With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase–related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature.

随着单细胞测序和机器学习方法的最新进展，人们对肝细胞癌（HCC）的进展提供了新的见解。蛋白激酶相关基因 (PKRG) 影响 HCC 进展过程中的细胞生长、分化、凋亡和信号传导，因此 PKRG 在 HCC 中的预测相关性对于个性化医疗非常必要。在本研究中，我们分析了HCC的单细胞数据，并使用LASSO回归的机器学习方法构建了六种主要细胞类型的PKRG预测模型。CDK4 和 AURKB 被发现是预测肝细胞中 HCC 患者总体生存（包括 TCGA、ICGC 和 GEO 数据集）的最佳 PKRG 预后特征。使用COX回归方法进一步筛选出独立的临床因素，并创建结合PKRG和癌症状态的列线图。Palbociclib（CDK4 抑制剂）和 Barasertib（AURKB 抑制剂）治疗可抑制 HCC 细胞迁移。PKRG 高风险组或低风险组的患者表现出不同的肿瘤突变负荷、免疫浸润和基因富集。PKRG高危组表现出较高的肿瘤突变负担，基因集富集分析表明这些患者的细胞周期、碱基切除修复和RNA降解途径更加丰富。此外，PKRG 高风险组的 Naïve CD8+ T 细胞、内皮细胞、M2 巨噬细胞和 Tregs 的浸润水平高于低风险组。总之，本研究通过在 HCC 中使用机器学习算法，建立了用于单细胞水平预后分层的肝细胞相关 PKRG 特征，并根据 PKRG 特征确定了潜在的 HCC 治疗靶点。