Integrative analysis of the characteristic of lipid metabolism-related genes for the prognostic prediction of hepatocellular carcinoma

A High-Throughput Sequencing Data-Based Classifier Reveals the Metabolic Heterogeneity of Hepatocellular Carcinoma

Metabolic heterogeneity plays a key role in poor outcomes in malignant tumors, but its role in hepatocellular carcinoma (HCC) remains largely unknown. In the present study, we aim to disentangle the metabolic heterogeneity features of HCC by developing a classification system based on metabolism pathway activities in high-throughput sequencing datasets. As a result, HCC samples were classified into two distinct clusters: cluster 1 showed high levels of glycolysis and pentose phosphate pathway activity, while cluster 2 exhibited high fatty acid oxidation and glutaminolysis status. This metabolic reprogramming-based classifier was found to be highly correlated with several clinical variables, including overall survival, prognosis, TNM stage, and 𝛼-fetoprotein (AFP) expression. Of note, activated oncogenic pathways, a higher TP53 mutation rate, and increased stemness were also observed in cluster 1, indicating a causal relationship between metabolic reprogramming and carcinogenesis. Subsequently, distinct metabolism-targeted therapeutic strategies were proven in human HCC cell lines, which exhibit the same metabolic properties as corresponding patient samples based on this classification system. Furthermore, the metabolic patterns and effects of different types of cells in the tumor immune microenvironment were explored by referring to both bulk and single-cell data. It was found that malignant cells had the highest overall metabolic activities, which may impair the anti-tumor capacity of CD8+ T cells through metabolic competition, and this provided a potential explanation for why immunosuppressive cells had higher overall metabolic activities than those with anti-tumor functions. Collectively, this study established an HCC classification system based on the gene expression of energy metabolism pathways. Its prognostic and therapeutic value may provide novel insights into personalized clinical practice in patients with metabolic heterogeneity.

Amino acid profiles in the tissue and serum of patients with liver cancer

Most patients with liver cancer were found late and lost the chance of surgery. Liquid biopsy can monitor the risk of tumor recurrence and metastasis, quickly evaluate the curative effect of tumor treatment, and is conducive to early screening and auxiliary diagnosis of high-risk groups. Amino acid (AA) profiling has been used to the diagnosis and the prognosis for cancers. However, little was known about the profiles of AA of liver cancer. In this study, we used tRNA in Cancer database to analyze the AA levels in liver cancer tissues. Blood samples of patients with liver cancer were collected and analyzed using the automatic AA analyzer. We found that valine, isoleucine, and leucine were decreased significantly both in the plasma and the tumor tissues of patients with liver cancer. However, upregulation of methionine was observed in tissues and plasma of patients with liver cancer. Interestingly, tyrosine, and phenylalanine were decreased in tumor tissue but increased in the plasma of patients with liver cancer. This is the first report provided an overview of AA profile in both plasma and tissue for patients with liver cancer. AA levels can be used as diagnostic and prognostic markers of patients with liver cancer.