{"title":"胰腺导管腺癌(PDAC)肿瘤及邻近非肿瘤组织的基因表达特征","authors":"Emine Güven","doi":"10.30498/ijb.2021.292558.3092","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>One of the deadliest and most prevalent cancer is pancreatic ductal adenocarcinoma (PDAC). Microarray has become an important tool in the research of PDAC genes and target therapeutic drugs.</p><p><strong>Objectives: </strong>This study intends to clarify the promising prognostic and biomarker targets in PDAC using GSE78229 and GSE62452 datasets, publicly accessible at the Gene Expression Omnibus database.</p><p><strong>Materials and methods: </strong>Utilizing GEOquery, Bio base, gplots, and ggplot2 packages in the R program, this study detects 428 differentially expressed genes that are further applied to build a co-expression network by the weighted correlation network analysis (WGCNA). The turquoise module presented a higher correlation with PDAC progression. 79 candidate genes were selected based on the co-expression and protein-protein interaction (PPI) networks. In addition, the functional enrichment analysis was studied.</p><p><strong>Results: </strong>Five significant KEGG pathways linked to PDAC were detected, in which the endoplasmic reticulum protein processing pathway was remarked to be vital. The resulting 19 hub genes as HSPA4, PABPC1, HSP90B1, PPP1CC, USP9X, EIF2S3, MSN, RAB10, BMPR2, P4HB, UBC, B2M, SLC25A5, MMP7, SPTBN1, RALB, DNAJB1, CENPE, and PDIA6 were identified by the Network Analyst web tool founded on PPI network by the STRING. These were identified as the most connected hub proteins. The quantification of the expression of levels and survival probabilities were analyzed overall survival (OS) of the real hub genes and were investigated by Kaplan-Meier (KM) plotter through The Cancer Genome Atlas Program (TCGA) database.</p><p><strong>Conclusions: </strong>The protein-protein interactions and KEGG pathway enrichment by DAVID indicated that some pathways were involved in PDAC, such as \"pathways in cancer (hsa05200)\", \"protein processing in the endoplasmic reticulum (hsa04141)\", \"antigen processing and presentation (hsa04612)\", \"dopaminergic synapse (hsa04728)\", and \"measles (hsa05162)\"; in which these pathways, the \"protein processing in endoplasmic reticulum (hsa04141)\", was further studied because of its closely relationship with PDAC. The rest of the hub genes reviewed throughout the study might be promising targets for diagnosing and treating PDAC and relevant diseases.</p>","PeriodicalId":14492,"journal":{"name":"Iranian Journal of Biotechnology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e7/8c/IJB-20-e3092.PMC9284245.pdf","citationCount":"2","resultStr":"{\"title\":\"Gene Expression Characteristics of Tumor and Adjacent Non-Tumor Tissues of Pancreatic Ductal Adenocarcinoma (PDAC) In-Silico.\",\"authors\":\"Emine Güven\",\"doi\":\"10.30498/ijb.2021.292558.3092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>One of the deadliest and most prevalent cancer is pancreatic ductal adenocarcinoma (PDAC). Microarray has become an important tool in the research of PDAC genes and target therapeutic drugs.</p><p><strong>Objectives: </strong>This study intends to clarify the promising prognostic and biomarker targets in PDAC using GSE78229 and GSE62452 datasets, publicly accessible at the Gene Expression Omnibus database.</p><p><strong>Materials and methods: </strong>Utilizing GEOquery, Bio base, gplots, and ggplot2 packages in the R program, this study detects 428 differentially expressed genes that are further applied to build a co-expression network by the weighted correlation network analysis (WGCNA). The turquoise module presented a higher correlation with PDAC progression. 79 candidate genes were selected based on the co-expression and protein-protein interaction (PPI) networks. In addition, the functional enrichment analysis was studied.</p><p><strong>Results: </strong>Five significant KEGG pathways linked to PDAC were detected, in which the endoplasmic reticulum protein processing pathway was remarked to be vital. The resulting 19 hub genes as HSPA4, PABPC1, HSP90B1, PPP1CC, USP9X, EIF2S3, MSN, RAB10, BMPR2, P4HB, UBC, B2M, SLC25A5, MMP7, SPTBN1, RALB, DNAJB1, CENPE, and PDIA6 were identified by the Network Analyst web tool founded on PPI network by the STRING. These were identified as the most connected hub proteins. The quantification of the expression of levels and survival probabilities were analyzed overall survival (OS) of the real hub genes and were investigated by Kaplan-Meier (KM) plotter through The Cancer Genome Atlas Program (TCGA) database.