Somatic Copy Number Alterations in Colorectal Cancer Lead to a Differentially Expressed ceRNA Network (ceRNet).

Colorectal cancer (CRC) represents the second deadliest malignancy worldwide. Around 75% of CRC patients exhibit high levels of chromosome instability that result in the accumulation of somatic copy number alterations. These alterations are associated with the amplification of oncogenes and deletion of tumor-ppressor genes and contribute to the tumoral phenotype in different malignancies. Even though this relationship is well known, much remains to be investigated regarding the effect of said alterations in long non-coding RNAs (lncRNAs) and, in turn, the impact these alterations have on the tumor phenotype. The present study aimed to evaluate the role of differentially expressed lncRNAs coded in regions with copy number alterations in colorectal cancer patient samples. We downloaded RNA-seq files of the Colorectal Adenocarcinoma Project from the The Cancer Genome Atlas (TCGA) repository (285 sequenced tumor tissues and 41 non-tumor tissues), evaluated differential expression, and mapped them over genome sequencing data with regions presenting copy number alterations. We obtained 78 differentially expressed (LFC > 1|< -1, padj < 0.05) lncRNAs, 410 miRNAs, and 5028 mRNAs and constructed a competing endogenous RNA (ceRNA) network, predicting significant lncRNA-miRNA-mRNA interactions. Said network consisted of 30 lncRNAs, 19 miRNAs, and 77 mRNAs. To understand the role that our ceRNA network played, we performed KEGG and GO analysis and found several oncogenic and anti-oncogenic processes enriched by the molecular players in our network. Finally, to evaluate the clinical relevance of the lncRNA expression, we performed survival analysis and found that C5orf64, HOTAIR, and RRN3P3 correlated with overall patient survival. Our results showed that lncRNAs coded in regions affected by SCNAs form a complex gene regulatory network in CCR.

Comprehensive Analysis of Potential ceRNA Network and Different Degrees of Immune Cell Infiltration in Acute Respiratory Distress Syndrome.

Acute respiratory distress syndrome (ARDS) is a leading cause of death in critically ill patients due to hypoxemic respiratory failure. The specific pathogenesis underlying ARDS has not been fully elucidated. In this study, we constructed a triple regulatory network involving competing endogenous RNA (ceRNA) to investigate the potential mechanism of ARDS and evaluated the immune cell infiltration patterns in ARDS patients. Overall, we downloaded three microarray datasets that included 60 patients with sepsis-induced ARDS and 79 patients with sepsis alone from the public Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs, including 9 DElncRNAs, 9 DEmiRNAs, and 269 DEmRNAs) by R software. The DEGs were subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional enrichment analysis, and a protein-protein interaction (PPI) network was generated for uncovering interactive relationships among DEmRNAs. Then, a ceRNA network that contained 5 DElncRNAs, 7 DEmiRNAs, and 71 DEmRNAs was established according to the overlapping genes in both DEGs and predicted genes by public databases. Finally, we identified the TUG1/miR-140-5p/NFE2L2 pathway as the hub pathway in the whole network through Cytoscape. In addition, we evaluated the distribution of 22 subtypes of immune cells and recognized three differentially expressed immune cells in patients with sepsis-induced ARDS by "Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT)" algorithm, namely, naive B cells, regulatory T cells, and eosinophils. Correlations between differentially expressed immune cells and hub genes in the ceRNA network were also performed. In conclusion, we demonstrated a new potential regulatory mechanism underlying ARDS (the TUG1/miR-140-5p/NFE2L2 ceRNA regulatory pathway), which may help in further exploring the pathogenesis of ARDS.

Development and Validation of a Nomogram and a Comprehensive Prognostic Analysis of an LncRNA-Associated Competitive Endogenous RNA Network Based on Immune-Related Genes for Locally Advanced Rectal Cancer With Neoadjuvant Therapy.

