Table_1_An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma.DOCX

BackgroundFor pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments.
MethodsUnivariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan–Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature.
ResultsLASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy.
ConclusionWe established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.

背景 对于胰腺导管腺癌（pancreatic ductal adenocarcinoma, PDAC）患者而言，化疗失败是术后复发及预后不良的主要诱因。构建新型生物标志物及化疗疗效预测模型，或可通过个体化治疗为患者带来生存获益。
方法 本研究采用单变量Cox回归分析，筛选出与无病生存期（disease-free survival, DFS）预后相关的上皮间质转化（epithelial-mesenchymal transition, EMT）相关基因。随后将上述基因纳入LASSO回归分析及多变量Cox回归分析，在癌症基因组图谱（The Cancer Genome Atlas, TCGA）训练队列中筛选得到最优基因特征。通过Kaplan-Meier（K-M）曲线、受试者工作特征（receiver operating characteristic, ROC）曲线及校准曲线评估该模型的预测效能，并在PACA-CA队列及本研究本地队列中进行验证。此外，通过通路富集分析与功能注释分析，阐明该风险特征的生物学意义。
结果 LASSO与多变量Cox回归分析筛选得到由DLX2、FGF9、IL6R、ITGB6、MYC、LGR5、S100A2及TNFSF12组成的8基因特征。该特征可在训练队列与验证队列中区分不同无病生存期的PDAC患者，其无病生存期预测效能优于临床指标。该基因特征与多条肿瘤相关通路存在关联，同时还可预测患者基于免疫检查点抑制剂（immune-checkpoint inhibitors, ICIs）的免疫治疗应答情况。
结论 本研究构建了一种新型上皮间质转化相关基因特征，该特征可预测辅助化疗与免疫治疗的治疗应答，有望助力PDAC患者的个体化治疗与精准管理。