目的 利用生物信息学分析方法筛选与肝细胞癌（HCC）患者预后、诊断和免疫细胞浸润相关的关键基因，并分析其潜在治疗药物。 方法 从基因表达汇编（GEO）数据库和癌症基因组图谱（TCGA）数据库下载HCC基因表达谱数据及相应的HCC患者临床信息。采用R软件limma包筛选HCC差异表达基因（DEGs），对DEGs进行基因本体论（GO）功能和京都基因与基因组百科全书（KEGG）信号通路富集分析，利用STRING数据库构建蛋白-蛋白互作（PPI）网络，使用 Cytoscape 软件对PPI 网络进行可视化并筛选关键基因。通过Kaplan-Meier 生存曲线和LASSO回归算法鉴定HCC预后相关关键基因，并利用外部数据集对其表达进行验证和诊断效能分析。采用CIBERSORT算法评估预后相关的关键基因表达与HCC免疫细胞浸润的关系。利用MiRNet和Network Analyst数据库分别构建微小RNA（miRNA）-关键基因mRNA和转录因子（TFs）-关键基因mRNA分子调控网络。利用CMap数据库筛选潜在治疗HCC的小分子药物。 结果 共筛选出146个DEGs，其中表达上调基因30个，表达下调基因116个。GO功能和KEGG信号通路富集分析，DEGs明显富集在类固醇、烯化合物和激素代谢等生物过程（BP）及视黄醇代谢、药物代谢-细胞色素P450（CYP450）、补体和凝血级联反应等信号通路。PPI网络分析筛选得到14个关键基因，其中甲酰氨基转移酶环化脱氨酶（FTCD）、分泌型磷蛋白2（SPP2）、凝血酶-抗凝血酶复合物（TAT）、补体C6（C6）和CYP450家族成员2C9（CYP2C9）与HCC患者预后、临床病理分期和组织学分级有明显相关性，在HCC诊断中具有较高的诊断效能，与HCC的免疫细胞浸润有密切关联。Hsa-mir-182-5p、同源框CUT样蛋白1（CUX1）、早期生长反应1（EGR1）、SMAD家族成员4（SMAD4）和肿瘤蛋白 P53（TP53）是靶向上述预后相关关键基因的重要调控因子。DL-硫沙芬（DL-thiorphan）、异丙嗪（promethazine）和芹菜素（apigenin）可能对HCC有治疗作用。 结论 FTCD、SPP2、TAT、C6和 CYP2C9可能是HCC诊断、预后判断和治疗的潜在靶点，预测得到的3种小分子药物DL-thiorphan，promethazine和apigenin可为HCC治疗药物的研发提供参考。

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.