Jie Li , Jing Dong , Ming Li , Hongbo Zhu , Peicheng Xin
{"title":"基于多重生物信息学预测多发性骨髓瘤与股骨头坏死合并症的潜在机制","authors":"Jie Li , Jing Dong , Ming Li , Hongbo Zhu , Peicheng Xin","doi":"10.1016/j.compbiolchem.2024.108220","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>This study aims to utilize multiple bioinformatics tools to elucidate the potential mechanisms underlying the comorbidity of Multiple Myeloma (MM) and Osteonecrosis of the Femoral Head (ONFH).</div></div><div><h3>Method</h3><div>High-throughput microarray datasets for MM and ONFH were retrieved from the GEO database, followed by separate preprocessing. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks within the MM datasets, further identifying modules and genes associated with MM clinical characteristics. Potential comorbid genes were enriched and analyzed using pathway and network analysis tools, and key genes for MM and ONFH comorbidity were preliminarily screened using Cytoscape. The gene expression capabilities and performance were validated using two disease-related datasets, and we evaluated the differences and consistencies in the immune microenvironment between the two diseases.</div></div><div><h3>Results</h3><div>Our screening identified 418 immune-related comorbid genes, showing consistent biological processes in ribosome synthesis, particularly protein synthesis across both diseases. Key genes were further identified through Protein-Protein Interaction (PPI) networks, and their performance was validated in a validation cohort, with Receiver Operating Characteristic (ROC) curve areas exceeding 0.8. The immune microenvironment analysis highlighted consistent plasma cell infiltration correlated with disease comorbidity, suggesting potential immune targets for future therapies.</div></div><div><h3>Conclusion</h3><div>MM and ONFH share common pathogenic genes that mediate changes in signaling pathways and immune cell dynamics, potentially influencing the comorbidity and progression of these diseases. Key genes identified, such as RPS19, RPL35, RPL24, RPL36, and EIF3G, along with plasma cell infiltration, may serve as central mechanisms in the development of both diseases. This study offers insights and references for further research into targeted treatments for these conditions<strong>.</strong></div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108220"},"PeriodicalIF":2.6000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Potential mechanisms for predicting comorbidity between multiple myeloma and femoral head necrosis based on multiple bioinformatics\",\"authors\":\"Jie Li , Jing Dong , Ming Li , Hongbo Zhu , Peicheng Xin\",\"doi\":\"10.1016/j.compbiolchem.2024.108220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>This study aims to utilize multiple bioinformatics tools to elucidate the potential mechanisms underlying the comorbidity of Multiple Myeloma (MM) and Osteonecrosis of the Femoral Head (ONFH).</div></div><div><h3>Method</h3><div>High-throughput microarray datasets for MM and ONFH were retrieved from the GEO database, followed by separate preprocessing. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks within the MM datasets, further identifying modules and genes associated with MM clinical characteristics. Potential comorbid genes were enriched and analyzed using pathway and network analysis tools, and key genes for MM and ONFH comorbidity were preliminarily screened using Cytoscape. The gene expression capabilities and performance were validated using two disease-related datasets, and we evaluated the differences and consistencies in the immune microenvironment between the two diseases.</div></div><div><h3>Results</h3><div>Our screening identified 418 immune-related comorbid genes, showing consistent biological processes in ribosome synthesis, particularly protein synthesis across both diseases. Key genes were further identified through Protein-Protein Interaction (PPI) networks, and their performance was validated in a validation cohort, with Receiver Operating Characteristic (ROC) curve areas exceeding 0.8. The immune microenvironment analysis highlighted consistent plasma cell infiltration correlated with disease comorbidity, suggesting potential immune targets for future therapies.</div></div><div><h3>Conclusion</h3><div>MM and ONFH share common pathogenic genes that mediate changes in signaling pathways and immune cell dynamics, potentially influencing the comorbidity and progression of these diseases. Key genes identified, such as RPS19, RPL35, RPL24, RPL36, and EIF3G, along with plasma cell infiltration, may serve as central mechanisms in the development of both diseases. This study offers insights and references for further research into targeted treatments for these conditions<strong>.</strong></div></div>\",\"PeriodicalId\":10616,\"journal\":{\"name\":\"Computational Biology and Chemistry\",\"volume\":\"113 \",\"pages\":\"Article 108220\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Biology and Chemistry\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1476927124002081\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Biology and Chemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1476927124002081","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
Potential mechanisms for predicting comorbidity between multiple myeloma and femoral head necrosis based on multiple bioinformatics
Objective
This study aims to utilize multiple bioinformatics tools to elucidate the potential mechanisms underlying the comorbidity of Multiple Myeloma (MM) and Osteonecrosis of the Femoral Head (ONFH).
Method
High-throughput microarray datasets for MM and ONFH were retrieved from the GEO database, followed by separate preprocessing. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks within the MM datasets, further identifying modules and genes associated with MM clinical characteristics. Potential comorbid genes were enriched and analyzed using pathway and network analysis tools, and key genes for MM and ONFH comorbidity were preliminarily screened using Cytoscape. The gene expression capabilities and performance were validated using two disease-related datasets, and we evaluated the differences and consistencies in the immune microenvironment between the two diseases.
Results
Our screening identified 418 immune-related comorbid genes, showing consistent biological processes in ribosome synthesis, particularly protein synthesis across both diseases. Key genes were further identified through Protein-Protein Interaction (PPI) networks, and their performance was validated in a validation cohort, with Receiver Operating Characteristic (ROC) curve areas exceeding 0.8. The immune microenvironment analysis highlighted consistent plasma cell infiltration correlated with disease comorbidity, suggesting potential immune targets for future therapies.
Conclusion
MM and ONFH share common pathogenic genes that mediate changes in signaling pathways and immune cell dynamics, potentially influencing the comorbidity and progression of these diseases. Key genes identified, such as RPS19, RPL35, RPL24, RPL36, and EIF3G, along with plasma cell infiltration, may serve as central mechanisms in the development of both diseases. This study offers insights and references for further research into targeted treatments for these conditions.
期刊介绍:
Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.
Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered.
Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.