PAC-5 Gene Expression Signature for Predicting Prognosis of Patients with Pancreatic Adenocarcinoma.
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SUBMITTER: Kim J
PROVIDER: S-EPMC6896100 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
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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.
| S-EPMC7945593 | biostudies-literature
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.
| S-EPMC8379629 | biostudies-literature
Project description:In patients with pancreatic ductal adenocarcinoma (PDAC), optimal treatment selection, including multimodality regimens such as neoadjuvant chemoradiotherapy (NACRT), can be clinically transformative. Unfortunately, currently no predictive biomarkers are available that can guide the use of NACRT in PDAC patients. Accordingly, herein we developed a novel gene signature that can preoperatively predict NACRT-sensitivity in PDAC patients. Herein, we evaluated the performance of a 10-gene panel in 749 PDAC cases, which included two public datasets (The Cancer Genome Atlas and International Cancer Genome Consortium; n = 276), and three clinical specimen cohorts (n = 417), and a pre-NACRT endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) biopsy cohort (n = 56). The potential predictive performance of this signature was evaluated and compared to CA-19-9 levels and key clinicopathological factors. We first evaluated the prognostic potential of a 10-gene panel which significantly predicted overall survival in both public datasets (P < .01, P < .01), and two in-house patient cohorts (P < .01, P = .04). In the pre-NACRT EUS-FNA cohort, we established a radio-sensitivity gene panel (RSGP) which yielded highly robust (area under the curve [AUC] = 0.91; 95% CI: 0.81-0.97) for predicting response to gemcitabine-based NACRT. Multivariate logistic regression analysis revealed that RSGP was an independent predictor for response to NACRT (OR = 2.70; 95% CI: 1.25-5.85), and this response-prediction was even more robust when CA-19-9 levels were included into the model. In conclusion, we have validated and developed a novel gene signature that is highly robust in predicting response to NACRT, even in preoperative settings, highlighting its clinical significance for optimizing and personalizing treatment strategies in PDAC patients.
| S-EPMC8221275 | biostudies-literature
Project description:Alternative splicing (AS), as an effective and universal mechanism of transcriptional regulation, is involved in the development and progression of cancer. Therefore, systematic analysis of alternative splicing in pancreatic adenocarcinoma (PAAD) is warranted. The corresponding clinical information of the RNA-Seq data and PAAD cohort was downloaded from the TCGA data portal. Then, a java application, SpliceSeq, was used to evaluate the RNA splicing pattern and calculate the splicing percentage index (PSI). Differentially expressed AS events (DEAS) were identified based on PSI values between PAAD cancer samples and normal samples of adjacent tissues. Kaplan-Meier and Cox regression analyses were used to assess the association between DEAS and patient clinical characteristics. Unsupervised cluster analysis used to reveal four clusters with different survival patterns. At the same time, GEO and TCGA combined with GTEx to verify the differential expression of AS gene and splicing factor. After rigorous filtering, a total of 45,313 AS events were identified, 1,546 of which were differentially expressed AS events. Nineteen DEAS were found to be associated with OS with a five-year overall survival rate of 0.946. And the subtype clusters results indicate that there are differences in the nature of individual AS that affect clinical outcomes. Results also identified 15 splicing factors associated with the prognosis of PAAD. And the splicing factors ESRP1 and RBM5 played an important role in the PAAD-associated AS events. The PAAD-associated AS events, splicing networks, and clusters identified in this study are valuable for deciphering the underlying mechanisms of AS in PAAD and may facilitate the establishment of therapeutic goals for further validation.
| S-EPMC8100795 | biostudies-literature
Project description:BackgroundThe aim of the present study was to construct a novel gene signature on the tumor microenvironment (TME) to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).MethodsWe downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735). Differentially expressed genes were generated by comparing high versus low score groups of immune/stromal subgroups based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses. Finally, functional enrichment analysis and protein-protein interactions were performed to predict the functional implication of the prognostic model.ResultsWe picked out 1,797 upregulated genes in immune groups and stromal groups. Through further analysis, we constructed a 7-gene signature on the TME. The risk score from the model effectively differentiated patients into high-risk and low-risk groups with different overall survival and was validated by GEO datasets. A functional analysis suggested that 7 selected genes and their co-expressed genes were mainly enriched in immune response, extracellular structure organization, and cell adhesion molecule binding.ConclusionsOur results showed that the 7-gene model on the TME can be used to assess the prognosis of patients with PDAC.
| S-EPMC8102236 | biostudies-literature
Project description:Esophageal adenocarcinoma (EAC) is a highly malignant type of digestive tract cancers with a poor prognosis despite therapeutic advances. Pyroptosis is an inflammatory form of programmed cell death, whereas the role of pyroptosis in EAC remains largely unknown. Herein, we identified a pyroptosis-related five-gene signature that was significantly correlated with the survival of EAC patients in The Cancer Genome Atlas (TCGA) cohort and an independent validation dataset. In addition, a nomogram based on the signature was constructed with novel prognostic values. Moreover, the downregulation of GSDMB within the signature is notably correlated with enhanced DNA methylation. The pyroptosis-related signature might be related to the immune response and regulation of the tumor microenvironment. Several inhibitors including GDC-0879 and PD-0325901 are promising in reversing the altered differentially expressed genes in high-risk patients. Our findings provide insights into the involvement of pyroptosis in EAC progression and are promising in the risk assessment as well as the prognosis for EAC patients in clinical practice.
