Dataset Information

Construction and integrated analysis of a lncRNA-associated competing endogenous RNA network reveal functional lncRNAs in pancreatic cancer

ABSTRACT:

Background

Long noncoding RNAs (lncRNAs) can serve as a competing endogenous RNA (ceRNA) in regulating gene expression in multiple cancers by sponging miRNA. However, this mechanism is poorly studied in pancreatic cancer. This study aims to identify functional lncRNAs and their potential regulatory mechanisms in pancreatic cancer.

Methods

Differentially expressed lncRNA (DE-lncRNA), miRNA (DE-miRNA) and mRNA (DE-mRNA) were analyzed using data from three datasets (GSE89139, GSE24279 and GSE62452) from the Gene Expression Omnibus (GEO). The lncRNA-miRNA-mRNA interactions were predicted using miRcode and Targetscan. Gene ontology (GO) and pathway analysis of DE-mRNAs were performed using clusterProfiler. Survival analysis was conducted using data from The Cancer Genome Atlas (TCGA) database.

Results

Three hundred sixty-six DE-lncRNAs, 28 DE-miRNAs and 330 DE-mRNAs from pancreatic cancer and adjacent tissue were identified. A ceRNA network including 75 DE-lncRNAs, 18 DE-miRNAs and 85 DE-mRNAs was constructed, among which 16 DE-lncRNAs were associated with overall survival and 13 DE-lncRNAs were correlated with tumor progression. Three functional lncRNAs, GABPB1-AS1, ST7-AS1 and PSMG3-AS1, were identified as key functional lncRNAs, and their differential expression and potential ceRNA regulatory mechanism were validated by qPCR using pancreatic cancer cell lines and tissues.

Conclusions

Our study identifies novel lncRNAs associated with progression and prognosis of pancreatic cancer and contributes to better understanding of lncRNA-associated ceRNA regulatory mechanisms in pancreatic cancer.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8798265 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Precise classification has been reported as a central challenge in the clinical research on diagnosis and prediction of treatment efficacy in asthma. In this study, the aim was to investigate the underlying competing endogenous RNA network mechanism of asthma, especially T2 asthma, as well as to find more diagnostic biomarkers and effective therapeutic targets. Methods: Multiple sets of T2 asthma airway biopsy transcription profiles were collected, which involved long non-coding RNA (lncRNA), mRNA, and microRNA (miRNA). DIANA-LncBase, targetscan, mirwalk, and miRDB databases were employed to predict interactions between lncRNAs, miRNAs and target mRNAs. To identify mRNAs correlated with T2 asthma, differential expression and network analyses were conducted through weighted gene co-expression network analysis (WGCNA). Subsequently, the expressions of potential biomarkers were examined through qRT-PCR analysis in the T2 asthma coreinteracting cellular factor (IL-13/IL-33) induced experimental model. Lastly, the ceRNA network was confirmed by plasmid transfection and RNAi experiments in a 16HBE cell line. Results: 30 lncRNAs, 22 miRNAs and 202 mRNAs were differentially expressed in airway biopsies from T2 asthma patients. As indicated by the ROC analysis, the lncRNA, PCAT19, had high diagnostic accuracy (AUC >0.9) in distinguishing T2 asthma patients from non-T2 asthma patients and healthy controls. Furthermore, a competing ceRNA network was established, consisting of 13 lncRNAs, 12 miRNAs, as well as eight mRNAs. The reliability of this network was verified by testing several representative interactions in the network. Conclusion: To the best of our knowledge, this study has been the first to establish an lncRNA-mediated ceRNA regulatory network for studying T2 asthma. The findings of this study may elucidate the pathogenesis and help find potential therapeutic targets for T2 asthma. In T2 asthma, PCAT19-dominated ceRNA regulation networks may play a critical role, and PCAT19 may serve as a potential immune-related biomarker for asthma and other respiratory diseases correlated with eosinophilic inflammation.