Original Article
The lncRNAs MIR600HG and TSPOAP1-AS1 may potentially act as biomarkers for predicting pancreatic cancer
Abstract
Background: The purpose of this study was to assess long non-coding RNAs (lncRNAs) as biomarkers of pancreatic cancer (PC).
Methods: The Cancer Genome Atlas (TCGA) database was used to obtain the expression profiles of lncRNAs and clinical characteristics of PC patients. Then, differentially expressed lncRNAs (DElncRNAs) between tumor and normal tissue samples were determined. A pancreatic cancer related lncRNA prognostic model was established by univariate and multiple Cox regression analyses of all DElncRNA expression data. The specificity and sensitivity of the developed prognostic model were evaluated by Receiver operating characteristic (ROC) curves analysis. A lncRNA-mRNA co-expression network using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analyses was built to predict the potential biological functions of lncRNAs.
Results: A total of 178 DElncRNAs were screened from TCGA. Through univariate and multiple Cox regression analyses, a two-lncRNA (MIR600HG and TSPOAP1-AS1) predictive model was established. Further analysis determined a risk score that predicted prognosis independently of other clinicopathological factors. ROC curves analysis showed that the lncRNA signature model had high sensitivity and specificity. GO and KEGG functional enrichment analysis revealed that the two-lncRNA signature was mostly concentrated in PC related biological processes (BP).
Conclusions: These data provide evidence that MIR600HG and TSPOAP1-AS1 may serve as potential biomarkers to predict PC prognosis.
Methods: The Cancer Genome Atlas (TCGA) database was used to obtain the expression profiles of lncRNAs and clinical characteristics of PC patients. Then, differentially expressed lncRNAs (DElncRNAs) between tumor and normal tissue samples were determined. A pancreatic cancer related lncRNA prognostic model was established by univariate and multiple Cox regression analyses of all DElncRNA expression data. The specificity and sensitivity of the developed prognostic model were evaluated by Receiver operating characteristic (ROC) curves analysis. A lncRNA-mRNA co-expression network using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analyses was built to predict the potential biological functions of lncRNAs.
Results: A total of 178 DElncRNAs were screened from TCGA. Through univariate and multiple Cox regression analyses, a two-lncRNA (MIR600HG and TSPOAP1-AS1) predictive model was established. Further analysis determined a risk score that predicted prognosis independently of other clinicopathological factors. ROC curves analysis showed that the lncRNA signature model had high sensitivity and specificity. GO and KEGG functional enrichment analysis revealed that the two-lncRNA signature was mostly concentrated in PC related biological processes (BP).
Conclusions: These data provide evidence that MIR600HG and TSPOAP1-AS1 may serve as potential biomarkers to predict PC prognosis.
