Comprehensive analysis of prognostic biomarkers in lung adenocarcinoma based on aberrant lncRNA-miRNA-mRNA networks and Cox regression models.

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.

Yao Y ，Zhang T ，Qi L ，Liu R ，Liu G ，Wang J ，Song Q ，Sun C ... -  《-》

Comprehensive Analysis of Aberrantly Expressed Profiles of lncRNAs and miRNAs with Associated ceRNA Network in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma.

Dong R ，Liu J ，Sun W ，Ping W ... -  《-》

Comprehensive analysis of differentially expressed profiles of lncRNAs, mRNAs, and miRNAs in laryngeal squamous cell carcinoma in order to construct a ceRNA network and identify potential biomarkers.

This study aimed to uncover a regulatory network comprised of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) in laryngeal squamous cell carcinoma (LSCC), to explore its underlying mechanisms and development, and to identify key genetic biomarkers for the prognosis of LSCC. Here, we compared mRNA, lncRNA, and miRNA expression profiles between 111 LSCC and 12 adjacent normal tissues using RNA sequencing (RNA-Seq) data from the Cancer Genome Atlas (TCGA) database. Based on the interaction information obtained from miRcode, TargetScan, miRTarBase, and miRDB, a lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network was constructed using differentially expressed lncRNAs (DElncRNA), miRNAs (DEmiRNA), and mRNAs (DEmRNA). By assessing the functional enrichment of DEmRNAs in this network, the potential underlying mechanisms were explored. In addition, Kaplan-Meier survival analysis was used to assess genetic biomarkers related to the prognosis of LSCC patients. Upon comparing LSCC and control tissues, 1640 DElncRNAs, 75 DEmiRNAs, and 3217 DEmRNAs were identified. Based on the prediction between lncRNA-miRNA and miRNA-mRNA relationships, we constructed a ceRNA network comprised of 93 lncRNAs, nine miRNAs, and nine mRNAs. This network predicted that two lncRNAs (AC016773.1 and C00299), two mRNAs (DIO1 and STC2), and two miRNAs (hsa-mir-137 and hsa-mir-210) were significant biomarkers of LSCC prognosis according to thorough topological and survival analyses (P < .05). Through a ceRNA network analysis, our study identifies new lncRNAs, miRNAs, and mRNAs, which can be used as potential biomarkers of LSCC and as therapeutic targets for treating LSCC, thus laying a foundation for future clinical studies.

Kong X ，Qi J ，Yan Y ，Chen L ，Zhao Y ，Fang Z ，Fan J ，Liu M ，Liu Y ... -  《-》

Predictions of the dysregulated competing endogenous RNA signature involved in the progression of human lung adenocarcinoma.

Yang D ，He Y ，Wu B ，Liu R ，Wang N ，Wang T ，Luo Y ，Li Y ，Liu Y ... -  《-》

A comprehensive study of construction and analysis of competitive endogenous RNA networks in lung adenocarcinoma.

Long noncoding RNAs (lncRNAs) have gain increasing attention in lung adenocarcinoma. In this study, we aimed at constructing and analyzing the lncRNAs and the related proteins based competitive endogenous RNA (ceRNA) network. RNA expression data of lung adenocarcinoma were extracted from the TCGA database. Differentially expressed (DE) lncRNAs, messenger RNAs (mRNAs) and microRNAs (miRNAs) were identified and then a DElncRNA-DEmiRNA-DEmRNA ceRNA network was constructed for lung adenocarcinoma. We also analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of the DEgenes. Kaplan-Meier survival curves were also been further utilized for exploring the prognostic factors. After compared and calculated lncRNA, mRNA and miRNA expression profiles between lung adenocarcinoma and normal samples, 1709 differential expressed lncRNAs, 2554 differential expressed mRNAs and 116 differential expressed miRNAs were finally identified. Afterwards, a lncRNA mediated ceRNA network was constructed, according to the interactions among 544 pairs of DElncRNA-DEmiRNA relationships and 47 pairs of DEmiRNA-DEmRNA relationships. As for the survival analyses, we found 10 DElncRNAs, 25 DEmRNAs and 7 miRNAs have statistically prognostic significance for overall survival, respectively. This study provides meaningful information for deeper understanding the underlying molecular mechanism of lung adenocarcinoma and for evaluating prognosis, which could monitor recurrence, guide clinical treatment drugs and subsequent related researches.

Gu C ，Shi X ，Huang Z ，Chen J ，Yang J ，Shi J ，Pan X ... -  《-》