Identification of the Roles of a Stemness Index Based on mRNA Expression in the Prognosis and Metabolic Reprograming of Pancreatic Ductal Adenocarcinoma

BackgroundCancer stem cells (CSCs) are widely thought to contribute to the dismal prognosis of pancreatic ductal adenocarcinoma (PDAC). CSCs share biological features with adult stem cells, such as longevity, self-renewal capacity, differentiation, drug resistance, and the requirement for a niche; these features play a decisive role in cancer progression. A prominent characteristic of PDAC is metabolic reprogramming, which provides sufficient nutrients to support rapid tumor cell growth. However, whether PDAC stemness is correlated with metabolic reprogramming remains unknown.MethodRNA sequencing data of PDAC, including read counts and fragments per kilobase of transcript per million mapped reads (FPKM), were collected from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA-PAAD) database. Single-sample gene set enrichment analysis (GSEA) was used to calculate the relative activities of metabolic pathways in each PDAC sample. Quantitative real-time PCR was performed to validate the expression levels of genes of interest.ResultsThe overall survival (OS) of patients with high mRNA expression-based stemness index (mRNAsi) values was significantly worse than that of their counterparts with low mRNAsi values (P = 0.003). This survival disadvantage was independent of baseline clinical characteristics. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GSEA showed that the differentially expressed genes between patients with high and low mRNAsi values were mainly enriched in oncogenic and metabolic pathways. Weighted gene coexpression network analysis (WGCNA) revealed 8 independent gene modules that were significantly associated with mRNAsi and 12 metabolic pathways. Unsupervised clustering based on the key genes in each module identified two PDAC subgroups characterized by different mRNAsi values and metabolic activities. Univariate Cox regression analysis identified 14 genes beneficial to OS from 95 key genes selected from the eight independent gene modules from WGCNA. Among them, MAGEH1, MAP3K3, and PODN were downregulated in both pancreatic tissues and cell lines.ConclusionThe present study showed that PDAC samples with high mRNAsi values exhibited aberrant activation of multiple metabolic pathways, and the patients from whom these samples were obtained had a poor prognosis. Future studies are expected to investigate the underlying mechanism based on the crosstalk between PDAC stemness and metabolic rewiring.

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Identification of Key Genes Involved in Pancreatic Ductal Adenocarcinoma with Diabetes Mellitus Based on Gene Expression Profiling Analysis

Pathology & Oncology Research ◽

10.3389/pore.2021.604730 ◽

2021 ◽

Vol 27 ◽

Author(s):

Weiyu Zhou ◽

Yujing Wang ◽

Hongmei Gao ◽

Ying Jia ◽

Yuanxin Xu ◽

...

Keyword(s):

Gene Expression ◽

Functional Analysis ◽

Pancreatic Ductal Adenocarcinoma ◽

Classification Model ◽

Ductal Adenocarcinoma ◽

Good Survival ◽

Support Vector ◽

Gene Modules ◽

Key Genes ◽

Functional Analyses

This study aimed to identify key genes involved in the progression of diabetic pancreatic ductal adenocarcinoma (PDAC). Two gene expression datasets (GSE74629 and GSE15932) were obtained from Gene Expression Omnibus. Then, differentially expressed genes (DEGs) between diabetic PDAC and non-diabetic PDAC were identified, followed by a functional analysis. Subsequently, gene modules related to DM were extracted by weighed gene co-expression network analysis. The protein-protein interaction (PPI) network for genes in significant modules was constructed and functional analyses were also performed. After that, the optimal feature genes were screened by support vector machine (SVM) recursive feature elimination and SVM classification model was built. Finally, survival analysis was conducted to identify prognostic genes. The correlations between prognostic genes and other clinical factors were also analyzed. Totally, 1546 DEGs with consistent change tendencies were identified and functional analyses showed they were strongly correlated with metabolic pathways. Furthermore, there were two significant gene modules, in which RPS27A and UBA52 were key genes. Functional analysis of genes in two gene modules revealed that these genes primarily participated in oxidative phosphorylation pathway. Additionally, 21 feature genes were closely related with diabetic PDAC and the corresponding SVM classifier markedly distinguished diabetic PDAC from non-diabetic PDAC patients. Finally, decreased KIF22 and PYGL levels had good survival outcomes for PDAC. Four genes (RPS27A, UBA52, KIF22 and PYGL) might be involved in the pathogenesis of diabetic PDAC. Furthermore, KIF22 and PYGL acted as prognostic biomarkers for diabetic PDAC.

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Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

Scientific Reports ◽

10.1038/s41598-021-87234-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sk Md Mosaddek Hossain ◽

Aanzil Akram Halsana ◽

Lutfunnesa Khatun ◽

Sumanta Ray ◽

Anirban Mukhopadhyay

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Mixture Model ◽

Cancer Progression ◽

Dirichlet Process ◽

Network Inference ◽

Correlation Method ◽

Gaussian Mixture ◽

Ductal Adenocarcinoma ◽

Gene Regulatory Network Inference ◽

Gene Modules

AbstractPancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.

