Integrated machine learning screened glutamine metabolism-associated biomarker SLC1A5 to predict immunotherapy response in hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) stands as one of the most prevalent malignancies. While PD-1 immune checkpoint inhibitors have demonstrated promising therapeutic efficacy in HCC, not all patients exhibit a favorable response to these treatments. Glutamine is a crucial immune cell regulatory factor, and tumor cells exhibit glutamine dependence. In this study, HCC patients were divided into two subtypes (C1 and C2) based on glutamine metabolism-related genes via consensus clustering. The C1 pattern, in contrast to C2, was associated with a lower survival probability among HCC patients. Additionally, the C1 pattern exhibited higher proportions of patients with advanced tumor stages. The activity of C1 in glutamine metabolism and transport is significantly enhanced, while its oxidative phosphorylation activity is reduced. And, C1 was mainly involved in the progression-related pathway of HCC. Furthermore, C1 exhibited high levels of immunosuppressive cells, cytokine-receptor interactions and immune checkpoint genes, suggesting C1 as an immunosuppressive subtype. After stepwise selection based on integrated four machine learning methods, SLC1A5 was finally identified as the pivotal gene that distinguishes the subtypes. The expression of SLC1A5 was significantly positively correlated with immunosuppressive status. SLC1A5 showed the most significant correlation with macrophage infiltration, and this correlation was confirmed through the RNA-seq data of CLCA project and our cohort. Low-SLC1A5-expression samples had better immunogenicity and responsiveness to immunotherapy. As expected, SubMap and survival analysis indicated that individuals with low SLC1A5 expression were more responsive to anti-PD1 therapy. Collectively, this study categorized HCC patients based on glutamine metabolism-related genes and proposed two subclasses with different clinical traits, biological behavior, and immune status. Machine learning was utilized to identify the hub gene SLC1A5 for HCC classification, which also could predict immunotherapy response.

Keywords

• Hepatocellular carcinoma
• Glutamine metabolism
• Machine learning
• SLC1A5
• Immunotherapy