Machine learning-based integration of omics and clinical data reveals an N-glycan biosynthesis signature predictive of the outcome in low-grade glioma: an in silico study

Abstract

Aim: This study explored the prognostic value of N-glycan biosynthesis (NGB) in lower-grade glioma (LGG) and aimed to develop a machine learning model for enhanced prognostic accuracy.

Method: LGG patient transcriptome data were analyzed to identify NGB-related genes. Consensus clustering identified subgroups based on NGB expression. A prognostic NGB signature (pNGB) was developed using machine learning. The pNGB score's association with cell proliferation, inflammation, treatment response, tumor recurrence, and the immune microenvironment was also explored.

Results: A 22-gene pNGB signature was identified, with MGAT1 and TUSC3 having the highest and lowest hazard ratios, respectively. Two distinct clusters (C1 and C2) with differential pNGB expression and survival outcomes were revealed. NGB pathway analysis indicated an overall poor prognosis, except for MGAT4C and TUSC3. The Enet-based survival model showed superior discriminatory power and reliability. The NGB risk score correlated with increased cell proliferation, inflammation, and altered immune landscape. Additionally, the score is linked to treatment response and tumor recurrence.

Conclusion: This study highlights the critical role of NGB in LGG progression and proposes a pNGB-based model for prognosis. The NGB risk score shows promise as a prognostic biomarker and potential therapeutic target in LGG.

Keywords

Low-grade glioma / N-glycan biosynthesis / machine learning / metabolic dysfunction / tumor recurrence

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