scholarly journals Comprehensive analysis of prognostic biomarkers in lung adenocarcinoma based on aberrant lncRNA–miRNA–mRNA networks and Cox regression models

2020 ◽  
Vol 40 (1) ◽  
Author(s):  
Yan Yao ◽  
Tingting Zhang ◽  
Lingyu Qi ◽  
Ruijuan Liu ◽  
Gongxi Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prediction Models ◽  
Cox Regression ◽  
Prognostic Index ◽  
The Cancer Genome Atlas ◽  
Prognostic Biomarkers ◽  
Prognostic Indicators ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
A Genome

Abstract Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide, and its underlying mechanism remains unclear. Accumulating evidence has highlighted that long non-coding RNA (lncRNA) acts as competitive endogenous RNA (ceRNA) and plays an important role in the occurrence and development of LUAD. Here, we comprehensively analyzed and provided an overview of the lncRNAs, miRNAs, and mRNAs associated with LUAD from The Cancer Genome Atlas (TCGA) database. Then, differentially expressed lncRNAs (DElncRNA), miRNAs (DEmiRNA), and mRNAs (DEmRNA) were used to construct a lncRNA–miRNA–mRNA regulatory network according to interaction information from miRcode, TargetScan, miRTarBase, and miRDB. Finally, the RNAs of the network were analyzed for survival and submitted for Cox regression analysis to construct prognostic indicators. A total of 1123 DElncRNAs, 95 DEmiRNAs, and 2296 DEmRNAs were identified (|log2FoldChange| (FC) > 2 and false discovery rate (FDR) or adjusted P value < 0.01). The ceRNA network was established based on this and included 102 lncRNAs, 19 miRNAs, and 33 mRNAs. The DEmRNAs in the ceRNA network were found to be enriched in various cancer-related biological processes and pathways. We detected 22 lncRNAs, 12 mRNAs, and 1 miRNA in the ceRNA network that were significantly associated with the overall survival of patients with LUAD (P < 0.05). We established three prognostic prediction models and calculated the area under the 1,3,5-year curve (AUC) values of lncRNA, mRNA, and miRNA, respectively. Among them, the prognostic index (PI) of lncRNA showed good predictive ability which was 0.737, 0.702 and 0.671 respectively, and eight lncRNAs can be used as candidate prognostic biomarkers for LUAD. In conclusion, our study provides a new perspective on the prognosis and diagnosis of LUAD on a genome-wide basis, and develops independent prognostic biomarkers for LUAD.

scholarly journals A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Fengxia Qin ◽  
Houxi Xu ◽  
Guoli Wei ◽  
Yi Ji ◽  
Jialin Yu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Prognostic Index ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Prognostic Indicators ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Cancer Tissues

BackgroundColorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.MethodsThe clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.ResultsA total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.ConclusionEight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

scholarly journals An integrated analysis of the competing endogenous RNA network associated of prognosis of stage I lung adenocarcinoma

2021 ◽  
Author(s):  
Yuan Xu ◽  
Guofu Lin ◽  
Yifei Liu ◽  
Xianbin Lin ◽  
Hai Lin ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Early Stage ◽  
Stage I ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background: Accumulating evidence indicates that long non-coding RNAs (lncRNAs) are involving in the tumorigenesis and metastasis of lung cancer. The aim of the study is to systematically characterize the lncRNA-associated competing endogenous RNA (ceRNA) network and identify key lncRNAs in the development of stage I lung adenocarcinoma (LUAD). Methods: Totally, 1,955 DEmRNAs, 165 DEmiRNAs and 1,107 DElncRNAs were obtained in 10 paired normal and LUAD tissues. And a total of 8,912 paired lncRNA-miRNA-mRNA network was constructed. Using the Cancer Genome Atlas (TCGA) dataset, the module of ME turquoise was revealed to be most relevant to the progression of LUAD though Weighted Gene Co-expression Network Analysis (WGCNA). Results: Of the lncRNAs identified, LINC00639, RP4-676L2.1 and FENDRR were in ceRNA network established by our RNA-sequencing dataset. Using univariate Cox regression analysis, FENDRR was a risk factor of progression free survival (PFS) of stage I LUAD patients (HRs=1.69, 95%CI 1.07-2.68, P< .050). Subsequently, differential expression of FENDRR in paired normal and LUAD tissues was detected significant by real-time quantitative (qRT-PCR) (P <0.001). Conclusions: This study, for the first time, deciphered the regulatory role of FENDRR/miR-6815-5p axis in the progression of early-stage LUAD, which is needed to be established in vitro and in vivo.

