Cervical Cancer Prognostic Model Based on Tumor Mechanics-Related Genes

Lu Zhang published a prognostic prediction model for cervical cancer on figshare in 2026. The model is based on seven tumor mechanics-related genes identified from mRNA data in TCGA, GTEx, and GEO databases. The study includes clinical validation correlating tumor stiffness with MMP1 gene expression.

Use Cases

• Stratifying cervical cancer patients into high- and low-risk subgroups based on the seven-gene signature.
• Investigating the association between tumor stiffness and MMP1 gene expression in clinical samples.
• Analyzing differences in gene mutation landscapes and immune cell infiltration between patient risk groups.
• Evaluating differential responses to chemotherapy based on the prognostic risk score.

Strengths

• Model constructed from mRNA data sourced from three major public repositories: TCGA, GTEx, and GEO.
• Includes clinical validation with a reported correlation (r=0.418, P=0.012) between MMP1 expression and tumor stiffness.
• Published under a permissive CC-BY-4.0 license, allowing for reuse and modification.

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 very small (18.0 KB), suggesting it contains model parameters or summary results rather than raw patient-level data.

Provenance

Source

mRNA data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) project, and Gene Expression Omnibus (GEO).

Collection Method

Bioinformatics analysis using LASSO regression to build a prognostic model, validated in a separate cohort (GSE44001). Clinical component used strain elastography and immunohistochemistry.

Freshness

Last updated 2026-06-03 06:08:33; freshness should be verified.