Abstract
BACKGROUND: The high heterogeneity of hepatocellular carcinoma (HCC) poses challenges for precision treatment strategies. This study aims to use multi-omics methodologies to better understand its pathogenesis and discover biomarkers. METHODS: Quantitative proteomics was used to investigate hepatocellular carcinoma tissues (HCT) and their corresponding adjacent non-tumor tissues (DNT), obtained from six HCC patients. Untargeted metabolomics was applied to analyze the metabolic profiles of HCT and DNT of ten HCC patients. Statistical analyses, such as the Student's t-test, were performed to identify differentially expressed proteins (DEPs) and metabolites (DEMs) between the two groups. The functions and metabolic pathways involving DEPs and DEMs were annotated and enriched using the gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) databases. Bioinformatics methods were then utilized to analyze consistency between proteomics and metabolomics results, leading to identification of potential biomarkers along with key altered pathways associated with HCC. RESULTS: This study identified 1556 DEPs between HCT and DNT samples. These DEPs were primarily enriched in crucial biological pathways such as amino acid degradation, fatty acid metabolism, and DNA replication. Subsequently, the analysis of metabolomics identified 500 DEMs that mainly participated in glycerophospholipid metabolism, the phospholipase D signaling pathway, and choline metabolism related to cancer. Integrated analysis of proteomics and metabolomics data unveiled significant dysfunctions in bile secretion, multiple amino acid and fatty acid metabolic pathways among HCC patients. Further investigation revealed that five proteins (PTP4A3, B4GALT5, GAB1, ME2, and PKM) along with seven metabolites (PI(6 keto-PGF1alpha/16:0), 13, 16, 19-docosatrienoic acid, PA(18:2(9Z, 12Z)/20:1(11Z)), Citric Acid, PG(20:3(6, 8, 11)-OH(5)/18:2(9Z, 12Z)), Spermidine, and N2-Acetylornithine) exhibited excellent diagnostic efficiency for HCC and could serve as its potential biomarkers. CONCLUSION: Our integrated proteome and metabolome analysis revealed 10 key HCC-related pathways and proposed 12 potential biomarkers, which may enhance our understanding of HCC pathophysiology and be helpful in facilitating early diagnosis and treatment strategies.