RAIR–ATC Transcriptional Axis and Drug-Response Model for Thyroid Cancer

An Excel dataset supporting research on radioiodine-refractory and anaplastic thyroid cancer. It contains derived gene modules, pathway annotations, and drug-response predictions from integrated RNA-seq and microarray cohorts. The dataset was authored by Siying Wang and last updated on May 18, 2026.

Use Cases

• Prioritizing VEGFR/KDR and RAF/BRAF inhibitor classes for thyroid cancer based on RAIR signature scores.
• Training multimodal ridge models for drug-response prediction using integrated cell and drug features.
• Analyzing associations between RAIR scores and hypoxia or interferon/inflammatory pathways in external cohorts.
• Stratifying thyroid tumours based on activation of EMT–angiogenesis–inflammatory programs.
• Assessing differential drug sensitivity (ΔAUC) in thyroid cell lines using PRISM drug-response data.

Strengths

• Dataset integrates RNA-seq and microarray data from six cohorts, including TCGA-THCA.
• Includes PRISM drug-response AUC data for 18 thyroid cell lines.
• The multimodal ridge model improved AUC prediction over baseline models.
• License is CC-BY-4.0, allowing for open reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is 19.5 KB, indicating a limited scope of derived results rather than raw data.

Provenance

Source

Integrated RNA-seq and microarray cohorts (TCGA-THCA, RAIR vs. radioiodine-avid PTC cohort, five ATC cohorts).

Collection Method

Data integration, gene module derivation, pathway annotation (MSigDB Hallmark), and multimodal ridge model training.

Freshness

Last updated 2026-05-18 05:30:50; freshness should be verified.