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

Area of Science:

• Molecular Biology
• Genomics
• Cancer Research

Background:

• Long non-coding RNAs (lncRNAs) are increasingly recognized for their regulatory roles in gene expression.
• Dysregulation of lncRNAs is implicated in various human diseases, including cancer.

Purpose of the Study:

• To identify novel differentially expressed lncRNAs in ovarian tumors using deep machine learning.
• To investigate the potential of specific lncRNAs as biomarkers for ovarian cancer diagnosis and prognosis.

Main Methods:

• Deep machine learning was employed to identify differentially expressed lncRNAs in ovarian tumor samples.
• Reverse transcription quantitative polymerase chain reaction (RT-PCR) was used to validate transcript levels of selected lncRNAs (lnc-CCL28, SNHG17, LINC00152, NEAT1).

Main Results:

• Four novel lncRNAs (TMEM92-AS1, FAM222A-AS, TXLNB, and lnc-CCL28) were identified as differentially expressed in ovarian tumors.
• For the first time, increased levels of lnc-CCL28 and SNHG17 were observed in ovarian tumors.
• Overexpression of LINC00152 and NEAT1 was confirmed, and their levels were significantly associated with advanced stages and metastasis in ovarian cancer.

Conclusions:

• lnc-CCL28 and SNHG17 represent novel potential biomarkers for ovarian cancer.
• The overexpression of LINC00152 and lnc-CCL28 is linked to disease progression and metastasis, suggesting their involvement in ovarian carcinogenesis.