RLPI: R-loop Prognostic Index for Lower-Grade Glioma from Multi-omics Analysis

A prognostic index (RLPI) for lower-grade glioma (LGG) constructed using 9 machine-learning algorithms and validated across 11 bulk transcriptomic, 2 single-cell RNA-sequencing, and 1 spatial transcriptomic cohort. The RLPI comprises 35 R-loop regulators and is associated with clinical outcomes, immune microenvironment, and metabolic features in 1556 patients. The dataset was created by Ronghua Huang and last updated on 2026-04-17.

Use Cases

• Prognostic risk stratification for lower-grade glioma patients based on the 35-gene R-loop index.
• Analysis of tumor immune microenvironment features, such as antigen presentation and immunosuppressive signaling, associated with RLPI status.
• Investigation of metabolic pathway enrichment, including angiogenesis and hypoxia, in high-RLPI glioma cells.
• Prediction of treatment response, such as resistance to radiotherapy and temozolomide or improved response to immune checkpoint blockade.
• Validation of key RLPI genes (e.g., INCENP, NCAPG) through functional assays related to cell proliferation and invasion.

Strengths

• The RLPI was validated across 6 independent LGG cohorts comprising 1556 patients.
• The index was constructed by benchmarking 9 machine-learning algorithms, with Elastic Net selected for its predictive performance.
• Analysis integrates data from 11 bulk transcriptomic, 2 single-cell RNA-sequencing, and 1 spatial transcriptomic cohort.

Limitations

• Row count and column-level documentation are unknown, which may limit suitability assessment.
• The dataset is a 5.7 MB DOC file; the specific data format and structure require inspection after download.
• Last updated 2026-04-17 05:18:20; freshness should be verified.

Provenance

Source

figshare, author Ronghua Huang

Collection Method

Constructed via machine-learning analysis of multi-omics data from multiple LGG cohorts.

Freshness

2026-04-17