Establishment of a prognostic prediction model for pancreatic cancer based on endoplasmic reticulum stress-related genes / 临床肝胆病杂志

ABSTRACT

ObjectiveTo investigate the role of endoplasmic reticulum stress genes in the prognosis of pancreatic cancer， and to establish a prognostic prediction model based on the prognostic markers for pancreatic cancer. MethodsTranscriptome sequencing data were downloaded from TCGA and GTEx databases， and MsigDB website was used to obtain endoplasmic reticulum stress genes. A univariate Cox regression analysis was performed to obtain the genes associated with the prognosis of pancreatic cancer， and a consensus clustering analysis was used to construct the molecular typing of pancreatic cancer， while the differentially expressed genes between the two subgroups were obtained. A Lasso regression analysis was used to obtain the core genes associated with the prognosis of pancreatic cancer， which were used to construct a prognostic prediction model for pancreatic cancer. Related datasets were obtained from the GEO database to validate the predictive performance of the model. The CIBERSORT analysis was used to investigate the correlation between risk score and immune infiltration. Quantitative real-time PCR was used to measure the expression of genes in pancreatic cancer tissue and cell lines. The independent-samples t test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between groups. Survival was compared using Log-rank test. The predictive value of the model was evaluated by evaluating the area under the ROC curve. ResultsThe endoplasmic reticulum stress genes CEBPB， MARCKS， PMAIP1， and UBXN10 were independent risk factors for the prognosis of pancreatic cancer， and based on the expression characteristics of these genes， the TCGA pancreatic cancer cohort was divided into two subgroups， i.e.， cluster A and cluster B， while the cluster A patients had a significantly shorter overall survival time than the cluster B patients （P<0.01）. The Lasso regression analysis obtained 5 core genes from the differentially expressed genes affecting the prognosis of pancreatic cancer， and the risk scoring system was established as risk score=0.156×CDA+0.135×AHNAK2+0.020×RHOV+0.095×LY6D+0.054×SPRR1B. The ROC curve analysis showed that this model had good overall predictive performance， with the area under the ROC curve of 0.731 at 1 year， 0.712 at 3 years， and 0.686 at 5 years， and the low-risk group based on this model had a significantly longer overall survival time than the high-risk group （χ2=11.733， P=0.001）. The model showed good predictive performance in the external dataset GSE57495. Quantitative real-time PCR results showed that the expression levels of CDA， AHNAK2， RHOV， LY6D， and SPRR1B in 40 pancreatic cancer tissue samples were significantly upregulated compared with those in normal adjacent tissue samples （t=2.529， 2.458， 3.314， 3.583， and 5.082， all P<0.05）. ConclusionThe expression characteristics of CDA， AHNAK2， RHOV， LY6D， and SPRR1B can be used to predict the prognosis of pancreatic cancer， and the high expression levels of these genes are associated with the poor prognosis of pancreatic cancer patients.