Peer Review #1 of "Identification of a novel four-lncRNA signature as a prognostic indicator in cirrhotic hepatocellular carcinoma (v0.1)"

Abstract

Background. Many studies have shown that long noncoding RNAs (lncRNA) are closely associated with the occurrence and development of various tumors and have the potential to be prognostic markers. Moreover, cirrhosis is an important prognostic risk factors in patients with liver cancer. Some studies have reported that lncRNA-related prognostic models have been used to predict overall survival (OS) and recurrence-free survival (RFS) in patients with hepatocellular carcinoma (HCC). However, no one has constructed a prognostic lncRNA model only in patients with cirrhotic HCC. Thus, it is necessary to screen novel potential lncRNA markers for improve the prognosis of cirrhotic HCC patients. Methods. The probe expression profile dataset (GSE14520-GPL3921) from the Gene Expression Omnibus (GEO), which included 204 cirrhotic HCC samples, was reannotated and the lncRNA and mRNA expression dataset was obtained. The patients were randomly assigned to either the training set (n = 103) and testing set (n = 100). Univariate cox regression and the least absolute shrinkage and selection operator (LASSO) model were applied to screen lncRNAs linked to the OS of cirrhotic HCC in the training set. The lncRNAs having significant correlation with OS were then selected and the multivariate Cox regression model was implemented to construct the prognostic score model. Whether or not this model was related to RFS in the training set was simultaneously determined. The testing set was used to validate the lncRNA risk score model. A risk score based on the lncRNA signature was used for stratified analysis of different clinical features to test their prognostic performance.The prognostic lncRNA-related protein genes were identified by the co-expression matrix of lncRNA-mRNA, and the function of these lncRNAs was predicted through the enrichment of these co-expression genes. Results. The signature consisted of four lncRNAs:AC093797.1,POLR2J4,AL121748.1 and AL162231.4. The risk model was closely correlated with the OS of cirrhotic HCC in the training cohort, with a hazard ratio (HR) of 3.650 (95% CI: 1.761-7.566) and log-rank P value of 0.0002. Moreover, this model also showed favorable prognostic significance for RFS in the training set (HR: 2.392, 95% CI: 1.374-4.164, logrank P = 0.0015). The predictive performance of the four-lncRNA model for OS and RFS was verified in the testing set. Furthermore, the results of stratified analysis revealed that the four-lncRNA model was an independent factor in the prediction of OS and RFS of patients with clinical characteristics such as TNM (Tumor, Node, Metastasis system) stages I-II, Barcelona Clinic Liver Cancer (BCLC) stages 0-A, and solitary tumors in both the training set and testing set. The results of functional prediction showed that four lncRNAs may be potentially involve in multiple metabolic processes, such as amino acid, lipid, and glucose metabolism in cirrhotic HCC.