Multiomics Data Integration of Lipid Metabolism in Hepatocellular Carcinoma Studies Using Bioinformatics Networks Based on Vertical and Horizontal Comparisons

Exploring changes in lipid metabolism is helpful for providing unique insight into hepatocellular carcinoma (HCC) pathogenesis mechanisms and early hepatocarcinogenesis. However, lipid metabolism involves different omics molecular interactions by means of both linear and nonlinear forms. Thus, we proposed a novel network construction method based on molecular pair evaluation from linear and nonlinear viewpoints (PELN) for clinical studies. In PELN, molecular relationships were explored in depth by horizontal comparison (linear relationship) and vertical comparison (nonlinear relationship) to reflect disease development for biomarker discovery. In the score calculated by PELN, case ratios and case frequencies were used to comprehensively measure the discriminative ability of the molecular pairs, which can reduce the influence of sampling variability resulting from different subjects. HCC genomics and metabolomics datasets related to lipid metabolism were analyzed by PELN, and the selected network warning signals were shown to effectively predict cancer onset. The experimental results showed that compared with other network methods, including DMNC, DNB-HC, ATSD-DN and MN-PCC, PELN was more robust and precise for distinguishing HCC samples from non-HCC samples. Further analysis using statistical methods demonstrated that studying changes in lipid metabolism using PELN based on multiomics data can help to further understand the pathological mechanisms associated with HCC development, contributing to early diagnosis and affecting clinical prognosis.