Single-cell profiling uncovers proliferative cells as key determinants of survival outcomes in lower-grade glioma patients

Abstract

Abstract Lower-grade gliomas (LGGs), despite their generally indolent clinical course, are characterized by invasive growth patterns and genetic heterogeneity, which can lead to malignant transformation, underscoring the need for improved prognostic markers and therapeutic strategies. This study utilized single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq to identify a novel cell type, referred to as "Prol," characterized by increased proliferation and linked to a poor prognosis in patients with LGG, particularly under the context of immunotherapy interventions. A signature, termed the Prol signature, was constructed based on marker genes specific to the Prol cell type, utilizing an artificial intelligence (AI) network that integrates traditional regression, machine learning, and deep learning algorithms. This signature demonstrated enhanced predictive accuracy for LGG prognosis compared to existing models and showed pan-cancer prognostic potential. The mRNA expression of the key gene PTTG1 from the Prol signature was further validated through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Our findings not only provide novel insights into the molecular and cellular mechanisms of LGG but also offer a promising avenue for the development of targeted biomarkers and therapeutic interventions.

Keywords

• Artificial intelligence
• Single-cell RNA sequencing
• Immunotherapy
• Prognosis