Department of Hepatobiliary Pancreatic Surgery, Zhengzhou University Fifth Affiliated Hospital, Kangfu Front Street 3#, Zhengzhou, 450052, China.
Department of Blood Transfusion, Zhengzhou University First Affiliated Hospital, Zhengzhou, China.
Sci Rep. 2023 Jun 30;13(1):10586. doi: 10.1038/s41598-023-37836-0.
Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package "SVA" was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.
人类端粒与遗传不稳定性和更高的癌症风险相关。因此，为了改善胰腺癌患者的惨淡预后，需要彻底研究端粒相关基因与胰腺癌之间的关联。使用 Combat 从“R 包 SVA”中进行校正 TCGA-PAAD 和 GTEx 数据集之间的批次效应。在评估差异表达基因 (DEGs) 后，我们通过单变量 Cox 回归、LASSO-Cox 回归和多变量 Cox 回归分析构建了一个预后风险模型。ICGC、GSE62452、GSE71729 和 GSE78229 队列的数据被用作验证预后特征的测试队列。还评估了该特征对肿瘤微环境及其对免疫检查点药物反应的主要影响。最后，构建了 PAAD 组织微阵列并进行免疫组织化学分析，以探索该特征在临床样本中的表达。在计算了 502 个端粒相关 DEGs 后，我们构建了一个三基因预后特征（DSG2、LDHA 和 RACGAP1），可以有效地应用于多个数据集（包括 TCGA、ICGC、GSE62452、GSE71729 和 GSE78229 队列）中胰腺癌患者的预后分类。此外，我们已经筛选出针对该特征的多种肿瘤敏感药物。最后，我们还发现通过免疫组织化学分析，DSG2、LDHA 和 RACGAP1 的蛋白水平在胰腺癌组织中较正常组织上调。我们建立并验证了一个与端粒基因相关的胰腺癌预后特征，并在临床样本中证实了 DSG2、LDHA 和 RACGAP1 表达的上调，这可能为个体化免疫治疗提供新的思路。
