Lung Adenocarcinoma Prognostic Gene Expression Modules from GEO and TCGA

292 early-stage lung adenocarcinoma patient gene expression profiles from NCBI GEO were reanalyzed to identify modules linked to recurrence risk. Jyothi Thyagabhavan Mony published this work in 2026, validating findings in an independent TCGA cohort of 90 treatment-naive patients. The analysis reveals a prognostic dichotomy between protective immune-related programs and risk-associated extracellular matrix remodeling.

Use Cases

• Training classifiers to stratify recurrence risk based on immune and extracellular matrix gene expression signatures.
• Validating prognostic biomarkers for early-stage lung adenocarcinoma using independent cohort survival data.
• Analyzing coordinated tumor states by integrating immune, stromal, and tumor-intrinsic transcriptional programs.
• Investigating biological mechanisms linking epithelial organization and fibrotic remodeling to patient outcomes.

Strengths

• Analysis is validated in an independent TCGA cohort of 90 treatment-naive patients.
• Data is derived from 292 patient gene expression profiles in NCBI GEO datasets.
• License is CC-BY-4.0, permitting open sharing and adaptation.

Limitations

• Row count and column-level documentation are unknown; field semantics must be inferred after download.
• Dataset is small at 78.6 KB, indicating limited scope, likely containing summary or module scores rather than raw expression data.

Provenance

Source

figshare, author Jyothi Thyagabhavan Mony.

Collection Method

Reanalysis of gene expression data from NCBI GEO datasets and TCGA-LUAD.

Time Range

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Freshness

Last updated 2026-04-16 05:25:58; freshness should be verified.

Geography

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