Identifying Proteins and Amino Acids Associated with Liver Cancer Risk: A Study Utilizing Mendelian Randomization and Bulk RNA Sequencing Analysis

Abstract

Background: Primary liver cancer (PLC) ranks third in terms of fatality rate among all malignant tumors worldwide. Proteomics and metabolomics have become widely utilized in identifying causes and diagnostic indicators of PLC. Nevertheless, in studies aiming to identify proteins/metabolites that experienced significant changes before PLC, the potential impact of reverse causation and confounding variables still needs to be fully addressed.

Methods: This study thoroughly investigated the causal relationship between 4719 blood proteins, 21 amino acids, and the risk of PLC using the Mendelian randomization (MR) method. In addition, through a comprehensive analysis of the TCGA-LIHC cohort and GEO databases, we evaluated the differentially expressed genes (DEGs) related to serine metabolism in diagnosing and predicting the prognosis of patients with PLC.

Results: A total of 63 proteins have been identified as connected to the risk of PLC. Additionally, there has been confirmation of a positive cause-effect between PLC and the concentration of serine. The integration of findings from both MR analyses determined that the protein associated with PLC risk exhibited a significant correlation with serine metabolism. Upon careful analysis of the TCGA-LIHC cohort, it was found that eight DEGs are linked to serine metabolism. After thoroughly validating the GEO database, two DEGs, TDO2 and MICB, emerged as potential biomarkers for diagnosing PLC.

Conclusions: Two proteins involved in serine metabolism, MICB and TDO2, are causally linked to the risk of PLC and could potentially be used as diagnostic indicators.

Keywords: amino acid; liver cancer; mendelian randomization; protein; risk.

Figure 1

Causal effects of plasma protein on PLC risk (positive correlation). Forest plot (A) and partial scatter plots (B) of plasma protein and PLC risk.

Figure 2

Causal effects of plasma protein on PLC risk (negative correlation). Forest plot (A) and partial scatter plots (B) of plasma protein and PLC risk.

Figure 3

Causal effects of amino acid and PLC. The scatter plot (A), funnel plot (B), and forest plot analyzed by LOO analysis (C) of PLC on serine concentration.

Figure 4

Identification of TCGA-LIHC cohort-related DEGs and enrichment analysis. (A). GO enrichment analysis of positive results of MR analysis. (B). Volcano plot of DEGs in the TCGA-LIHC cohort. (C). Venn diagram of serine metabolism-related genes, DEGs in TCGA-LIHC cohort, and positive results of MR analysis. (D). Heat map of the expression of 8 key DEGs in the TCGA-LIHC cohort. (E). Lollipop chart for correlation analysis of 8 key DEGs and immune cell infiltration. *, ** and *** are expressed as p-Values less than 0.05, 0.01 and 0.001, respectively. ns indicates no statistical significance.

Figure 5

Diagnostic and prognostic value of 8 key DEGs in TCGA-LIHC cohort. (A). Violin plot of 8 key DEGs expression in TCGA-LIHC cohort. (B). ROC curves of 8 key DEGs. (C). Kaplan–Meier plots of 2 key DEGs. *** indicates a p-Value less than 0.001.

Figure 6

Correlation analysis of 8 key genes and clinical features. Comparison of expression levels of 8 key DEGs with histologic grade (A), TMN stage (B), Child–Pugh grade (C), vascular invasion (D), AFP (E), and gender (F). *, ** and *** are expressed as p-Values less than 0.05, 0.01 and 0.001, respectively.

Figure 7

Volcano plot (A) and heat map (B) of GSE62232 cohort-related DEGs. Volcano plot (C) and heat map (D) of GSE101685 cohort-related DEGs. (E). Venn diagram of DEGs for 8 key DEGs and GSE62232 and GSE101685 cohort. (F). Bean plot of TDO2 and MICB expression in GSE62232 and GSE101685 cohort. ROC curves of TDO2 and MICB in GSE62232 (G) and GSE101685 (H) cohort. *** indicates a p-Value less than 0.001.