scholarly journals Identification of a stemness-related prognostic model in Pancreatic ductal adenocarcinoma by Weighted gene co-expression network analysis

2021 ◽  
Author(s):  
Xiao-Yan Huang ◽  
Wen-Tao Qin ◽  
Qi-Sheng Su ◽  
Cheng-Cheng Qiu ◽  
Ruo-Chuan Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cox Proportional Hazards Model ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ductal Adenocarcinoma ◽  
Cox Regression Analysis ◽  
Key Genes

Abstract Objective: Aim of this study was to identify the stemness-related genes in Pancreatic ductal adenocarcinoma (PDAC). Methods: The RNA-seq data of PADC were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. mRNA expression base-index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC were evaluated. The mRNAsi-based gene sets in PADC were identified by Weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analyses were performed with key genes. Kaplan–Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognosis-associated hub genes were applied to establish nomograms. The receiver operating characteristic curves (ROC) and concordance index (C-index) were utilized to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database and immunohistochemistry (IHC). Results: 36 key genes associate with mRNAsi were screened via WGCNA. Next a five-gene signature compromised TPX2, NCAPH, UBE2C, CCNB2, CEP55. Based on the expression of the signature, the PADC patients were classify patients into high- and low-risk groups. Cox regression analysis revealed that the high-risk group was significantly positive with overall survival (OS). Moreover, the nomogram has better sensitivity and specificity for predicting the OS. And the ROC, C-index indicated good performance of the prognostic signature in the TCGA and GEO dataset. Conclusion: Prognostic model associated with cancer stem cells (CSCs) reliably predict OS in PADC. this might be beneficial for the treatment of PADC patients.

scholarly journals A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiao-Yan Huang ◽  
Wen-Tao Qin ◽  
Qi-Sheng Su ◽  
Cheng-Cheng Qiu ◽  
Ruo-Chuan Liu ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ductal Adenocarcinoma ◽  
Cox Regression Analysis ◽  
Calibration Curves ◽  
Key Genes

Objective. This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC). Methods. The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index ( C -index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database. Results. A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C -index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients. Conclusion. The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

scholarly journals Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

2020 ◽  
Vol 19 ◽  
pp. 153303382098417
Author(s):  
Ting-ting Liu ◽  
Shu-min Liu
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Small Gtpase ◽  
Functional Enrichment ◽  
Clinical Correlation ◽  
Cox Regression Analysis ◽  
Prognostic Analysis ◽  
Key Genes

Objective: The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC). Methods: This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes. Results: Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation. Conclusions: In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

scholarly journals Transcriptomic Profiling Identifies DCBLD2 as a Diagnostic and Prognostic Biomarker in Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Zengyu Feng ◽  
Kexian Li ◽  
Yulian Wu ◽  
Chenghong Peng
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Extracellular Vesicles ◽  
Immune Cell ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Functional Enrichment ◽  
Ductal Adenocarcinoma ◽  
Survival Curves ◽  
Cox Regression Analysis ◽  
Calibration Curves

Background: Accumulating evidence shows that the elevated expression of DCBLD2 (discoidin, CUB and LCCL domain-containing protein 2) is associated with unfavorable prognosis of various cancers. However, the correlation of DCBLD2 expression value with the diagnosis and prognosis of pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated. Methods: Univariate Cox regression analysis was used to screen robust survival-related genes. Expression pattern of selected genes was investigated in PDAC tissues and normal tissues from multiple cohorts. Kaplan–Meier (K–M) survival curves, ROC curves and calibration curves were employed to assess prognostic performance. The relationship between DCBLD2 expression and immune cell infiltrates was conducted by CIBERSORT software. Biological processes and KEGG pathway enrichment analyses were adopted to clarify the potential function of DCBLD2 in PDAC. Results: Univariate analysis, K–M survival curves and calibration curves indicated that DCBLD2 was a robust prognostic factor for PDAC with cross-cohort compatibility. Upregulation of DCBLD2 was observed in dissected PDAC tissues as well as extracellular vesicles from both plasma and serum samples of PDAC patients. Both DCBLD2 expression in tissue and extracellular vesicles had significant diagnostic value. Besides, DCBLD2 expression was correlated with infiltrating level of CD8+ T cells and macrophage M2 cells. Functional enrichment revealed that DCBLD2 might be involved in cell motility, angiogenesis, and cancer-associated pathways. Conclusion: Our study systematically analyzed the potential diagnostic, prognostic and therapeutic value of DCBLD2 in PDAC. All the findings indicated that DCBLD2 might play a considerably oncogenic role in PDAC with diagnostic, prognostic and therapeutic potential. These preliminary results of bioinformatics analyses need to be further validated in more prospective studies.

scholarly journals BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Jin Zhou ◽  
Zheming Liu ◽  
Huibo Zhang ◽  
Tianyu Lei ◽  
Jiahui Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Early Stage ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

scholarly journals Transcriptomic Profiling Identifies DCBLD2 as a Diagnostic and Prognostic Biomarker in Pancreatic Ductal Adenocarcinoma