</p><p><strong>Conclusions: </strong>The protein-protein interactions and KEGG pathway enrichment by DAVID indicated that some pathways were involved in PDAC, such as \\\"pathways in cancer (hsa05200)\\\", \\\"protein processing in the endoplasmic reticulum (hsa04141)\\\", \\\"antigen processing and presentation (hsa04612)\\\", \\\"dopaminergic synapse (hsa04728)\\\", and \\\"measles (hsa05162)\\\"; in which these pathways, the \\\"protein processing in endoplasmic reticulum (hsa04141)\\\", was further studied because of its closely relationship with PDAC. The rest of the hub genes reviewed throughout the study might be promising targets for diagnosing and treating PDAC and relevant diseases.</p>\",\"PeriodicalId\":14492,\"journal\":{\"name\":\"Iranian Journal of Biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e7/8c/IJB-20-e3092.PMC9284245.pdf\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iranian Journal of Biotechnology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.30498/ijb.2021.292558.3092\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iranian Journal of Biotechnology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.30498/ijb.2021.292558.3092","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 2
摘要
背景:胰腺导管腺癌(PDAC)是最致命和最常见的癌症之一。微阵列技术已成为PDAC基因研究和靶向治疗药物研究的重要工具。目的:本研究旨在利用GSE78229和GSE62452数据集阐明PDAC中有希望的预后和生物标志物靶点,这些数据集可在Gene Expression Omnibus数据库公开获取。材料与方法:本研究利用R程序中的GEOquery、Bio base、gplots、ggplot2包,检测428个差异表达基因,通过加权相关网络分析(weighted correlation network analysis, WGCNA)构建共表达网络。绿松石模块与PDAC进展有较高的相关性。通过共表达和蛋白相互作用(PPI)网络筛选出79个候选基因。此外,还进行了功能富集分析。结果:检测到与PDAC相关的5条重要的KEGG通路,其中内质网蛋白加工通路被认为是至关重要的。通过STRING建立在PPI网络上的Network Analyst网络工具,鉴定得到的19个枢纽基因为HSPA4、PABPC1、HSP90B1、PPP1CC、USP9X、EIF2S3、MSN、RAB10、BMPR2、P4HB、UBC、B2M、SLC25A5、MMP7、SPTBN1、RALB、DNAJB1、CENPE和PDIA6。这些被鉴定为连接最紧密的枢纽蛋白。通过The Cancer Genome Atlas Program (TCGA)数据库,通过Kaplan-Meier (KM)绘图仪分析真实枢纽基因的总生存期(OS),定量分析表达水平和生存概率。结论:蛋白-蛋白相互作用及DAVID扩增KEGG通路提示PDAC参与“肿瘤通路(hsa05200)”、“内质网蛋白加工(hsa04141)”、“抗原加工和递呈(hsa04612)”、“多巴胺能突触(hsa04728)”、“麻疹(hsa05162)”等通路;其中这些通路“内质网蛋白加工(hsa04141)”因其与PDAC的密切关系而被进一步研究。在整个研究中回顾的其余枢纽基因可能是诊断和治疗PDAC及相关疾病的有希望的靶点。
Gene Expression Characteristics of Tumor and Adjacent Non-Tumor Tissues of Pancreatic Ductal Adenocarcinoma (PDAC) In-Silico.
Background: One of the deadliest and most prevalent cancer is pancreatic ductal adenocarcinoma (PDAC). Microarray has become an important tool in the research of PDAC genes and target therapeutic drugs.
Objectives: This study intends to clarify the promising prognostic and biomarker targets in PDAC using GSE78229 and GSE62452 datasets, publicly accessible at the Gene Expression Omnibus database.
Materials and methods: Utilizing GEOquery, Bio base, gplots, and ggplot2 packages in the R program, this study detects 428 differentially expressed genes that are further applied to build a co-expression network by the weighted correlation network analysis (WGCNA). The turquoise module presented a higher correlation with PDAC progression. 79 candidate genes were selected based on the co-expression and protein-protein interaction (PPI) networks. In addition, the functional enrichment analysis was studied.
Results: Five significant KEGG pathways linked to PDAC were detected, in which the endoplasmic reticulum protein processing pathway was remarked to be vital. The resulting 19 hub genes as HSPA4, PABPC1, HSP90B1, PPP1CC, USP9X, EIF2S3, MSN, RAB10, BMPR2, P4HB, UBC, B2M, SLC25A5, MMP7, SPTBN1, RALB, DNAJB1, CENPE, and PDIA6 were identified by the Network Analyst web tool founded on PPI network by the STRING. These were identified as the most connected hub proteins. The quantification of the expression of levels and survival probabilities were analyzed overall survival (OS) of the real hub genes and were investigated by Kaplan-Meier (KM) plotter through The Cancer Genome Atlas Program (TCGA) database.
Conclusions: The protein-protein interactions and KEGG pathway enrichment by DAVID indicated that some pathways were involved in PDAC, such as "pathways in cancer (hsa05200)", "protein processing in the endoplasmic reticulum (hsa04141)", "antigen processing and presentation (hsa04612)", "dopaminergic synapse (hsa04728)", and "measles (hsa05162)"; in which these pathways, the "protein processing in endoplasmic reticulum (hsa04141)", was further studied because of its closely relationship with PDAC. The rest of the hub genes reviewed throughout the study might be promising targets for diagnosing and treating PDAC and relevant diseases.
期刊介绍:
Iranian Journal of Biotechnology (IJB) is published quarterly by the National Institute of Genetic Engineering and Biotechnology. IJB publishes original scientific research papers in the broad area of Biotechnology such as, Agriculture, Animal and Marine Sciences, Basic Sciences, Bioinformatics, Biosafety and Bioethics, Environment, Industry and Mining and Medical Sciences.