Genetic variants of cell cycle pathway genes predict disease‐free survival of hepatocellular carcinoma

Abstract

Disruption of the cell cycle pathway has previously been related to development of human cancers. However, associations between genetic variants of cell cycle pathway genes and prognosis of hepatocellular carcinoma (HCC) remain largely unknown. In this study, we evaluated the associations between 24 potential functional single nucleotide polymorphisms (SNPs) of 16 main cell cycle pathway genes and disease‐free survival (DFS) of 271 HCC patients who had undergone radical surgery resection. We identified two SNPs, i.e., SMAD3 rs11556090 A>G and RBL2 rs3929G>C, that were independently predictive of DFS in an additive genetic model with false‐positive report probability (FPRP) <0.2. The SMAD3 rs11556090G allele was associated with a poorer DFS, compared with the A allele [hazard ratio (HR) = 1.46, 95% confidential interval (95% CI) = 1.13–1.89, P = 0.004]; while the RBL2 rs3929 C allele was associated with a superior DFS, compared with the G allele (HR = 0.74, 95% CI = 0.57–0.96, P = 0.023). Additionally, patients with an increasing number of unfavorable genotypes (NUGs) of these loci had a significant shorter DFS (P trend = 0.0001). Further analysis using receiver operating characteristic (ROC) curves showed that the model including the NUGs and known prognostic clinical variables demonstrated a significant improvement in predicting the 1‐year DFS (P = 0.011). Moreover, the RBL2 rs3929 C allele was significantly associated with increased mRNA expression levels of RBL2 in liver tissue (P = 1.8 × 10−7) and the whole blood (P = 3.9 × 10−14). Our data demonstrated an independent or a joint effect of SMAD3 rs11556090 and RBL2 rs3929 in the cell cycle pathway on DFS of HCC, which need to be validated by large cohort and biological studies.

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

“…For example, Xie et al (2018) showed that DNA replication is associated with tumor cell proliferation and prognosis of patients with HCC. Moreover, genetic variations in cell cycle pathway genes affect the disease-free survival of patients with HCC ( Liu et al, 2017 ). The TP53 mutation is considered one of the molecular mechanisms of HCC pathogenesis ( Hussain et al, 2007 ).…”

“…In addition, GO and KEGG analyses showed enrichment of both modules in multiple tumor-related pathways. For instance, DNA replication is associated with tumor cell proliferation and the prognosis of HCC ( Xie et al, 2018 ), variations in cell cycle pathway genes affect disease-free survival of patients with HCC ( Liu et al, 2017 ), TP53 mutation is considered one of the critical molecular mechanisms of HCC pathogenesis ( Hussain et al, 2007 ), an abnormal cellular senescence phenotype is observed in various cancer types ( Chen S. L. et al, 2019 ), and cytochrome P450 is severely damaged and dysregulated in HCC ( Yan et al, 2015 ).…”

Upregulation of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in Tumor Tissues Predicted Worse Overall Survival and Disease-Free Survival in Hepatocellular Carcinoma Patients

“…It has been well studied that cell cycle regulators are strongly implicated in progression of cancer development [ 16 ]. Disruption of the cell cycle pathway has previously been associated with development of several kinds of cancers, including HCC [ 17 ]. Although recent progress has enabled improved diagnosis and management of HCC, its prognosis remains dismal.…”

<p>Identification of a Functional ceRNA Network to Explore Potential Biomarkers for Hepatocellular Carcinoma</p>

“…Reports indicate that changes in cell cycle rhythm are closely related to the occurrence and development of HCC. 31 The imbalance of the p53 signaling pathway promotes the proliferation of HCC cells, thereby contributing to the occurrence of HCC. 32 To further determine the key genes in the ceRNA network, we considered the top 10 genes in the PPI network as Hub genes including, CDK1, CCNA2, CCNB1, PPP2R1A, CHEK1, KIF23, BIRC5, OIP5, RAD51, and RACGAP1.…”