Tian Xiong, Zheng Jing, Mou Wanlan, Lu Guoguang, Chen Shuaishuai, Du Juping, Zheng Yufen, Chen Shiyong, Shen Bo, Li Jun, Wang Na
Department of Public Research Platform, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.
Department of Clinical Laboratory, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.
Front Pharmacol. 2022 Jul 21;13:939542. doi: 10.3389/fphar.2022.939542. eCollection 2022.
Pancreatic adenocarcinoma (PAAD) is one of the most aggressive and fatal gastrointestinal malignancies with high morbidity and mortality worldwide. Accumulating evidence has revealed the clinical significance of the interaction between the hypoxic microenvironment and cancer stemness in pancreatic cancer progression and therapies. This study aims to identify a hypoxia-stemness index-related gene signature for risk stratification and prognosis prediction in PAAD. The mRNA expression-based stemness index (mRNAsi) data of PAAD samples from The Cancer Genome Atlas (TCGA) database were calculated based on the one-class logistic regression (OCLR) machine learning algorithm. Univariate Cox regression and LASSO regression analyses were then performed to establish a hypoxia-mRNAsi-related gene signature, and its prognostic performance was verified in both the TCGA-PAAD and GSE62452 corhorts by Kaplan-Meier and receiver operating characteristic (ROC) analyses. Additionally, we further validated the expression levels of signature genes using the TCGA, GTEx and HPA databases as well as qPCR experiments. Moreover, we constructed a prognostic nomogram incorporating the eight-gene signature and traditional clinical factors and analyzed the correlations of the risk score with immune infiltrates and immune checkpoint genes. The mRNAsi values of PAAD samples were significantly higher than those of normal samples ( < 0.001), and PAAD patients with high mRNAsi values exhibited worse overall survival (OS). A novel prognostic risk model was successfully constructed based on the eight-gene signature comprising JMJD6, NDST1, ENO3, LDHA, TES, ANKZF1, CITED, and SIAH2, which could accurately predict the 1-, 3-, and 5-year OS of PAAD patients in both the training and external validation datasets. Additionally, the eight-gene signature could distinguish PAAD samples from normal samples and stratify PAAD patients into low- and high-risk groups with distinct OS. The risk score was closely correlated with immune cell infiltration patterns and immune checkpoint molecules. Moreover, calibration analysis showed the excellent predictive ability of the nomogram incorporating the eight-gene signature and traditional clinical factors. We developed a hypoxia-stemness-related prognostic signature that reliably predicts the OS of PAAD. Our findings may aid in the risk stratification and individual treatment of PAAD patients.
胰腺腺癌（PAAD）是全球发病率和死亡率极高的侵袭性最强且致命的胃肠道恶性肿瘤之一。越来越多的证据揭示了缺氧微环境与癌症干性之间的相互作用在胰腺癌进展和治疗中的临床意义。本研究旨在鉴定一种与缺氧-干性指数相关的基因特征，用于PAAD的风险分层和预后预测。基于单类逻辑回归（OCLR）机器学习算法计算了来自癌症基因组图谱（TCGA）数据库的PAAD样本基于mRNA表达的干性指数（mRNAsi）数据。随后进行单变量Cox回归和LASSO回归分析，以建立与缺氧-mRNAsi相关的基因特征，并通过Kaplan-Meier分析和受试者工作特征（ROC）分析在TCGA-PAAD和GSE62452队列中验证其预后性能。此外，我们使用TCGA、GTEx和HPA数据库以及qPCR实验进一步验证了特征基因的表达水平。此外，我们构建了一个包含八基因特征和传统临床因素的预后列线图，并分析了风险评分与免疫浸润和免疫检查点基因的相关性。PAAD样本的mRNAsi值显著高于正常样本（<0.001），mRNAsi值高的PAAD患者总生存期（OS）较差。基于包含JMJD6、NDST1、ENO3、LDHA、TES、ANKZF1、CITED和SIAH2的八基因特征成功构建了一种新的预后风险模型，该模型可以在训练数据集和外部验证数据集中准确预测PAAD患者的1年、3年和5年OS。此外，八基因特征可以区分PAAD样本与正常样本，并将PAAD患者分为OS不同的低风险和高风险组。风险评分与免疫细胞浸润模式和免疫检查点分子密切相关。此外，校准分析表明包含八基因特征和传统临床因素的列线图具有出色的预测能力。我们开发了一种与缺氧-干性相关的预后特征，可可靠地预测PAAD的OS。我们的研究结果可能有助于PAAD患者的风险分层和个体化治疗。
