A curated set of 160 lactylation-related genes was analyzed using a multi-step machine learning framework. The dataset, created by Mingyang Zou and last updated in March 2026, identifies AARS2 as a key biomarker. It integrates bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic data from public repositories like TCGA and GEO.
Use Cases
- Constructing prognostic signatures based on lactylation-related gene expression profiles.
- Validating biomarker expression at the protein level using immunohistochemistry data.
- Analyzing associations between gene expression and tumor microenvironment heterogeneity.
- Investigating functional roles in pathways like epithelial-mesenchymal transition and hypoxia response.
Strengths
- Integrates multi-omics data from multiple public repositories (TCGA, GEO).
- Analysis is based on a curated set of 160 lactylation-related genes.
- Includes in vitro validation data from the HCT116 cell line.
- Released under a permissive CC-BY-4.0 license.
Limitations
- Dataset size is 12.1 KB, indicating a very limited scope.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- figshare
- Collection Method
- Integrative multi-omics analysis of public data, supplemented by in vitro validation.
- Time Range
- null
- Freshness
- Last updated 2026-03-18 07:38:39; freshness should be verified.
- Geography
- null