Supplementary file 8_Decoding atherosclerosis through lactylation: multi-omics integration

Supplementary file 8 from a research paper published on figshare by Yirong Ma in May 2026. The dataset likely contains integrated multi-omics data, including proteomic and transcriptomic results, used to identify lactylation-linked biomarkers for atherosclerosis. The analysis involved 472 differentially expressed proteins, 2,544 DEGs, and machine-learning validation.

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.
• Prioritizing candidate therapeutic compounds based on Connectivity Map (CMap) analysis results.
• Validating lactylation-related gene (LRG) networks and hub genes like UAP1, NRP1, QPRT, and NDST1.

Strengths

• Integrated multi-omics data from proteomics and public transcriptomes (GEO), with batch correction applied.
• Machine-learning validation using an ensemble of 12 algorithms with cross-validation and external validation, achieving a mean AUC of 0.979.
• Experimental validation of core genes (NRP1, NDST1, QPRT, UAP1) via RT-qPCR, Western blot, and immunofluorescence in Apoe−/− mice.

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 a single 798.2 KB PDF file, which may require extraction and parsing of embedded tables or figures.

Provenance

Source

figshare, author Yirong Ma.

Collection Method

Data-independent proteomics on HUVECs exposed to oxidized LDL, integrated with public AS transcriptomes (GEO) and analyzed via consensus clustering, WGCNA, and machine learning.

Time Range

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Freshness

Last updated 2026-05-08 05:52:44; freshness should be verified.

Geography

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