Though patients with hepatocellular carcinoma (HCC) benefit from the treatment of immune checkpoint inhibitor (ICB), it is still of vital significance to develop more effective drugs and predict patients’ response to ICB therapy. Herein, we utilized single sample gene set enrichment analysis (ssGSEA) to score the downloaded tumor samples from TCGA-LIHC based on 29 immune gene sets, thus reflecting the immunologic competence of samples. Then samples were classified into high, moderate, and low immunity groups. Additionally, we utilized survival analysis and ESTIMATE score to verify the reliability of the immunity grouping. We then performed differential expression analysis on the samples in these two groups and obtained 716 differentially expressed genes (DEGs). Next, the DEGs mentioned above were subjected to GO and KEGG analyses. The outcomes demonstrated that these DEGs were mostly correlated with the immune-related biological functions. To further verify biological processes in which DEGs might be involved, we constructed a protein–protein interaction network. Afterward, we used MCODE plugin to conduct subnetwork analysis. Thereafter, KEGG enrichment analysis was performed on two genes with the highest score in the subnetwork. The results exhibited that these genes were gathered in pathways such as Th1 and Th2 cell differentiation and NF-κB. Finally, we utilized Connectivity Map to find possible drugs for the treatment of HCC and obtained complex methyl-angolensate. The above results may contribute to distinguishing HCC patients who are eligible for immunotherapy and providing the foundations for the development of therapeutic drugs for HCC.

尽管肝细胞癌（HCC）患者从免疫检查点抑制剂（ICB）的治疗中获益，但开发更有效的药物并预测患者对 ICB 治疗的反应仍然具有重要意义。在此，我们利用单样本基因集富集分析（ssGSEA）基于 29 个免疫基因集对从 TCGA-LIHC 下载的肿瘤样本进行评分，从而反映样本的免疫能力。然后将样本分为高、中、低免疫组。此外，我们利用生存分析和 ESTIMATE 评分来验证免疫分组的可靠性。然后我们对这两组样本进行了差异表达分析，得到了716个差异表达基因（DEGs）。接下来，对上述 DEG 进行 GO 和 KEGG 分析。结果表明，这些 DEG 大多与免疫相关的生物学功能相关。为了进一步验证可能涉及 DEG 的生物过程，我们构建了一个蛋白质-蛋白质相互作用网络。之后，我们使用 MCODE 插件进行子网分析。此后，对子网络中得分最高的两个基因进行了 KEGG 富集分析。结果表明，这些基因聚集在Th1和Th2细胞分化和NF-κB等途径中。最后，我们利用 Connectivity Map 寻找治疗 HCC 的可能药物，并获得了复杂的甲基-安格列酸。上述结果可能有助于区分符合免疫治疗条件的HCC患者，为HCC治疗药物的开发奠定基础。