Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma - Tan, Zhen; Lei, Yubin; Zhang, Bo; Shi, Si; Liu, Jiang; Yu, Xianjun; Xu, Jin; Liang, Chen (Frontiers in Oncology, 2021)
Background: Pancreatic ductal adenocarcinoma is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed existence certain curative effect in treating PDAC. Aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients. Methods: In this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO datasets, and identify modules related to CD4+ T cells. Lasso Cox regression analysis and Kaplan-Meier curve was applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analysis were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.Results: A four-genes signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verify by Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). We also found a positive correlation between the risk score of gene signature and inherited germline mutations in PDAC predisposition gene. Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset. Conclusions: We constructed a novel four genes signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.