An Immune Microenvironment-Associated Gene Signature Predicts Outcomes and Therapeutic Response in Pediatric Medulloblastoma

Author:

Han DongMing1,Jia Zetian2,Wang Ziwei3,Chen Xuan1,Yang Zhengtao1,Zou Wanjing4,Liu Raynald4,Jiang Yifei5,Jin Xin3,Hu Yuhua2,Qiu Xiaoguang4,Li Chunde4,Liu Hailong4,Li Shuaicheng6,Li Jiankang7,Jiang Tao4

Affiliation:

1. College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

2. Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, People's Republic of China

3. BGI Research, Shenzhen 518083, China

4. Department of Neurosurgery, Beijing TianTan Hospital, Capital Medical University, Beijing 100050, People's Republic of China

5. University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.

6. Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China

7. BGI-Shenzhen, Shenzhen 518083, China

Abstract

Abstract Purpose: The tumor microenvironment (TME) is a pivotal factor in the progression and treatment response of cancers, including medulloblastoma (MB), the most common malignant brain tumor in children. This study investigates TME-specific gene signatures to create a prognostic model for MB patient outcomes. Methods: We sequenced 240 MB patient samples at Beijing Tiantan Hospital with RNASeq and analyzed TME components. Through co-expression network analysis and regression models, we identified an eight-gene signature that correlated with TME elements. This signature was tested against an independent dataset (GSE85217) for validation. Results: Our study presents the TME-associated risk score (TMErisk), which incorporates the eight-gene (CEBPB, OLFML2B, GGTA1, GZMA, TCIM, OLFML3, NAT1, and CD1C) signature. Higher TMErisk scores were associated with decreased overall survival and lower immune cell infiltration, immune checkpoint activity, and human leukocyte antigen expression. There was also a notable negative correlation between TMErisk scores and both TMB and IPS, varying across MB molecular subtypes. Moreover, the TME-risk was inversely related to the tumor mRNAsi, implying the influence of the TME on tumor stemness. Conclusions:Our findings identify a TME-specific eight-gene prognostic model that may serve as a predictive biomarker for MB patient outcomes and responses to immunotherapy. This gene signature model offers a supplementary tool to current WHO molecular subtypes and provides a potential target for future TME-focused MB treatment strategies. However, the distinct TME landscapes across MB subtypes pose new challenges for further research. Keywords: gene signature, immune cells, medulloblastoma, tumor microenvironment, prognostic model, tumor, stromal cells

Publisher

Research Square Platform LLC

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