NSCLC TAM Polarity: CXCL9/SPP1 Axis and Six-Gene Prognostic Signature

A six-gene prognostic model for non-small cell lung cancer patients, constructed from single-cell and bulk transcriptomic data using 101 machine learning algorithms. The dataset, published by Houqiang Li on figshare, stratifies patients into high-risk and low-risk groups based on the CXCL9/SPP1 polarity axis in tumor-associated macrophages. It was last updated on 2026-04-17.

Use Cases

• Stratifying NSCLC patient risk based on the validated six-gene signature.
• Analyzing the correlation between TAM polarity and immune status within the tumor microenvironment.
• Studying the dynamic plasticity of TAM subpopulations through pseudotime trajectory analysis.
• Validating the prognostic significance of the CXCL9/SPP1 expression ratio in NSCLC.

Strengths

• Model was constructed and validated using 101 machine learning algorithms.
• Data integrates findings from both single-cell and bulk transcriptomic analyses.
• The prognostic model is based on a novel, NSCLC-specific functional framework for TAMs.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 10.3 KB, indicating limited scope.

Provenance

Source

Houqiang Li via figshare

Collection Method

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

Freshness

Last updated 2026-04-17 05:20:09; freshness should be verified.