[Identification of Novel Differentially Expressing Long Non- Coding RNAs with Oncogenic Potential]

Abstract

Recently, a wealth of data have been accumulating on the role of long non-coding RNAs (lncRNAs) in the fine-tuning of mRNA expression. Four new lncRNAs, namely, TMEM92-AS1, FAM222A-AS, TXLNB, and lnc-CCL28, were identified as differentially expressed in ovarian tumors using deep machine learning. The levels of lnc-CCL28 transcripts in both tumors and normal tissue samples were sufficient for further analysis by RT-PCR. In addition, the promising ovarian cancer biomarkers, lncRNAs LINC00152, NEAT 1 and SNHG17 were added to RT-PCR analysis. For the first time, an increase in the level of lnc-CCL28 and SNHG 17 lncRNAs was found in ovarian tumors, and the overexpression of LINC00152 and NEAT1 was confirmed. It seems that lnc-CCL28 is involved in carcinogenesis and, in particular, in ovarian cancer progression. Overexpression of LINC00152 and lnc-CCL28 was significantly associated with the later stages and metastasis.

Keywords: deep machine learning; differential expression; lnc-CCL28; long noncoding RNAs; oncogenic potential; prognostic marker.