CXCL9/SPP1 Polarity Axis in NSCLC: Six-Gene Prognostic Model for Tumor Microenvironment

A six-gene prognostic model for non-small cell lung cancer (NSCLC) patients, constructed using 101 machine learning algorithms. The dataset likely contains gene expression signatures related to the CXCL9/SPP1 polarity axis in tumor-associated macrophages, enabling patient risk stratification. Author Houqiang Li published this data on figshare under a CC-BY-4.0 license, with a last update in April 2026.

Use Cases

• Stratifying NSCLC patients into high-risk and low-risk groups based on the six-gene signature.
• Analyzing the immunosuppressive tumor microenvironment associated with M0/M2 macrophage enrichment.
• Investigating the dynamic plasticity of tumor-associated macrophage polarity using pseudotime trajectory concepts.
• Validating the CXCL9/SPP1 expression ratio as a functional framework for macrophage classification in NSCLC.

Strengths

• Model constructed and validated using an extensive optimization process involving 101 machine learning algorithms.
• Data is derived from the integration of single-cell and bulk transcriptomic data sources.
• Published under a permissive CC-BY-4.0 license, facilitating reuse and redistribution.

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 at 12.4 KB, indicating limited scope, likely containing summary or signature data rather than raw observations.

Provenance

Source

Author Houqiang Li via figshare.

Collection Method

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

Time Range

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Freshness

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

Geography

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