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

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.

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

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.

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

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.

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

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.

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

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.

scholarly journals High IKBIP expression as a biomarker for poor prognosis and correlated with immune infiltrates in hepatocellular carcinoma (HCC)

2021 ◽  
Author(s):  
Mingxin Lin ◽  
Jingping Liu ◽  
Xiaoli Shi ◽  
Jiajun Li ◽  
Huiming Ye
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Poor Prognosis ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Rank Test ◽  
Roc Curve Analysis ◽  
Ppi Networks ◽  
Diagnostic Potential

Abstract Background: I kappa B kinase interacting protein (IKBIP) is dysregulated and closely correlated with prognosis in tumor. However, the potential functions of IKBIP have not been utterly revealed in hepatocellular carcinoma (HCC). Thus, we explored the clinical values of IKBIP and its correlation with immune infiltrates in HCC.Methods: IKBIP expression in different tumor types was analyzed through the Tumor Immunity Evaluation Resource (TIMER) database. IKBIP expression between HCC and normal tissues were readily available for retrospective analyses from GEPIA, UALCAN and the Cancer Genome Atlas (TCGA) databases. The association between clinical features and IKBIP expression in HCC was analyzed by the Wilcoxon signed-rank test and logistic regression. Human Protein Atlas (HPA) was used to analyze the protein expression of IKBIP. A receiver operating characteristic (ROC) curve was performed to explore diagnostic potential of IKBIP. Kaplan-Meier (K-M) method and Cox regression were conducted to evaluate the influence of IKBIP on the prognosis of HCC patients. Protein-protein interaction (PPI) networks were established using STRING. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene functional enrichment analysis. The interrelation between IKBIP expression and immune infiltrates in HCC was analyzed through TIMER and tumor-immune system interaction database (TISIDB).Results: Our results revealed that IKBIP expression was abnormally upregulated in HCC, and ROC curve analysis confirmed the diagnostic power of IKBIP. High IKBIP expression was related to tumor sizes, pathologic stage and histologic grade. K-M plotter analysis showed that high IKBIP expression was correlated with poor prognosis of HCC patients. Moreover, multivariate Cox analysis further verified that high IKBIP expression was an independent risk factor in HCC patients. Correlation analysis found that IKBIP expression was associated with infiltrating immune cells.Conclusion: High IKBIP expression is associated with poor prognosis and immune infiltrates, which may serve as a prognostic biomarker and therapeutic target for HCC.

scholarly journals LIMD2 is a Prognostic and Predictive Marker in Patients With Esophageal Cancer Based on a ceRNA Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuanmei Chen ◽  
Xinyi Huang ◽  
Kunshou Zhu ◽  
Changkun Li ◽  
Haiyan Peng ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Cox Regression ◽  
Predictive Marker ◽  
Gene Expression Omnibus ◽  
Akt Signaling ◽  
The Cancer Genome Atlas ◽  
Circular Rnas ◽  
Messenger Rnas ◽  
Cerna Network ◽  
Functional Pathway

Globally, esophageal cancer (ECA) is the seventh most common cancer and sixth most common cause of cancer-associated mortality. However, there are no reliable prognostic and predictive molecular markers for ECA; in addition, the pathogenesis of ECA is not fully elucidated. The expressions of circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) of ECA and control groups were obtained from the RNA-sequencing (RNA-seq) data of our hospital, the Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) datasets. Analyses of differentially expressed genes, the circRNA–miRNA–mRNA–competing endogenous RNA (ceRNA) network, and functional/pathway enrichment were conducted. The key targets in the ceRNA network that showed significant results in survival Cox regression analyses were selected. Furthermore, analyses of immune infiltration and autophagy genes related to the key targets were performed. Seven circRNAs, 22 miRNAs, and 34 mRNAs were identified as vital genes in ECA; the nuclear factor-κ-gene binding (NF-κB) and phosphatidylinositol-3 kinase/protein kinase B (PI3K-Akt) signaling were identified as the most enriched pathways. In addition, the LIM domain containing 2 (LIMD2) was an independent predictor of prognosis in ECA patients and closely associated with immunity and autophagy. Moreover, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) revealed significant upregulation of LIMD2 expression in ECA tissues. ECA may be closely correlated with NF-κB and PI3K/Akt signaling. In addition, LIMD2 could be a potential prognostic and predictive marker of ECA.

scholarly journals Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Honglan Guo ◽  
Qinqiao Fan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

scholarly journals Identification of a Ubiquitin Related Genes Signature for Predicting Prognosis of Prostate Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Guoda Song ◽  
Yucong Zhang ◽  
Hao Li ◽  
Zhuo Liu ◽  
Wen Song ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Post Translational Modifications ◽  
Training Cohort ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Background: Ubiquitin and ubiquitin-like (UB/UBL) conjugations are one of the most important post-translational modifications and involve in the occurrence of cancers. However, the biological function and clinical significance of ubiquitin related genes (URGs) in prostate cancer (PCa) are still unclear.Methods: The transcriptome data and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA), which was served as training cohort. The GSE21034 dataset was used to validate. The two datasets were removed batch effects and normalized using the “sva” R package. Univariate Cox, LASSO Cox, and multivariate Cox regression were performed to identify a URGs prognostic signature. Then Kaplan-Meier curve and receiver operating characteristic (ROC) curve analyses were used to evaluate the performance of the URGs signature. Thereafter, a nomogram was constructed and evaluated.Results: A six-URGs signature was established to predict biochemical recurrence (BCR) of PCa, which included ARIH2, FBXO6, GNB4, HECW2, LZTR1 and RNF185. Kaplan-Meier curve and ROC curve analyses revealed good performance of the prognostic signature in both training cohort and validation cohort. Univariate and multivariate Cox analyses showed the signature was an independent prognostic factor for BCR of PCa in training cohort. Then a nomogram based on the URGs signature and clinicopathological factors was established and showed an accurate prediction for prognosis in PCa.Conclusion: Our study established a URGs prognostic signature and constructed a nomogram to predict the BCR of PCa. This study could help with individualized treatment and identify PCa patients with high BCR risks.

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

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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