Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis

Yu-Duo Guo; Jie Sun; Chao Zhao; Le Han; Chun-Jiang Yu; Hong-Wei Zhang

doi:10.3389/fonc.2023.1198414

Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis

Front Oncol. 2023 May 12:13:1198414. doi: 10.3389/fonc.2023.1198414. eCollection 2023.

Authors

Yu-Duo Guo¹, Jie Sun², Chao Zhao¹, Le Han³, Chun-Jiang Yu¹, Hong-Wei Zhang¹

Affiliations

¹ Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.
² Rehabilitation Department of Integrated Chinese and Western Medicine, Beijing Xiaotangshan Hospital, Beijing, China.
³ Chinese Academy of Sciences (CAS) Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.

Abstract

Background: The activation of YAP/TAZ transcriptional co-activators, downstream effectors of the Hippo/YAP pathway, is commonly observed in human cancers, promoting tumor growth and invasion. The aim of this study was to use machine learning models and molecular map based on the Hippo/YAP pathway to explore the prognosis, immune microenvironment and therapeutic regimen of patients with lower grade glioma (LGG).

Methods: SW1783 and SW1088 cell lines were used as in vitro models for LGG, and the cell viability of the XMU-MP-1 (a small molecule inhibitor of the Hippo signaling pathway) treated group was evaluated using a Cell Counting Kit-8 (CCK-8). Univariate Cox analysis on 19 Hippo/YAP pathway related genes (HPRGs) was performed to identify 16 HPRGs that exhibited significant prognostic value in meta cohort. Consensus clustering algorithm was used to classify the meta cohort into three molecular subtypes associated with Hippo/YAP Pathway activation profiles. The Hippo/YAP pathway's potential for guiding therapeutic interventions was also investigated by evaluating the efficacy of small molecule inhibitors. Finally, a composite machine learning models was used to predict individual patients' survival risk profiles and the Hippo/YAP pathway status.

Results: The findings showed that XMU-MP-1 significantly enhanced the proliferation of LGG cells. Different Hippo/YAP Pathway activation profiles were associated with different prognostic and clinical features. The immune scores of subtype B were dominated by MDSC and Treg cells, which are known to have immunosuppressive effects. Gene Set Variation Analysis (GSVA) indicated that subtypes B with a poor prognosis exhibited decreased propanoate metabolic activity and suppressed Hippo pathway signaling. Subtype B had the lowest IC50 value, indicating sensitivity to drugs that target the Hippo/YAP pathway. Finally, the random forest tree model predicted the Hippo/YAP pathway status in patients with different survival risk profiles.

Conclusions: This study demonstrates the significance of the Hippo/YAP pathway in predicting the prognosis of patients with LGG. The different Hippo/YAP Pathway activation profiles associated with different prognostic and clinical features suggest the potential for personalized treatments.

Keywords: Hippo signaling pathway; LGG; immune microenvironment; machine learning; prognosis.

Grants and funding

This research was supported by National Natural Science Foundation of China (No. 82101932), and Beijing Municipal Funding Program for Traditional Chinese Medicine Science Development (No. JJ-2020-29).