Machine Learning-Determined Sex-Related Bladder Cancer Biomarkers from RNA-Seq Data

A gene panel dataset derived from gender and disease-stratified RNA-seq data to identify sex-specific bladder cancer biomarkers. The data was generated by Joseph R. Pizzi using machine learning feature selection methods and was last updated on 2026-04-29. Male and female-specific panels achieved areas under the ROC curve of 0.932 and 0.914, respectively, for distinguishing cancer from non-tumor controls.

Use Cases

• Training classifiers for sex-specific bladder cancer diagnosis based on identified gene panels.
• Investigating biological pathways like PI3K-AKT signaling and extracellular matrix reorganization in bladder cancer.
• Validating potential biomarker genes such as PRAC1, PCDH11Y, AR, PLXNA1, USP54, and PMEPA1 in external studies.

Strengths

• Performance metrics are provided, with male and female-specific panels achieving AUCs of 0.932 and 0.914.
• The dataset is focused, derived from RNA-seq data using four distinct machine learning feature selection methods.
• Data is openly licensed under CC-BY-4.0.

Limitations

• The dataset is very small at 8.6 KB, indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Machine learning analysis of gender and disease-stratified RNA-seq data, using differential expression analysis, recursive feature elimination, logistic regression, and random forest.

Freshness

Last updated 2026-04-29 05:58:43; freshness should be verified.