Dataset Information

A robust 6-mRNA signature for prognosis prediction of pancreatic ductal adenocarcinoma.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. PDAC prognostic and diagnostic biomarkers are still being explored. The aim of this study is to establish a robust molecular signature that can improve the ability to predict PDAC prognosis. 155 overlapping differentially expressed genes between tumor and non-tumor tissues from three Gene Expression Omnibus (GEO) datasets were explored. A least absolute shrinkage and selection operator method (LASSO) Cox regression model was employed for selecting prognostic genes. We developed a 6-mRNA signature that can distinguish high PDAC risk patients from low risk patients with significant differences in overall survival (OS). We further validated this signature prognostic value in three independent cohorts (GEO batch, P < 0.0001; ICGC, P = 0.0036; Fudan, P = 0.029). Furthermore, we found that our signature remained significant in patients with different histologic grade, TNM stage, locations of tumor entity, age and gender. Multivariate cox regression analysis showed that 6-mRNA signature can be an independent prognostic marker in each of the cohorts. Receiver operating characteristic curve (ROC) analysis also showed that our signature possessed a better predictive role of PDAC prognosis. Moreover, the gene set enrichment analysis (GSEA) analysis showed that several tumorigenesis and metastasis related pathways were indeed associated with higher scores of risk. In conclusion, identifying the 6-mRNA signature could provide a valuable classification method to evaluate clinical prognosis and facilitate personalized treatment for PDAC patients. New therapeutic targets may be developed upon the functional analysis of the classifier genes and their related pathways.

SUBMITTER: Zhou C

PROVIDER: S-EPMC6775308 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A robust 6-mRNA signature for prognosis prediction of pancreatic ductal adenocarcinoma.

Zhou Chenhao C Zhao Yue Y Yin Yirui Y Hu Zhiqiu Z Atyah Manar M Chen Wanyong W Meng Zhefeng Z Mao Huarong H Zhou Qiang Q Tang Weiguo W Wang Pengcheng P Li Zhanming Z Weng Jialei J Bruns Christiane C Popp Marie M Popp Felix F Dong Qiongzhu Q Ren Ning N

International journal of biological sciences 20190822 11

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. PDAC prognostic and diagnostic biomarkers are still being explored. The aim of this study is to establish a robust molecular signature that can improve the ability to predict PDAC prognosis. 155 overlapping differentially expressed genes between tumor and non-tumor tissues from three Gene Expression Omnibus (GEO) datasets were explored. A least absolute shrinkage and selection operator method (LASSO) Cox reg ...[more]

PMID: 31595147

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Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is characterized by an important heterogeneity, reflected by different clinical outcomes and chemoresistance. During carcinogenesis, tumor cells display aberrant glycosylated structures, synthetized by deregulated glycosyltransferases, supporting the tumor progression. In this study, we aimed to determine whether PDAC could be stratified through their glycosyltransferase expression profiles better than the current binary classification (basal-like and classical) in order to improve detection of patients with poor prognosis.MethodsBioinformatic analysis of 169 glycosyltransferase RNA sequencing data were performed for 74 patient-derived xenografts (PDX) of resected and unresectable tumors. The Australian cohort of International Cancer Genome Consortium and the microarray dataset from Puleo patient's cohort were used as independent validation datasets.FindingsNew PDAC stratification based on glycosyltransferase expression profile allowed to distinguish different groups of patients with distinct clinical outcome (p-value = 0.007). A combination of 19 glycosyltransferases differentially expressed in PDX defined a glyco-signature, whose prognostic value was validated on datasets including resected whole tumor tissues. The glyco-signature was able to discriminate three clusters of PDAC patients on the validation cohorts, two clusters displaying a short overall survival compared to one cluster having a better prognosis. Both poor prognostic clusters having different glyco-profiles in Puleo patient's cohort were correlated with stroma activated or desmoplastic subtypes corresponding to distinct microenvironment features (p-value < 0.0001). Besides, differential expression and enrichment analyses revealed deregulated functional pathways specific to different clusters.InterpretationThis study identifies a glyco-signature relevant for a prognostic use, potentially applicable to resected and unresectable PDAC. Furthermore, it provides new potential therapeutic targets.FundingThis work was supported by INCa (Grants number 2018-078 and 2018-079), Fondation ARC (Grant number ARCPJA32020070002326), Cancéropôle PACA, DGOS (labelization SIRIC, Grant number 6038), Amidex Foundation and Ligue Nationale Contre le Cancer and by institutional fundings from INSERM and the Aix-Marseille Université.

