肝细胞癌
未折叠蛋白反应
基因
免疫系统
生物
癌症研究
微阵列
基因表达
免疫学
遗传学
作者
Shuqiao Zhang,Xinyu Li,Yilu Zheng,Hao Hu,Jiahui Liu,Shijun Zhang,Chunzhi Tang,Zhuomao Mo,Weihong Kuang
出处
期刊:Current Protein & Peptide Science
[Bentham Science]
日期:2023-08-17
卷期号:24 (8): 666-683
被引量:2
标识
DOI:10.2174/1389203724666230816090504
摘要
Aims: To reveal the prognostic role of unfolded protein response (UPR) -related genes in hepatocellular carcinoma (HCC). Background: Hepatocellular carcinoma is a genetically heterogeneous tumor, and the prediction of its prognosis remains a challenge. Studies elucidating the molecular mechanisms of UPR have rapidly increased. However, the UPR molecular subtype characteristics of the related genes in HCC progression have yet to be thoroughly studied. Objective: Conducting a comprehensive assessment of the prognostic signature of genes related to the UPR in patients with HCC can advance our understanding of the cellular processes contributing to the progression of HCC and offer innovative strategies in precise therapy. Methods: Based on the gene expression profiles associated with UPR in HCC, we explored the molecular subtypes mediated by UPR-related genes and constructed a UPR-related genes signature that could precisely predict the prognosis for HCC. Results: Using microarray data of HCC patients, differentially expressed UPR-related genes (DEGs) were discovered in malignancies and normal tissues. The HCC was classified into two molecular subtypes by the NMF algorithm based on DEGs modification of the UPR. Moreover, we developed a UPR-related model for predicting HCC patients' prognosis. The robustness of the UPR- related model was confirmed in external validation. Moreover, we analyzed immune responses in different risk groups. Analysis of immune functions revealed that Treg, Macrophages, aDCs, and MHC class-I were significantly up-regulated in high-risk HCC. At the same time, cytolytic activity and type I and II INF response were higher in a low-risk subgroup. Conclusion: This study identified two UPR molecular subtypes of HCC and developed a ten-gene HCC prognostic signature model (EXTL3, PPP2R5B, ZBTB17, CCT3, CCT4, CCT5, GRPEL2, HSP90AA1, PDRG1, and STC2), which can robustly forecast the progression of HCC.
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