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Identification of ARAP3 as a regulator of tumor progression, macrophage infiltration and osteoclast differentiation in a tumor microenvironment-related prognostic model of Ewing sarcoma.


ABSTRACT: Understanding the specificity and complexity of the tumor microenvironment (TME) of Ewing sarcoma (ES) is essential for identifying the immune characteristics of ES, improving the prediction of immunotherapeutic response, and facilitating therapeutic target discovery. In this study, we not only evaluated the gene sets associated with TME in ES using ESTIMATE and WGCNA algorithms based on the transcriptome data of ES, but also constructed a prognostic model (ES Score) using univariate Cox regression and Lasso regression and assessed its predictive ability on immune cell infiltration. Subsequently, we identified ARAP3 as a key gene affecting the TME of ES. In addition, bioinformatic analyses and in vitro experiments proved that the high expression of ARAP3 regulated ES cell proliferation, migration, as well as apoptosis via the p53 signaling pathway and affected macrophage infiltration and osteoclast differentiation through regulating IL1B and IL11 secretion of tumor cells.

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10492096 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Identification of ARAP3 as a regulator of tumor progression, macrophage infiltration and osteoclast differentiation in a tumor microenvironment-related prognostic model of Ewing sarcoma.

Wang Yao Y   Jiang Runyi R   Wang Ting T   Wu Zhipeng Z   Gong Haiyi H   Cai Xiaopan X   Liu Jialiang J   Yang Xinghai X   Wei Haifeng H   Jiao Jian J   Jia Qi Q   Yang Cheng C   Zhao Chenglong C   Xiao Jianru J  

American journal of cancer research 20230815 8


Understanding the specificity and complexity of the tumor microenvironment (TME) of Ewing sarcoma (ES) is essential for identifying the immune characteristics of ES, improving the prediction of immunotherapeutic response, and facilitating therapeutic target discovery. In this study, we not only evaluated the gene sets associated with TME in ES using ESTIMATE and WGCNA algorithms based on the transcriptome data of ES, but also constructed a prognostic model (ES Score) using univariate Cox regress  ...[more]

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