Acute Myeloid Leukemia Lactylation Biomarkers and Drug Candidates

This dataset presents seven hub lactylation-related genes (LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA, POU2F2) identified as prognostic biomarkers for Acute Myeloid Leukemia (AML). It integrates findings from RNA and single-cell sequencing data from the GEO and TCGA databases, validated through machine learning algorithms and Mendelian randomization. The data supports the investigation of lactate regulation's role in AML pathogenesis and potential therapeutic strategies.

Use Cases

• Validate the prognostic power of the seven hub LRGs (LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA, POU2F2) using the provided GLM model on independent AML cohorts.
• Analyze the relationship between key LRGs like GZMB and LSP1 and immune cell infiltration patterns using CIBERSORT methodology.
• Screen for drug candidates by performing molecular docking simulations targeting the GZMB and LSP1 protein structures.
• Investigate the enrichment of the identified hub genes in immune and inflammatory pathways through functional annotation.
• Correlate pan-lactylation levels from cell line experiments with the mRNA expression of GZMB and LSP1 in AML patient samples.

Strengths

• Identifies seven specific hub lactylation-related genes (LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA, POU2F2) with prognostic significance.
• Integrates data from two major public repositories, GEO and TCGA, for AML.
• Validation employs multiple machine learning algorithms (LASSO, SVM-RFE, Boruta) and Mendelian randomization analysis.
• Includes experimental validation via qRT–PCR and immunohistochemistry on patient samples for GZMB and LSP1.

Limitations

• The dataset is very small (10.0 KB), indicating it likely contains summary results rather than raw genomic or clinical data.
• Specific row and column counts, sample sizes, and detailed clinical variables are not provided, limiting direct analytical utility.
• The temporal and geographic coverage of the source patient data is unspecified, potentially introducing bias.

Provenance

Source

Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases.

Collection Method

Computational analysis of RNA and single-cell sequencing data using Seurat, WGCNA, and machine learning algorithms, supplemented by experimental validation (Western blot, qRT–PCR, IHC).

Freshness

Last updated March 2026.