Henan University of Traditional Chinese Medicine, Zhengzhou, 450046, Henan, China.
Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
J Transl Med. 2023 Oct 27;21(1):759. doi: 10.1186/s12967-023-04567-9.
The unfolding protein response is a critical biological process implicated in a variety of physiological functions and disease states across eukaryotes. Despite its significance, the role and underlying mechanisms of the response in the context of ischemic stroke remain elusive. Hence, this study endeavors to shed light on the mechanisms and role of the unfolding protein response in the context of ischemic stroke.
In this study, mRNA expression patterns were extracted from the GSE58294 and GSE16561 datasets in the GEO database. The screening and validation of protein response-related biomarkers in stroke patients, as well as the analysis of the immune effects of the pathway, were carried out. To identify the key genes in the unfolded protein response, we constructed diagnostic models using both random forest and support vector machine-recursive feature elimination methods. The internal validation was performed using a bootstrapping approach based on a random sample of 1,000 iterations. Lastly, the target gene was validated by RT-PCR using clinical samples. We utilized two algorithms, CIBERSORT and MCPcounter, to investigate the relationship between the model genes and immune cells. Additionally, we performed uniform clustering of ischemic stroke samples based on expression of genes related to the UPR pathway and analyzed the relationship between different clusters and clinical traits. The weighted gene co-expression network analysis was conducted to identify the core genes in various clusters, followed by enrichment analysis and protein profiling for the hub genes from different clusters.
Our differential analysis revealed 44 genes related to the UPR pathway to be statistically significant. The integration of both machine learning algorithms resulted in the identification of 7 key genes, namely ATF6, EXOSC5, EEF2, LSM4, NOLC1, BANF1, and DNAJC3. These genes served as the foundation for a diagnostic model, with an area under the curve of 0.972. Following 1000 rounds of internal validation via randomized sampling, the model was confirmed to exhibit high levels of both specificity and sensitivity. Furthermore, the expression of these genes was found to be linked with the infiltration of immune cells such as neutrophils and CD8 T cells. The cluster analysis of ischemic stroke samples revealed three distinct groups, each with differential expression of most genes related to the UPR pathway, immune cell infiltration, and inflammatory factor secretion. The weighted gene co-expression network analysis showed that all three clusters were associated with the unfolded protein response, as evidenced by gene enrichment analysis and the protein landscape of each cluster. The results showed that the expression of the target gene in blood was consistent with the previous analysis.
The study of the relationship between UPR and ischemic stroke can help to better understand the underlying mechanisms of the disease and provide new targets for therapeutic intervention. For example, targeting the UPR pathway by blocking excessive autophagy or inducing moderate UPR could potentially reduce tissue injury and promote cell survival after ischemic stroke. In addition, the results of this study suggest that the use of UPR gene expression levels as biomarkers could improve the accuracy of early diagnosis and prognosis of ischemic stroke, leading to more personalized treatment strategies. Overall, this study highlights the importance of the UPR pathway in the pathology of ischemic stroke and provides a foundation for future studies in this field.
unfolded protein response（未折叠蛋白反应）是一种关键的生物学过程，涉及真核生物中的各种生理功能和疾病状态。尽管它很重要，但在缺血性中风的背景下，该反应的作用和潜在机制仍然难以捉摸。因此，本研究旨在阐明 unfolded protein response 在缺血性中风中的机制和作用。
本研究从 GEO 数据库中的 GSE58294 和 GSE16561 数据集提取 mRNA 表达模式。筛选和验证中风患者的蛋白反应相关生物标志物，并分析该途径的免疫效应。为了确定 unfolded protein response 中的关键基因，我们使用随机森林和支持向量机递归特征消除方法构建了诊断模型。内部验证采用基于 1000 次随机抽样的自举方法进行。最后，使用临床样本通过 RT-PCR 验证靶基因。我们使用了两种算法，CIBERSORT 和 MCPcounter，来研究模型基因与免疫细胞之间的关系。此外，我们基于与 UPR 途径相关的基因的表达对缺血性中风样本进行了均匀聚类，并分析了不同聚类与临床特征之间的关系。进行加权基因共表达网络分析以确定不同聚类中的核心基因，然后对不同聚类的枢纽基因进行富集分析和蛋白图谱分析。
我们的差异分析显示，有 44 个与 UPR 途径相关的基因具有统计学意义。两种机器学习算法的整合导致确定了 7 个关键基因，即 ATF6、EXOSC5、EEF2、LSM4、NOLC1、BANF1 和 DNAJC3。这些基因作为诊断模型的基础，曲线下面积为 0.972。通过 1000 轮随机抽样的内部验证，证实该模型具有较高的特异性和敏感性。此外，这些基因的表达与中性粒细胞和 CD8 T 细胞等免疫细胞的浸润有关。对缺血性中风样本的聚类分析显示，有三个不同的组，每个组与与 UPR 途径、免疫细胞浸润和炎症因子分泌相关的大多数基因的表达不同。加权基因共表达网络分析表明，所有三个聚类均与 unfolded protein response 相关，这可以通过基因富集分析和每个聚类的蛋白图谱得到证明。结果表明，血液中靶基因的表达与之前的分析一致。
对 UPR 与缺血性中风之间关系的研究有助于更好地了解疾病的潜在机制，并为治疗干预提供新的靶点。例如，通过阻断过度自噬或诱导适度 UPR 来靶向 UPR 途径，可能会减少缺血性中风后的组织损伤并促进细胞存活。此外，本研究的结果表明，使用 UPR 基因表达水平作为生物标志物可以提高缺血性中风早期诊断和预后的准确性，从而制定更个性化的治疗策略。总体而言，本研究强调了 UPR 途径在缺血性中风病理中的重要性，并为该领域的未来研究奠定了基础。
