A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

Xin Fan; Xiyi Nie; Junwen Huang; Lingling Zhang; Xifu Wang; Min Lu

doi:10.1155/2022/9886044

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

J Oncol. 2022 Oct 4:2022:9886044. doi: 10.1155/2022/9886044. eCollection 2022.

Authors

Xin Fan^{1

2}, Xiyi Nie³, Junwen Huang⁴, Lingling Zhang⁵, Xifu Wang¹, Min Lu¹

Affiliations

¹ Department of Emergency, Shangrao Hospital Affiliated to Nanchang University, Shangrao People's Hospital, Shangrao 334000, China.
² Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Nanchang University, Nanchang 330000, China.
³ Department of Neurosurgery, Yichun Hospital Affiliated to Nanchang University, Yichun People's Hospital, Yichun 334000, China.
⁴ The First Clinical Medical College of Nanchang University, Nanchang 330000, China.
⁵ School of Stomatology, Nanchang University, Nanchang 330000, China.

Abstract

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.