Glioma Chromatin Remodeling Data from CGGA, GTEx, and TCGA Cohorts

117 machine-learning algorithm combinations were screened to develop a prognostic signature for glioma. The signature stratifies patients into high- and low-risk cohorts based on chromatin remodeling-related genes. Kun Wang authored this dataset, which was last updated on May 8, 2026.

Use Cases

• Stratify glioma patients into prognostic risk groups based on chromatin remodeling-related gene expression.
• Identify potential therapeutic targets, such as LMNA, for glioma treatment.
• Validate gene expression patterns using single-cell RNA sequencing, spatial transcriptomics, and proteomic data.
• Screen for candidate LMNA inhibitors using molecular docking analyses.

Strengths

• The prognostic signature was derived from systematically screening 117 machine-learning algorithm combinations.
• Expression patterns were cross-validated using single-cell RNA sequencing, spatial transcriptomics, and proteomic data.
• The dataset includes experimental validation from in vitro and in vivo assays.

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 1.4 MB, suggesting a relatively small scale.

Provenance

Source

RNA-seq profiles from CGGA, GTEx, and TCGA cohorts.

Collection Method

Data integration and analysis leveraging multiomics approaches.

Freshness

Last updated 2026-05-08 06:15:54; freshness should be verified.