Fang Lishan, Chen Shaojing, Gong Hui, Xia Shaohua, Guan Sainan, Quan Nali, Li Yajie, Zeng Chao, Chen Ya, Du Jianhang, Liu Shuguang
Department of Medical Research Center, The Eighth Affiliated Hospital, Sun Yat-Sun University, Shenzhen, China.
Department of Laboratory Medicine Center, Huazhong University of Science and Technology Union Shenzhen Hospital and the 6th Affliated Hospital of Shenzhen University, Shenzhen, China.
Front Oncol. 2023 Jan 13;12:1060508. doi: 10.3389/fonc.2022.1060508. eCollection 2022.
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive lethal malignancy. An effective prognosis prediction model is urgently needed for treatment optimization.
The differentially expressed unfolded protein response (UPR)‒related genes between pancreatic tumor and normal tissue were analyzed using the TCGA-PDAC dataset, and these genes that overlapped with UPR‒related prognostic genes from the E-MTAB-6134 dataset were further analyzed. Univariate, LASSO and multivariate Cox regression analyses were applied to establish a prognostic gene signature, which was evaluated by Kaplan‒Meier curve and receiver operating characteristic (ROC) analyses. E‒MTAB‒6134 was set as the training dataset, while TCGA-PDAC, GSE21501 and ICGC-PACA-AU were used for external validation. Subsequently, a nomogram integrating risk scores and clinical parameters was established, and gene set enrichment analysis (GSEA), tumor immunity analysis and drug sensitivity analysis were conducted.
A UPR-related signature comprising twelve genes was constructed and divided PDAC patients into high- and low-risk groups based on the median risk score. The UPR-related signature accurately predicted the prognosis and acted as an independent prognostic factor of PDAC patients, and the AUCs of the UPR-related signature in predicting PDAC prognosis at 1, 2 and 3 years were all more than 0.7 in the training and validation datasets. The UPR-related signature showed excellent performance in outcome prediction even in different clinicopathological subgroups, including the female (p<0.0001), male (p<0.0001), grade 1/2 (p<0.0001), grade 3 (p=0.028), N0 (p=0.043), N1 (p<0.001), and R0 (p<0.0001) groups. Furthermore, multiple immune-related pathways were enriched in the low-risk group, and risk scores in the low-risk group were also associated with significantly higher levels of tumor-infiltrating lymphocytes (TILs). In addition, DepMap drug sensitivity analysis and our validation experiment showed that PDAC cell lines with high UPR-related risk scores or UPR activation are more sensitive to floxuridine, which is used as an antineoplastic agent.
Herein, we identified a novel UPR-related prognostic signature that showed high value in predicting survival in patients with PDAC. Targeting these UPR-related genes might be an alternative for PDAC therapy. Further experimental studies are required to reveal how these genes mediate ER stress and PDAC progression.
胰腺导管腺癌（PDAC）是一种具有高度侵袭性的致命恶性肿瘤。迫切需要一种有效的预后预测模型来优化治疗方案。
使用TCGA-PDAC数据集分析胰腺肿瘤组织与正常组织之间差异表达的未折叠蛋白反应（UPR）相关基因，并进一步分析这些与E-MTAB-6134数据集中UPR相关预后基因重叠的基因。应用单因素、LASSO和多因素Cox回归分析建立预后基因特征，并通过Kaplan-Meier曲线和受试者工作特征（ROC）分析进行评估。将E-MTAB-6134设定为训练数据集，而将TCGA-PDAC、GSE21501和ICGC-PACA-AU用于外部验证。随后，建立了一个整合风险评分和临床参数的列线图，并进行了基因集富集分析（GSEA）、肿瘤免疫分析和药物敏感性分析。
构建了一个包含12个基因的UPR相关特征，并根据中位风险评分将PDAC患者分为高风险组和低风险组。UPR相关特征准确预测了预后，并作为PDAC患者的独立预后因素，在训练和验证数据集中，UPR相关特征预测PDAC患者1年、2年和3年预后的AUC均大于0.7。即使在不同的临床病理亚组中，包括女性（p<0.0001）、男性（p<0.0001）、1/2级（p<0.0001）、3级（p=0.028）、N0（p=0.043）、N1（p<0.001）和R0（p<0.0001）组，UPR相关特征在预后预测方面也表现出优异的性能。此外，多个免疫相关通路在低风险组中富集，低风险组中的风险评分也与肿瘤浸润淋巴细胞（TILs）水平显著升高相关。此外，DepMap药物敏感性分析和我们的验证实验表明，具有高UPR相关风险评分或UPR激活的PDAC细胞系对用作抗肿瘤药物的氟尿苷更敏感。
在此，我们鉴定了一种新的UPR相关预后特征，其在预测PDAC患者生存方面具有很高价值。靶向这些UPR相关基因可能是PDAC治疗的一种替代方法。需要进一步的实验研究来揭示这些基因如何介导内质网应激和PDAC进展。
