PFAS-Prostate Cancer Multi-Omics Analysis: 219 Shared Targets and Prognostic Model

Kuiyuan Zhang's research dataset, last updated June 1, 2026, supports a study on per- and polyfluoroalkyl substances (PFAS) and prostate cancer. It contains results from a computational toxicology and multi-omics approach, including a list of 219 shared molecular targets and a 10-gene machine-learning prognostic model. The dataset is 162.6 KB in size and is shared under a CC-BY-4.0 license.

Use Cases

• Validating a 10-gene prognostic model for prostate cancer based on machine-learning algorithms described in the study.
• Investigating shared molecular pathways like PPAR and p53 signaling between PFAS exposure and prostate cancer.
• Identifying potential natural compound interventions, such as quercetin, for mitigating PFAS effects based on molecular docking results.
• Cross-referencing core hub genes like ALB, PPARG, and CDC20 with other single-cell or spatial transcriptomics datasets.

Strengths

• Dataset is derived from a described multi-omics framework integrating toxicogenomic and transcriptomic data.
• Includes a specific list of 219 common molecular targets identified between PFAS and prostate cancer.
• Prognostic model achieved an average C-index of 0.710, as reported in the description.

Limitations

• Row count and column-level documentation are unknown; field semantics must be inferred after download.
• Findings are primarily based on computational predictions and validation in a single cell line (DU145), as noted in the conclusion.
• The dataset is small (162.6 KB), indicating limited scope, likely containing summary results rather than raw experimental data.

Provenance

Source

Kuiyuan Zhang, via figshare.

Collection Method

Generated through a computational and experimental framework including toxicogenomic data integration, network analysis, machine learning, and in vitro validation in DU145 prostate cancer cells.

Freshness

Last updated 2026-06-01 05:25:20; freshness should be verified.