HMMR Biomarker Analysis: Transcriptomic Data from OSCC Cohorts in TCGA and GEO

245 common differentially expressed genes were identified across three cohorts of oral squamous cell carcinoma (OSCC) patients. Lingling Liu integrated transcriptomic data from the TCGA OSCC cohort and two GEO cohorts (GSE37991, GSE41613) to analyze the prognostic biomarker HMMR. The analysis, last updated in June 2026, links HMMR overexpression to advanced tumor stage, poor survival, and immune cell infiltration.

Use Cases

• Identify prognostic biomarkers for oral squamous cell carcinoma based on differential gene expression analysis.
• Correlate gene expression with immune cell infiltration levels based on immune infiltration analysis results.
• Explore protein-protein interaction networks related to cell cycle regulation based on PPI network analysis.
• Validate biomarker predictive performance using receiver operating characteristic (ROC) analysis.
• Investigate associations between gene expression and clinical pathological features like T stage and N stage.

Strengths

• Analysis integrates data from three independent cohorts (TCGA OSCC, GSE37991, GSE41613).
• HMMR demonstrated strong predictive value with an AUC of 0.919 in ROC analysis.
• The study identified 245 common differentially expressed genes across datasets.
• HMMR expression was correlated with 11 immune cell subsets, including Th2 cells (r=0.465).

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 a 163.2 KB DOCX file, suggesting limited scope and likely contains summary tables rather than raw transcriptomic data.

Provenance

Source

TCGA and GEO databases (specifically GSE37991 and GSE41613).

Collection Method

Multi-cohort bioinformatics analysis integrating transcriptomic data.

Freshness

Last updated 2026-06-04 22:02:17; freshness should be verified.