Inflammatory factors and risk of meningiomas: a bidirectional mendelian-randomization study.

Abstract

Background:

Meningiomas are one of the most common intracranial tumors, and the current understanding of meningioma pathology is still incomplete. Inflammatory factors play an important role in the pathophysiology of meningioma, but the causal relationship between inflammatory factors and meningioma is still unclear.

Method:

Mendelian randomization (MR) is an effective statistical method for reducing bias based on whole genome sequencing data. It's a simple but powerful framework, that uses genetics to study aspects of human biology. Modern methods of MR make the process more robust by exploiting the many genetic variants that may exist for a given hypothesis. In this paper, MR is applied to understand the causal relationship between exposure and disease outcome.

Results:

This research presents a comprehensive MR study to study the association of genetic inflammatory cytokines with meningioma. Based on the results of our MR analysis, which examines 41 cytokines in the largest GWAS datasets available, we were able to draw the relatively more reliable conclusion that elevated levels of circulating TNF-β, CXCL1, and lower levels of IL-9 were suggestive associated with a higher risk of meningioma. Moreover, Meningiomas could cause lower levels of interleukin-16 and higher levels of CXCL10 in the blood.

Conclusion:

These findings suggest that TNF-β, CXCL1, and IL-9 play an important role in the development of meningiomas. Meningiomas also affect the expression of cytokines such as IL-16 and CXCL10. Further studies are needed to determine whether these biomarkers can be used to prevent or treat meningiomas.

• Authors: Zhiyun Zhang, Shengnan Wang, Fei Ren, Laiyu Yang, Haoqun Xie, Lin Pan, Yifan Li, Bingcheng Yu, Yifan Yang, Haoyi Su, Youqi Chen, Chuyi Zhang, Hongyu Chen, Wenzhuo Yang, Nan An, Yang Bai
• Journal: Frontiers in neuroscience (Front Neurosci) Vol. 17 Pg. 1186312 ( 2023) ISSN: 1662-4548 [Print] Switzerland
• PMID: 37425011 (Publication Type: Journal Article)
• Copyright: Copyright © 2023 Zhang, Wang, Ren, Yang, Xie, Pan, Li, Yu, Yang, Su, Chen, Zhang, Chen, Yang, An and Bai.