Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
BMC Gastroenterol. 2023 Sep 14;23(1):312. doi: 10.1186/s12876-023-02916-y.
Pancreatic adenocarcinoma is one of the most lethal tumors in the world with a poor prognosis. Thus, an accurate prediction model, which identify patients within high risk of pancreatic adenocarcinoma is needed to adjust the treatment and elevate the prognosis of these patients.
We obtained RNAseq data of The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) from UCSC Xena database, identified immune-related lncRNAs (irlncRNAs) by correlation analysis, and identified differential expressed irlncRNAs (DEirlncRNAs) between pancreatic adenocarcinoma tissues from TCGA and normal pancreatic tissues from TCGA and Genotype-Tissue Expression (GTEx). Further univariate and lasso regression analysis were performed to construct prognostic signature model. Then, we calculated the areas under curve and identified the best cut-off value to identify high- and low-risk patients with pancreatic adenocarcinoma. The clinical characteristics, immune cell infiltration, immunosuppressive microenvironment, and chemoresistance were compared between high- and low-risk patients with pancreatic adenocarcinoma.
We identified 20 DEirlncRNA pairs and grouped the patients by the best cut-off value. We proved that our prognostic signature model possesses a remarkable efficiency to predict prognosis of PAAD patients. The AUC for ROC curve was 0.905 for 1-year prediction, 0.942 for 2-year prediction, and 0.966 for 3-year prediction. Patients in high-risk group have poor survival rate and worse clinical characteristics. We also proved that patients in high-risk groups were in immunosuppressive status and may be resistant to immunotherapy. Anti-cancer drug evaluation was performed based on in-silico predated tool, such as paclitaxel, sorafenib, and erlotinib, may be suitable for PAAD patients in high-risk group.
Overall, our study constructed a novel prognostic risk model based on pairing irlncRNAs, exhibited a promising prediction value in patients with pancreatic adenocarcinoma. Our prognostic risk model may help distinguish PAAD patients suitable for medical treatments.
胰腺导管腺癌是世界上预后最差的致命肿瘤之一。因此，需要一种准确的预测模型来识别处于高风险的胰腺导管腺癌患者，以便调整治疗方案，提高这些患者的预后。
我们从 UCSC Xena 数据库中获取了癌症基因组图谱（TCGA）胰腺导管腺癌（PAAD）的 RNAseq 数据，通过相关性分析鉴定免疫相关 lncRNA（irlncRNA），并鉴定 TCGA 胰腺导管腺癌组织与 TCGA 正常胰腺组织和基因型组织表达（GTEx）之间差异表达的irlncRNA（DEirlncRNA）。进一步进行单变量和套索回归分析，构建预后特征模型。然后，我们计算了曲线下面积，并确定了最佳截断值来识别高风险和低风险的胰腺导管腺癌患者。比较了高风险和低风险胰腺导管腺癌患者的临床特征、免疫细胞浸润、免疫抑制微环境和化学耐药性。
我们鉴定了 20 对 DEirlncRNA 对，并根据最佳截断值对患者进行分组。我们证明了我们的预后特征模型能够显著预测 PAAD 患者的预后。ROC 曲线的 AUC 对于 1 年预测为 0.905，对于 2 年预测为 0.942，对于 3 年预测为 0.966。高风险组患者的生存率较低，临床特征较差。我们还证明了高风险组患者处于免疫抑制状态，可能对免疫治疗有抵抗。我们还基于紫杉醇、索拉非尼和厄洛替尼等预测工具进行了抗癌药物评估，这些药物可能适用于高风险组的 PAAD 患者。
总之，我们基于配对的irlncRNA 构建了一个新的预后风险模型，在胰腺导管腺癌患者中表现出了有前途的预测价值。我们的预后风险模型可能有助于区分适合医学治疗的 PAAD 患者。
