A signature of 18 immune‐related gene pairs to predict the prognosis of pancreatic cancer patients. Issue 4 (31st October 2020) - Bu, Fanqin
Abstract: Pancreatic cancer is one of the most lethal malignancies. With the promising prospects conveyed by immunotherapy in cancers, we aimed to construct an immune‐related gene pairs (IRGPs) signature to predict the prognosis of pancreatic cancer patients. We downloaded clinical and transcriptional data of pancreatic cancer patients from The Cancer Genome Atlas data set as the training group and GSE57495 data set as the verification group. We filtered immune‐related transcriptional data by IMMPORT. With the assistance of lasso penalized Cox regression, we constructed our prognostic IRGPs signature and divided all samples into high‐/low‐risk groups by receiver operating characteristic curve for further comparisons. The comparisons between high‐ and low‐risk groups including survival rate, multivariate, and univariate Cox proportional‐hazards analysis, infiltration of immune cells, and Gene Set Enrichment Analysis (GSEA). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) are facilitated to analyze the proceedings in which our IRGPs signature may involve in. The results revealed that 18 IRGPs were defined as our prognostic signature. The prognostic value of this IRGPs signature was verified from the GSE57495 data set. We further demonstrated the independent prognostic value of this IRGPs signature. The contents of six immune cells between high‐/low‐risk groups were different, which was associated with the progression of diverse cancers. Results from GO, KEGG, and GSEA revealed that this IRGPs signature was involved in extracellular space, immune response, cancer pathways, cation channel, and gated channel activities. Evidently, this IRGPs signature will provide remarkable value for the therapy of pancreatic cancer patients. Abstract : In our study, we aimed to construct an immune‐related gene pairs (IRGPs) signature to predict the prognosis of pancreatic cancer patients. We will verify the validity of our model by receiver operating characteristic curve, prognostic analysis, multivariate and univariate Cox proportional‐hazards analysis, and infiltration of different immune cells. Unlike traditional model, the pairwise comparison of each IRGP and the calculation of the score were absolutely based on the genetic expressions in the same patient thus it is not a necessity in our IRGPs signature to standardize the genetic expression profiles from different sequencing platforms.