAs (AC078778.1, ADAMTS9-AS1, ADAMTS9-AS2, and NAV2-AS2) can be independently used as prognostic markers of bladder cancer.ConclusionsBased on the construction of the bladder cancer ceRNA network, a new prognostic signature of four lncRNA-based has been discovered. It will help to better understand the mechanism of bladder cancer occurrence, development and metastasis, and provide direction for future research.

scholarly journals The construction and validation of an RNA binding protein-related prognostic model for bladder cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fengxia Chen ◽  
Qingqing Wang ◽  
Yunfeng Zhou
Keyword(s):  
Bladder Cancer ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Sequence Data ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Depth Analysis

Abstract Background RNA-binding proteins (RBPs) play crucial and multifaceted roles in post-transcriptional regulation. While RBPs dysregulation is involved in tumorigenesis and progression, little is known about the role of RBPs in bladder cancer (BLCA) prognosis. This study aimed to establish a prognostic model based on the prognosis-related RBPs to predict the survival of BLCA patients. Methods We downloaded BLCA RNA sequence data from The Cancer Genome Atlas (TCGA) database and identified RBPs differentially expressed between tumour and normal tissues. Then, functional enrichment analysis of these differentially expressed RBPs was conducted. Independent prognosis-associated RBPs were identified by univariable and multivariable Cox regression analyses to construct a risk score model. Subsequently, Kaplan–Meier and receiver operating characteristic curves were plotted to assess the performance of this prognostic model. Finally, a nomogram was established followed by the validation of its prognostic value and expression of the hub RBPs. Results The 385 differentially expressed RBPs were identified included 218 and 167 upregulated and downregulated RBPs, respectively. The eight independent prognosis-associated RBPs (EFTUD2, GEMIN7, OAS1, APOBEC3H, TRIM71, DARS2, YTHDC1, and RBMS3) were then used to construct a prognostic prediction model. An in-depth analysis showed lower overall survival (OS) in patients in the high-risk subgroup compared to that in patients in the low-risk subgroup according to the prognostic model. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve were 0.795 and 0.669 for the TCGA training and test datasets, respectively, showing a moderate predictive discrimination of the prognostic model. A nomogram was established, which showed a favourable predictive value for the prognosis of BLCA. Conclusions We developed and validated the performance of a prognostic model for BLCA that might facilitate the development of new biomarkers for the prognostic assessment of BLCA patients.

scholarly journals A Simple Competing Endogenous RNA Network Identifies Novel mRNA, miRNA, and lncRNA Markers in Human Cholangiocarcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Cheng Zhang ◽  
Chunlin Ge
Keyword(s):  
Cox Regression ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Primary Liver Tumor ◽  
Normal Tissues ◽  
Cerna Network ◽  
Tumorigenesis And Progression ◽  
Competing Endogenous Rna Network ◽  
Cancer Genome Atlas ◽  
Novel Biomarkers

Background. Cholangiocarcinoma (CCA) is the second most common malignant primary liver tumor and has shown an alarming increase in incidence over the last two decades. However, the mechanisms behind tumorigenesis and progression remain insufficient. The present study aimed to uncover the underlying regulatory mechanism on CCA and find novel biomarkers for the disease prognosis. Method. The RNA-sequencing (RNA-seq) datasets of lncRNAs, miRNAs, and mRNAs in CCA as well as relevant clinical information were obtained from the Cancer Genome Atlas (TCGA) database. After pretreatment, differentially expressed RNAs (DERNAs) were identified and further interrogated for their correlations with clinical information. Prognostic RNAs were selected using univariate Cox regression. Then, a ceRNA network was constructed based on these RNAs. Results. We identified a total of five prognostic DEmiRNAs, 63 DElncRNAs, and 90 DEmRNAs between CCA and matched normal tissues. Integrating the relationship between the different types of RNAs, an lncRNA-miRNA-mRNA network was established and included 28 molecules and 47 interactions. Screened prognostic RNAs involved in the ceRNA network included 3 miRNAs (hsa-mir-1295b, hsa-mir-33b, and hsa-mir-6715a), 7 lncRNAs (ENSG00000271133, ENSG00000233834, ENSG00000276791, ENSG00000241155, COL18A1-AS1, ENSG00000274737, and ENSG00000235052), and 18 mRNAs (ANO9, FUT4, MLLT3, ABCA3, FSCN2, GRID2IP, NCK2, MACC1, SLC35E4, ST14, SH2D3A, MOB3B, ACTL10, RAB36, ATP1B3, MST1R, SEMA6A, and SEL1L3). Conclusions. Our study identified novel prognostic makers and predicted a previously unknown ceRNA regulatory network in CCA and may provide novel insight into a further understanding of lncRNA-mediated ceRNA regulatory mechanisms in CCA.

