In silico identification of novel lncRNAs with a potential role in diagnosis of gastric cancer. Issue 7 (2nd May 2020)
- Record Type:
- Journal Article
- Title:
- In silico identification of novel lncRNAs with a potential role in diagnosis of gastric cancer. Issue 7 (2nd May 2020)
- Main Title:
- In silico identification of novel lncRNAs with a potential role in diagnosis of gastric cancer
- Authors:
- Delshad, Encieh
Shamsabadi, Fatemeh T.
Bahramian, Shabbou
Mehravar, Fatemeh
Maghsoudi, Hossein
Shafiee, Mohammad - Abstract:
- Abstract: Gastric cancer (GC) is the second leading cause of cancer-related deaths in the world. Due to the shortage of adequate symptoms in the early stages, it is diagnosed when the tumor has spread to distant organs. Early recognition of GC enhances the chance of successful treatment. Molecular mechanisms of GC are still poorly understood. LncRNAs are emerging as new players in cancer in both oncogene and tumor suppressor roles. High-throughput technologies such as RNA-Seq, have revealed thousands of lncRNAs which are dysregulated in GC. In this study, we retrieved lncRNAs obtained by High-throughput technologies from OncoLnc database. Consequently, retrieved lncRNAs were compared in literature-based databases including PubMed. As a result, two lists, including experimentally validated lncRNAs and predicted lncRNAs were provided. We found 43 predicted lncRNAs that had not been experimentally validated in GC, so far. Further Bioinformatics analyses were performed to obtain the expression profile of predicted lncRNAs in tumor and normal tissues. Also, the roles and targets of predicted lncRNAs in GC were identified by related databases. Finally, using the GEPIA database was reviewed the significant relationship of predicted lncRNAs with the survival of GC patients. By recognizing the lncRNAs involved in initiation and progression of GC, they may be considered as potential biomarkers in the GC early diagnosis or targeted treatment and lead to novel therapeutic strategies.Abstract: Gastric cancer (GC) is the second leading cause of cancer-related deaths in the world. Due to the shortage of adequate symptoms in the early stages, it is diagnosed when the tumor has spread to distant organs. Early recognition of GC enhances the chance of successful treatment. Molecular mechanisms of GC are still poorly understood. LncRNAs are emerging as new players in cancer in both oncogene and tumor suppressor roles. High-throughput technologies such as RNA-Seq, have revealed thousands of lncRNAs which are dysregulated in GC. In this study, we retrieved lncRNAs obtained by High-throughput technologies from OncoLnc database. Consequently, retrieved lncRNAs were compared in literature-based databases including PubMed. As a result, two lists, including experimentally validated lncRNAs and predicted lncRNAs were provided. We found 43 predicted lncRNAs that had not been experimentally validated in GC, so far. Further Bioinformatics analyses were performed to obtain the expression profile of predicted lncRNAs in tumor and normal tissues. Also, the roles and targets of predicted lncRNAs in GC were identified by related databases. Finally, using the GEPIA database was reviewed the significant relationship of predicted lncRNAs with the survival of GC patients. By recognizing the lncRNAs involved in initiation and progression of GC, they may be considered as potential biomarkers in the GC early diagnosis or targeted treatment and lead to novel therapeutic strategies. Communicated by Ramaswamy H. Sarma … (more)
- Is Part Of:
- Journal of biomolecular structure & dynamics. Volume 38:Issue 7(2020)
- Journal:
- Journal of biomolecular structure & dynamics
- Issue:
- Volume 38:Issue 7(2020)
- Issue Display:
- Volume 38, Issue 7 (2020)
- Year:
- 2020
- Volume:
- 38
- Issue:
- 7
- Issue Sort Value:
- 2020-0038-0007-0000
- Page Start:
- 1954
- Page End:
- 1962
- Publication Date:
- 2020-05-02
- Subjects:
- Gastric cancer -- predicted lncRNAs -- in silico analysis -- bioinformatics databases -- high-throughput technology
Biomolecules -- Periodicals
Molecular structure -- Periodicals
Molecular Biology -- Periodicals
Biomechanics -- Periodicals
572 - Journal URLs:
- http://www.tandfonline.com/loi/tbsd20 ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/07391102.2019.1624615 ↗
- Languages:
- English
- ISSNs:
- 0739-1102
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4953.850000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 13631.xml