Comprehensive analysis of prognostic immune-related genes associated with the tumor microenvironment of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a specific tumor immune microenvironment (TIME). Therefore, investigating prognostic immune-related genes (IRGs) that are closely associated with TIME to predict PDAC clinical outcomes is necessary. In the present study, 459 samples of PDAC from the Genotype-Tissue Expression database, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) were included and a survival-associated module was identified using weighted gene co-expression network analysis. Based on the Cox regression analysis and least absolute shrinkage and selection operator analysis, four IRGs (2'-5'-oligoadenylate synthetase 1, MET proto-oncogene, receptor tyrosine kinase, interleukin 1 receptor type 2 and interleukin 20 receptor subunit β) were included in the prognostic model to calculate the risk score (RS), and patients with PDAC were divided into high- and low-RS groups. Kaplan-Meier survival and receiver operating characteristic curve analyses demonstrated that the low-RS group had significantly improved survival conditions compared with the high-RS group in TCGA training set. The prognostic function of the model was also validated using ICGC and GEO cohorts. To investigate the mechanism of different overall survival between the high- and low-RS groups, the present study included Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data and Cell Type Identification by Estimating Relative Subset of Known RNA Transcripts algorithms to investigate the state of the tumor microenvironment and immune infiltration inpatients in the cohort from TCGA. In summary, four genes associated with the TIME of PDAC were identified, which may provide a reference for clinical treatment.

Yan S ，Fang J ，Zhu Y ，Xie Y ，Fang F ... -  《-》

A novel immunogenomic signature to predict prognosis and reveal immune infiltration characteristics in pancreatic ductal adenocarcinoma.

The immune response in the tumor microenvironment (TME) plays a crucial role in cancer progression and recurrence. We aimed to develop an immune-related gene (IRG) signature to improve prognostic predictive power and reveal the immune infiltration characteristics of pancreatic ductal adenocarcinoma (PDAC). The Cancer Genome Atlas (TCGA) PDAC was used to construct a prognostic model as a training cohort. The International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO) databases were set as validation datasets. Prognostic genes were screened by using univariate Cox regression. Then, a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator (LASSO) Cox regression. Cell type identification by estimating the relative subsets of RNA transcripts (CIBERSORT) and estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithms were used to characterize tumor immune infiltrating patterns. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict immunotherapy responsiveness. A prognostic signature based on five IRGs (MET, ERAP2, IL20RB, EREG, and SHC2) was constructed in TCGA-PDAC and comprehensively validated in ICGC and GEO cohorts. Multivariate Cox regression analysis demonstrated that this signature had an independent prognostic value. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve at 1, 3, and 5 years of survival were 0.724, 0.702, and 0.776, respectively. We further demonstrated that our signature has better prognostic performance than recently published ones and is superior to traditional clinical factors such as grade and tumor node metastasis classification (TNM) stage in predicting survival. Moreover, we found higher abundance of CD8+ T cells and lower M2-like macrophages in the low-risk group of TCGA-PDAC, and predicted a higher proportion of immunotherapeutic responders in the low-risk group. We constructed an optimal prognostic model which had independent prognostic value and was comprehensively validated in external PDAC databases. Additionally, this five-genes signature could predict immune infiltration characteristics. Moreover, the signature helped stratify PDAC patients who might be more responsive to immunotherapy.

Li A ，Ye B ，Lin F ，Wang Y ，Miao X ，Jiang Y ... -  《-》

N6-methylandenosine-related lncRNAs play an important role in the prognosis and immune microenvironment of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive, fatal tumor. N6-methylandenosine (m6A) methylation is the major epigenetic modification of RNA including lncRNAs. The roles of m6A-related lncRNAs in PDAC have not been fully clarified. This study aims to assess gene signatures and prognostic value of m6A-related lncRNAs in PDAC. The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset were explored to identify m6A-related lncRNAs. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to construct the m6A-related lncRNAs prognostic riskscore (m6A-LPR) model to predict the overall survival (OS) in the TCGA training cohort. Kaplan-Meier curve with log-rank test and receiver operating characteristic (ROC) curve were used to evaluate the prognostic value of the m6A-LPR. Furthermore, the robustness of the m6A-LPR was further validated in the ICGC cohort. Tumor immunity was evaluated using ESTIMATE and CIBERSORT algorithms. A total of 262 m6A-related lncRNAs were identified in two datasets. In the TCGA training cohort, 28 prognostic m6A-related lncRNAs were identified and the m6A-LPR including four m6A-related lncRNAs was constructed. The m6A-LPR was able to identify high-risk patients with significantly poorer OS and accurately predict OS in both the TCGA training cohort and the ICGC validation cohort. Analysis of tumor immunity revealed that high-risk groups had remarkably lower stromal, immune, and ESTIMATE scores. Moreover, high-risk groups were associated with significantly higher levels of plasma B cells and resting NK cells infiltration, and lower levels of infiltrating resting memory CD4 T cells, monocytes, and resting mast cells. Our study proposed a robust m6A-related prognostic signature of lncRNAs for predicting OS in PDAC, which provides some clues for further studies focusing on the mechanism process underlying m6A modification of lncRNAs.

