Proteomic Profiles of 22 Lung Cancer Specimens for Chemoradiotherapy Response

22 pairs of lung cancer specimens were analyzed via proteomics profiling to compare CRT-sensitive versus CRT-resistant subtypes. The dataset includes a LASSO regression-based predictive model identifying a protein signature involving IKBIP and CIT. It was authored by Weiwei Ouyang and last updated on June 2, 2026.

Use Cases

• Train predictive models for chemoradiotherapy efficacy based on identified protein signatures like IKBIP and CIT.
• Analyze pathway enrichment differences between treatment-sensitive and treatment-resistant cancer subtypes.
• Validate prognostic biomarkers for lung cancer patient survival following chemoradiotherapy.

Strengths

• Proteomic profiling of 22 paired clinical specimens provides a direct biological comparison.
• Includes a developed predictive model with specific protein targets (IKBIP, CIT) for validation.
• Dataset is shared under a CC-BY-NC-4.0 license, allowing for non-commercial reuse with attribution.

Limitations

• The dataset is small at 1.6 MB, with an unknown row count, limiting statistical power for some analyses.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Proteomics profiling performed on clinical specimens, with functional and survival analysis conducted.

Freshness

Last updated 2026-06-02 18:04:35; freshness should be verified.