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Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

International Journal of Molecular Sciences ◽

10.3390/ijms22126505 ◽

2021 ◽

Vol 22 (12) ◽

pp. 6505

Author(s):

Jishizhan Chen ◽

Jia Hua ◽

Wenhui Song

Keyword(s):

Stem Cells ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Negative Control ◽

Gene Set Enrichment Analysis ◽

Venn Diagram ◽

Hub Genes ◽

Protein Protein Interaction ◽

Two Samples ◽

Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

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Pancreatic ductal adenocarcinoma cell lines display a plastic ability to bi-directionally convert into cancer stem cells

International Journal of Oncology ◽

10.3892/ijo.2014.2796 ◽

2014 ◽

Vol 46 (3) ◽

pp. 1099-1108 ◽

Cited By ~ 22

Author(s):

ELISA DALLA POZZA ◽

ILARIA DANDO ◽

GIULIA BIONDANI ◽

JESSICA BRANDI ◽

CHIARA COSTANZO ◽

...

Keyword(s):

Stem Cells ◽

Cancer Stem Cells ◽

Pancreatic Ductal Adenocarcinoma ◽

Cell Lines ◽

Ductal Adenocarcinoma ◽

Adenocarcinoma Cell

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Coexpression of CD44‐positive/CD133‐positive cancer stem cells and CD204‐positive tumor‐associated macrophages is a predictor of survival in pancreatic ductal adenocarcinoma

Cancer ◽

10.1002/cncr.28774 ◽

2014 ◽

Vol 120 (17) ◽

pp. 2766-2777 ◽

Cited By ~ 49

Author(s):

Ya‐Chin Hou ◽

Ying‐Jui Chao ◽

Hui‐Ling Tung ◽

Hao‐Chen Wang ◽

Yan‐Shen Shan

Keyword(s):

Stem Cells ◽

Cancer Stem Cells ◽

Pancreatic Ductal Adenocarcinoma ◽

Ductal Adenocarcinoma ◽

Tumor Associated Macrophages

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High mRNA expression of LY6 gene family is associated with overall survival outcome in pancreatic ductal adenocarcinoma

Oncotarget ◽

10.18632/oncotarget.27880 ◽

2021 ◽

Vol 12 (3) ◽

pp. 145-159

Author(s):

Eric Russ ◽

Krithika Bhuvaneshwar ◽

Guisong Wang ◽

Benjamin Jin ◽

Michele M. Gage ◽

...

Keyword(s):

Overall Survival ◽

Gene Family ◽

Mrna Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Survival Outcome ◽

Ductal Adenocarcinoma

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Integrated Bioinformatics Analysis of Gene Expression Profiles for Potential Biomarker Identification Towards Early Therapeutic Intervention in Pancreatitis and Pancreatic Ductal Adenocarcinoma

10.21203/rs.3.rs-879048/v1 ◽

2021 ◽

Author(s):

Manoj M Wagle ◽

Ananya Rao Kedige ◽

Shama P Kabekkodu ◽

Sandeep Mallya

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Ductal Adenocarcinoma ◽

Rapid Progression ◽

Hub Genes ◽

Altered Expression ◽

Biochemical Pathways ◽

Potential Biomarker ◽

Key Genes

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a malignancy associated with rapid progression and an abysmal prognosis. It has been reported that chronic pancreatitis can increase the risk of developing PDAC by 16-fold. Our study aims to identify the key genes and biochemical pathways mediating pancreatitis and PDAC. The gene expression datasets were retrieved from the EMBL-EBI ArrayExpress and NCBI GEO database. A total of 172 samples of normal pancreatic tissue, 68 samples of pancreatitis, and 306 samples of PDAC were used in this study. The differentially expressed genes (DEGs) identified were used to perform downstream analysis for ontology, interaction, and associated pathways. Furthermore, hub gene expression was validated using the GEPIA2 tool and survival analysis using the Kaplan-Meier (KM) plotter. The potential druggability of the hub genes identified was determined using the Drug-Gene Interaction Database (DGIdb). Our study identified a total of 45 genes found to have altered expression levels in both PDAC and pancreatitis. Over-representation analysis revealed that protein digestion and absorption pathway, ECM-receptor interaction pathway, PI3k-Akt signaling pathway, and proteoglycans in cancer pathways as significantly enriched. Module analysis revealed 15 hub genes with 92 edges, of which 14 were found to be in the druggable genome category. Through bioinformatics analysis, we identified key genes and biochemical pathways disrupted in pancreatitis and PDAC. The results can provide new insights into targeted therapy and intervening therapeutically at an earlier stage can be used as an effective strategy to decrease the incidence and severity of PDAC.

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