Early-Stage Lung Adenocarcinoma Gene Expression Data for Recurrence Risk Stratification

292 patient gene expression profiles from NCBI GEO datasets were reanalyzed to identify transcriptional programs linked to recurrence risk in early-stage lung adenocarcinoma. The study, authored by Jyothi Thyagabhavan Mony and last updated in April 2026, further validates findings in an independent cohort of 90 treatment-naive patients from TCGA-LUAD. It identifies 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.
• Analyzing the correlation between B-cell, antibody response, and vascular homeostasis gene modules with patient survival.
• Investigating the relationship between epithelial organization and extracellular matrix remodeling programs and poor prognosis.
• Validating prognostic gene expression modules in independent cohorts of early-stage, treatment-naive lung adenocarcinoma patients.

Strengths

• Data is derived from 292 patient samples from NCBI GEO, providing a substantive initial cohort.
• Findings are validated in an independent cohort of 90 patients from TCGA-LUAD.
• Released under a permissive CC-BY-4.0 license, facilitating reuse and redistribution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the primary data file is unknown, which may limit suitability assessment.
• The description metadata is limited to the study's methodology and results; actual data structure requires inspection.

Provenance

Source

figshare, author Jyothi Thyagabhavan Mony.

Collection Method

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

Time Range

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Freshness

Last updated 2026-04-16 05:25:56.

Geography

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