Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages
Background: Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and pr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.881948/full |
| _version_ | 1818465693271588864 |
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| author | Zhiyuan Yu Zhiyuan Yu Chen Liang Huaiyu Tu Shuzhong Qiu Xiaoyu Dong Yonghui Zhang Chao Ma Peiyu Li Peiyu Li |
| author_facet | Zhiyuan Yu Zhiyuan Yu Chen Liang Huaiyu Tu Shuzhong Qiu Xiaoyu Dong Yonghui Zhang Chao Ma Peiyu Li Peiyu Li |
| author_sort | Zhiyuan Yu |
| collection | DOAJ |
| description | Background: Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and progression using bioinformatics and meta-analysis methods.Methods: Eligible microarray datasets were downloaded and integrated using the meta-analysis method. According to the tumor stage, GC gene chips were classified into three groups. Thereafter, the three groups’ differentially expressed genes (DEGs) were identified by comparing the gene data of the tumor groups with those of matched normal specimens. Enrichment analyses were conducted based on common DEGs among the three groups. Then protein–protein interaction (PPI) networks were constructed to identify relevant hub genes and subnetworks. The effects of significant DEGs and hub genes were verified and explored in other datasets. In addition, the analysis of mutated genes was also conducted using gene data from The Cancer Genome Atlas database.Results: After integration of six microarray datasets, 1,229 common DEGs consisting of 1,065 upregulated and 164 downregulated genes were identified. Alpha-2 collagen type I (COL1A2), tissue inhibitor matrix metalloproteinase 1 (TIMP1), thymus cell antigen 1 (THY1), and biglycan (BGN) were selected as significant DEGs throughout GC development. The low expression of ghrelin (GHRL) is associated with a high lymph node ratio (LNR) and poor survival outcomes. Thereafter, we constructed a PPI network of all identified DEGs and gained 39 subnetworks and the top 20 hub genes. Enrichment analyses were performed for common DEGs, the most related subnetwork, and the top 20 hub genes. We also selected 61 metabolic DEGs to construct PPI networks and acquired the relevant hub genes. Centrosomal protein 55 (CEP55) and POLR1A were identified as hub genes associated with survival outcomes.Conclusion: The DEGs, hub genes, and enrichment analysis for GC with different stages were comprehensively investigated, which contribute to exploring the new biomarkers and therapeutic targets. |
| first_indexed | 2024-04-13T20:49:04Z |
| format | Article |
| id | doaj.art-623c898653654c098ed6ffb5ab977093 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-13T20:49:04Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-623c898653654c098ed6ffb5ab9770932022-12-22T02:30:35ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-07-011310.3389/fgene.2022.881948881948Common Core Genes Play Vital Roles in Gastric Cancer With Different StagesZhiyuan Yu0Zhiyuan Yu1Chen Liang2Huaiyu Tu3Shuzhong Qiu4Xiaoyu Dong5Yonghui Zhang6Chao Ma7Peiyu Li8Peiyu Li9School of Medicine, Nankai University, Tianjin, ChinaDepartment of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaFirst Department of Liver Disease / Beijing Municipal Key Laboratory of Liver Failure and Artificial Liver Treatment Research, Beijing You’an Hospital, Capital Medical University, Beijing, ChinaDepartment of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaDepartment of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaSchool of Medicine, Nankai University, Tianjin, ChinaDepartment of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, ChinaBackground: Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and progression using bioinformatics and meta-analysis methods.Methods: Eligible microarray datasets were downloaded and integrated using the meta-analysis method. According to the tumor stage, GC gene chips were classified into three groups. Thereafter, the three groups’ differentially expressed genes (DEGs) were identified by comparing the gene data of the tumor groups with those of matched normal specimens. Enrichment analyses were conducted based on common DEGs among the three groups. Then protein–protein interaction (PPI) networks were constructed to identify relevant hub genes and subnetworks. The effects of significant DEGs and hub genes were verified and explored in other datasets. In addition, the analysis of mutated genes was also conducted using gene data from The Cancer Genome Atlas database.Results: After integration of six microarray datasets, 1,229 common DEGs consisting of 1,065 upregulated and 164 downregulated genes were identified. Alpha-2 collagen type I (COL1A2), tissue inhibitor matrix metalloproteinase 1 (TIMP1), thymus cell antigen 1 (THY1), and biglycan (BGN) were selected as significant DEGs throughout GC development. The low expression of ghrelin (GHRL) is associated with a high lymph node ratio (LNR) and poor survival outcomes. Thereafter, we constructed a PPI network of all identified DEGs and gained 39 subnetworks and the top 20 hub genes. Enrichment analyses were performed for common DEGs, the most related subnetwork, and the top 20 hub genes. We also selected 61 metabolic DEGs to construct PPI networks and acquired the relevant hub genes. Centrosomal protein 55 (CEP55) and POLR1A were identified as hub genes associated with survival outcomes.Conclusion: The DEGs, hub genes, and enrichment analysis for GC with different stages were comprehensively investigated, which contribute to exploring the new biomarkers and therapeutic targets.https://www.frontiersin.org/articles/10.3389/fgene.2022.881948/fullgastric cancermicroarray datasetsdifferentially expressed genesmetabolismmeta-analysis |
| spellingShingle | Zhiyuan Yu Zhiyuan Yu Chen Liang Huaiyu Tu Shuzhong Qiu Xiaoyu Dong Yonghui Zhang Chao Ma Peiyu Li Peiyu Li Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages Frontiers in Genetics gastric cancer microarray datasets differentially expressed genes metabolism meta-analysis |
| title | Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages |
| title_full | Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages |
| title_fullStr | Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages |
| title_full_unstemmed | Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages |
| title_short | Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages |
| title_sort | common core genes play vital roles in gastric cancer with different stages |
| topic | gastric cancer microarray datasets differentially expressed genes metabolism meta-analysis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.881948/full |
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