Journal of Jilin University(Medicine Edition) ›› 2024, Vol. 50 ›› Issue (4): 1062-1075.doi: 10.13481/j.1671-587X.20240421

• Research in clinical medicine • Previous Articles     Next Articles

Bioinformatics analysis on key genes related to prognosis, diagnosis, and immune cell infiltration of hepatocellular carcinoma and their potential therapeutic drugs

Jinlian LI1,2,Lanzhen HUANG3,Xishi HUANG3,Kangzhi LI1,Jiali JIANG4,Miaomiao ZHANG1,Qunying WU1()   

  1. 1.Department of Cell and Genetics,School of Intelligent Medicine and Biotechnology,Guilin Medical University,Guilin 541004,China
    2.Joint Laboratory of Transfusion-Transmitted Diseases,Nanning Blood Center,Nanning 530007,China
    3.Research Center for Science,Guilin Medical University,Guilin 541004,China
    4.School of General Medicine,Guilin Medical University,Guilin 541004,China
  • Received:2023-07-18 Online:2024-07-28 Published:2024-08-01
  • Contact: Qunying WU E-mail:wuqunying@glmc.edu.cn

Abstract:

Objective To screen the key genes related to the prognosis, diagnosis, and immune infiltration of the hepatocellular carcinoma (HCC) patients by bioinformatics analysis methods, and to analyze their potential therapeutic drugs. Methods The HCC gene expression profile data and corresponding clinical informations of the HCC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The R software package limma was used to screen the differentially expressed genes (DEGs) in HCC. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the DEGs. The STRING database was used to construct the protein-protein interaction (PPI) network; the Cytoscape software was used to visualize the PPI network and screen the key genes; Kaplan-Meier survival curve and LASSO regression algorithm were used to identify the key genes related to the HCC prognosis;external data sets were used to validate their expressions and analyze the diagnostic efficacy;CIBERSORT algorithm was used to detect the relationship between the expression of prognosis-related key genes and HCC immune cell infiltration. The MiRNet and Network Analyst databases were used to construct the microRNA (miRNA)-key gene mRNA and transcription factors (TFs)-key gene mRNA molecular regulatory networks; CMap database was used to screen the potential small molecule drugs for HCC treatment. Results A total of 146 DEGs were screened, including 30 upregulated genes and 116 downregulated genes. The GO functional enrichment analysis and KEGG pathway enrichment analysis results showed that the DEGs were significantly enriched in biological processes (BP) such as steroid, alkene compound, and hormone metabolism, as well as signaling pathways such as retinol metabolism, drug metabolism-cytochrome P450 (CYP450), complement and coagulation cascades. The PPI network analysis identified 14 key genes, among which formimidoyltransferase cyclodeaminase (FTCD), secreted phosphoprotein 2 (SPP2), thrombin-antithrombin complex (TAT), complement C6 (C6), and cytochrome CYP450 family member 2C9 (CYP2C9) were significantly associated with the prognosis, clinical pathological stage, and histological grade of the HCC patients and also had high diagnostic efficacy for HCC and were closely related to immune cell infiltration in HCC. Hsa-mir-182-5p, CUT-like homeobox 1 (CUX1), early growth response 1 (EGR1), SMAD family member 4 (SMAD4), and tumor protein P53 (TP53) were identified as the important regulators targeting the above-mentioned prognosis-related key genes. DL-thiorphan, promethazine, and apigenin may have the therapeutic effects on HCC. Conclusion FTCD, SPP2, TAT, C6, and CYP2C9 may be the potential targets for the diagnosis, prognosis, and treatment of HCC. Three predicted small molecule drugs, DL-thiorphan, promethazine, and apigenin, may provide the references for the development of therapeutic drugs for HCC.

Key words: Hepatocellular carcinoma, Biomarker, Small molecule drug, Bioinformatics

CLC Number: 

  • Q811.4