Colorectal cancer (CRC) is a common digestive tract tumor worldwide. In recent years, neoadjuvant chemoradiotherapy (CRT) has been the most comprehensive treatment for locally advanced rectal cancer (LARC). In this study, we explored immune infiltration in rectal cancer (RC) and identified immune-related differentially expressed genes (IRDEGs). Then, we identified response markers in datasets in GEO databases by principal component analysis (PCA). We also utilized three GEO datasets to identify the up- and downregulated response-related genes simultaneously and then identified genes shared between the PCA markers and three GEO datasets. Based on the hub IRDEGs, we identified target mRNAs and constructed a ceRNA network. Based on the ceRNA network, we explored prognostic biomarkers to develop a prognostic model for RC through Cox regression. We utilized the specimen to validate the expression of the two biomarkers. We also utilized LASSO regression to screen hub IRDEGs and built a nomogram to predict the response of LARC patients to CRT. All of the results show that the nomogram and prognostic model offer good prognostic value and that the ceRNA network can effectively highlight the regulatory relationship. hsa-mir-107 and WDFY3-AS2 may be prognostic biomarkers for RC.

Comprehensive analysis of an immune-related ceRNA network in identifying a novel lncRNA signature as a prognostic biomarker for hepatocellular carcinoma.

The function of competitive endogenous RNA (ceRNA) network in the immune regulation of hepatocellular carcinoma (HCC) is unclear. Our study aimed to construct an immune-related ceRNA network and develop an immune-related long noncoding RNA (lncRNA) signature to assess the prognosis of HCC patients and to optimize the treatment methods. We firstly constructed a ceRNA regulatory network for HCC using differentially expressed lncRNAs, mRNAs and microRNAs (miRNAs) from the Cancer Genome Atlas. A signature was constructed by 11 immune-related prognostic lncRNAs from the ceRNA network. The survival analysis and receiver operating characteristic analysis validated the reliability of the signature. Multivariate Cox regression analysis revealed that the signature could act an independent prognostic indicator. This signature also showed high association with immune cell infiltration and immune check blockades. LINC00491 was identified as the hub lncRNA in the signature. In vitro and in vivo evidence demonstrated that silencing of LINC00491 significantly inhibited HCC growth. Finally, 59 lncRNAs, 21 miRNAs, and 26 mRNAs were obtained to build the immune-related ceRNA network for HCC. In conclusion, our novel immune-related lncRNA prognostic signature and the immune-related ceRNA network might provide in-depth insights into tumor-immune interaction of HCC and promote better individual treatment strategies in HCC patients.

Competitive endogenous RNA network identifies four long non-coding RNA signature as a candidate prognostic biomarker for lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most commonly histological subtype of lung cancer (LC) and the prognoses of the majority of LUAD patients are still very poor. The present study aimed at integrating long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) expression data to construct lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network and identify importantly potential lncRNA signature in ceRNA network as a candidate prognostic biomarker for LUAD patients. METHODS: lncRNA, miRNA and mRNA expression data as well as clinical characteristics of LUAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DElncRNAs), differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNA (DEmiRNA) between LUAD and normal lung tissues samples were analyzed. A lncRNA-miRNA-mRNA ceRNA network was constructed and the biological functions of DEmRNAs in ceRNA network were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Univariate and multivariate Cox regression analyses of DElncRNAs in ceRNA network were implemented to predict the overall survival (OS) in LUAD patients. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. RESULTS: A total of 1,664 DElncRNAs, 120 DEmiRNAs and 2,503 DEmRNAs was identified between LUAD and normal lung tissues samples. A lncRNA-miRNA-mRNA ceRNA network including 140 DElncRNAs, 33 DEmiRNAs and 57 DEmRNAs was established. Kaplan-Meier (KM) [Log-rank (LR) test] and univariate regression analysis of those 140 DElncRNAs revealed that 7 DElncRNAs (LINC00518, UCA1, NAV2-AS2, MED4-AS1, SYNPR-AS1, AC011483.1, AP002478.1) were simultaneously identified to be associated with OS of LUAD patients. A multivariate Cox regression analysis of those 7 DElncRNAs showed that a group of 4 DElncRNAs including AP002478.1 (Cox P=4.66E-03), LINC00518 (Cox P=2.34E-04), MED4-AS1 (Cox P=6.42E-03) and NAV2-AS2 (Cox P=6.66E-02) had significantly prognostic value in OS of LUAD patients. The cumulative risk score indicated that the 4-lncRNA signature was significantly associated with OS of LUAD patients (P=0). The area under the curve (AUC) of the 4-lncRNA signature related with 3-year survival was 0.669. CONCLUSIONS: The present study provides novel insights into the lncRNA-related regulatory mechanisms in LUAD, and identifying 4-lncRNA signature may serve as a candidate prognostic biomarker in predicting the OS of LUAD patients.