Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

Abstract

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan–Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma

“…A study using patients from the TCGA dataset demonstrated that the high expression levels of STC2 , CA12 , CDC20 , DNASE1L3 , GBA3 , and MT1G in patients with HCC had a significantly shorter survival time ( Guan et al, 2019 ). Nonetheless, a four-novel-gene-based prognostic model to predict the patients with a high-risk HCC excluded DNASE1L3 because no statistical association was found between DNASE1L3 and the patient survival ( Dessie et al, 2021 ), while our study sufficiently showed that DNASE1L3 is an independent prognostic predictor of HCC. PTTG1 has been found overexpressed in many types of cancer cells, including the hepatoma cell line HepG2 ( Fujii et al, 2006 ).…”

“…Individual gene expression signatures in the multiple-gene-expression panel indicated specific behavior to trait tumor evolution. To appropriately predict the OS of the patients with HCC, the predictive systems should replenish certain clinicopathological criteria ( Du and Cao, 2012 ; Quetglas et al, 2014 ), whereas most of the traditionally existing multiple-gene-prediction signatures were constructed by bioinformatic analysis through public databases or focused on only a specific signal pathway or certain biological processed ( Dvorchik et al, 2007 ; Guan et al, 2019 ; Dessie et al, 2021 ). Thus, these existing predictive models are found to be inaccurate or arbitrary sometimes.…”

Establishment and Validation of a Peroxisome-related Gene Signature for Prognostic Prediction and Immune Distinction in Hepatocellular Carcinoma

“…However, the specific molecular mechanism of the 9 genes in HCC needs further exploration. Owing to the development of microarray and next-generation sequencing technologies, many multigene prognostic models have been developed to predict survival for HCC patients [50,51]. However, this is the first study about a prognostic model in HCC patients constructed using multiple peroxisomerelated genes.…”

Comprehensive Analysis Identified Mutation-Gene Signature Impacts the Prognosis Through Immune Function in Hepatocellular Carcinoma

“…In addition to the prognostic model based on traditional prognostic markers, genomics and bioinformatics have made it possible to identify prognostic gene signatures. Some research confirmed that a number of gene signatures have recently been developed to predict outcomes of patients with HCC (21,22,26). However, these gene signatures could not predict the immune status of the body and drug resistance of cancer cells.…”