| S-EPMC8637127 | biostudies-literature
Project description:Colorectal cancer (CRC) is the third leading cause of global cancer mortality. Recent studies have proposed several gene signatures to predict CRC prognosis, but none of those have proven reliable for predicting prognosis in clinical practice yet due to poor reproducibility and molecular heterogeneity. Here, we have established a prognostic signature of 113 probe sets (CRC-113) that include potential biomarkers and reflect the biological and clinical characteristics. Robustness and accuracy were significantly validated in external data sets from 19 centers in five countries. In multivariate analysis, CRC-113 gene signature showed a stronger prognostic value for survival and disease recurrence in CRC patients than current clinicopathological risk factors and molecular alterations. We also demonstrated that the CRC-113 gene signature reflected both genetic and epigenetic molecular heterogeneity in CRC patients. Furthermore, incorporation of the CRC-113 gene signature into a clinical context and molecular markers further refined the selection of the CRC patients who might benefit from postoperative chemotherapy. Conclusively, CRC-113 gene signature provides new possibilities for improving prognostic models and personalized therapeutic strategies.
| S-EPMC4741632 | biostudies-literature
Project description:BackgroundGenomic analysis is the promising tool to clear understanding of the tumorigenesis and guide molecular classification for pancreatic cancer. Our purpose was to develop a critical predictive model for prognosis in pancreatic carcinoma, based on the genomic data.MethodsThe online The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were queried as training and validation cohorts for comprehensive bioinformatic analysis. We applied Lasso and multivariate Cox regression to shrink genes and construct predictive model.ResultsA four genes model (DNAH10: HR = 0.71, 95% CI = 0.57-0.88, HSBP1L1: HR = 1.51, 95% CI = 1.18-1.92, KIAA0513: HR = 0.69, 95% CI = 0.50-0.96, and MRPL3: HR = 3.73, 95% CI = 2.03-6.86), was proposed and validated. The C-index was 0.73 (95% CI: 0.7-0.77). Patients in high-risk and low-risk group, stratified by model, suffered significantly different overall survival time (15.1 vs. 49.3 months, p < 0.0001 in TCGA; 423 vs. 618 days, p = 0.038 in ICGC). Taken clinical parameters into consideration, the risk-score was independent marker in clinical subpopulation. To explore the molecular mechanisms, 579 differential expression genes (DEG) in two groups were identified by edgeR. Functional enrichment of DEG indicated neuro-endocrine activity was the potential mechanism for the discrepant prognosis.ConclusionA specific four genes signature with the ability to predicted survival of pancreatic carcinoma was generated, which may indicate the connection between neuro-endocrine activity and patients' prognosis.
| S-EPMC6625361 | biostudies-literature
Project description:Pancreatic adenocarcinoma (PAC) is the most common type of pancreatic cancer, which commonly has an unfavorable prognosis. The present study aimed to develop a novel prognostic prediction strategy for PAC patients. mRNA sequencing data of PAC (the training dataset) were extracted from The Cancer Genome Atlas database, and the validation datasets (GSE62452 and GSE79668) were acquired from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between good and poor prognosis groups were analyzed by limma package, and then prognosis?associated genes were screened using Cox regression analysis. Subsequently, the risk score system was constructed and confirmed using Kaplan?Meier (KM) survival analysis. After the survival associated?clinical factors were screened using Cox regression analysis, they were performed with stratified analysis. Using DAVID tool, the DEGs correlated with risk scores were conducted with enrichment analysis. The results revealed that there were a total of 242 DEGs between the poor and good prognosis groups. Afterwards, a risk score system was constructed based on 6 prognosis?associated genes (CXCL11, FSTL4, SEZ6L, SPRR1B, SSTR2 and TINAG), which was confirmed in both the training and validation datasets. Cox regression analysis showed that risk score, targeted molecular therapy, and new tumor (the new tumor event days after the initial treatment according to the TCGA database) were significantly related to clinical prognosis. Under the same clinical condition, 6 clinical factors (age, history of chronic pancreatitis, alcohol consumption, radiation therapy, targeted molecular therapy and new tumor (event days) had significant associations with clinical prognosis. Under the same risk condition, only targeted molecular therapy was significantly correlated with clinical prognosis. In conclusion, the 6?gene risk score system may be a promising strategy for predicting the outcome of PAC patients.
| S-EPMC6365694 | biostudies-literature
Project description:Myeloma Overexpressed (MYEOV) is closely related to cell growth and differentiation in many cancer types. However, the role of this protein-coding gene in pancreatic ductal adenocarcinoma (PDAC) has rarely been investigated. In this study, we demonstrated that MYEOV was higher expressed in tumor tissues compared with adjacent normal pancreas tissues (ANPTs) both in mRNA and protein levels. We also performed bioinformatic analysis and found high MYEOV expression was positively correlated with tumor differentiation (P = 0.004), lymph node metastasis (P = 0.016) and TNM stage (P = 0.001). Moreover, Kaplan-Meier and Cox proportional-hazards analyses indicated that high MYEOV expression was significantly associated with poor survival in patients with PDAC and that MYEOV was an independent prognostic factor for overall survival in patients with PDAC. Geneset Enrichment Analysis (GSEA) result showed that high expression of MYEOV facilitates glycolysis of tumor cells in PDAC and validated in cellular assays. In conclusion, our results suggest that MYEOV acts as an oncogene in PDAC and can therefore serve as a biomarker for the prognosis of patients with PDAC.
| S-EPMC7469688 | biostudies-literature