Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis

Abstract

Background Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide. Despite recent advances in imaging techniques and therapeutic intervention for HCC, the low overall 5-year survival rate of HCC patients remains unsatisfactory. This study aims to find a gene signature to predict clinical outcomes in HCC. Methods Bioinformatics analysis including Cox’s regression analysis, Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analysis and the random survival forest algorithm were performed to mine the expression profiles of 553 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public database. Results We selected a signature comprising eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) in the training dataset (AUC = 0.77 at five years, n = 332). The signature stratified patients into high- and low-risk groups with significantly different survival in the training dataset (median 2.20 vs. 8.93 years, log-rank test P < 0.001) and in the test dataset (median 2.68 vs. 4.24 years, log-rank test P = 0.004, n = 221, GSE14520). Further multivariate Cox regression analysis showed that the signature was an independent prognostic factor for patients with HCC. Compared with TNM stage and another reported three-gene model, the signature displayed improved survival prediction power in entire dataset (AUC signature = 0.66 vs. AUC TNM = 0.64 vs. AUC gene model = 0.60, n = 553). Stratification analysis shows that it can be used as an auxiliary marker for many traditional staging models. Conclusions We constructed an eight-gene signature that can be a novel prognostic marker to predict the survival of HCC patients.

An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma

“…In recent years, the identification of prognostic gene signature for HCC has been noted in many studies. For example, an eightgene signature with a 5-year survival prediction AUC of 0.770 containing eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) was established (Qiao et al, 2019). Subsequently, a six-gene signature (CSE1L, CSTB, MTHFR, DAGLA, MMP10, and GYS2) with a 5-year survival prediction AUC of 0.718 was established (Liu et al, 2019).…”

A novel six-gene signature for prognosis prediction in ovarian cancer

“…For example, researchers presented a data-interlinked platform called BIOOPENER, which enabled the query of different types of mutations and genomic alterations to contribute to molecular and clinical insights on cancer, resulting in the discovery of three potential pathways that cause promoter changes in gynecological cancers [32]. Gene signatures containing several genes are superior to individual biomarkers that contribute to OC prognostic prediction and can be applied widely [33][34][35].…”

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma

“…WDR12 as a new oncogene contributes to liver cancer spread (Yin et al, 2018). DCAF13 is upregulated in liver cancer and significantly related to poor survival (Cao et al, 2017;Qiao et al, 2019). NSUN2 as a tRNA methyltransferase, is significantly related to cancer progression in several tumors, such as esophageal squamous cell carcinoma (Li et al, 2018), breast cancer (Yi et al, 2017), and ovarian cancer (Yang et al, 2017).…”