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

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.

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.

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.

scholarly journals Development and Validation of a Novel Prognosis Prediction Model for Patients With Stomach Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Tong Wang ◽  
Weiwei Wen ◽  
Hongfei Liu ◽  
Jun Zhang ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Differential Expression ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Stomach Adenocarcinoma ◽  
Core Genes

Background: Stomach adenocarcinoma (STAD) is a significant global health problem. It is urgent to identify reliable predictors and establish a potential prognostic model.Methods: RNA-sequencing expression data of patients with STAD were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database. Gene expression profiling and survival analysis were performed to investigate differentially expressed genes (DEGs) with significant clinical prognosis value. Overall survival (OS) analysis and univariable and multivariable Cox regression analyses were performed to establish the prognostic model. Protein–protein interaction (PPI) network, functional enrichment analysis, and differential expression investigation were also performed to further explore the potential mechanism of the prognostic genes in STAD. Finally, nomogram establishment was undertaken by performing multivariate Cox regression analysis, and calibration plots were generated to validate the nomogram.Results: A total of 229 overlapping DEGs were identified. Following Kaplan–Meier survival analysis and univariate and multivariate Cox regression analysis, 11 genes significantly associated with prognosis were screened and five of these genes, including COL10A1, MFAP2, CTHRC1, P4HA3, and FAP, were used to establish the risk model. The results showed that patients with high-risk scores have a poor prognosis, compared with those with low-risk scores (p = 0.0025 for the training dataset and p = 0.045 for the validation dataset). Subsequently, a nomogram (including TNM stage, age, gender, histologic grade, and risk score) was created. In addition, differential expression and immunohistochemistry stain of the five core genes in STAD and normal tissues were verified.Conclusion: We develop a prognostic-related model based on five core genes, which may serve as an independent risk factor for survival prediction in patients with STAD.

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

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.

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

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.

scholarly journals A signature related to lung adenocarcinoma prognosis: A study based on TCGA database

2020 ◽  
Author(s):  
Jia Wang ◽  
Xiaolu Zhang ◽  
Xiaoming Zhang ◽  
Yan Yao ◽  
Xiaoran Ma ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Risk Groups ◽  
Clinical Treatment ◽  
Low Risk ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background: The intrinsic molecular subtypes of lung adenocarcinoma (LUAD) impact clinical treatment decision-making, but the molecular mechanisms are still unclear. Therefore, we aimed to identify sensitive biomarkers to evaluate LUAD patient prognosis. Methods: Differentially expressed RNAs from LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database and they were used to construct a competitive endogenous RNA (ceRNA) network. Based on the examination of clinical data, long noncoding RNAs (lncRNAs) and mRNAs in the network were selected by univariate and multivariate Cox regression analysis. Finally, functional enrichment analysis was used to reveal prognostic signatures based on the classification into high and low-risk groups, survival analysis, and an independence test. Results: The ceRNA network consisted of 21 mRNAs, 53 lncRNAs, and 8 miRNAs that were selected from the differentially expressed RNAs identified. Next, based on univariate and multivariate Cox regression analysis, a prognostic signature, including two mRNAs (HOXA10 and CBX2) and four lncRNAs (LINC00460, LINC00330, DGCR5, and C14orf132) was constructed. Eventually, survival analysis showed that significant differences in survival rates between high and low-risk groups and the area under the curve (AUC) for three‐year survival was 0.714. Compared with clinical risk factors, including age, pathological stage, and TNM stage, our risk score had a higher prognostic value. Conclusion: By screening from a ceRNA network, we constructed a signature, including two mRNAs (HOXA10 and CBX2) and four lncRNAs (LINC00460, LINC00330, DGCR5, and C14orf132), that can be utilized as a prognostic biomarker in LUAD. This signature may provide options for clinical treatment.

Prognostic factors in advanced seminoma in the era of etoposide- and cisplatin-based chemotherapy: Importance of pretreatment LDH-level

