CheckDyn: Immune Checkpoint Gene Expression Dynamics in Cancer Immunotherapy

A computational framework analyzing paired pre- and post-treatment transcriptomes from 64 tumor samples across melanoma, basal cell carcinoma, and non-small-cell lung cancer. The dataset, created by Yanan Hu and last updated in 2026, profiles treatment-induced changes across 38 immune checkpoint genes and includes predictions for adaptive resistance to immune checkpoint blockade.

Use Cases

• Identify consistently upregulated immune checkpoint genes (e.g., LAG3, PDCD1) based on paired transcriptomic analysis.
• Model temporal trajectory of checkpoint gene expression based on linear upregulation patterns described.
• Predict adaptive resistance to immune checkpoint blockade based on pre-to-post expression deltas using the described ensemble classifier.
• Analyze co-expression network rewiring during treatment based on hub-degree shifts like ENTPD1.

Strengths

• Integrates data from 64 paired tumor samples across three cancer types.
• Profiles a specific set of 38 immune checkpoint and exhaustion-associated genes.
• Includes a predictive model for adaptive resistance with a reported AUC of 0.812.
• Applies cross-cohort harmonization methods including pseudo-bulk aggregation and meta-analysis.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (10.9 KB), indicating it likely contains summary results rather than raw expression data.

Provenance

Source

Integrated publicly available RNA-seq and scRNA-seq data from studies GSE91061, GSE120575, GSE123813, GSE176021.

Collection Method

Computational framework applying pseudo-bulk aggregation, Z-score batch correction, Stouffer meta-analysis, paired Wilcoxon tests, and linear mixed-effects meta-analysis.

Freshness

Last updated 2026-05-28 05:30:34; freshness should be verified.