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Bioinformatics analysis combined with experiments to explore potential prognostic factors for pancreatic cancer.

ABSTRACT: Background:Pancreatic cancer is a common malignant tumor of the digestive tract. It has a high degree of malignancy and poor prognosis. Finding effective molecular markers has great significance for pancreatic cancer diagnosis and treatment. This study aimed to investigate DLGAP5 expression in pancreatic cancer and explore the possible mechanisms and clinical value of DLGAP5 in tumorigenesis and tumor development. Methods:Differentially expressed genes were screened using the Gene Expression Omnibus (GEO) data set GSE16515. Gene Ontology (GO)-based functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis were performed on the corresponding proteins of the above genes using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Kaplan-Meier Plotter database was used to analyze the relationship between differentially expressed genes and pancreatic cancer prognosis. The most prognostic gene, DLGAP5, was screened out, and the Oncomine and gene expression profiling interactive analysis (GEPIA) databases were used to analyze its expression in pancreatic cancer and other cancer tissues. The Cancer Genome Atlas (TCGA) database was used to analyze the overall survival of DLGAP5. Gene set enrichment analysis (GSEA) was performed to explore its possible molecular mechanisms in pancreatic cancer. Furthermore, the biological behavior of DLGAP5 in pancreatic cancer was verified by cell function experiments. Results:A total of 201 significant upregulated differentially expressed genes and 79 downregulated genes were selected. The biological processes with significant enrichment of differential genes included cell adhesion, apoptosis, wound healing, leukocyte migration, angiogenesis. Pathways were mainly enriched in tumor-related signaling pathways such as cancer pathways, the extracellular matrix-receptor interaction pathway, and the p53 signaling pathway. DLGAP5 was significantly expressed in pancreatic cancer, and its expression level had a significant effect on patients' survival time and progression-free survival. GSEA results indicated that DLGAP5 had significantly enriched into signaling pathways such as the cell cycle, the p53 signaling pathway, and oocyte meiosis. The experimental results showed that when we knocked down the expression of DLGAP5 in pancreatic cancer cells, their proliferation ability was significantly inhibited, and their invasion and migration ability significantly decreased. Conclusions:DLGAP5 can be used as a prognostic indicator for pancreatic cancer and affect the occurrence and development of pancreatic cancer.

SUBMITTER: Ke MJ

PROVIDER: S-EPMC7414559 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Bioinformatics analysis combined with experiments to explore potential prognostic factors for pancreatic cancer.

Ke Mu-Jing MJ Ji Lian-Dong LD Li Yi-Xiong YX

Cancer cell international 20200808

<h4>Background</h4>Pancreatic cancer is a common malignant tumor of the digestive tract. It has a high degree of malignancy and poor prognosis. Finding effective molecular markers has great significance for pancreatic cancer diagnosis and treatment. This study aimed to investigate DLGAP5 expression in pancreatic cancer and explore the possible mechanisms and clinical value of DLGAP5 in tumorigenesis and tumor development.<h4>Methods</h4>Differentially expressed genes were screened using the Gene ...[more]

PMID: 32782440

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Project description:BackgroundPancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms.MethodsTo identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot.ConclusionsAltogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

Project description:BackgroundIn this study, miRNAs and their critical target genes related to the prognosis of pancreatic cancer were screened based on bioinformatics analysis to provide targets for the prognosis and treatment of pancreatic cancer.MethodsR software was used to screen differentially expressed miRNAs (DEMs) and genes (DEGs) downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. A miRNA Cox proportional hazards regression model was constructed based on the miRNAs, and a miRNA prognostic model was generated. The target genes of the prognostic miRNAs were predicted using TargetScan and miRDB and then intersected with the DEGs to obtain common genes. The functions of the common genes were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. A protein-protein interaction (PPI) network of the common genes was constructed with the STRING database and visualized with Cytoscape software. Key genes were also screened with the MCODE and cytoHubba plug-ins of Cytoscape. Finally, a prognostic model formed by the key gene was also established to help evaluate the reliability of this screening process.ResultsA prognostic model containing four downregulated miRNAs (hsa-mir-424, hsa-mir-3613, hsa-mir-4772 and hsa-mir-126) related to the prognosis of pancreatic cancer was constructed. A total of 118 common genes were enriched in two KEGG pathways and 33 GO functional annotations, including extracellular matrix (ECM)-receptor interaction and cell adhesion. Nine key genes related to pancreatic cancer were also obtained: MMP14, ITGA2, THBS2, COL1A1, COL3A1, COL11A1, COL6A3, COL12A1 and COL5A2. The prognostic model formed by nine key genes also possessed good prognostic ability.ConclusionsThe prognostic model consisting of four miRNAs can reliably predict the prognosis of patients with pancreatic cancer. In addition, the screened nine key genes, which can also form a reliable prognostic model, are significantly related to the occurrence and development of pancreatic cancer. Among them, one novel miRNA (hsa-mir-4772) and two novel genes (COL12A1 and COL5A2) associated with pancreatic cancer have great potential to be used as prognostic factors and therapeutic targets for this tumor.