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e16038-e16038
Author(s):  
A. Tryakin ◽  
M. Fedyanin ◽  
A. Bulanov ◽  
D. Titov ◽  
G. Allakhverdiyeva ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Prognostic Factors ◽  
Prognostic Factor ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Testicular Tumor ◽  
Progression Free Survival ◽  
Risk Groups ◽  
Cox Regression Analysis ◽  
Metastatic Sites

e16038 Background: The commonly used IGCCCG classification probably underestimates other prognostic factors (tumor markers, stage) for advanced seminoma, which was shown later (Fossa S., 1997). Furthermore, in contrast to nonseminoma different cisplatin-based regimens have not been directly compared in this population. We performed an analysis to review the outcome and prognostic factors of patients (pts) with advanced seminoma treated in our center during the last two decades. Methods: From 1983 to 2005, 250 chemotherapy (CT)-naïve pts with advanced seminoma received induction platinum-based CT, which was divided as an “older” (76 pts) and “modern” (174 pts) one. “Older CT” included cyclophosphamide + cisplatin (46 pts), ifosfamide + carboplatin (12 pts), PVB (8 pts) and other regimens (10 pts). “Modern CT” contained BEP (26 pts) and EP (148 pts) regimens. 227 (91%) pts had primary testicular tumor, 241 (96%) pts belonged to IGCCCG good prognostic group. Median follow-up was 57 (range, 3–276) months for the pts who survived. Prognostic factors were analyzed in “modern CT” group. Progression-free survival (PFS) was an end-point for Cox‘ stepwise regression analysis. Results: “Modern CT” significantly improved PFS (5-years, 91% and 74%, p = 0.002) but not OS (5-years, 92% and 89%, p = 0.28), which could be explained by effective salvage CT. Univariate analysis revealed following factors as significant: number of metastatic sites, presence of pulmonary metastases, RPLN size, hCG level, and LDH level. Cox‘ regression analysis showed pre-CT LDH as the only prognostic factor for PFS (HR 7,6, 95% CI 1,6–36.3). Using cut-off 2 x upper limit of normal for LDH level, “modern CT” group can be divided into favorable (105 [60%] pts) and unfavorable (69 (40%) pts) groups with 5-years DFS 98% vs. 78% (HR 11.1, 95% CI 3.2–33.3) and 5-years OS 99% vs. 80% (HR 11.07, 95% CI 3.09–27.92), respectively. Conclusions: Comparing with older cisplatin-based regimens, the new ones (BEP or EP) improved PFS without significant influence on OS in pts with advanced seminoma. Pre-treatment LDH level is an important independent prognostic factor, which could help stratify pts better into risk groups. Further studies with risk-adapted policy in advanced seminoma are warranted. No significant financial relationships to disclose.

scholarly journals Prognostic Significance of EIF4G1 in Patients with Pancreatic Ductal Adenocarcinoma

2019 ◽  
Author(s):  
Tae Sik Goh ◽  
Mihyang Ha ◽  
Jung Sub Lee ◽  
Dae Cheon Jeong ◽  
Eun Sang Jung ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Survival Rate ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Prognostic Biomarker ◽  
Prognostic Significance ◽  
Ductal Adenocarcinoma ◽  
Operating Characteristics ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Background: The advances of genomics have greatly improved the survival rate cancer patients. However, due to genetic heterogeneity, pancreatic ductal adenocarcinoma (PDAC) is still difficult to diagnose early and the survival rate is extremely low. Therefore, we identified biomarkers that predict the prognosis of PDAC patients by using independent cohort data. Methods: To develop a novel prognostic biomarker, we used gene expression and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). In Kaplan-Meier survival curve using median values of genes as cut off, the only statistically significant gene in the three cohorts was EIF4G1. We analyzed prognostic significance of EIF4G1 using the time-dependent area under the curve (AUC) of the Uno's C-index, the AUC value of the receiver operating characteristics (ROC) at 3 years, and multivariate cox analysis. Also, we compare EIF4G1 levels between tumor and matched non-tumor. Results: EIF4G1 is the only prognostic gene patients with PDAC which was selected by Kaplan-Meier survival analysis. Kaplan-Meier survival analysis showed that high expression of EIF4G1 was associated with poor prognosis of PDAC with good discriminative ability in 3 independent cohorts. Risk stratifying ability of EIF4G1 was demonstrated by analyzing C-indices and AUC values. Multivariate cox regression analysis confirmed its prognostic significance. EIF4G1 expression was significantly higher in the PDAC tissues than in the matched normal tissues. Conclusions: Herein, the novel prognostic biomarker EIF4G1 could be used as prognostic maker for PDAC and determining suitable treatment options.

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 Identification of Prognostic RBPs in Osteosarcoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110049
Author(s):  
Bei Li ◽  
Long Fang ◽  
Baolong Wang ◽  
Zengkun Yang ◽  
Tingbao Zhao
Keyword(s):  
Regression Analysis ◽  
Prognostic Factors ◽  
Differential Expression ◽  
Ribosome Biogenesis ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Malignant Tumors ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

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