Original Article

VOLUME: 37 | ISSUE: 1 | Mar 31, 2021 | PAGE: (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


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.