{"title":"通过网络分析对胰腺导管腺癌进行分期分析。","authors":"Ayad Bahadorimonfared, Masoumeh Farahani, Mostafa Rezaei Tavirani, Zahra Razzaghi, Babak Arjmand, Mitra Rezaei, Abdolrahim Nikzamir, Mohammad Javad Ehsani Ardakani, Vahid Mansouri","doi":"10.22037/ghfbb.v17i3.2887","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>This study aimed to introduce a biomarker panel to detect pancreatic ductal adenocarcinoma (PDAC) in the early stage, and also differentiate of stages from each other.</p><p><strong>Background: </strong>PDAC is a lethal cancer with poor prognosis and overall survival.</p><p><strong>Methods: </strong>Gene expression profiles of PDAC patients were extracted from the Gene Expression Omnibus (GEO) database. The genes that were significantly differentially expressed (DEGs) for Stages I, II, and III in comparison to the healthy controls were identified. The determined DEGs were assessed via protein-protein interaction (PPI) network analysis, and the hub-bottleneck nodes of analyzed networks were introduced.</p><p><strong>Results: </strong>A number of 140, 874, and 1519 significant DEGs were evaluated via PPI network analysis. A biomarker panel including ALB, CTNNB1, COL1A1, POSTN, LUM, and ANXA2 is presented as a biomarker panel to detect PDAC in the early stage. Two biomarker panels are suggested to recognize other stages of illness.</p><p><strong>Conclusion: </strong>It can be concluded that ALB, CTNNB1, COL1A1, POSTN, LUM, and ANXA2 and also FN1, HSP90AA1, LOX, ANXA5, SERPINE1, and WWP2 beside GAPDH, AKT1, EGF, CASP3 are suitable sets of gene to separate stages of PDAC.</p>","PeriodicalId":12636,"journal":{"name":"Gastroenterology and Hepatology From Bed to Bench","volume":"17 3","pages":"297-3030"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413388/pdf/","citationCount":"0","resultStr":"{\"title\":\"Stage analysis of pancreatic ductal adenocarcinoma via network analysis.\",\"authors\":\"Ayad Bahadorimonfared, Masoumeh Farahani, Mostafa Rezaei Tavirani, Zahra Razzaghi, Babak Arjmand, Mitra Rezaei, Abdolrahim Nikzamir, Mohammad Javad Ehsani Ardakani, Vahid Mansouri\",\"doi\":\"10.22037/ghfbb.v17i3.2887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aim: </strong>This study aimed to introduce a biomarker panel to detect pancreatic ductal adenocarcinoma (PDAC) in the early stage, and also differentiate of stages from each other.</p><p><strong>Background: </strong>PDAC is a lethal cancer with poor prognosis and overall survival.</p><p><strong>Methods: </strong>Gene expression profiles of PDAC patients were extracted from the Gene Expression Omnibus (GEO) database. The genes that were significantly differentially expressed (DEGs) for Stages I, II, and III in comparison to the healthy controls were identified. The determined DEGs were assessed via protein-protein interaction (PPI) network analysis, and the hub-bottleneck nodes of analyzed networks were introduced.</p><p><strong>Results: </strong>A number of 140, 874, and 1519 significant DEGs were evaluated via PPI network analysis. A biomarker panel including ALB, CTNNB1, COL1A1, POSTN, LUM, and ANXA2 is presented as a biomarker panel to detect PDAC in the early stage. Two biomarker panels are suggested to recognize other stages of illness.</p><p><strong>Conclusion: </strong>It can be concluded that ALB, CTNNB1, COL1A1, POSTN, LUM, and ANXA2 and also FN1, HSP90AA1, LOX, ANXA5, SERPINE1, and WWP2 beside GAPDH, AKT1, EGF, CASP3 are suitable sets of gene to separate stages of PDAC.</p>\",\"PeriodicalId\":12636,\"journal\":{\"name\":\"Gastroenterology and Hepatology From Bed to Bench\",\"volume\":\"17 3\",\"pages\":\"297-3030\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413388/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gastroenterology and Hepatology From Bed to Bench\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22037/ghfbb.v17i3.2887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gastroenterology and Hepatology From Bed to Bench","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22037/ghfbb.v17i3.2887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
Stage analysis of pancreatic ductal adenocarcinoma via network analysis.
Aim: This study aimed to introduce a biomarker panel to detect pancreatic ductal adenocarcinoma (PDAC) in the early stage, and also differentiate of stages from each other.
Background: PDAC is a lethal cancer with poor prognosis and overall survival.
Methods: Gene expression profiles of PDAC patients were extracted from the Gene Expression Omnibus (GEO) database. The genes that were significantly differentially expressed (DEGs) for Stages I, II, and III in comparison to the healthy controls were identified. The determined DEGs were assessed via protein-protein interaction (PPI) network analysis, and the hub-bottleneck nodes of analyzed networks were introduced.
Results: A number of 140, 874, and 1519 significant DEGs were evaluated via PPI network analysis. A biomarker panel including ALB, CTNNB1, COL1A1, POSTN, LUM, and ANXA2 is presented as a biomarker panel to detect PDAC in the early stage. Two biomarker panels are suggested to recognize other stages of illness.
Conclusion: It can be concluded that ALB, CTNNB1, COL1A1, POSTN, LUM, and ANXA2 and also FN1, HSP90AA1, LOX, ANXA5, SERPINE1, and WWP2 beside GAPDH, AKT1, EGF, CASP3 are suitable sets of gene to separate stages of PDAC.
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