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

2020 ◽  
Vol 21 (24) ◽  
pp. 9359
Author(s):  
Shuzhen Liu ◽  
Qing Cao ◽  
Guoyan An ◽  
Bianbian Yan ◽  
Lei Lei
Keyword(s):  
Colorectal Cancer ◽  
Noncoding Rna ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cell Counting ◽  
Luciferase Assay ◽  
Clinical Feature ◽  
Cox Regression Analysis ◽  
Cerna Network

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.

scholarly journals Identification of Key Iron Metabolism-Related Long Non-Coding RNAs in Hepatocellular Carcinoma Based on Bioinformatics Analysis

2021 ◽  
Author(s):  
yu chen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Roc Curve Analysis ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Non Coding Rnas

Abstract Background： The present study explored the regulatory mechanisms and functional roles of iron metabolism-related long non-coding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) and their potential impact on prognosis of HCC patients. Methods：RNA-seq data and clinical information of HCC samples and normal samples were downloaded from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) portal. Iron metabolism-related genes were downloaded from Reactome database and AmiGo2 database. Differential expression and correlation analysis were performed to identify iron metabolism-related differentially expressed lncRNAs (DElncRNAs). Moreover, Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) analysis were used to screen the possible prognostic and diagnostic biomarkers of HCC. Results: A total of 20 differentially expressed and iron metabolism-related genes (DEIMRG) were identified by overlapping 3746 differentially expressed genes (DEGs) and 86 IMRGs. Next, ARHGAP11B, LINC00205, LINC00261 and SNHG12 were screened through Univariate Cox regression. Kaplan-Meier survival curves indicated that ARHGAP11B, LINC00205, LINC00261 and SNHG12 were related to overall survival (OS) in HCC patient in TCGA database. ARHGAP11B, LINC00205 and LINC00261 were finally identified as prognostic DEIMRGs related with OS of HCC patients after validate the survival results in ICGC portal. ARHGAP11B, LINC00205 and LINC00261 all achieved an AUC value of >0.80 in ROC curve analysis. Furthermore, LINC00205 was identified as independently prognostic factor by multivariate Cox analysis combined with clinicopathological factors. Moreover, a ceRNA network including 25 DEmRNAs, 15 DEmiRNA and 3 DElncRNAs was successfully constructed, based on prognostic DElncRNAs and key target miRNAs and mRNAs of them predicted by starBase database and miRwalk. The PPI network illustrated that CDC25A, CHEK1, CCNE2 and ANLN proteins interact more with other proteins. Conclusions: In the present study, we identified iron metabolism related LINC00205 as a prognostic and diagnostic biomarker and constructed a metabolism-related ceRNA network, which may contribute to the treatment of HCC.

scholarly journals Building a Competing Endogenous RNA Network to Find Potential Long Non-Coding RNA Biomarkers for Pheochromocytoma

2018 ◽  
Vol 51 (6) ◽  
pp. 2916-2924 ◽  
Author(s):  
Ying-Chun Liang ◽  
Yu-Peng Wu ◽  
Dong-Ning Chen ◽  
Shao-Hao Chen ◽  
Xiao-Dong Li ◽  
...  
Keyword(s):  
Digital Gene Expression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Competing Endogenous Rna Network ◽  
Patient Prognosis

Background/Aims: Accumulating evidence has shown that long non-coding RNAs (lncRNAs) in competing endogenous RNA (ceRNA) networks play crucial roles in tumor survival and patient prognosis; however, studies investigating ceRNA networks in pheochromocytoma (PCC) are lacking. In this study, we investigated the pathogenesis of PCC and whether lncRNAs acting through ceRNAs networks were associated with prognosis. Methods: A total of 183 PCC samples and 3 control samples from The Cancer Genome Atlas database were analyzed. The Empirical Analysis of Digital Gene Expression Data package in R (edgeR) was used to analyze differentially expressed RNAs. Biological processes and pathways functional enrichment analysis were performed based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. LncRNA/mRNA/miRNA ceRNA network was constructed by Cytoscape v3.0 software based on the differentially expressed RNAs Survival package in R was used to perform survival analysis. Results: In total, 554 differentially expressed lncRNAs, 1775 mRNAs and 40 miRNAs were selected for further analysis. Subsequently, 23 lncRNAs, 22 mRNAs, and 6 miRNAs were included in the constructed ceRNA network. Meanwhile, two of the 23 lncRNAs (C9orf147 and BSN-AS2) were identified as independent predictors of overall survival in PCC patients (P< 0.05). Conclusion: This study improves the understanding of lncRNA-related ceRNA networks in PCC and suggests that the lncRNAs C9orf147 and BSN-AS2 could be independent prognostic biomarkers and potential therapeutic targets for PCC.