2021 ◽  
Author(s):  
Zengyu Feng ◽  
Kexian Li ◽  
Minmin Shi ◽  
Yeliu Feng ◽  
Hao Chen ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Therapeutic Potential ◽  
Univariate Analysis ◽  
Functional Enrichment ◽  
Ductal Adenocarcinoma ◽  
Survival Curves ◽  
Cox Regression Analysis ◽  
Calibration Curves

Abstract Background: Accumulating evidence showed that the elevated expression of DCBLD2 is associated with unfavorable prognosis of various cancers. However, the correlation of DCBLD2 expression value with the diagnosis and prognosis of pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated.Methods: Univariate Cox regression analysis was used to screen robust survival-related genes. Expression pattern of selected genes was investigated in PDAC tissues and normal tissues from multiple cohorts. Kaplan–Meier (K-M) survival curves, ROC curves and calibration curves were employed to assess prognostic performance. The relationship between DCBLD2 expression and immune cell infiltrates was conducted by CIBERSORT software. Biological processes and KEGG pathway enrichment analyses were adopted to clarify the potential function of DCBLD2 in PDAC.Results: Univariate analysis, K-M survival curves and calibration curves indicated that DCBLD2 was a robust prognostic factor for PDAC with cross-cohort compatibility. Upregulation of DCBLD2 was observed in dissected PDAC tissues as well as extracellular vesicles from both plasma and serum samples of PDAC patients. Both tissue and extracellular vesicle-encapsulated DCBLD2 expression have significant diagnostic value. Besides, DCBLD2 expression is correlated with infiltrating level of CD8 + T cells and macrophage M2 cells. Functional enrichment revealed that DCBLD2 may be involved in cell motility, angiogenesis, and cancer-associated pathways.Conclusions: Our study systematically analyzed the potential diagnostic, prognostic and therapeutic value of DCBLD2 in PDAC. All the findings indicated that DCBLD2 may play a considerably oncogenic role in PDAC with diagnostic, prognostic and therapeutic potential. These preliminary results of bioinformatics analyses need to be further validated in more prospective studies.

scholarly journals Identification of Robust Immune‐Associated lncRNAs Signature for Immune Checkpoint Blockade and Prognosis in Pancreatic Ductal Adenocarcinoma

2020 ◽  
Author(s):  
Qianhui Xu ◽  
Yuxin Wang ◽  
Wen Huang
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Immune Checkpoint Blockade ◽  
Checkpoint Blockade ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Cox Regression Analysis

Abstract Background: An increasing body of evidence has suggested that long non-coding RNAs (lncRNAs) can serve as essential regulators in cancer immunity. We aimed to establish a robust immune-associated lncRNA signature for pancreatic ductal adenocarcinoma (PDAC) outcome prediction to enhance prognostic accuracy.Methods: Pancreatic cancer samples were obtained from the The Cancer Genome Atlas (TCGA) project. Immune‐related lncRNAs displaying significant association with overall survival (OS) were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. The prediction reliability was further estimated in the internal validation set and combination set. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was performed for functional annotation. The correlation between immune checkpoint inhibitors and this signature was examined. Results: 5 immune-related lncRNAs were confirmed to establish five‐immune-related lncRNAs prognostic signature. The constructed risk model showed significant correlation with PDAC OS. The area under the curve (AUC) for this lncRNA model was up to 0.747. This immune‐related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our signature mediated chondrocyte development, laminin binding and so forth. This risk score model was associated with immune cell infiltration (i.e., CD4 T cells, etc.,) and immune checkpoint blockade (ICB) immunotherapy‐related molecules (i.e., PDCD1 and CTLA4).Conclusion: The immune‐related lncRNA signature we established possesses latent prognostic value for patients with PDAC and may have the potential to measure the response to ICB immunotherapy, which could guide for immunological treatment in PDAC.

scholarly journals A Novel Ferroptosis-Related Gene Signature Predicts Recurrence in Patients With Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Zengyu Feng ◽  
Peng Chen ◽  
Kexian Li ◽  
Jianyao Lou ◽  
Yulian Wu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cox Regression ◽  
Validation Cohort ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis ◽  
The Stability

Background: Recurrence after surgery is largely responsible for the extremely poor outcomes for patients with pancreatic ductal adenocarcinoma (PDAC). Ferroptosis is implicated in chemotherapy sensitivity and tumor recurrence, we aimed to find out survival-associated ferroptosis-related genes and use them to build a practical risk model with the purpose to predict PDAC recurrence.Methods: Univariate Cox regression analysis was conducted to obtain prognostic ferroptosis-related genes in The Cancer Genome Atlas (TCGA, N = 140) cohort. Multivariate Cox regression analysis was employed to construct a reliable and credible gene signature. The prognostic performance was verified in a MTAB-6134 (N = 286) validation cohort and a PACA-CA (N = 181) validation cohort. The stability of the signature was tested in TCGA and MTAB-6134 cohorts by ROC analyses. Pathway enrichment analysis was adopted to preliminary illuminate the biological relevance of the gene signature.Results: Univariate and multivariate Cox regression analyses identified a 5-gene signature that contained CAV1, DDIT4, SLC40A1, SRXN1 and TFAP2C. The signature could efficaciously stratify PDAC patients with different recurrence-free survival (RFS), both in the training and validation cohorts. Results of subgroup receiver operating characteristic curve (ROC) analyses confirmed the stability and the independence of this signature. Our signature outperformed clinical indicators and previous reported models in predicting RFS. Moreover, the signature was found to be closely associated with several cancer-related and drug response pathways.Conclusion: This study developed a precise and concise prognostic model with the clinical implication in predicting PDAC recurrence. These findings may facilitate individual management of postoperative recurrence in patients with PDAC.

