肝细胞癌（HCC）具有发病机制复杂、治疗方法有限、预后差等特点。内质网应激（ERS）在HCC的发生发展中起着重要作用，因此，我们仍需要进一步研究HCC和ERS的分子机制，以实现早期诊断和有希望的治疗目标。GEO数据集（GSE25097、GSE62232和GSE65372）整合以识别与 HCC 相关的差异表达基因 (ERSRG)。应用随机森林（RF）和支持向量机（SVM）机器学习技术筛选与内质网应激相关的ERSRG，并构​​建人工神经网络（ANN）诊断预测模型。利用ESTIMATE算法分析ERSRGs与免疫微环境之间的相关性。使用药物特征数据库 (DSigDB) 探索了 ERSRG 的潜在治疗药物。通过单细胞测序和细胞通讯评估ERSRGs中心基因PPP1R16A的免疫学景观，并通过细胞学实验验证其生物学功能。基于SRPX、THBS4、CTH、PPP1R16A、CLGN构建了与ERS模型相关的ANN和 THBS1。模型在训练集中的曲线下面积（AUC）为0.979，三个验证集中的AUC值分别为0.958、0.936和0.970，具有较高的可靠性和有效性。 Spearman相关分析表明ERSRGs的表达水平与免疫细胞浸润和免疫相关通路显着相关，表明它们有可能成为免疫治疗的重要靶点。根据其最高的结合评分，莫米松被预测为最有前途的治疗药物。 6个ERSRGs中，PPP1R16A突变率最高，主要是拷贝数突变，可能是ERSRGs模型的核心基因。单细胞分析和细胞通讯表明PPP1R16A主要分布在肝脏恶性实质细胞中，可能通过增强巨噬细胞迁移抑制因子（MIF）/CD74 CXCR4信号通路重塑肿瘤微环境。功能实验显示，siRNA敲除后，PPP1R16A表达下调，抑制HCCLM3和Hep3B细胞的体外增殖、迁移和侵袭能力。各种机器学习算法和人工智能神经网络的共识，建立了一种新的预测模型。与 ERS ​​相关的肝癌诊断模型。这项研究为 HCC 的诊断和治疗提供了新的方向。© 2024。作者。

Hepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets.The GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments.An ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro.The consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.© 2024. The Author(s).