Abstract

Long non-coding RNAs (lncRNAs) have been acknowledged to serve a significant role in cancer biology and abnormal expression in tumors is frequently observed. However, their mechanisms in cervical cancer remain unclear. With a genome-wide analysis of lncRNA expression in cervical cancer tissues, the present study aimed to identify lncRNA targets for the further study of cervical cancer. To elucidate the specific role of lncRNAs in the pathogenesis of this type of cancer, 6 cervical cancer samples paired with normal cervical tissues were obtained. Expression profiles of lncRNAs and mRNAs were constructed through microarray analysis and confirmed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) methods. Gene Ontology and pathway enrichment analyses were performed with computational methods. On the basis of correlations between the differential expression levels of lncRNAs and mRNAs, a coding-non-coding gene co-expression network (CNC network) was established. The differential expression of 5,844 lncRNAs and 4,436 mRNAs were discovered in cervical cancer samples compared with normal cervical tissues. Among the differentially expressed lncRNAs, 14 were chosen at random and validated by RT-qPCR; the majority of the results measured were consistent with the microarray results. Furthermore, the lncRNA ENST00000551152 was found to be upregulated and TCO. NS_00001368 lncRNA was downregulated in cervical cancer cell lines. The CNC network included 592 network nodes and 934 associations between 12 lncRNAs and 580 protein-coding genes, indicating that one lncRNA could act on a maximum of 141 coding genes, and that one coding gene may corresponded with a maximum of 5 lncRNAs. Overall, the present study has provided a complete expression profile of lncRNAs and mRNAs in cervical cancer, which may now be used to establish a solid foundation for cervical cancer research. These results may provide significant information for improving the understanding of the pathogenesis of cervical cancer and indicate potential therapeutic targets.

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