scholarly journals Prognostic Nomogram for Patients With Pancreatic Ductal Adenocarcinoma of Pancreatic Head After Pancreaticoduodenectomy

2021 ◽  
Vol 15 ◽  
pp. 117955492110241
Author(s):  
Hongkai Zhuang ◽  
Zixuan Zhou ◽  
Zuyi Ma ◽  
Shanzhou Huang ◽  
Yuanfeng Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Perineural Invasion ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Pancreatic Head ◽  
Ductal Adenocarcinoma ◽  
R0 Resection ◽  
Cox Regression Analysis ◽  
Ajcc Stage

Background: The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) of pancreatic head remains poor, even after potentially curative R0 resection. The aim of this study was to develop an accurate model to predict patients’ prognosis for PDAC of pancreatic head following pancreaticoduodenectomy. Methods: We retrospectively reviewed 112 patients with PDAC of pancreatic head after pancreaticoduodenectomy in Guangdong Provincial People’s Hospital between 2014 and 2018. Results: Five prognostic factors were identified using univariate Cox regression analysis, including age, histologic grade, American Joint Committee on Cancer (AJCC) Stage 8th, total bilirubin (TBIL), CA19-9. Using all subset analysis and multivariate Cox regression analysis, we developed a nomogram consisted of age, AJCC Stage 8th, perineural invasion, TBIL, and CA19-9, which had higher C-indexes for OS (0.73) and RFS (0.69) compared with AJCC Stage 8th alone (OS: 0.66; RFS: 0.67). The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve for the nomogram for OS and RFS were significantly higher than other single parameter, which are AJCC Stage 8th, age, perineural invasion, TBIL, and CA19-9. Importantly, our nomogram displayed higher C-index for OS than previous reported models, indicating a better predictive value of our model. Conclusions: A simple and practical nomogram for patient prognosis in PDAC of pancreatic head following pancreaticoduodenectomy was established, which shows satisfactory predictive efficacy and deserves further evaluation in the future.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Dipeptidyl peptidase like 6 promoter methylation is a potential prognostic biomarker for pancreatic ductal adenocarcinoma

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Xin Zhao ◽  
Di Cao ◽  
Zhangyong Ren ◽  
Zhe Liu ◽  
Shaocheng Lv ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Promoter Methylation ◽  
Methylation Level ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Dipeptidyl Peptidase ◽  
Ductal Adenocarcinoma ◽  
Gene Promoters ◽  
Cox Regression Analysis

Abstract Background: Hypermethylation of gene promoters plays an important role in tumorigenesis. The present study aimed to identify and validate promoter methylation-driven genes (PMDGs) for pancreatic ductal adenocarcinoma (PDAC). Methods: Based on GSE49149 and the PDAC cohort of The Cancer Genome Atlas (TCGA), differential analyses of promoter methylation, correlation analysis, and Cox regression analysis were performed to identify PMDGs. The promoter methylation level was assessed by bisulfite sequencing polymerase chain reaction (BSP) in paired tumor and normal tissues of 72 PDAC patients. Kaplan−Meier survival analyses were performed to evaluate the clinical value of PMDGs. Results: In GSE49149, the β-value of the dipeptidyl peptidase like 6 (DPP6) promoter was significantly higher in tumor compared with normal samples (0.50 vs. 0.24, P<0.001). In the PDAC cohort of TCGA, the methylation level of the DPP6 promoter was negatively correlated with mRNA expression (r = −0.54, P<0.001). In a multivariate Cox regression analysis, hypermethylation of the DPP6 promoter was an independent risk factor for PDAC (hazard ratio (HR) = 543.91, P=0.002). The results of BSP revealed that the number of methylated CG sites in the DPP6 promoter was greater in tumor samples than in normal samples (7.43 vs. 2.78, P<0.001). The methylation level of the DPP6 promoter was moderately effective at distinguishing tumor from normal samples (area under ROC curve (AUC) = 0.74, P<0.001). Hypermethylation of the DPP6 promoter was associated with poor overall (HR = 3.61, P<0.001) and disease-free (HR = 2.01, P=0.016) survivals for PDAC patients. Conclusion: These results indicate that DPP6 promoter methylation is a potential prognostic biomarker for PDAC.

Sign in / Sign up

Export Citation Format

Share Document