A metabolism-related gene signature for predicting the prognosis and therapeutic responses in patients with hepatocellular carcinoma

Abstract

Background

Hepatocellular carcinoma (HCC) often has an insidious onset and rapid progression. Often, when the disease is first diagnosed, the opportune time for surgical intervention has already lapsed. In addition, the effects of systemic treatment is relatively unsatisfactory. Metabolic reprogramming is one of the hallmarks of cancer. This study aimed to identify a set of genes related to metabolism to construct a predictive model for the prognosis of HCC.

Methods

The transcriptomic and clinical data of 352 HCC patients were obtained from The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC) dataset and divided into a training cohort (n=212) and a testing cohort (n=140) at a ratio of 6:4. Univariate Cox regression analysis and the LASSO Cox regression model were used to identify 5 genes to establish a risk score for predicting the prognosis of HCC patients. Subsequently, the molecular characteristics of the model were assessed and the ability of the model to predict the tumor immune microenvironment and patient response to immunotherapy and chemotherapy was also examined.

Results

The risk score model was constructed based on the five genes, methyltransferase-like protein 6 (METTL6), RNA polymerase III subunit G (POLR3G), phosphoribosyl pyrophosphate amidotransferase (PPAT), SET Domain Bifurcated 2 (SETDB2), and suppressor of variegation 3-9 homolog 2 (SUV39H2). The Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) curves demonstrated that high-risk patients had a poorer overall survival (OS) compared to low-risk patients. he nomogram score had a better predictive ability compared to the common factors. Our results finally showed that high-risk cases were associated with cell proliferation and cell cycle related gene sets, high tumor protein P53 (TP53) mutation rate, suppressive immunity and increased sensitivity to cisplatin, gemcitabine and docetaxel. Meanwhile, low-risk cases were associated with cell cycle and immune response related pathways, low TP53 mutation rate, active immunity and more benefit from immunotherapy.

Conclusions

This study provided novel insights into the role of metabolism-related genes in HCC, and demonstrated that our model could be a promising prognostic biomarker for distinguishing the molecular and immune characteristics and inferring the potential response to chemotherapy and immunotherapy.