Hu Y ，Chen Y 《Scientific Reports》

Identification and Validation of Three PDAC Subtypes and Individualized GSVA Immune Pathway-Related Prognostic Risk Score Formula in Pancreatic Ductal Adenocarcinoma Patients.

With the progress of precision medicine treatment in pancreatic ductal adenocarcinoma (PDAC), individualized cancer-related medical examination and prediction are of great importance in this high malignant tumor and tumor-immune microenvironment with changed pathways highly enrolled in the carcinogenesis of PDAC. High-throughput data of pancreatic ductal adenocarcinoma were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. After batch normalization, the enrichment pathway and relevant scores were identified by the enrichment of immune-related pathway signature using gene set variation analysis (GSVA). Then, cancerous subtype in TCGA and GEO samples was defined through the NMF methods by cancertypes packages in R software, respectively. Subsequently, the significance between the characteristics of each TCGA sample and cancer type and the significant prognosis-related pathway with risk score formula is calculated through t-test and univariate Cox analysis. Next, the prognostic value of gained risk score formula and each significant prognosis-related pathway were validated in TCGA and GEO samples by survival analysis. The pivotal hub genes in the enriched significant prognosis-related pathway are identified and validated, and the TIMER database was used to identify the potential role of hub genes in the PDAC immune environment. The potential role of hub genes is promoting the transdifferentiation of cancer-associated fibroblasts. The enrichment pathway and relevant scores were identified by GSVA, and 3 subtypes of pancreatic ductal adenocarcinoma were defined in TCGA and GEO samples. The clinical stage, tumor node metastasis classification, and tumor grade are strongly relative to the subtype above in TCGA samples. A risk formula about GSVA significant pathway "GSE45365_WT_VS_IFNAR_KO_CD11B_DC_MCMV_INFECTION_DN ∗ 0.80 + HALLMARK_GLYCOLYSIS ∗ 16.8 + GSE19888_CTRL_VS_T_CELL_MEMBRANES_ACT_MAST_CELL_DN ∗ 14.4" was identified and validated in TCGA and GEO samples through survival analysis with significance. DCN, VCAN, B4GALT7, SDC1, SDC2, B3GALT6, B3GAT3, SDC3, GPC1, and XYLT2 were identified as hub genes in these GSVA significant pathways and validated in silico. Three pancreatic ductal adenocarcinoma subtypes are identified, and an individualized GSVA immune pathway score-related prognostic risk score formula with 10 hub genes is identified and validated. The predicted function of the 10 upregulated hub genes in tumor-immune microenvironment was promoting the infiltration of cancer-associated fibroblasts. These findings will contribute to the precision medicine of pancreatic ductal adenocarcinoma treatment and tumor immune-related basic research.

Zhang D ，Wang M ，Peng L ，Yang X ，Li K ，Yin H ，Xia C ，Cui F ，Huang H ，Jin Z ... -  《-》

Metabolic syndrome related gene signature predicts the prognosis of patients with pancreatic ductal carcinoma. A novel link between metabolic dysregulation and pancreatic ductal carcinoma.

Pancreatic cancer is one of the most common malignancies worldwide. In recent years, specific metabolic activities, which involves the development of tumor, caused wide public concern. In this study, we wish to explore the correlation between metabolism and progression of tumor. A retrospective analysis including 95 patients with pancreatic ductal adenocarcinoma (PDAC) and PDAC patients from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and The Gene Expression Omnibus (GEO) database were involved in our study. Multivariate Cox regression analysis was used to construct the prognosis model. The potential connection between metabolism and immunity of PDAC was investigated through a weighted gene co-expression network analysis (WGCNA). 22 types of Tumor-infiltrating immune cells (TIICs) between high-risk and low-risk groups were estimated through CIBERSORT. Moreover, the potential immune-related signaling pathways between high-risk and low-risk groups were explored through the gene set enrichment analysis (GSEA). The role of key gene GMPS in developing pancreatic tumor was further investigated through CCK-8, colony-information, and Transwell. The prognostic value of the MetS factors was analyzed using the Cox regression model, and a clinical MetS-based nomogram was established. Then, we established a metabolism-related signature to predict the prognosis of PDAC patients based on the TCGA databases and was validated in the ICGC database and the GEO database to find the distinct molecular mechanism of MetS genes in PDAC. The result of WGCNA showed that the blue module was associated with risk score, and genes in the blue module were found to be enriched in the immune-related signaling pathway. Furthermore, the result of CIBERSORT demonstrated that proportions of T cells CD8, T cells Regulatory, Tregs NK cells Activated, Dendritic cells Activated, and Mast cells Resting were different between high-risk and low-risk groups. These differences are potential causes of different prognoses of PDAC patients. GSEA and the protein-protein interaction network (PPI) further revealed that our metabolism-related signature was significantly enriched in immune-related biological processes. Moreover, knockdown of GMPS in PDAC cells suppressed proliferation, migration, and invasion of tumor cells, whereas overexpression of GMPS performed oppositely. The results shine light on fundamental mechanisms of metabolic genes on PDAC and establish a reliable and referable signature to evaluate the prognosis of PDAC. GMPS was identified as a potential candidate oncogene with in PDAC, which can be a novel biomarker and therapeutic target for PDAC treatment.

Cai W ，Bao W ，Chen S ，Yang Y ，Li Y ... -  《Cancer Cell International》