Construction and Validation of an Immune-Based Prognostic Model for Pancreatic Adenocarcinoma Based on Public Databases

Abstract

BackgroundPancreatic adenocarcinoma (PAAD) is a highly lethal and aggressive tumor with poor prognoses. The predictive capability of immune-related genes (IRGs) in PAAD has yet to be explored. We aimed to explore prognostic-related immune genes and develop a prediction model for indicating prognosis in PAAD.MethodsThe messenger (m)RNA expression profiles acquired from public databases were comprehensively integrated and differentially expressed genes were identified. Univariate analysis was utilized to identify IRGs that related to overall survival. Whereafter, a multigene signature in the Cancer Genome Atlas cohort was established based on the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Moreover, a transcription factors regulatory network was constructed to reveal potential molecular processes in PAAD. PAAD datasets downloaded from the Gene Expression Omnibus database were applied for the validations. Finally, correlation analysis between the prognostic model and immunocyte infiltration was investigated.ResultsTotally, 446 differentially expressed immune-related genes were screened in PAAD tissues and normal tissues, of which 43 IRGs were significantly related to the overall survival of PAAD patients. An immune-based prognostic model was developed, which contained eight IRGs. Univariate and multivariate Cox regression revealed that the risk score model was an independent prognostic indicator in PAAD (HR > 1, P < 0.001). Besides, the sensitivity of the model was evaluated by the receiver operating characteristic curve analysis. Finally, immunocyte infiltration analysis revealed that the eight-gene signature possibly played a pivotal role in the status of the PAAD immune microenvironment.ConclusionA novel prognostic model based on immune genes may serve to characterize the immune microenvironment and provide a basis for PAAD immunotherapy.

An Inflammatory Response Related Gene Signature Associated with Survival Outcome and Gemcitabine Response in Patients with Pancreatic Ductal Adenocarcinoma

“…In each cohort, patients were divided into the high- or low-risk group according to the median value of the risk score. To compare the survival difference between high- and low-risk groups, Kaplan–Meier survival and receiver operating characteristic (ROC) analyses were conducted by using the R package “survival” and “timeROC.” We also calculated the time-varying concordance index (C-index) of our signature and compared it with six previous published signatures which were based on glycolysis ( Song et al, 2021 ), ferroptosis ( Feng et al, 2021a ), hypoxia ( Ding et al, 2021 ), m6A ( Meng et al, 2020 ), autophagy ( Yu J. et al, 2021 ), and immune ( Mao et al, 2021 ) related genes. The information of these six signatures are listed in Supplementary Table S2 .…”

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

“…S5). Furthermore, the comparison of the constructed model with 7 previously published prognostic models were also conducted [33][34][35][36][37][38][39]. All signatures exhibited excellent prognostic discrimination, with significantly lower survival times observed in the high-risk group compared to the low-risk group (Fig.…”