PDF(Pubmed)
Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：肝细胞癌（HCC）的特点是发病机制复杂，有限的治疗方法，预后不良。内质网应激(ERS)在肝癌的发生、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，以便早期诊断和有希望的治疗靶点。
方法：整合了GEO数据集（GSE25097、GSE62232和GSE65372），以鉴定与HCC相关的差异表达基因（ERSRGs）。随机森林(RF)和支持向量机(SVM)机器学习技术被应用于筛选与内质网应激相关的ERSRGs,建立了人工神经网络(ANN)诊断预测模型。利用ESTIMATE算法分析ERSRGs与免疫微环境的相关性。使用药物特征数据库(DSigDB)探索用于ERSRG的潜在治疗剂。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了其生物学功能。
结果：基于SRPX构建了与ERS模型相关的ANN，THBS4,CTH,PPP1R16A,CLGN,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,表明高可靠性和有效性。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著相关,表明它们作为免疫疗法重要靶点的潜力。根据莫米松的最高结合评分，预测莫米松是最有前途的治疗药物。在六个ERSRG中，PPP1R16A突变率最高，主要是拷贝数突变，这可能是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移动抑制因子（MIF）/CD74+CXCR4信号通路重塑肿瘤微环境。功能实验表明，在siRNA敲低后，PPP1R16A的表达下调，抑制了增殖，迁移,HCCLM3和Hep3B细胞的体外侵袭能力。
结论：各种机器学习算法和人工智能神经网络的共识为诊断与ERS相关的肝癌建立了一种新颖的预测模型。本研究为HCC的诊断和治疗提供了新的方向。
方法：整合了GEO数据集（GSE25097、GSE62232和GSE65372），以鉴定与HCC相关的差异表达基因（ERSRGs）。随机森林(RF)和支持向量机(SVM)机器学习技术被应用于筛选与内质网应激相关的ERSRGs,建立了人工神经网络(ANN)诊断预测模型。利用ESTIMATE算法分析ERSRGs与免疫微环境的相关性。使用药物特征数据库(DSigDB)探索用于ERSRG的潜在治疗剂。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了其生物学功能。
结果：基于SRPX构建了与ERS模型相关的ANN，THBS4,CTH,PPP1R16A,CLGN,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,表明高可靠性和有效性。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著相关,表明它们作为免疫疗法重要靶点的潜力。根据莫米松的最高结合评分，预测莫米松是最有前途的治疗药物。在六个ERSRG中，PPP1R16A突变率最高，主要是拷贝数突变，这可能是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移动抑制因子（MIF）/CD74+CXCR4信号通路重塑肿瘤微环境。功能实验表明，在siRNA敲低后，PPP1R16A的表达下调，抑制了增殖，迁移,HCCLM3和Hep3B细胞的体外侵袭能力。
结论：各种机器学习算法和人工智能神经网络的共识为诊断与ERS相关的肝癌建立了一种新颖的预测模型。本研究为HCC的诊断和治疗提供了新的方向。
