Using multi-omics analysis to explore diagnostic tool and optimize drug therapy selection for patients with glioma based on cross-talk gene signature  

作　　者：YUSHI YANG CHUJIAO HU SHAN LEI XIN BAO ZHIRUI ZENG WENPENG CAO 

机构地区：[1]Department of Pathology and Pathophysiology,School of Basic Medicine,Guizhou Medical University,Guiyang,550025,China [2]Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D,State Key Laboratory of Functions and Applications of Medicinal Plants,Guizhou Medical University,Guiyang,550014,China [3]Department of Physiology,School of Basic Medicine,Guizhou Medical University,Guiyang,550025,China [4]Department of Anatomy,School of Basic Medicine,Guizhou Medical University,Key Laboratory of Human Brain Bank for Functions and Diseases of Department of Education of Guizhou Province,Guizhou Medical University,Guiyang,550025,China

出　　处：《Oncology Research》2024年第12期1921-1934,共14页肿瘤学研究（英文）

基　　金：funded by the Scientific Research Project of the Higher Education Department of Guizhou Province[Qianjiaoji 2022(187)];Department of Education of Guizhou Province[Guizhou Teaching and Technology(2023)015];Guizhou Medical University National Natural Science Foundation Cultivation Project(22NSFCP45);China Postdoctoral Science Foundation Project(General Program No.2022M720929).

摘　　要：Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited.Therefore,we aimed to develop a model that can effectively predict prognosis,differentiate microenvironment signatures,and optimize drug selection for patients with glioma.Materials and Methods:The CIBERSORT algorithm,bulk sequencing analysis,and single-cell RNA(scRNA)analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues.A predictive model was constructed based on cross-talk gene expression,and its effect on prognosis,recurrence prediction,and microenvironment characteristics was validated in multiple cohorts.The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments.Results:A high abundance of M2 macrophages in glioma tissues indicates poor prognosis,and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment.Eight genes involved in the cross-talk between macrophages and cancer cells were identified.Among them,periostin(POSTN),chitinase 3 like 1(CHI3L1),serum amyloid A1(SAA1),and matrix metallopeptidase 9(MMP9)were selected to construct a predictive model.The developed model demonstrated significant efficacy in distinguishing patient prognosis,recurrent cases,and characteristics of high inflammation,hypoxia,and immunosuppression.Furthermore,this model can serve as a valuable tool for guiding the use of trametinib.Conclusions:In summary,this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma;utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis,recurrence instances,and microenvironment characteristics;and aids in optimizing th

关 键 词：GLIOMA CROSS-TALK MACROPHAGES Prognosis Drug therapy selection 

分 类 号：R739.4[医药卫生—肿瘤]