CXCL9/SPP1 Polarity Axis in NSCLC: Six-Gene Prognostic Model and TAM Subpopulations

A 10.2 KB Excel file by Houqiang Li, last updated April 17, 2026, presents data from a study on tumor-associated macrophages in non-small cell lung cancer. The research introduces a functional framework based on the CXCL9/SPP1 expression ratio, identifying four distinct TAM subpopulations. It details a validated six-gene prognostic signature constructed using 101 machine learning algorithms for patient risk stratification.

Use Cases

• Stratifying NSCLC patients into high-risk and low-risk groups based on the six-gene prognostic signature.
• Analyzing the correlation between TAM polarity and immune status within the tumor microenvironment.
• Investigating the dynamic plasticity of TAM subpopulations through pseudotime trajectory analysis.
• Validating the expression of key genes associated with the CXCL9/SPP1 polarity axis.

Strengths

• Data is derived from the integration of single-cell and bulk transcriptomic data.
• The prognostic model was constructed and validated using 101 machine learning algorithms.
• Dataset is shared under a permissive CC-BY-4.0 license.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• The dataset is very small at 10.2 KB, indicating limited scope.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Houqiang Li via figshare

Collection Method

Integration of single-cell and bulk transcriptomic data, with machine learning optimization.

Freshness

Last updated 2026-04-17 05:20:08.