Bioinformatics Analysis of the Association between Ewing’s Sarcoma and Tuberculosis Comorbidity  

作　　者：Jun Huang Qi Lu Junxiu Zhou Wenzhao Zhang Chongyao Xu Guiyun Wei Jun Huang;Qi Lu;Junxiu Zhou;Wenzhao Zhang;Chongyao Xu;Guiyun Wei(Department of Orthopedic Trauma, Napo People’s Hospital, Baise, China;Department of Anesthesiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China;Pharmacy Department, Baise Maternal and Child Hospital, Baise, China;School of Clinical Medicine, Youjiang Medical University for Nationalities, Baise, China;Pharmaceutical College, Guangxi Medical University, Nanning, China;Department of Pharmacy, Guangxi Zhuang Autonomous Region Maternal and Child Health Hospital, Baise, China)

机构地区：[1]Department of Orthopedic Trauma, Napo People’s Hospital, Baise, China [2]Department of Anesthesiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China [3]Pharmacy Department, Baise Maternal and Child Hospital, Baise, China [4]School of Clinical Medicine, Youjiang Medical University for Nationalities, Baise, China [5]Pharmaceutical College, Guangxi Medical University, Nanning, China [6]Department of Pharmacy, Guangxi Zhuang Autonomous Region Maternal and Child Health Hospital, Baise, China

出　　处：《Journal of Biosciences and Medicines》2024年第8期128-140,共13页生物科学与医学（英文）

摘　　要：Objective To screen and analyze the differentially expressed genes of Ewing’s sarcoma (ES) and Tuberculosis (TB) by bioinformatics. Methods GEO gene chip public database in NCBI was used for data retrieval, and chip data GSE17674 and GSE57736 were selected as analysis objects. The R language limma toolkit was used to screen DEmRNAs, and the data were standardized, and the common differentially expressed genes were screened by Venn diagram. The GO function and KEGG pathway enrichment of common differentially expressed genes were analyzed by using the R cluster Profiler package. String database was selected for PPI analysis, and the results were imported into Cytoscape software to obtain PPI interaction map, core module and Hub gene. Import Hub gene into BioGPS database. Results: A total of 3 Hub genes were screened, namely CD3D, LCK, KLRB1;The genes were imported into BioGPS database to obtain the specific genes. Conclusion The selected differential genes and related signaling pathways are helpful to understand the molecular mechanism of ES and TB, and can provide the basis for early diagnosis of ES complicated with TB. It also provides new ideas for clinical treatment and diagnosis.Objective To screen and analyze the differentially expressed genes of Ewing’s sarcoma (ES) and Tuberculosis (TB) by bioinformatics. Methods GEO gene chip public database in NCBI was used for data retrieval, and chip data GSE17674 and GSE57736 were selected as analysis objects. The R language limma toolkit was used to screen DEmRNAs, and the data were standardized, and the common differentially expressed genes were screened by Venn diagram. The GO function and KEGG pathway enrichment of common differentially expressed genes were analyzed by using the R cluster Profiler package. String database was selected for PPI analysis, and the results were imported into Cytoscape software to obtain PPI interaction map, core module and Hub gene. Import Hub gene into BioGPS database. Results: A total of 3 Hub genes were screened, namely CD3D, LCK, KLRB1;The genes were imported into BioGPS database to obtain the specific genes. Conclusion The selected differential genes and related signaling pathways are helpful to understand the molecular mechanism of ES and TB, and can provide the basis for early diagnosis of ES complicated with TB. It also provides new ideas for clinical treatment and diagnosis.

关 键 词：ES NFH Bioinformatics Analysis Differential Gene Signal Path 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]