Machine learning derived development and validation of extracellular matrix related signature for predicting prognosis in adolescents and young adults glioma

Abstract

Abstract The mortality rates have been increasing for glioma in adolescents and young adults (AYAs, aged 15–39 years). However, current biomarkers for clinical assessment in AYAs glioma are limited, prompting the urgent need for identifying ideal prognostic signature. Extracellular matrix is involved in the development of tumors, while their prognostic significance in AYAs glioma remains unclear. By an integrated machine learning workflow and circuit training and validation procedure, we developed a machine learning-derived prognostic signature (MLDPS) based on 1,026 extracellular matrix-related genes and 3 AYAs glioma cohorts. MLDPS exhibited robust and consistent predictive performance in overall survival and could serve as an independent prognostic factor for AYAs glioma. Simultaneously, MLDPS outperformed previous 89 published prognostic signatures and traditional clinical characteristics, confirming the robust predictive capability. Besides, MLDPS had the potential to stratify prognosis in patients with other cancer types. In addition, the tumor microenvironment between high and low MLDPS groups displayed different patterns while more tumor-infiltrating immune cells were observed in high MLDPS group. Additionally, patients in low MLDPS group had significantly prolonged survival when received immunotherapy in cancers including glioblastoma, urothelial carcinoma and melanoma. Overall, our study proposes a promising signature, which can be utilized for clinicians to evaluate prognosis and might provide individualized clinical management for AYAs glioma.