Abstract No. 467 A panel of mRNA molecules as prognostic biomarkers for patients with hepatocellular carcinoma

Abstract

Selection of a specific endovascular locoregional treatment for hepatocellular cancer (HCC) is based on imaging findings and operator experience. Treatment responses using imaging have not reliably translated to survival outcomes between different modalities. Discovery and validation of prognostic biomarkers may be useful in optimizing personalized therapy in patients with HCC. The purpose of this study is to identify a panel of mRNA molecules for prognosis of patient survival using the online Kaplan-Meier plotter database. The Kaplan-Meier plotter database contains overall survival data for 364 HCC patients. The mRNA expression data and survival information of HCC patients were downloaded from Gene Expression Omnibus and The Cancer Genome Atlas. To analyze the prognostic value of the selected mRNA in different cohorts, patients were divided into groups according to stage of cancer (4 stages). These cohorts were compared with a Kaplan–Meier survival plot. Hazard ratio, false discovery rate, and log rank p values were subsequently determined. A p value <0.01 was considered statistically significant to reduce the false positive rate. High mRNA expression of ANXA2 and AFP are significantly correlated with lower survival, whereas high mRNA expression of KNG1 and CD274 (PDL-1 protein) were associated with higher survival in all combined stages. In stage 3, mRNA levels of the four genes are significantly related to HCC patients’ overall survival. Multigene analysis of ANXA2 and AFP, or KNG1 and CD274 demonstrated notably different in the Kaplan-Meier overall survivals. There were 11.6% vs. 27.9% survival rate in high vs. low expression of ANXA2 and AFP groups; whereas 30% vs. 10% survival rate in high vs. low expression of KNG1 and CD274 groups. This study identified a panel of 4 mRNA molecules which may be used as potential prognostic biomarkers for HCC patients. Future studies with a larger sample size are needed to validate our findings. Future applications of biomarker selection may include selecting appropriate patients for treatment and monitoring response to current therapies.