A Three-Gene Prognostic Signature Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

Abstract Background: Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, functions of circRNAs in lung adenocarcinoma (LUAD) are still unknown. It is necessary to investigate the regulatory mechanism of circRNAs based on competing endogenous RNA (ceRNA) network in LUAD procession and further construct a prognostic signature for predicting overall survival of LUAD patients.Methods: Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs) and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network based on TargetScan prediction tool and Pearson correlation coefficient. Functional and pathway enrichment analysis were performed using DAVID database. A PPI network was constructed and then visualized by Cytoscape software. Finally, we constructed a prognostic signature for LUAD patients using LASSO method and assessed the prognostic performance in the validation cohort.Results: A total of 38 DEcircRNAs, 56 DEmiRNAs, and 960 DEmRNAs were identifed. Based on the interactions predicted by TargetScan, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs and 49 DEmRNAs. GO and KEGG pathway analysis indicated that the circRNA-associated ceRNA network might be involved in regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using LASSO method. By applying the signature, patients in the training cohort could be categorized into high-risk or low-risk subgroup with significant survival difference (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009). The prognostic performance was confirmed in an independent GEO cohort (HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004). Multivariate cox regression analysis proved that the three-gene signature was an independent prognostic factor for LUAD.Conclusions: Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and constructed a prognostic signature that could be a useful guide for personalized treatment of LUAD patients.

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Prognostic Nomogram Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/9978206 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Yang Li ◽

Rongrong Sun ◽

Rui Li ◽

Yonggang Chen ◽

He Du

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Regulatory Mechanism ◽

Cox Regression ◽

Gene Signature ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009 ). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004 ). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C -indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.

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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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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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Five-mRNA Signature for the Prognosis of Breast Cancer Based on the ceRNA Network

BioMed Research International ◽

10.1155/2020/9081852 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Wenjie Shi ◽

Daojun Hu ◽

Sen Lin ◽

Rui Zhuo

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Differentially Expressed ◽

Risk Scores ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Protein Levels ◽

Cerna Network

Background. The purpose of this study was to investigate the regulatory mechanisms of ceRNAs in breast cancer (BC) and construct a new five-mRNA prognostic signature. Methods. The ceRNA network was constructed by different RNAs screened by the edgeR package. The BC prognostic signature was built based on the Cox regression analysis. The log-rank method was used to analyse the survival rate of BC patients with different risk scores. The expression of the 5 genes was verified by the GSE81540 dataset and CPTAC database. Results. A total of 41 BC-adjacent tissues and 473 BC tissues were included in this study. A total of 2,966 differentially expressed lncRNAs, 5,370 differentially expressed mRNAs, and 359 differentially expressed miRNAs were screened. The ceRNA network was constructed using 13 lncRNAs, 267 mRNAs, and 35 miRNAs. Kaplan-Meier (K-M) methods showed that two lncRNAs (AC037487.1 and MIR22HG) are related to prognosis. Five mRNAs (VPS28, COL17A1, HSF1, PUF60, and SMOC1) in the ceRNA network were used to establish a prognostic signature. Survival analysis showed that the prognosis of patients in the low-risk group was significantly better than that in the high-risk group (p=0.0022). ROC analysis showed that this signature has a good diagnostic ability (AUC=0.77). Compared with clinical features, this signature was also an independent prognostic factor (HR: 1.206, 95% CI 1.108−1.311; p<0.001). External verification results showed that the expression of the 5 mRNAs differed between the normal and tumour groups at the chip and protein levels (p<0.001). Conclusions. These ceRNAs may play a key role in the development of BC, and the new 5-mRNA prognostic signature can improve the prediction of survival for BC patients.

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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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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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Comprehensive Bioinformatics Analysis of Lipopolysaccharide-Induced Altered Autophagy in Acute Lung Injury and Construction of Underlying Competing Endogenous RNA Regulatory Mechanism

BioMed Research International ◽

10.1155/2021/6831770 ◽

2021 ◽

Vol 2021 ◽

pp. 1-21

Author(s):

Jian-Yu Liu ◽

Ying-Xiao Jiang ◽

Meng-Yu Zhang ◽

Chen Huo ◽

Yi-Can Yang ◽

...

