Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC

Abstract

Hepatocellular carcinoma (HCC), which has become one of the most significant malignancies causing cancer-related mortality, presents genetic and phenotypic heterogeneity that makes predicting prognosis challenging. Aging-related genes have been increasingly reported as significant risk factors for many kinds of malignancies, including HCC. In this study, we comprehensively dissected the features of transcriptional aging-relevant genes in HCC from multiple perspectives. We applied public databases and self-consistent clustering analysis to classify patients into C1, C2, and C3 clusters. The C1 cluster had the shortest overall survival time and advanced pathological features. Least absolute shrinkage and selection operator (LASSO) regression analysis was adopted to build the prognostic prediction model based on six aging-related genes (HMMR, S100A9, SPP1, CYP2C9, CFHR3, and RAMP3). These genes were differently expressed in HepG2 cell lines compared with LO2 cell lines measured by the mRNA expression level. The high-risk score group had significantly more immune checkpoint genes, higher tumor immune dysfunction and exclusion score, and stronger chemotherapy response. The results indicated that the age-related genes have a close correlation with HCC prognosis and immune characteristics. Overall, the model based on six aging-associated genes demonstrated great prognostic prediction ability.

An aging-related gene signature to predict the prognosis of hepatocellular carcinoma

“…This study established an aging-related prognostic model for patients with HCC, which performed well in several independent datasets. Compared with other aging-related signatures of HCC, [39,40] this study adopted strict and reasonable criteria to select key prognostic genes, and the significance of each gene in HCC progression was well supported by previous findings. Moreover, we analyzed the expression of the prognostic genes at the single-cell level, thereby illustrating the spatiotemporal dynamics and potential pathogenic cellular modules.…”

Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma

“…Each sample was categorised either into a high risk or low risk score subgroup. 18 The Kaplan-Meier method was applied to plot survival curves for both subgroups in the testing and validation datasets. The clinical characteristics of both subgroups were also compared.…”