Exploring prognostic potential of long noncoding RNAs in colorectal cancer based on a competing endogenous RNA network

• Published in: World journal of gastroenterology : WJG Vol. 26; no. 12; pp. 1298 - 1316
• Main Authors: Yang, Zhi-Dong, Kang, Hui
• Format: Journal Article
• Language: English
• Published: United States Baishideng Publishing Group Inc 28.03.2020
• Subjects: Aged; Basic Study; Biomarkers, Tumor - genetics; Colorectal Neoplasms - genetics; Colorectal Neoplasms - mortality; Databases, Genetic; Female; Gene Regulatory Networks - genetics; Humans; Male; Middle Aged; Predictive Value of Tests; Prognosis; Proportional Hazards Models; Risk Assessment; RNA, Long Noncoding - analysis; Survival Analysis; Survival Rate; Transcriptome; Colorectal cancer; miRNA; mRNA; Long noncoding RNA; Transcription factor; Survival; Competing endogenous RNA
• ISSN: 1007-9327; 2219-2840
• DOI: 10.3748/wjg.v26.i12.1298

Colorectal cancer (CRC) is one of the most prevalent tumors worldwide. Recently, long noncoding RNAs (lncRNAs) have been shown to influence tumorigenesis and tumor progression by acting as competing endogenous RNAs (ceRNAs). It is difficult to extract prognostic lncRNAs and useful bioinformation from most ceRNA networks constructed previously. To construct a prognostic related ceRNA regulatory network and lncRNA related signature based on risk score in CRC. RNA transcriptome profile and clinical information of 506 CRC patients were downloaded from the Cancer Genome Atlas database. R packages and Perl program were used for data processing. Cox regression analysis was used for prognostic model construction. Quantitative real-time polymerase chain reaction was used to detect the expression of lncRNAs. A prognostic-related ceRNA network was constructed, including 9 lncRNAs, 44 mRNAs, and 30 miRNAs. In addition, a four-lncRNA model was constructed using multivariate Cox regression analysis, which could be an independent prognostic model in CRC. The risk score for each patient was calculated, and the 506 patients were divided into high and low-risk groups (253 for each group) based on the median risk score. The results of the survival analysis showed that patients with a high-risk score had a poor survival rate. Furthermore, the predictive value of the four-lncRNA model was evaluated in GSE38832. Patient survival probabilities could be better predicted when combing the risk score and clinical features. Gene Set Enrichment Analysis results verified that a number of cancer-related signaling pathways were enriched with a high-risk score in CRC. Finally, we validated a novel lncRNA ( ) using quantitative real-time polymerase chain reaction in 22 paired CRC patient tumor tissues compared to adjacent non-tumor tissues. The four-lncRNA model could give better predictive value for CRC patients. Our understanding of the lncRNA-related ceRNA regulatory mechanism could provide a potential diagnostic indicator for CRC patients.