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

2018 ◽  
Vol 48 (5) ◽  
pp. 1953-1967 ◽  
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.

scholarly journals Comprehensive analysis of competitive endogenous RNA associated with immune infiltration in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenjie Chen ◽  
Wen Li ◽  
Zhenkun Liu ◽  
Guangzhi Ma ◽  
Yunfu Deng ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Mast Cells ◽  
Correlation Analysis ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cerna Network ◽  
Monocytes And Macrophages ◽  
Competitive Endogenous Rna

AbstractTo identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan–Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

scholarly journals Identification and validation of immunotypes for clear cell RCC prognosis

2021 ◽  
Author(s):  
Zhaolin Yang ◽  
Jiale Zhou ◽  
Yizheng Xue ◽  
Yu Zhang ◽  
Kaijun Zhou ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Clear Cell ◽  
Survival Rates ◽  
Prognostic Index ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Metastasis Model

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.

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 Integrated analysis of the functions and prognostic values of RNA binding proteins in colon adenocarcinoma

2020 ◽  
Author(s):  
Xinhong Liu ◽  
Fang Tan ◽  
Xingyao Long ◽  
Ruokun Yi ◽  
Dingyi Yang ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Prediction Models ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Cox Regression Analysis

Abstract Background RNA binding proteins (RBPs) play an important role in a variety of cancers. However, the role of RBPs in colorectal adenocarcinoma (COAD) has not been studied. Integrated analysis of RBPs will provide a better understanding of disease genesis and new insights into COAD treatment. Methods The gene expression data and corresponding clinical information for COAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was used to screen for RBPs associated with COAD recurrence, and multivariate Cox proportional hazards regression analyses were used to identify genes that were associated with COAD recurrence. A nomogram was constructed to predict the recurrence of COAD, and a receiver operating characteristic (ROC) curve analysis was performed to determine the accuracy of the prediction models. The Human Protein Atlas database was used in prediction models to confirm the expression of key genes in COAD patients. Result A total of 177 differentially expressed RBPs was obtained, comprising 123 upregulated and 54 downregulated. GO and KEGG enrichment analysis showed that the differentially expressed RBPs were mainly related to mRNA metabolism, RNA processing and translation regulation. Seven RBP genes (TDRD6, POP1, TDRD7, PPARGC1A, LIN28B, LRRFIP2 and PNLDC1) were identified as prognosis-associated genes and were used to construct the prognostic model. Conclusion We constructed a COAD prognostic model through bioinformatics analysis, which indicated that prognostic model RBPs have a potential role in the diagnosis and prognosis of COAD. Moreover, the nomogram can effectively predict the 1-year, 3-year, and 5-year survival rate for COAD patients.

scholarly journals Upregulation of GNPNAT1 Predicts Poor Prognosis and Correlates With Immune Infiltration in Lung Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Wenting Liu ◽  
Kaiting Jiang ◽  
Jingya Wang ◽  
Ting Mei ◽  
Min Zhao ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathways ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Clinical Stage ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Cox Regression Analysis ◽  
Normal Tissues

