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

Abstract

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.

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

“…Several previous studies have also constructed risk scoring systems to predict the prognosis of patients with HCC (24)(25)(26)(27)(28)(29)(30)(31). However, those risk scoring systems were based on DElncRNAs between HCC and normal samples, while the risk scoring systems in the present study are based on the DElncRNAs between HCC Table IX.…”

Risk Scoring System based on lncRNA Expression for Predicting Survival in Hepatocellular Carcinoma with Cirrhosis

“…Several previous studies constructed risk scoring systems to predict the prognosis of HCC patients (Gu et al, 2018;Liao et al, 2018;Ma et al, 2018;Shi et al, 2018;Sui et al, 2018;Wu et al, 2018b;Zhao et al, 2018;Bai et al, 2019;Yan et al, 2019;Ye et al, 2019;Zhang et al, 2019). However, all these risk scoring systems are based on lncRNAs differentially expressed between HCC and normal samples, while our systems are based on lncRNAs differentially expressed between HCC patients with or without cirrhosis.…”

Development and validation of a novel pseudogene pair-based prognostic signature for prediction of overall survival in patients with hepatocellular carcinoma

“…For example, in HCC, upregulation of the pseudogene RP11-564D11.3 has been found to be associated with adverse survival [20]. Numerous researches have built gene expression profile-based signatures for survival prediction in patients with HCC [9][10][11][12][13][14]. However, previous reports aiming to build a prognostic model have focused on mRNAs, lncRNAs, and miRNAs, neglecting pseudogenes as potential biomarkers in HCC.…”

“…Numerous studies have established mRNA expression profile-based signatures for outcome prediction in HCC patients [9][10][11][12][13][14]. However, these models have been failed to utilize clinically due to the diversity of data types, batch effects, and subsequent normalization of expression data, which poses a daunting obstacles for data processing given the possible biological heterogeneity among various data series and technical differences across different platforms [15].…”