Lung Cancer Proteomic Profiles for 22 Patient Specimens

22 pairs of lung cancer specimens were analyzed via proteomics to compare chemoradiotherapy-sensitive and resistant subtypes. The data, published by Weiwei Ouyang in 2026, includes a predictive model identifying IKBIP and CIT proteins as key biomarkers. Functional analysis revealed distinct pathway enrichments, linking fatty acid oxidation to sensitivity and immune activation to resistance.

Use Cases

• Training predictive models for chemoradiotherapy efficacy based on protein signature biomarkers.
• Analyzing pathway enrichment differences between treatment-sensitive and resistant cancer subtypes.
• Validating the prognostic relevance of IKBIP and CIT proteins in independent survival analyses.

Strengths

• Proteomic profiling of 22 matched patient specimens provides a direct comparative basis.
• Includes a LASSO regression-based predictive model with identified protein signatures (IKBIP, CIT).
• Functional analysis results, such as pathway enrichment for fatty acid oxidation and immune activation, are described.

Limitations

• The dataset is very small at 10.9 KB, indicating limited scope.
• Row count and column-level documentation are unknown, requiring manual inspection after download.
• Data may reflect bias inherent to the specific patient cohort and collection method on figshare.

Provenance

Source

Weiwei Ouyang via figshare.

Collection Method

Proteomics profiling of patient specimens, likely from a clinical study.

Freshness

Last updated 2026-06-02 18:04:33.