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

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.

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A Novel DNA Replication-Related Signature Predicting Recurrence After R0 Resection of Pancreatic Ductal Adenocarcinoma: Prognostic Value and Clinical Implications

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.619549 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zengyu Feng ◽

Kexian Li ◽

Jianyao Lou ◽

Mindi Ma ◽

Yulian Wu ◽

...

Keyword(s):

Dna Replication ◽

Pancreatic Ductal Adenocarcinoma ◽

Risk Model ◽

Cox Regression ◽

External Validation ◽

Gene Signature ◽

Ductal Adenocarcinoma ◽

R0 Resection ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.

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Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.676291 ◽

2021 ◽

Vol 8 ◽

Author(s):

Zengyu Feng ◽

Hao Qian ◽

Kexian Li ◽

Jianyao Lou ◽

Yulian Wu ◽

...

Keyword(s):

Regression Analysis ◽

Pancreatic Ductal Adenocarcinoma ◽

Cox Regression ◽

Predictive Accuracy ◽

Disease Free Survival ◽

Roc Curves ◽

Gene Signature ◽

Ductal Adenocarcinoma ◽

Survival Prediction ◽

Cox Regression Analysis

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported.Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan–Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature.Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways.Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.

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An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.665161 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zengyu Feng ◽

Kexian Li ◽

Jianyao Lou ◽

Yulian Wu ◽

Chenghong Peng

Keyword(s):

Regression Analysis ◽

Adjuvant Chemotherapy ◽

Pancreatic Ductal Adenocarcinoma ◽

Cox Regression ◽

Predictive Accuracy ◽

Checkpoint Inhibitors ◽

Gene Signature ◽

Ductal Adenocarcinoma ◽

Related Gene ◽

Cox Regression Analysis

BackgroundFor pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments.MethodsUnivariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan–Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature.ResultsLASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy.ConclusionWe established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.

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

Clinical Medicine Insights Oncology ◽

10.1177/11795549211024149 ◽

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.

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

Bioscience Reports ◽

10.1042/bsr20200214 ◽

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.

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The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

Scientific Reports ◽

10.1038/s41598-021-00364-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Dankun Luo ◽

Wenchao Yao ◽

Qiang Wang ◽

Qiu Yang ◽

Xuxu Liu ◽

...

Keyword(s):

Regression Analysis ◽

Prognostic Model ◽

Invasive Carcinoma ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis ◽

Pathway Enrichment

AbstractLong non-coding RNA (lncRNA) is a prognostic biomarker for many types of cancer. Here, we aimed to study the prognostic value of lncRNA in Breast Invasive Carcinoma (BRCA). We downloaded expression profiles from The Cancer Genome Atlas (TCGA) datasets. Subsequently, we screened the differentially expressed genes between normal tissues and tumor tissues. Univariate Cox, LASSO regression, and multivariate Cox regression analysis were used to construct a lncRNA prognostic model. Finally, a nomogram based on the lncRNAs model was developed, and weighted gene co-expression network analysis (WGCNA) was used to predict mRNAs related to the model, and to perform function and pathway enrichment. We constructed a 6-lncRNA prognostic model. Univariate and multivariate Cox regression analysis showed that the 6-lncRNA model could be used as an independent prognostic factor for BRCA patients. We developed a nomogram based on the lncRNAs model and age, and showed good performance in predicting the survival rates of BRCA patients. Also, functional pathway enrichment analysis showed that genes related to the model were enriched in cell cycle-related pathways. Tumor immune infiltration analysis showed that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group. In general, the 6-lncRNA prognostic model and nomogram could be used as a practical and reliable prognostic tool for invasive breast cancer.

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

BioMed Research International ◽

10.1155/2021/6669570 ◽

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.

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A novel epigenetic signature for predicting the prognosis of pancreatic ductal adenocarcinoma

10.21203/rs.3.rs-27415/v1 ◽

2020 ◽

Author(s):

Zhiming Zhao ◽

Mengyang Li ◽

Xianglong Tan ◽

Rong Liu

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Regression Analysis ◽

Survival Analysis ◽

Prognostic Factors ◽

Pancreatic Ductal Adenocarcinoma ◽

Cox Regression ◽

Risk Groups ◽

Ductal Adenocarcinoma ◽

Cox Regression Analysis

Abstract Background Aberrant DNA methylation is often involved in carcinogenesis. This study is designed to establish an epigenetic signature to predict overall survival (OS) of pancreatic ductal adenocarcinoma (PDAC). Methods DNA methylation and RNA-seq data of PDAC patients were downloaded from the Cancer Genome Atlas database, Genotype-Tissue Expression (GTEx), and International Cancer Genome Consortium (ICGC) database. Methylation-related differentially expressed genes (DEGs) were identified using an R package MethylMix. Epigenetic signature and nomogram were established by the LASSO and multivariate Cox regression analysis, respectively. In addition, a joint survival analysis of the gene expression and methylation was performed to identify potential prognostic factors for patients with PDAC. Results There were a total of 56 methylation-related DEGs by MethylMix criteria. After LASSO Cox regression analysis, we developed an epigenetic signature composed of five genes according to their methylation level. The signature was able to divide patients into high-risk and low-risk groups, and the OS between the high-and low-risk groups was more significantly different in both training and validation cohort. The signature is independent of clinicopathological variables and indicated better predictive power. Moreover, we developed a novel prognostic nomogram that combines risk scores with three clinicopathological factors. The joint survival analysis of gene expression and methylation revealed that 24 genes could be independent prognostic factors for OS in PDAC. Conclusions The qualitative signature and nomogram that predict OS at the individualized level and guide therapy for patients with PDAC.

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

10.21203/rs.3.rs-113463/v1 ◽

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.

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

10.21203/rs.3.rs-245344/v1 ◽

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.

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