Lung Adenocarcinoma Exosome Gene Prognostic Model

A prognostic model for Lung Adenocarcinoma (LUAD) was constructed using four exosome-related genes (CLIC6, ANLN, FAM83A, RHOV) identified from TCGA and GEO data. The model stratifies patients into risk subgroups with distinct immune characteristics and immunotherapy response potential. The dataset includes analysis of immune cell infiltration differences and single-cell RNA sequencing validation.

Use Cases

• Validate the four-gene exosome-related signature (CLIC6, ANLN, FAM83A, RHOV) for prognostic risk stratification in LUAD cohorts.
• Analyze immune cell infiltration differences, such as eosinophils and T cells, between low- and high-RiskScore patient groups.
• Investigate the association between high-risk group status and elevated expression of immune checkpoint genes like CD274, PDCD1, and LAG3.
• Explore the expression patterns of CLIC6 across cell types, particularly its higher levels in epithelial cells, using single-cell data.

Strengths

• Model built on data from authoritative genomic repositories, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).
• Prognostic model validated with ROC curve analysis, confirming its predictive performance.
• Analysis includes multi-modal validation through immune infiltration assessment, single-cell RNA-seq, and in vitro cell line assays.

Limitations

• The dataset is small in file size (81.6 KB), indicating it contains summary or model results rather than raw, high-dimensional genomic data.
• Specific row and column counts, sample sizes, and the full feature set are not provided, limiting reproducibility and direct analysis.
• Clinical and genomic data are derived from specific public databases, which may introduce selection biases inherent to those sources.

Provenance

Source

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

Collection Method

Bioinformatic analysis including differential expression, consensus clustering, LASSO regression, immune infiltration assessment (TIMER, MCPcounter, ssGSEA), TIDE algorithm, and single-cell RNA-seq processing with Seurat.

Freshness

Last updated on 2026-03-23.