Deng Xiangying, He Xu, Yang Zehua, Huang Jing, Zhao Lin, Wen Min, Hu Xiyuan, Zou Zizheng
Yiyang Key Laboratory of Chemical Small Molecule Anti-Tumor Targeted Therapy, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Yiyang Medical College, Yiyang, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Front Oncol. 2023 Apr 14;13:1112104. doi: 10.3389/fonc.2023.1112104. eCollection 2023.
Pancreatic cancer is one of most aggressive malignancies with a dismal prognosis. Activation of PI3K/AKT signaling is instrumental in pancreatic cancer tumorigenesis. The aims of this study were to identify the molecular clustering, prognostic value, relationship with tumor immunity and targeting of PI3K/AKT-related genes (PARGs) in pancreatic cancer using bioinformatics.
The GSEA website was searched for PARGs, and pancreatic cancer-related mRNA data and clinical profiles were obtained through TCGA downloads. Prognosis-related genes were identified by univariate Cox regression analysis, and samples were further clustered by unsupervised methods to identify significant differences in survival, clinical information and immune infiltration between categories. Next, a prognostic model was constructed using Lasso regression analysis. The model was well validated by univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis and ROC curves, and correlations between risk scores and patient pathological characteristics were identified. Finally, GSEA, drug prediction and immune checkpoint protein analyses were performed.
Pancreatic cancers were divided into Cluster 1 (C1) and Cluster 2 (C1) according to PARG mRNA expression. C1 exhibited longer overall survival (OS) and higher immune scores and CTLA4 expression, whereas C2 exhibited more abundant PD-L1. A 6-PARG-based prognostic model was constructed to divide pancreatic cancer patients into a high-risk score (HRS) group and a low-risk score (LRS) group, where the HRS group exhibited worse OS. The risk score was defined as an independent predictor of OS. The HRS group was significantly associated with pancreatic cancer metastasis, aggregation and immune score. Furthermore, the HRS group exhibited immunosuppression and was sensitive to radiotherapy and guitarbine chemotherapy. Multidrug sensitivity prediction analysis indicated that the HRS group may be sensitive to PI3K/AKT signaling inhibitors (PIK-93, GSK2126458, CAL-101 and rapamycin) and ATP concentration regulators (Thapsigargin). In addition, we confirmed the oncogenic effect of protein phosphatase 2 regulatory subunit B'' subunit alpha (PPP2R3A) in pancreatic cancer and .
PARGs predict prognosis, tumor immune profile, radiotherapy and chemotherapy drug sensitivity and are potential predictive markers for pancreatic cancer treatment that can help clinicians make decisions and personalize treatment.
胰腺癌是侵袭性最强的恶性肿瘤之一，预后很差。PI3K/AKT信号通路的激活在胰腺癌的肿瘤发生过程中起重要作用。本研究旨在利用生物信息学方法，鉴定胰腺癌中PI3K/AKT相关基因（PARGs）的分子聚类、预后价值、与肿瘤免疫的关系以及靶向性。
在GSEA网站搜索PARGs，并通过TCGA下载获得胰腺癌相关的mRNA数据和临床资料。通过单因素Cox回归分析鉴定预后相关基因，并采用无监督方法对样本进行进一步聚类，以确定不同类别之间在生存、临床信息和免疫浸润方面的显著差异。接下来，使用Lasso回归分析构建预后模型。通过单因素和多因素Cox回归分析、Kaplan-Meier生存分析和ROC曲线对模型进行了充分验证，并确定了风险评分与患者病理特征之间的相关性。最后，进行了GSEA、药物预测和免疫检查点蛋白分析。
根据PARG mRNA表达，胰腺癌分为Cluster 1（C1）和Cluster 2（C2）。C1表现出更长的总生存期（OS）、更高的免疫评分和CTLA4表达，而C2表现出更丰富的PD-L1。构建了一个基于6个PARG的预后模型，将胰腺癌患者分为高风险评分（HRS）组和低风险评分（LRS）组，其中HRS组的OS较差。风险评分被定义为OS的独立预测因子。HRS组与胰腺癌转移、聚集和免疫评分显著相关。此外，HRS组表现出免疫抑制，对放疗和吉他滨化疗敏感。多药敏感性预测分析表明，HRS组可能对PI3K/AKT信号通路抑制剂（PIK-93、GSK2126458、CAL-101和雷帕霉素）和ATP浓度调节剂（毒胡萝卜素）敏感。此外，我们证实了蛋白磷酸酶2调节亚基B''亚基α（PPP2R3A）在胰腺癌中的致癌作用。
PARGs可预测预后、肿瘤免疫特征、放疗和化疗药物敏感性，是胰腺癌治疗的潜在预测标志物，有助于临床医生做出决策并实现个性化治疗。
