Identification of a novel cell cycle-related risk signature predicting prognosis in patients with pancreatic adenocarcinoma. Issue 46 (18th November 2022) - Xu, Dapeng; Qin, Rong; Li, Ming; Shen, Jun; Mao, Yongmin; Tang, Kai; Zhang, Aiguo; Wang, Dafeng; Shi, Yingzuo
Abstract : Background: Growing evidence have indicated that cell cycle-related genes (CRGs) play an essential role in the progression of pancreatic adenocarcinoma (PAAD). Nevertheless, the application of CRGs in estimating the prognosis of PAAD patients is still lacking. This study aimed to establish a risk signature based on CRGs that can predict patients' overall survival for PAAD. Methods: The expression and corresponding clinical data of PAAD patients from The Cancer Genome Atlas database and 200 cell cycle-related genes from the MSigDB were used for the generation and validation of the signature. LASSO Cox regression was applied to build the prediction model. The diagnostic value of signature was evaluated by receiver operating characteristic curves. Univariate and multivariate regression was used to construct the nomogram providing the clinicians a useful tool. Results: A total of 103 CRGs were identified. Seven genes (RBM14, SMAD3, CENPA, KIF23, NUSAP1, INCENP, SMC4) with non-zero coefficients in LASSO analysis were used to construct the prognostic signature. The 7-gene signature significantly stratified patients into high- and low-risk groups in terms of overall survival, and the area under the receiver operating characteristic curve of 5-year survival reached 0.749. Multivariate analysis showed that the signature is an independent prognostic factor. We then mapped a nomogram to predict 1-, 3-, and 5-year survival for PAAD patients. The calibration curves indicated that the model was reliable. Finally, we discovered that TP53 and KRAS mutated most frequently in low and high-risk groups, respectively. Conclusion: Our findings suggested that the seven genes identified in this study are valuable prognostic predictors for patients with PAAD. These findings provided us with a novel insight that it is useful for understanding cell cycle mechanisms and for identifying patients with PAAD with poor prognosis.