Multi-Omics Integration for Atherosclerosis Biomarkers Linked to Lactylation

A research dataset from figshare, authored by Yirong Ma and last updated in May 2026. It integrates proteomic and transcriptomic data to identify lactylation-related biomarkers for atherosclerosis, validated through machine learning and experimental models.

Use Cases

• Training diagnostic models for atherosclerosis based on the identified hub genes UAP1, NRP1, QPRT, and NDST1.
• Analyzing immune cell infiltration in atherosclerotic plaques using the CIBERSORT method described.
• Prioritizing candidate therapeutic compounds via Connectivity Map analysis and molecular docking simulations.
• Validating lactylation-linked gene expression patterns in single-cell data from macrophages and smooth muscle cells.

Strengths

• Integrates multiple data types: 472 differentially expressed proteins from proteomics and 2,544 DEGs from transcriptomics.
• Validation includes an ensemble of 12 machine-learning algorithms achieving a mean AUC of 0.979.
• Includes experimental validation in Apoe−/− mice using RT-qPCR, Western blot, and immunofluorescence.

Limitations

• Dataset is a single 11.8 KB DOCX file; the underlying raw data tables are not directly provided.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale analysis.

Provenance

Source

figshare

Collection Method

Data-independent proteomics on HUVECs exposed to oxidized LDL, integrated with public AS transcriptomes from GEO.

Time Range

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Freshness

Last updated 2026-05-08 05:52:41; freshness should be verified.

Geography

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