Lactylation Gene Biomarkers for Acute Myeloid Leukemia Prognosis

Zhibo Guo's research dataset identifies seven lactylation-related genes (LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA, POU2F2) as prognostic biomarkers in acute myeloid leukemia. It integrates data from GEO and TCGA databases, analyzed using machine learning algorithms like LASSO-logistic and SVM-RFE. The 14.4 KB XLSX file contains results from this integrative profiling study.

Use Cases

• Validate the seven hub LRGs (LSP1, MPO, GZMB, SPINK2, HLA-DRB1, HLA-DRA, POU2F2) as prognostic markers using the GLM model identified in the study.
• Analyze the relationship between key LRGs like GZMB and LSP1 and immune cell infiltration patterns using CIBERSORT methodology.
• Screen for potential drugs targeting GZMB and LSP1 by evaluating binding potentials identified through molecular docking results.

Strengths

• Identifies seven specific hub lactylation-related genes (LRGs) validated through multiple machine learning algorithms.
• Integrates data from two major public repositories, GEO and TCGA, for analysis.
• Results include MR test validation for GZMB and LSP1 genes and molecular docking for drug screening.

Limitations

• Very small file size (14.4 KB) suggests the dataset contains summary results, not the underlying raw sequencing data.
• Unknown row and column structure limits direct analytical utility without the source study context.
• Biomarker findings are specific to acute myeloid leukemia and may not generalize to other cancers.

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, limma, WGCNA, and machine learning algorithms (LASSO-logistic, SVM-RFE, Boruta).

Freshness

Last updated March 23, 2026.