RLPI: R-Loop Prognostic Index for Lower-Grade Glioma from Multi-Omics Analysis

A 35-gene R-loop prognostic index (RLPI) was constructed using Elastic Net from 9 benchmarked machine learning algorithms. The index predicts clinical outcomes for 1556 patients across 6 independent lower-grade glioma cohorts, linking to immune microenvironment and metabolic features. Author Ronghua Huang published this data on figshare in April 2026 under a CC-BY-4.0 license.

Use Cases

• Prognostic risk stratification for lower-grade glioma patients based on the 35-gene RLPI score.
• Analyzing associations between R-loop aberrations and immune cell infiltration patterns described in the study.
• Investigating links between RLPI and metabolic features like angiogenesis and hypoxia pathways.
• Modeling treatment response, such as resistance to radiotherapy or sensitivity to immune checkpoint blockade, using RLPI as a feature.

Strengths

• The RLPI index is derived from benchmarking 9 machine learning algorithms, with Elastic Net selected for its performance.
• Validation involves data from 1556 patients across 6 independent lower-grade glioma cohorts.
• Analysis integrates multiple data types, including 11 bulk transcriptomic, 2 single-cell RNA-sequencing, and 1 spatial transcriptomic cohort.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 412.5 KB, indicating limited scope, likely containing summary results rather than raw omics data.

Provenance

Source

figshare, author Ronghua Huang.

Collection Method

Constructed via machine learning analysis of multi-omics data from multiple lower-grade glioma cohorts.

Freshness

Last updated 2026-04-17 05:18:21; freshness should be verified.