Exosome-Related Gene Prognostic Model for Lung Adenocarcinoma
by Yajun Miao·Updated 3mo ago
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Description
A 2026 research paper presents a prognostic model for Lung Adenocarcinoma (LUAD) based on four identified exosome-related genes (CLIC6, ANLN, FAM83A, RHOV). The study analyzes data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), using differential expression, clustering, and LASSO regression. It links the gene signature to immune infiltration differences and immunotherapy response, validated with in vitro assays on LUAD cell lines.
Use Cases
Validate the four-gene exosome-related signature (CLIC6, ANLN, FAM83A, RHOV) for prognostic stratification of LUAD patients.
Analyze immune cell infiltration differences (e.g., eosinophils, T cells, myeloid dendritic cells) between high- and low-risk patient groups defined by the model.
Investigate the association between high-risk scores and elevated expression of immune checkpoint genes CD274, PDCD1, and LAG3 to predict immunotherapy response.
Explore single-cell RNA-seq data to examine CLIC6 expression levels specifically within epithelial cell populations.
Strengths
Identifies four specific prognostic genes (CLIC6, ANLN, FAM83A, RHOV) with validation in LUAD cell lines A549 and NCI-H838.
Integrates multi-source data from TCGA and GEO databases for model construction and validation.
Links the prognostic model to specific immune characteristics and immunotherapy response markers (CD274, PDCD1, LAG3).
Limitations
Dataset is a 10.1 KB DOCX file containing a research paper, not a structured, machine-readable data table.
No raw data, column definitions, or sample data are provided, limiting direct computational analysis.
The study's findings are specific to Lung Adenocarcinoma and the four identified genes, limiting generalizability.
Provenance
Source
The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).
Collection Method
Bioinformatics analysis including differential expression, consensus clustering, LASSO regression, immune infiltration assessment (TIMER, MCPcounter, ssGSEA), TIDE algorithm, and in vitro cell line assays.
Freshness
Last updated March 23, 2026.
Primary content is a research paper in DOCX format; users seeking the underlying gene expression or clinical data must obtain it from the original TCGA/GEO sources using appropriate identifiers. License is CC BY 4.0.