The discovery of a novel eight‐mRNA‐lncRNA signature predicting survival of hepatocellular carcinoma patients

• Published in: Journal of cellular biochemistry Vol. 120; no. 5; pp. 7539 - 7550
• Main Authors: Shi, Ye‐Min, Li, Yan‐Yan, Lin, Jia‐Yun, Zheng, Lei, Zhu, Yi‐Ming, Huang, Jian
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.05.2019
• Subjects: Cancer; Carcinogenesis; Carcinogens; Cell proliferation; Computer applications; Confidence intervals; Cox regression model; Data processing; Datasets; Division; Gene set enrichment analysis; Genomes; Hepatocellular carcinoma; hepatocellular carcinoma (HCC); Liver cancer; Mathematical models; Medical prognosis; mRNA; nomogram; Nomograms; Patients; Predictions; Prognosis; prognostic signature; Risk analysis; Signatures; Survival; TCGA; Training; hepatocellular carcinoma (HCC); Cox regression model; nomogram; TCGA; prognostic signature
• ISSN: 0730-2312; 1097-4644; 1097-4644
• DOI: 10.1002/jcb.28028

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non‐coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA‐lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA‐lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high‐risk group and low‐risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648‐6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well‐performed nomogram integrating the prognostic signature and other clinical information to predict 3‐ and 5‐year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA‐lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC. We figured out an innovative prognostic signature consisting of eight genes in hepatocellular carcinoma (HCC) by exploring and analyzing currently available sequencing data. This signature showed independence of tumor stage. Also, it outperforms other known clinical information, indicating that the eight‐messenger RNA‐long non‐coding RNA signature may serve as a potential biomarker for prognostic prediction in HCC.