Osteosarcoma Multi-Omics Data for Lactylation and HDGF Analysis

A 4.9 MB dataset from a 2026 study by Shidong Hu, integrating single-cell RNA sequencing and spatial transcriptomics to investigate lactylation-driven metabolic reprogramming in osteosarcoma. The data supports the identification of a tumor subpopulation with high lactylation activity and the role of hepatoma-derived growth factor (HDGF) in tumor progression and immune modulation. It was made available under a CC-BY-4.0 license on figshare.

Use Cases

• Identify prognostic biomarkers based on lactylation-associated gene expression patterns.
• Validate machine learning models for predicting osteosarcoma patient outcomes using multi-omics features.
• Investigate spatial co-localization of metabolic activity and gene expression within tumor regions.
• Perform virtual screening for drug candidates targeting specific proteins like HDGF.

Strengths

• Integrates multiple data modalities, including single-cell RNA-seq and spatial transcriptomics, as described.
• Includes data from functional validation studies (e.g., HDGF knockdown experiments) mentioned in the description.
• Dataset is openly available under a permissive CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale analyses.
• Description metadata is limited; actual data quality and structure require manual inspection after download.

Provenance

Source

figshare, author Shidong Hu

Collection Method

Integrative multi-omics approach combining single-cell RNA sequencing, spatial transcriptomics, and machine learning-based modeling.

Freshness

Last updated 2026-05-20 05:23:36; freshness should be verified.