Cervical Cancer Prognostic Model Based on Seven Tumor Mechanics-Related Genes

A prognostic model for cervical cancer was constructed using seven key genes identified from mRNA data across GTEx, TCGA, and GEO databases. The 13.9 KB document, authored by Lu Zhang and last updated in June 2026, details the model's validation and its clinical association with tumor stiffness. The model stratifies patients into high- and low-risk subgroups with distinct mutation landscapes and immune cell infiltration.

Use Cases

• Validating a seven-gene prognostic signature for cervical cancer based on the identified TMRGs.
• Investigating correlations between tumor stiffness, measured by strain elastography, and gene expression like MMP1.
• Analyzing differences in gene mutation landscapes and immune cell infiltration between high- and low-risk patient subgroups.
• Assessing differential responses to chemotherapy based on the prognostic risk score.

Strengths

• Model constructed from mRNA data across three major public repositories: GTEx, TCGA, and GEO.
• Clinical validation component includes preoperative strain ratio measurements and immunohistochemistry scores for MMP1.
• Reported a moderate positive correlation (r=0.418, P=0.012) between MMP1 expression and tumor stiffness.

Limitations

• The dataset is a 13.9 KB DOCX file; the underlying tabular data (rows, columns) is not directly accessible.
• Column-level documentation is absent; field semantics must be inferred from the manuscript.
• Row count is unknown, which may limit suitability assessment for large-scale ML training.

Provenance

Source

mRNA data from Genotype-Tissue Expression project (GTEx), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO); clinical samples from an unspecified cohort.

Collection Method

Bioinformatics analysis using LASSO regression; clinical evaluation using strain elastography and immunohistochemistry.

Freshness

Last updated 2026-06-03 06:08:36; freshness should be verified.