Lipids in Health and Disease (Aug 2024)

Screening and molecular docking verification of feature genes related to phospholipid metabolism in hepatocarcinoma caused by hepatitis B

  • Jian Zhang,
  • Fengmei Zhang,
  • Lei Zhang,
  • Meiling Zhang,
  • Shuye Liu,
  • Ying Ma

DOI
https://doi.org/10.1186/s12944-024-02253-3
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 14

Abstract

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Abstract Background The progression of tumours is related to abnormal phospholipid metabolism. This study is anticipated to present a fresh perspective for disease therapy targets of hepatocarcinoma caused by hepatitis B virus in the future by screening feature genes related to phospholipid metabolism. Methods This study analysed GSE121248 to pinpoint differentially expressed genes (DEGs). By examining the overlap between the metabolism-related genes and DEGs, the research focused on the genes involved in phospholipid metabolism. To find feature genes, functional enrichment studies were carried out and a network diagram was proposed. These findings were validated via data base of The Cancer Genome Atlas (TCGA). Further analyses included immune infiltration studies and metabolomics. Finally, the relationships between differentially abundant metabolites and feature genes were confirmed by molecular docking, providing a thorough comprehension of the molecular mechanisms. Results The seven genes with the highest degree of connection (PTGS2, IGF1, SPP1, BCHE, NR1I2, NAMPT, and FABP1) were identified as feature genes. In the TCGA database, the seven feature genes also had certain diagnostic efficiency. Immune infiltration analysis revealed that feature genes regulate the infiltration of various immune cells. Metabolomics successfully identified the different metabolites of the phospholipid metabolism pathway between patients and normal individuals. The docking study indicated that different metabolites may play essential roles in causing disease by targeting feature genes. Conclusions In this study, for the first time, it reveals the possible involvement of genes linked to phospholipid metabolism-related genes using bioinformatics analysis. Identifying genes and probable therapeutic targets could provide clues for the further treatment of disease.

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