Lung Adenocarcinoma Prognostic Risk Model Based on Histidine Metabolism Genes

A study by Chaofan Mao integrates bulk and single-cell RNA sequencing data to analyze histidine metabolism in lung adenocarcinoma (LUAD). It identifies seven epithelial cell-specific prognostic genes (WIF1, GATA2, CD69, ID1, C4BPA, WFDC2, CCL20) and constructs a risk model. The dataset, last updated on 2026-04-20, includes analysis files in DOCX and XLS formats totaling 1.3 MB.

Use Cases

• Constructing prognostic risk models for lung adenocarcinoma based on seven identified histidine metabolism-related genes.
• Investigating immune heterogeneity across patient groups using algorithms like ESTIMATE, ssGSEA, and CIBERSORT.
• Identifying molecular subtypes of lung adenocarcinoma through consensus clustering analysis.
• Exploring potential sensitivity to CTLA-4 and PD-L1 inhibitors in patient subgroups based on immune infiltration analysis.

Strengths

• Integrates both bulk and single-cell RNA sequencing data from sources like UCSC Xena and Code Ocean.
• Identifies seven specific prognostic genes (WIF1, GATA2, CD69, ID1, C4BPA, WFDC2, CCL20) linked to histidine metabolism.
• Employs multiple analytical methods including differential expression, Cox analysis, LASSO regression, and consensus clustering.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect bias inherent to the specific sequencing platforms and patient cohorts used.

Provenance

Source

Bulk RNA-seq data from UCSC Xena; single-cell RNA-seq data from Code Ocean.

Collection Method

Integration of differential expression analysis, univariate/multivariate Cox analysis, LASSO regression, and consensus clustering.

Freshness

Last updated 2026-04-20 08:34:46; freshness should be verified.