CALCRL Gene Expression Data for Uterine Leiomyosarcoma vs. Leiomyoma

Shiying Chen published a dataset on figshare in April 2026 containing gene expression data for differentiating uterine leiomyosarcoma (ULMS) from uterine leiomyoma (ULM). The data includes 25 ULMS and 25 ULM samples from the GEO database (GSE64763), with validation across three additional datasets. It is a 200.7 KB ZIP file licensed under CC-BY-4.0.

Use Cases

• Training diagnostic classifiers based on differentially expressed immune and inflammation-related genes (DIIRGs).
• Validating the biomarker CALCRL using ROC curves and principal component analysis as described.
• Analyzing immune cell infiltration patterns in ULMS using the Cibersort algorithm mentioned in the methods.
• Conducting functional enrichment analysis on gene sets using GO and KEGG pathways referenced in the study.

Strengths

• Includes a validation cohort from three independent GEO datasets (GSE9511, GSE68295, GSE36610).
• Candidate biomarker CALCRL demonstrated an AUC of 0.898 in the training set and 0.792 in validation.
• Analysis includes immunohistochemistry (IHC) validation on clinical tissue samples.

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 small at 200.7 KB, indicating limited scope.

Provenance

Source

GEO database (GSE64763, GSE9511, GSE68295, GSE36610) and clinical tissue samples.

Collection Method

Gene expression analysis, differential expression identification, and machine learning algorithms (two unspecified).

Time Range

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Freshness

Last updated 2026-04-10 05:58:32; freshness should be verified.

Geography

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