scholarly journals Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets

2019 ◽  
Author(s):  
Liying Liu ◽  
Siqi Wang ◽  
Chunyuan Cen ◽  
Shuyi Peng ◽  
Yan Chen ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Ductal Adenocarcinoma ◽  
Microarray Datasets

scholarly journals Network Pharmacology-based Elucidation of Molecular Biological Mechanisms of Kanglaite Injection for Treatment of Pancreatic Ductal Adenocarcinoma

2020 ◽  
Author(s):  
Bowen Xu ◽  
Wenchao Dan ◽  
Jie Lj ◽  
Xiaoxiao Zhang ◽  
Luchang Cao ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
Drug Targets ◽  
Differentially Expressed ◽  
Ductal Adenocarcinoma ◽  
Network Pharmacology ◽  
Biological Mechanisms ◽  
Pathway Network ◽  
Gene Pathway

Abstract Background: Kanglaite injection (KLTi) has shown good clinical efficacy in the treatment of pancreatic ductal adenocarcinoma (PDAC). However, its molecular biological mechanisms are still unclear. This study used network pharmacology approach to investigate the molecular biological mechanisms of KLTi.Methods: Compounds in KLTi were screened using TCMSP and drug targets were obtained from the DRUGBANK. Next, the GEO database was searched for differentially expressed genes in cancerous tissues and healthy tissues of PDAC patients to identify targets. Subsequently, the protein-protein interaction data of KLTi and PDAC targets were constructed by BisoGenet. A visual analysis was done to extract KLTi candidate genes for PDAC. The candidate genes were enriched using GO and KEGG by Metascape, and the gene-pathway network was constructed to further screen the key genes.Results: A total of 10 active compounds and 36 drug targets were screened for KLTi, 919 differentially expressed genes associated with PDAC were identified from GEO, and 139 KLTi candidate genes against PDAC were excavated by BisoGenet. The gene-pathway network showed RELA, NFKB1, IKBKG, JUN, MAPK1, TP53, and AKT1 as the core genes, predicting that KLTi intervenes in PDAC by acting on these genes.Conclusions: Our study suggested that KLTi plays an anti-PDAC role by intervening in the cell cycle, inducing apoptosis, regulating protein binding, inhibiting nerve invasion, and down-regulating the NF-κB, MAPK, and PI3K-Akt signaling pathways. In addition, it might also directly participate in the pancreatic cancer pathway. These results provide new evidence and therapeutic direction for subsequent clinical applications and basic research on KLTi in PDAC.

scholarly journals Autophagy Regulatory Genes MET and RIPK2 Play a Prognostic Role in Pancreatic Ductal Adenocarcinoma: A Bioinformatic Analysis Based on GEO and TCGA

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xingyu Li ◽  
Zhiqiang Li ◽  
Hongwei Zhu ◽  
Xiao Yu
Keyword(s):  
Single Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cancer Cells ◽  
Differentially Expressed Genes ◽  
Clinical Application ◽  
Regulatory Genes ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Ductal Adenocarcinoma ◽  
Rna Seq

Pancreatic ductal adenocarcinoma is a common malignant tumor with a poor prognosis. Autophagy activity changes in both cancer cells and microenvironment and affects the progression of pancreatic ductal adenocarcinoma. The purpose of this study was to predict the prognostic autophagy regulatory genes and their role in the regulation of autophagy in pancreatic ductal adenocarcinoma. We draw conclusions based on gene expression data from different platforms: GSE62165 and GSE85916 from the array platform, TCGA from the bulk RNA-seq platform, and GSE111672 from the single-cell RNA-seq platform. At first, we detected differentially expressed genes in pancreatic ductal adenocarcinoma compared with normal pancreatic tissue based on GSE62165. Then, we screened prognostic genes based on GSE85916 and TCGA. Furthermore, we constructed a risk signature composed of the prognostic differentially expressed genes. Finally, we predicted the probable role of these genes in regulating autophagy and the types of cell expressing these genes. According to our screening criteria, there were only two genes: MET and RIPK2, selected into the development of the risk signature. However, evaluated by log-rank tests, receiver operating characteristic curves, and calibration curves, the risk signature was worth considering its clinical application because of good sensitivity, specificity, and stability. Besides, we predicted that both MET and RIPK2 promote autophagy in pancreatic ductal adenocarcinoma by gene set enrichment analysis. Analysis of single-cell RNA-seq data from GSE111672 revealed that both MET and RIPK2 were expressed in cancer cells while RIPK2 was also expressed in monocytes and neutrophils. After comprehensive analysis, we found that both MET and RIPK2 are related to the prognosis of pancreatic ductal adenocarcinoma and provided some associated clues for clinical application and basic experiment research.