Project description:BackgroundPancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDA), is an aggressive malignancy associated with a low 5-year survival rate. Poor outcomes associated with PDA are attributable to late detection and inoperability. Most patients with PDA are diagnosed with locally advanced and metastatic disease. Such cases are primarily treated with chemotherapy and radiotherapy. Because of the lack of effective molecular targets, early diagnosis and successful therapies are limited. The purpose of this study was to screen key candidate genes for PDA using a bioinformatic approach and to research their potential functional, pathway mechanisms associated with PDA progression. It may help to understand the role of associated genes in the development and progression of PDA and identify relevant molecular markers with value for early diagnosis and targeted therapy.Materials and methodsTo identify novel genes associated with carcinogenesis and progression of PDA, we analyzed the microarray datasets GSE62165, GSE125158, and GSE71989 from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A protein-protein interaction (PPI) network was constructed using STRING, and module analysis was performed using Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to evaluate the differential expression of hub genes in patients with PDA. In addition, we verified the expression of these genes in PDA cell lines and normal pancreatic epithelial cells.ResultsA total of 202 DEGs were identified and these were found to be enriched for various functions and pathways, including cell adhesion, leukocyte migration, extracellular matrix organization, extracellular region, collagen trimer, membrane raft, fibronectin-binding, integrin binding, protein digestion, and absorption, and focal adhesion. Among these DEGs, 12 hub genes with high degrees of connectivity were selected. Survival analysis showed that the hub genes (HMMR, CEP55, CDK1, UHRF1, ASPM, RAD51AP1, DLGAP5, KIF11, SHCBP1, PBK, and HMGB2) may be involved in the tumorigenesis and development of PDA, highlighting their potential as diagnostic and therapeutic factors in PDA.ConclusionsIn summary, the DEGs and hub genes identified in the present study not only contribute to a better understanding of the molecular mechanisms underlying the carcinogenesis and progression of PDA but may also serve as potential new biomarkers and targets for PDA.

Project description:Lung cancer is one of the most common types of cancer worldwide. Understanding the molecular mechanisms underlying the development and progression of lung cancer may improve early diagnosis, treatment and prognosis. The aim of the present study was to examine the pathogenesis of lung cancer and to identify potentially novel biomarkers. Gene expression datasets of patients with lung cancer were obtained from the Gene Expression Omnibus. Genes which were most closely associated with lung cancer (core genes) were screened by weighted gene co‑expression network analysis. In vitro cell based experiments were further utilized to verify the effects of the core genes on the proliferation of lung cancer cells, adhesion between cells and the matrix, and the associated metabolic pathways. Based on WGCNA screening, two gene modules and five core genes closely associated with lung cancer, including immunoglobulin superfamily member 10 (IGSF10) from the turquoise module, and ribonucleotide reductase regulatory subunit M2, protein regulator of cytokinesis 1, kinesin family member (KIF)14 and KIF2C from the brown module were identified as relevant. Survival analysis and differential gene expression analysis showed that there were significant differences in IGSF10 expression levels between the healthy controls and patients with lung cancer. In patients with lung cancer, IGSF10 expression was decreased, and the overall survival time of patients with lung cancer was significantly shortened. An MTT and colony formation assay showed that IGSF10‑knockout significantly increased proliferation of lung cancer cells, and Transwell assays and adhesion experiments further suggested that the adhesion between cells and the matrix was significantly increased in IGSF10‑knockout cells. Gene Set Enrichment Analysis showed that the expression level of IGSF10 was significantly associated with the activation of the integrin‑β1/focal adhesion kinase (FAK) pathway. Western blotting revealed that knockout of IGSF10 resulted in the activation of the integrin‑β1/FAK pathway, as the protein expression levels of integrin‑β1, phosphorylated (p)‑FAK and p‑AKT were significantly upregulated. Activation of the integrin‑β1/FAK pathway, following knockout of IGSF10, affected the proliferation and adhesion of lung cancer cells. Therefore, IGSF10 my serve as a potential prognostic marker of lung cancer.

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Bioinformatics analysis combined with experiments to explore potential prognostic factors for pancreatic cancer.

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Bioinformatics analysis combined with experiments to explore potential prognostic factors for pancreatic cancer.

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