Identification of pancreatic duct adenocarcinoma prognostic-related tumor microenvironment genes using multi-platform data
10.3760/cma.j.cn115667-20200311-00035
- VernacularTitle:基于多平台数据识别胰腺导管腺癌预后相关肿瘤微环境基因
- Author:
Yinquan PU
1
;
Yufan MA
;
Li PENG
;
Xiaowei TANG
;
Yan PENG
Author Information
1. 西南医科大学附属医院消化内科，泸州 646000
- From:
Chinese Journal of Pancreatology
2020;20(2):93-101
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the tumor microenvironment (TME) module associated with pancreatic ductal adenocarcinoma (PDAC) and identify prognostic biomarkers and potential immunotherapeutic targets.Methods:The genetic expression profile data were collected and selected from a dataset of 142 PDAC patients in The Cancer Genome Altas (TCGA) database and 2 microarray datasets (GSE2150, GSE62452) of 168 PDAC patients in Gene Expression Omnibus (GEO) database, and the cell type enrichment analysis of PDAC gene expression data was analyzed by xCell network tool. According to the median cell enrichment score, 142 patients from TCGA were divided into high score group and low score group, and the cell types with prognostic value were determined by univariate survival analysis and validated by GEO datasets. According to the cell type, the differential expression gene analysis and univariate survival analysis were performed to determine the prognosis related differential expression genes (DEGs), and the prognostic DEGs were analyzed by function enrichment analysis and protein-protein interaction (PPI) network analysis. At the same time, GEO dataset was used to verify the prognosis related DEGs of TCGA datasets. Finally, TISIDB database was searched for the common DEGs of TCGA and GEO database, and its correlation with immune system was analyzed.Results:Cell type enrichment analysis showed that Th1 cell and keratinocyte had the same prognostic value in both TCGA and GEO dataset; the overall survival rate of patients with high score was lower than that of those with low score, and the differences were statistically significant (all P values <0.05). 216 prognosis related DEGs were identified, and the results of functional enrichment showed that 9 of the 21 biological process items were closely related to the immune process, and 4 of the 5 KEGG (Kyoto Encyclopedia Of Genes and Genomes) pathways were closely related to the immune process. Through PPI network analysis, CCR7, CD 27, CD 5, CXCL13, ZAP70, MS4A1 and CCL19 were proved to be possibly closely associated with central genes. Through the validation of GEO datasets, there were 15 DEGs with similar prognostic value in GEO and TCGA datasets, which was searched in TISIDB dataset, and the result showed that GIMAP7 was closely related with the immune process of PDAC. Conclusions:A group of 216 TME genes and 7 central genes related to the prognosis of PDAC were identified, and 5 potential targets for immunotherapy of PDAC were provided, including CCR7, CCL19, CD 27, CXCL13 and GIMAP7.