BackgroundGlucosamine 6-phosphate N-acetyltransferase (GNPNAT1) is a key enzyme in the hexosamine biosynthetic pathway (HBP), which functions as promoting proliferation in some tumors, yet its potential biological function and mechanism in lung adenocarcinoma (LUAD) have not been explored.MethodsThe mRNA differential expression of GNPNAT1 in LUAD and normal tissues was analyzed using the Cancer Genome Atlas (TCGA) database and validated by real-time PCR. The clinical value of GNPNAT1 in LUAD was investigated based on the data from the TCGA database. Then, immunohistochemistry (IHC) of GNPNAT1 was applied to verify the expression and clinical significance in LUAD from the protein level. The relationship between GNPNAT1 and epigenetics was explored using the cBioPortal database, and the miRNAs regulating GNPNAT1 were found using the miRNA database. The association between GNPNAT1 expression and tumor-infiltrating immune cells in LUAD was observed through the Tumor IMmune Estimation Resource (TIMER). Finally, Gene set enrichment analysis (GSEA) was used to explore the biological signaling pathways involved in GNPNAT1 in LUAD.ResultsGNPNAT1 was upregulated in LUAD compared with normal tissues, which was verified through qRT-PCR in different cell lines (P &lt; 0.05), and associated with patients’ clinical stage, tumor size, and lymphatic metastasis status (all P &lt; 0.01). Kaplan–Meier (KM) analysis suggested that patients with upregulated GNPNAT1 had a relatively poor prognosis (P &lt; 0.0001). Furthermore, multivariate Cox regression analysis indicated that GNPNAT1 was an independent prognostic factor for LUAD (OS, TCGA dataset: HR = 1.028, 95% CI: 1.013–1.044, P &lt; 0.001; OS, validation set: HR = 1.313, 95% CI: 1.130–1.526, P &lt; 0.001). GNPNAT1 overexpression was correlated with DNA copy amplification (P &lt; 0.0001), low DNA methylation (R = −0.52, P &lt; 0.0001), and downregulation of hsa-miR-30d-3p (R = −0.17, P &lt; 0.001). GNPNAT1 expression was linked to B cells (R = −0.304, P &lt; 0.0001), CD4+T cells (R = −0.218, P &lt; 0.0001), and dendritic cells (R = −0.137, P = 0.002). Eventually, GSEA showed that the signaling pathways of the cell cycle, ubiquitin-mediated proteolysis, mismatch repair and p53 were enriched in the GNPNAT1 overexpression group.ConclusionGNPNAT1 may be a potential prognostic biomarker and novel target for intervention in LUAD.

scholarly journals NDRG2 combined with EGFR improved its prognostic value for lung adenocarcinoma

2019 ◽  
Author(s):  
Bo Yang ◽  
Xiao-Ping Li ◽  
Hong-Gang Zhou ◽  
Tao Jiang ◽  
Ting Xiao ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Growth Factor Receptor ◽  
Prognostic Indicators ◽  
Clinicopathological Parameters ◽  
Clinical Value ◽  
Cox Regression Analysis ◽  
Egfr Expression ◽  
Serum Carcinoembryonic Antigen ◽  
Prediction Of Prognosis

Abstract Background N-Myc downstream-regulated gene2 (NDRG2) plays an important role in lung adenocarcinoma (LUAD). Epidermal growth factor receptor (EGFR) mutation has significantly improved prognosis in patients with adenocarcinoma. We aimed to elucidate the clinical value of NDRG2/EGFR as a prediction of prognosis in patients with lung adenocarcinoma.Materials and Methods Immunohistochemistry and western blot analysis were conducted to detect the expression of NDRG2 protein. Association between NDRG2/EGFR expression and clinicopathological parameters of the patients were examined. Serum Carcinoembryonic antigen (CEA) level was examined prior to treatment in patients with LUAD. Patients’ survival rate was assessed by Kaplan–Meier. Candidates for independent prognostic biomarkers were analyzed using a COX proportional hazard model.Results NDRG2 levels were significantly decreased in patients with lung adenocarcinoma. NDRG2 levels were positively correlated with CEA and EGFR. Advanced stages were significantly associated with low expression of NDRG2. Patients with NDRG2-high combined with EGFR-positive expression had the best prognosis during the 5-year follow-up period. Meanwhile, COX regression analysis showed that the conjoined expressions of NDRG2-low/EGFR-positive, NDRG2-high/EGFR-positive and vascular invasion were independent prognostic indicators for lung adenocarcinoma.Conclusion NDRG2 is of more prognosis value as the biomarker for lung adenocarcinoma when analyzed combined with the EGFR expression.

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

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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