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

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.

scholarly journals A Novel Signature Constructed by Immune-Related LncRNA Predicts the Immune Landscape of Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Mengyu Sun ◽  
Tongyue Zhang ◽  
Yijun Wang ◽  
Wenjie Huang ◽  
Limin Xia
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Cox Regression Analysis ◽  
Chemotherapy Drugs

Colorectal cancer (CRC) has the characteristics of high morbidity and mortality. LncRNA not only participates in the progression of CRC through genes and transcription levels, but also regulates the tumor microenvironment and leads to the malignant phenotype of tumors. Therefore, we identified immune-related LncRNAs for the construction of clinical prognostic model. We searched The Cancer Genome Atlas (TCGA) database for original data. Then we identified differentially expressed irlncRNA (DEirlncRNA), which was paired and verified subsequently. Next, univariate analysis, Lasso and Cox regression analysis were performed on the DEirlncRNA pair. The ROC curve of the signature was drawn, and the optimal cut-off value was found. Then the cohort was divided into a high-risk and a low-risk group. Finally, we re-evaluated the signature from different perspectives. A total of 16 pairs of DEirlncRNA were included in the construction of the model. After regrouping according to the cut-off value of 1.275, the high-risk group showed adverse survival outcomes, progressive clinicopathological features, specific immune cell infiltration status, and high sensitivity to some chemotherapy drugs. In conclusion, we constructed a signature composed of immune-related LncRNA pair with no requirement of the specific expression level of genes, which shows promising clinical predictive value in CRC patients.

scholarly journals Development and validation of a cancer stem cell-related signature for prognostic prediction in pancreatic ductal adenocarcinoma

2020 ◽  
Author(s):  
Zengyu Feng ◽  
Minmin Shi ◽  
Kexian Li ◽  
Lingxi Jiang ◽  
Hao Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Calibration Curves ◽  
Risk Patients

Abstract Background Cancer stem cells (CSCs) are crucial to malignant behavior and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). In recent years, CSC biology has been widely studied, but practical prognostic signatures based on CSC-related genes have not been established and reported in PDAC. Methods This signature was developed and validated in seven independent PDAC datasets. MTAB-6134 cohort was used as training set, while one Chinese local cohort and five other public cohorts were used for external validation. CSC-related genes with credible prognostic roles were selected to form the signature, and its predictive performance was evaluated by Kaplan-Meier survival, receiver operating characteristic (ROC), and calibration curves. Correlation analysis was employed to clarify the potential biological characteristics. Results A robust signature comprising DCBLD2, GSDMD, PMAIP1, and PLOD2 was developed. It could classify patients into high-risk and low-risk groups, and high risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) compared with low risk patients. Calibration curves and Cox regression analysis demonstrated the powerful predictive performance. ROC curves showed an improved survival prediction provided by this model. Functional analysis revealed positive association between risk score and CSC marker. These results had cross-dataset compatibility. Conclusions We established a novel four-gene signature based on CSC-related genes which could serve as a powerful prognostic tool in PDAC. Impact This signature can offer potential help for further improving current TNM staging system, and providing data for the development of novel personalized therapeutic strategies in the future.

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 Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Huiling Wang ◽  
Shuo You ◽  
Meng Fang ◽  
Qian Fang
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Regression Analysis ◽  
Clustering Algorithm ◽  
Immune Cell ◽  
Cox Regression ◽  
Therapeutic Potential ◽  
Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Background. Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. Method. The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set enrichment (ssGSEA). The immune feature was constructed by Cox regression analysis. In addition, the coexpression of differential expression genes (DEGs) was identified. Through enrichment analysis, the function and pathway of module genes were identified. The somatic mutations related to immune characteristics were analyzed by Maftools. By using the consistency clustering algorithm, the molecular subtypes were constructed, and the overall survival time (OS) was predicted. Results. Immune landscape can be divided into low immune infiltration and high immune infiltration. Cox regression analysis identified seven immune cells as protective factors of BC. In the coexpression modules for DEGs of high and low immune infiltration, module 1 was related to T cells and high immune infiltration. In particular, the area under the curve (AUC) value of hub gene SASH3 was the highest, and the correlation with T cells was stronger in the high immune infiltration. Enrichment analysis found that oxidative stress, T cell aggregation, and apoptosis were observed in high immune infiltration. In addition, TP53 was identified as the most important somatic gene mutation related to immune characteristics. Importantly, we also constructed seven immune cell-based breast cancer subtypes to predict OS. Conclusion. We evaluated the immune landscape of BC and constructed the gene characteristics related to the immune landscape. The potential of T cells and SASH3 in immunotherapy of BC was revealed, which may guide the development of new clinical treatment strategies.

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