Supplementary File 1: PFAS-Prostate Cancer Multi-Omics Analysis and Prognostic Model

219 shared molecular targets between per- and polyfluoroalkyl substances (PFAS) and prostate cancer were identified through toxicogenomic and transcriptomic integration. A 10-gene machine-learning prognostic model was built, achieving an average C-index of 0.710, and core gene CDC20 was validated in vitro. The 6.0 MB DOCX file by Kuiyuan Zhang was last updated on June 1, 2026, under a CC-BY-4.0 license.

Use Cases

• Building prognostic models for prostate cancer based on the identified 10-gene signature.
• Investigating molecular docking interactions between PFAS compounds and cancer-related proteins like CDC20.
• Analyzing protein-protein interaction networks and pathway enrichment for inflammation and oxidative stress.
• Validating computational toxicology predictions with single-cell or spatial transcriptomics data.

Strengths

• Identifies 219 common molecular targets between PFAS and prostate cancer.
• Prognostic model built using multiple machine-learning algorithms with an average C-index of 0.710.
• Includes in vitro functional validation of key findings in DU145 prostate cancer cells.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Findings are primarily based on computational predictions and a single cell line, as noted in the conclusion.

Provenance

Source

figshare

Collection Method

Generated through a computational and experimental framework integrating toxicogenomic, transcriptomic, and in vitro data.

Freshness

Last updated 2026-06-01 05:25:21; freshness should be verified.