TNBC Matrix Stiffness Prognostic Model Based on TCGA and GEO Transcriptome Data

A 12.6 KB DOCX file by Wenjie Shi, last updated 2026-05-07, presents a prognostic model for triple-negative breast cancer based on matrix stiffness-related genes. The model was constructed using transcriptome data from the TCGA and GEO databases, integrated with single-cell RNA-sequencing data. It aims to predict patient survival risk, immune microenvironment characteristics, and treatment response.

Use Cases

• Constructing prognostic risk models based on matrix stiffness-related gene signatures.
• Predicting immunotherapy and chemotherapy response based on matrix stiffness scores.
• Analyzing the tumor microenvironment and cell functional states using integrated single-cell and bulk transcriptome data.

Strengths

• Model validated in multiple independent patient cohorts, as stated in the description.
• The study integrates data from two major public repositories, TCGA and GEO.
• The 12.6 KB file size suggests a focused, concise presentation of the model and findings.

Limitations

• The dataset is a 12.6 KB DOCX document, which is a very small file likely containing summary results rather than raw data.
• Row count and column-level documentation are unknown, limiting assessment of the underlying data's structure and scale.
• The description does not specify the exact number of patients or samples used from the TCGA and GEO sources.

Provenance

Source

Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.

Collection Method

Integrated analysis of bulk transcriptome and single-cell RNA-sequencing data, using differential expression screening, Cox regression, and machine-learning methods.

Freshness

Last updated 2026-05-07 04:45:25; freshness should be verified.