Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis
Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequen...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.756784/full |
| _version_ | 1818890481569890304 |
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| author | Chenxi Xiang Huimin Ni Zhina Wang Binbin Ji Binbin Ji Bo Wang Bo Wang Xiaoli Shi Xiaoli Shi Wanna Wu Nian Liu Ying Gu Dongshen Ma Hui Liu |
| author_facet | Chenxi Xiang Huimin Ni Zhina Wang Binbin Ji Binbin Ji Bo Wang Bo Wang Xiaoli Shi Xiaoli Shi Wanna Wu Nian Liu Ying Gu Dongshen Ma Hui Liu |
| author_sort | Chenxi Xiang |
| collection | DOAJ |
| description | Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds. |
| first_indexed | 2024-12-19T17:25:36Z |
| format | Article |
| id | doaj.art-6e3801f87a49416ea8d1d863ea5cc041 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-19T17:25:36Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-6e3801f87a49416ea8d1d863ea5cc0412022-12-21T20:12:34ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-10-011210.3389/fgene.2021.756784756784Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation AnalysisChenxi Xiang0Huimin Ni1Zhina Wang2Binbin Ji3Binbin Ji4Bo Wang5Bo Wang6Xiaoli Shi7Xiaoli Shi8Wanna Wu9Nian Liu10Ying Gu11Dongshen Ma12Hui Liu13Department of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, ChinaDepartment of Pathology, Xuzhou Medical University, Xuzhou, ChinaDepartment of Oncology, Emergency General Hospital, Beijing, ChinaGenies Beijing Co., Ltd., Beijing, ChinaQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, ChinaGenies Beijing Co., Ltd., Beijing, ChinaQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, ChinaGenies Beijing Co., Ltd., Beijing, ChinaQingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, ChinaDepartment of Pathology, Xuzhou Medical University, Xuzhou, ChinaDepartment of Pathology, Xuzhou Medical University, Xuzhou, ChinaDepartment of Pathology, Xuzhou Medical University, Xuzhou, ChinaDepartment of Pathology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, ChinaDepartment of Pathology, Xuzhou Medical University, Xuzhou, ChinaOver 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.https://www.frontiersin.org/articles/10.3389/fgene.2021.756784/fulldiffuse large B-cell lymphomadrug repurposingdifferentially expressed genesdifferential module analysiskey driver analysis |
| spellingShingle | Chenxi Xiang Huimin Ni Zhina Wang Binbin Ji Binbin Ji Bo Wang Bo Wang Xiaoli Shi Xiaoli Shi Wanna Wu Nian Liu Ying Gu Dongshen Ma Hui Liu Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis Frontiers in Genetics diffuse large B-cell lymphoma drug repurposing differentially expressed genes differential module analysis key driver analysis |
| title | Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis |
| title_full | Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis |
| title_fullStr | Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis |
| title_full_unstemmed | Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis |
| title_short | Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis |
| title_sort | agent repurposing for the treatment of advanced stage diffuse large b cell lymphoma based on gene expression and network perturbation analysis |
| topic | diffuse large B-cell lymphoma drug repurposing differentially expressed genes differential module analysis key driver analysis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.756784/full |
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