An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma - Feng, Zengyu; Li, Kexian; Lou, Jianyao; Wu, Yulian; Peng, Chenghong (Frontiers in Cell and Developmental Biology, 2021)
Abstract Background: For 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. Methods: Univariate 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. Results: LASSO and multivariate Cox regression analyses selected a 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. Conclusion: A credible and reliable response prediction model based on EMT-related genes were constructed with cross-cohort accuracy. This signature might facilitate individualized treatment and systematic management of PDAC patients.