OLFML3, as a potential predictor of prognosis and therapeutic target for glioma, is closely related to immune cell infiltration.

The low response rate to immunosuppressant is mainly due to the lack of adequate knowledge about the tumor microenvironment (TME) and screening biomarkers for gliomas. We aimed to identify the promising immune biomarkers and new immune classification of glioma. In this study, multiple‐immune algorithms were used to calculate immune‐infiltration scores. Unsupervised and supervised machine learning methods were used to perform the classification. We observed that OLFML3 overexpression was indicated in gliomas and linked to poor prognosis. OLFML3 knockdown inhibited proliferation, invasion and increased the sensitivity of glioma cells to temozolomide. OLFML3 expression could also reflect the aberrant immune status. Based on the immune‐related signature, patients were divided into three immune subtypes via consensus clustering. Patients with C2 subtype presented poorer prognosis and shorter progression free survival than patients with other two subtypes. The TME patterns among subtypes were different. C2 and C3 subtypes are the immune‐inflamed and immune‐desert tumors, respectively. Additionally, compared with C3 subtype, patients with C1/C2 subtypes were more likely to respond to immunotherapy. The pRRophetic algorithm indicated patients with C1/C2 subtypes were more resistant to temozolomide, but sensitive to paclitaxel and cisplatin. To conclude, OLFML3 overexpression affects glioma cell proliferation, invasion, and TMZ sensitivity and has been proved to be an independent prognostic‐ and immune‐related biomarker. Additionally, the novel immune subtype's classification could provide the prognostic and predictive predictors for glioma patients and may guide physicians in selecting potential responders. [ABSTRACT FROM AUTHOR]