Immune-related genes LAMA2 and IL1R1 correlate with tumor sites and predict poor survival in pancreatic adenocarcinoma

2021 ◽  
Author(s):  
Mengna Zhang ◽  
Lirong Zeng ◽  
Yanan Peng ◽  
Bin Fan ◽  
PengFei Chen ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Pancreatic Head ◽  
Differentially Expressed ◽  
High Expression ◽  
Ductal Adenocarcinoma ◽  
Head Cancer ◽  
Pancreatic Head Cancer ◽  
Gene Sets ◽  
Immune Related Genes

Aims: The aim of this study was to identify the immune- and locus-associated genes in pancreatic ductal adenocarcinoma and evaluate their value in prognosis. Methods: The pancreatic ductal adenocarcinoma stromal and immune scores were calculated with the estimation of stromal and immune cells in malignant tumor tissues using expression data algorithm. The authors screened the differentially expressed genes to generate immune- and stromal-related differentially expressed genes. Next, the authors conducted weighted correlation network analysis to find the gene sets related to tumor sites. Results: IL1R1 and LAMA2 were identified as the site- and immune-related genes in pancreatic ductal adenocarcinoma, and their high expression in pancreatic head cancer exhibited high immune scores and predicted unfavorable prognosis. Conclusion: The authors identified IL1R1 and LAMA2 as immune- and locus-associated genes, and their high expression predicted a poor prognosis.

scholarly journals Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

2019 ◽  
Author(s):  
ChenChen Yang ◽  
Aifeng Gong
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Mrna Levels ◽  
Pcr Assay ◽  
Microarray Datasets ◽  
Geo Database

Abstract Background Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1and MMP1 in GC tissues and cell lines, respectively.Results We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1.Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

scholarly journals Identification of novel genes associated with a poor prognosis in pancreatic ductal adenocarcinoma via a bioinformatics analysis

2019 ◽  
Vol 39 (8) ◽  
Author(s):  
Jun Zhou ◽  
Xiaoliang Hui ◽  
Ying Mao ◽  
Liya Fan
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ductal Adenocarcinoma ◽  
Peroxisome Proliferator ◽  
Integrin Subunit ◽  
Hub Genes ◽  
Potential Biomarker ◽  
Km Plotter

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a class of the commonest malignant carcinomas. The present study aimed to elucidate the potential biomarker and prognostic targets in PDAC. The array data of GSE41368, GSE43795, GSE55643, and GSE41369 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRNAs) in PDAC were obtained by using GEO2R, and overlapped DEGs were acquired with Venn Diagrams. Functional enrichment analysis of overlapped DEGs and DEmiRNAs was conducted with Metascape and FunRich, respectively. The protein–protein interaction (PPI) network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) of DEmiRNAs and hub genes were investigated by Kaplan–Meier (KM) plotter (KM plotter). Transcriptional data and correlation analyses among hub genes were verified through GEPIA and Human Protein Atlas (HPA). Additionally, miRNA targets were searched using miRTarBase, then miRNA–DEG regulatory network was visualized with Cytoscape. A total of 32 DEmiRNAs and 150 overlapped DEGs were identified, and Metascape showed that DEGs were significantly enriched in cellular chemical homeostasis and pathways in cancer, while DEmiRNAs were mainly enriched in signal transduction and Glypican pathway. Moreover, seven hub genes with a high degree, namely, V-myc avian myelocytomatosis viral oncogene homolog (MYC), solute carrier family 2 member 1 (SLC2A1), PKM, plasminogen activator, urokinase (PLAU), peroxisome proliferator activated receptor γ (PPARG), MET proto-oncogene, receptor tyrosine kinase (MET), and integrin subunit α 3 (ITGA3), were identified and found to be up-regulated between PDAC and normal tissues. miR-135b, miR-221, miR-21, miR-27a, miR-199b-5p, miR-143, miR-196a, miR-655, miR-455-3p, miR-744 and hub genes predicted poor OS of PDAC. An integrative bioinformatics analysis identified several hub genes that may serve as potential biomarkers or targets for early diagnosis and precision target treatment of PDAC.

scholarly journals NDUFA13/YJEFN3 is differentially expressed in the brains of patients with schizophrenia.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Frontal Cortex ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Schizoaffective Disorder ◽  
Psychotic Disorders ◽  
Auditory Hallucinations ◽  
Differentially Expressed ◽  
Significant Differential Expression ◽  
Microarray Datasets ◽  
Cardinal Feature

Visual and auditory hallucinations are a cardinal feature of psychotic disorders (1). We mined published and public microarray datasets (2, 3) to discover differentially expressed genes in schizophrenia and schizoaffective disorder. We found significant differential expression of transcripts overlapping NDUFA13 and YJEFN3 genes in neurons of the dorsolateral pre-frontal cortex from patients with schizophrenia and schizoaffective disorder.

scholarly journals A pseudogene of Brd7, Brd7p3 is differentially expressed in the brains of patients with schizophrenia.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Frontal Cortex ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Schizoaffective Disorder ◽  
Psychotic Disorders ◽  
Auditory Hallucinations ◽  
Differentially Expressed ◽  
Significant Differential Expression ◽  
Microarray Datasets ◽  
Cardinal Feature

Visual and auditory hallucinations are a cardinal feature of psychotic disorders (1). We mined published and public microarray datasets (2, 3) to discover differentially expressed genes in schizophrenia and schizoaffective disorder. We found significant differential expression of pseudogene 3 of the bromodomain containing 7 molecule, Brd7p3, in neurons of the dorsolateral pre-frontal cortex from patients with schizophrenia and schizoaffective disorder.

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