scholarly journals A Robust Circular RNA-Associated Three-Gene Prognostic Signature for Patients with Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yang Li ◽  
Rui Li ◽  
Xiuli Wang ◽  
Yuan Yuan ◽  
Yangmei Zhang
Keyword(s):  
Gastric Cancer ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regulation Of Transcription ◽  
Prognostic Signature ◽  
Mesodermal Cell ◽  
Cerna Network ◽  
Significant Difference ◽  
Normal Rank

Accumulating evidence has demonstrated that circular RNAs (circRNAs) play vital roles in cancer progression. However, the underlying molecular mechanisms of circRNAs remain poorly elucidated in gastric cancer (GC). The main purpose of present study is to explore the underlying regulatory mechanism by constructing a circRNA-associated competitive endogenous RNA (ceRNA) network and further establish a robust prognostic signature for patients with GC. Based on expression data of circRNA, microRNA, and mRNA derived from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, a circRNA-associated ceRNA network, containing 15 cirRNAs, 9 microRNAs, and 35 mRNAs, was constructed using the Starbase database. Functional enrichment analysis showed that the ceRNA network might be involved in many cancer-related pathways, such as regulation of transcription from RNA polymerase II promoter, mesodermal cell differentiation, and focal adhesion. A protein-protein interaction network was constructed based on genes within the circRNA-associated ceRNA network. We found that six of ten hub genes within the PPI network were significantly associated with overall survival (OS). Thus, using the LASSO method, we constructed a three-gene prognostic signature based on TCGA-GC cohort, which could classify GC patients into low-risk and high-risk groups with significant difference in OS ( HR = 1.9 , 95 % CI = 1.14 ‐ 3.2 , and log-rank p = 0.001 ). The prognostic performance of the three-gene signature was verified in GSE15459 ( HR = 1.9 , 95 % CI = 1.27 ‐ 3.0 , and log − rank   p = 2.2 E − 05 ) and GSE84437 ( HR = 1.5 , 95 % CI = 1.17 ‐ 2.0 , and log − rank   p = 6.3 E − 04 ). Multivariate Cox analysis further revealed that the three-gene prognostic signature could serve as an independent risk factor for OS. Taken together, our findings contribute to a better understanding of the underlying mechanisms of circRNAs in GC progression. Furthermore, a robust prognostic signature is meaningful to facilitate individualized treatment for patients with GC.

scholarly journals Identification of a Novel Ferroptosis-Related Gene Prognostic Signature in Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiale Sun ◽  
Wenchang Yue ◽  
Jiawei You ◽  
Xuedong Wei ◽  
Yuhua Huang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Experimental Studies ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Molecular Alteration

BackgroundFerroptosis is a newly found non-apoptotic forms of cell death that plays an important role in tumors. However, the prognostic value of ferroptosis-related genes (FRG) in bladder cancer (BLCA) have not been well examined.MethodsFRG data and clinical information were collected from The Cancer Genome Atlas (TCGA). Then, significantly different FRGs were investigated by functional enrichment analyses. The prognostic FRG signature was identified by univariate cox regression and least absolute shrinkage and selection operator (LASSO) analysis, which was validated in TCGA cohort and Gene Expression Omnibus (GEO) cohort. Subsequently, the nomogram integrating risk scores and clinical parameters were established and evaluated. Additionally, Gene Set Enrichment Analyses (GSEA) was performed to explore the potential molecular mechanisms underlying our prognostic FRG signature. Finally, the expression of three key FRGs was verified in clinical specimens.ResultsThirty-two significantly different FRGs were identified from TCGA–BLCA cohort. Enrichment analyses showed that these genes were mainly related to the ferroptosis. Seven genes (TFRC, G6PD, SLC38A1, ZEB1, SCD, SRC, and PRDX6) were then identified to develop a prognostic signature. The Kaplan–Meier analysis confirmed the predictive value of the signature for overall survival (OS) in both TCGA and GEO cohort. A nomogram integrating age and risk scores was established and demonstrated high predictive accuracy, which was validated through calibration curves and receiver operating characteristic (ROC) curve [area under the curve (AUC) = 0.690]. GSEA showed that molecular alteration in the high- or low-risk group was closely associated with ferroptosis. Finally, experimental results confirmed the expression of SCD, SRC, and PRDX6 in BLCA.ConclusionHerein, we identified a novel FRG prognostic signature that maybe involved in BLCA. It showed high values in predicting OS, and targeting these FRGs may be an alternative for BLCA treatment. Further experimental studies are warranted to uncover the mechanisms that these FRGs mediate BLCA progression.

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

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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