Supplementary File 3: Multi-Omics Integration for Atherosclerosis Biomarker Discovery

A supplementary file from a study integrating proteomic and transcriptomic data to investigate lactylation-linked biomarkers for atherosclerosis. The research involved data-independent proteomics on HUVECs, integration of public transcriptomes, and validation in Apoe−/− mice. The file was authored by Yirong Ma and last updated on 2026-05-08.

Use Cases

• Training diagnostic machine learning models based on the ensemble of 12 algorithms described in the study.
• Analyzing immune cell infiltration patterns in atherosclerosis based on CIBERSORT and single-cell data mentioned in the description.
• Prioritizing candidate therapeutic compounds based on Connectivity Map analysis and molecular docking results.
• Validating hub gene expression (UAP1, NRP1, QPRT, NDST1) in experimental models as described in the validation pipeline.

Strengths

• The underlying study integrated multiple data types, including proteomics identifying 472 differentially expressed proteins and transcriptomics yielding 2,544 DEGs.
• Machine learning validation was performed using an ensemble of 12 algorithms with cross-validation and external validation, achieving a mean AUC of 0.979.
• Core findings were validated through multiple experimental methods: RT-qPCR, Western blotting, and immunofluorescence in a mouse model.

Limitations

• The dataset is a 801.5 KB PDF file; the underlying structured data (e.g., gene expression matrices) is not directly accessible.
• Column-level documentation is absent; field semantics must be inferred from the manuscript text after download.
• Row count is unknown, which may limit suitability assessment for direct computational analysis.

Provenance

Source

figshare, authored by Yirong Ma.

Collection Method

Generated from a study combining experimental proteomics on HUVECs, public transcriptomic data integration, and computational analyses.

Time Range

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Freshness

Last updated 2026-05-08 05:52:42; freshness should be verified.

Geography

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