Keyword(s):

Acute Lung Injury ◽

Epithelial Cell ◽

Lung Injury ◽

Regulatory Mechanism ◽

Bronchial Epithelial Cell ◽

Functional Enrichment ◽

Differentially Expressed ◽

Competing Endogenous Rna ◽

Bronchial Epithelial ◽

Cerna Network

Background. Acute lung injury (ALI) is a fatal syndrome frequently induced by lipopolysaccharide (LPS) released from the bacterial cell wall. LPS could also trigger autophagy of lung bronchial epithelial cell to relieve the inflammation, while the overwhelming LPS would impair the balance of autophagy consequently inducing serious lung injury. Methods. We observed the autophagy variation of 16HBE, human bronchial epithelial cell, under exposure to different concentrations of LPS through western blot, immunofluorescence staining, and electron microscopy. Eight strands of 16HBE were divided into two groups upon 1000 ng/ml LPS stimulation or not, which were sent to be sequenced at whole transcriptome. Subsequently, we analyzed the sequencing data in functional enrichment, pathway analysis, and candidate gene selection and constructed a hsa-miR-663b-related competing endogenous RNA (ceRNA) network. Results. We set a series of concentrations of LPS to stimulate 16HBE and observed the variation of autophagy in related protein expression and autophagosome count. We found that the effective concentration of LPS was 1000 ng/ml at 12 hours of exposure and sequenced the 1000 ng/ml LPS-stimulated 16HBE. As a result, a total of 750 differentially expressed genes (DEGs), 449 differentially expressed lncRNAs (DElncRNAs), 76 differentially expressed circRNAs (DEcircRNAs), and 127 differentially expressed miRNAs (DEmiRNAs) were identified. We constructed the protein-protein interaction (PPI) network to visualize the interaction between DEGs and located 36 genes to comprehend the core discrepancy between LPS-stimulated 16HBE and the negative control group. In combined analysis of differentially expressed RNAs (DERNAs), we analyzed all the targeted relationships of ceRNA in DERNAs and figured hsa-miR-663b as a central mediator in the ceRNA network to play when LPS induced the variation of autophagy in 16HBE. Conclusion. Our research indicated that the hsa-miR-663b-related ceRNA network may contribute to the key regulatory mechanism in LPS-induced changes of autophagy and ALI.

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Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2021/4093426 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Lixian Chen ◽

Zhonglu Ren ◽

Yongming Cai

Keyword(s):

Prognostic Factors ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Cox Regression ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Competing Endogenous Rna ◽

Cox Regression Analysis ◽

Cerna Network

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

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A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.612952 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zaisheng Ye ◽

Miao Zheng ◽

Yi Zeng ◽

Shenghong Wei ◽

He Huang ◽

...

Keyword(s):

Risk Score ◽

Molecular Mechanisms ◽

Cancer Metabolism ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Stomach Adenocarcinoma ◽

Prognostic Prediction

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice.

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Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

10.21203/rs.3.rs-32147/v1 ◽

2020 ◽

Author(s):

Pinping Jiang ◽

Wei Sun ◽

Ningmei Shen ◽

Qiang Wang ◽

Shouyu Wang ◽

...

Keyword(s):

Clinical Features ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Autophagy, as a lysosomal degradation pathway, has been reported to be involved in various pathologies, including cancer. However, the expression profiles of autophagy-related genes (ARGs) in endometrial cancer (EC) remain poorly understood. Methods In this study, we analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated to EC patients’ prognosis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DE-MRGs were investigated. LASSO algorithm and Cox regression analysis were performed to select MRGs closely related to EC patients’ outcomes. A prognostic signature was developed and the efficacy were validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability. Results Ninety-four ARGs significantly dysregulated in EC samples compared with the normal control samples. Functional enrichment analysis showed these differentially expressed ARGs (DE-ARGs) were highly enriched in apoptosis, P53 signaling pathway, and various cancer development. Among the 94 DE-ARGs, we subsequently screen out four-ARGs closely related to EC patients outcomes, which are ERBB2, PTEN, TP73 and ARSA. Based on the expression and coefficiency of 4 DE-ARGs, we developed a prognostic signature and further validated its efficacy in part of and the entire TCGA EC cohort. The four ARGs signature was independent of other clinical features, and was proved to effectively distinguish high- or low-risk EC patients and predicted patients' OS accurately. Moreover, the nomogram showed the excellent consistency between the prediction and actual observation in terms of patients' 3- and 5-year survival rates. Conclusions It was suggested that the ARG prognostic model and the comprehensive nomogram may guide the precise outcome prediction and rational therapy in clinical practice.

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