Hypoxia- and Lactylation-Related Gene Signature for Lung Adenocarcinoma Prognosis

A prognostic model for lung adenocarcinoma (LUAD) was constructed using hypoxia- and lactylation-related genes via LASSO, XGBoost, and Random Forest algorithms. The model's core gene, PABPC1, was validated experimentally in two LUAD cell lines using qRT-PCR, CCK-8, colony formation, wound healing, and Transwell assays. The dataset, authored by Guannan Wang and last updated in May 2026, is shared under a CC-BY-4.0 license.

Use Cases

• Developing prognostic models for lung adenocarcinoma based on hypoxia- and lactylation-related gene signatures.
• Analyzing immune infiltration and TIDE-based immunotherapy response predictions for LUAD subtypes.
• Identifying potential drug sensitivities based on the defined gene signature.
• Validating gene expression findings with single-cell RNA-seq data integration.

Strengths

• The model integrates multiple machine learning algorithms (LASSO, XGBoost, Random Forest) for robustness.
• Includes experimental validation of the core gene PABPC1 in two LUAD cell lines using five distinct assays.
• Dataset is licensed under CC-BY-4.0, allowing for broad reuse.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• The dataset is very small (13.1 KB), suggesting it contains model results or summary statistics rather than raw patient-level data.

Provenance

Source

figshare

Collection Method

Constructed via bioinformatic analysis of gene signatures and validated with cell-based experiments.

Freshness

Last updated 2026-05-01 05:24:50; freshness should be verified.