Project description:PurposePancreatic ductal adenocarcinoma (PDAC) is one of the highest fatality rate cancers with poor survival rates. The tumor microenvironment (TME) is vital for tumor immune responses, leading to resistance to chemotherapy and poor prognosis of PDAC patients. This study aimed to provide a comprehensive evaluation of the immune genes and microenvironment in PDAC that might help in predicting prognosis and guiding clinical treatments.MethodsWe developed a prognosis-associated immune signature (i.e., PAIS) based on immune-associated genes to predict the overall survival of patients with PDAC. The clinical significance and immune landscapes of the signature were comprehensively analyzed.ResultsOwing to gene expression profiles from TCGA database, functional enrichment analysis revealed a significant difference in the immune response between PDAC and normal pancreas. Using transcriptome data analysis of a training set, we identified an immune signature represented by 5 genes (ESR2, IDO1, IL20RB, PPP3CA, and PLAU) related to the overall survival of patients with PDAC, significantly. This training set was well-validated in a test set. Our results indicated a clear association between a high-risk score and a very poor prognosis. Stratification analysis and multivariate Cox regression analysis revealed that PAIS was an important prognostic factor. We also found that the risk score was positively correlated with the inflammatory response, antigen-presenting process, and expression level of some immunosuppressive checkpoint molecules (e.g., CD73, PD-L1, CD80, and B7-H3). These results suggested that high-risk patients had a suppressed immune response. However, they could respond better to chemotherapy. In addition, PAIS was positively correlated with the infiltration of M2 macrophages in PDAC.ConclusionsThis study highlighted the relationship between the immune response and prognosis in PDAC and developed a clinically feasible signature that might serve as a powerful prognostic tool and help further optimize the cancer therapy paradigm.

Project description:BackgroundThe 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).MethodsThe 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.ResultsA 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.ConclusionsWe 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.

Project description:OBJECTIVE:The tumour microenvironment is one of the significant factors driving the carcinogenesis of Pancreatic adenocarcinoma (PAAD). However, the underlying mechanism of how the tumour microenvironment impacts the prognosis of PAAD is not completely clear. RESULTS:The transcriptome and clinical data of 182 PAAD program cases were downloaded from the TCGA database. Three hundred thirty-three differentially expressed genes (DEGs) between high and low stromal groups and 314 DEGs between high and low immune score groups were identified using ESTIMATE score. Based on the 203 genes differentially expressed simultaneously in two score-related comparisons, we established an 8-mRNA signature to evaluate the prognosis of PAAD patients. Kaplan-Meier curves showed significantly worse survival for patients with high-risk scores in both the training and validation groups. The risk score was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. By searching the TCGA database, we showed that CA9, CXCL9, and GIMAP7 from the 8-mRNA signature were associated with the infiltration levels of immunocytes by regulating FOXO1 expression in PAAD. CONCLUSIONS:Unlike traditional methods of screening for differential genes in cancer and healthy tissues, we constructed a novel 8-mRNA signature to predict the prognosis of PAAD patients by applying ESTIMATE scoring to RNA-seq-based transcriptome data. Most importantly, we identified CA9, CXCL9, and GIMAP7 from the above eight genes as regulators of immunocyte infiltration by adjusting the expression of FOXO1 in PAAD. Thus, CA9, CXCL9, and GIMAP7 might be the ideal targets of immune therapy of PAAD. METHODS:ESTIMATE scoring was used to determine the stromal and immune scores of transcriptome datasets downloaded from the TCGA database. An mRNA-based prognostic signature was built for the training cohort via the LASSO Cox regression model. The signature was verified using a validation cohort. Kaplan-Meier curves and log-rank analysis were used to identify survival differences. Western blot analysis and RT-qPCR analysis were carried out to analyze the expression of specific proteins and mRNAs. IHC was performed to assess the protein levels of Forkhead box-O 1 (FOXO1), Carbonic anhydrase 9 (CA9), C-X-C motif chemokine ligand 9 (CXCL9), and GTPase, IMAP family member 7 (GIMAP7) in the tissue microarray of PAAD.

Dataset Information

A robust 6-mRNA signature for prognosis prediction of pancreatic ductal adenocarcinoma.

Publications

A robust 6-mRNA signature for prognosis prediction of pancreatic ductal adenocarcinoma.

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