Original Article
Volume: 37 | Issue: 1 | Published: Mar 31, 2021 | Pages: 29 - 38 | DOI: 10.51441/BioMedica/5-167
A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer
Authors: Wang Yuhang , Chen Lu , Pei Lixia , Chen Yufeng , Hu Yue , Hou Wenzhen , Song Yafang , Sun Mengzhu , Sun Jianhua
Article Info
Authors
Wang Yuhang
Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing- China.
Chen Lu
Affiliated Hospital of Nanjing University of Chinese Medicine,Nanjing –China.
Pei Lixia
Affiliated Hospital of Nanjing University of Chinese Medicine,Nanjing –China.
Chen Yufeng
Affiliated Hospital of Nanjing University of Chinese Medicine,Nanjing –China.
Hu Yue
Affiliated Hospital of Nanjing University of Chinese Medicine,Nanjing –China
Hou Wenzhen
Outpatient Department, Nanjing University of Traditional Chinese Medicine, Nanjing – China.
Song Yafang
Affiliated Hospital of Nanjing University of Chinese Medicine,Nanjing –China.
Sun Mengzhu
Affiliated Hospital of Nanjing University of Chinese Medicine,Nanjing –China.
Sun Jianhua
Affiliated Hospital of Nanjing University of Chinese Medicine,Nanjing –China.
Publication History
Received: January 26, 2021
Revised: March 21, 2021
Accepted: March 23, 2021
Published: March 31, 2021
Abstract
Background and Objective:
Background and Objective: Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.
Methods: Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.
Results: This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.
Conclusion: The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.
Keywords: Bioinformatics, Long non coding (lnc) RNA, Signature, Colon cancer.