Supplementary file 7_Decoding atherosclerosis through lactylation: multi-omics integration

472 differentially expressed proteins and 2,544 differentially expressed genes were integrated to identify lactylation-linked biomarkers for atherosclerosis. The dataset, authored by Yirong Ma and last updated in May 2026, supports a machine-learning ensemble that achieved a mean AUC of 0.979. It includes results from proteomics, transcriptomics, and network analyses validated in an Apoe−/− mouse model.

Use Cases

• Training diagnostic classifiers for atherosclerosis based on the identified hub genes UAP1, NRP1, QPRT, and NDST1.
• Analyzing immune cell infiltration patterns in atherosclerotic plaques using the CIBERSORT method described.
• Prioritizing candidate drug compounds for atherosclerosis via Connectivity Map analysis and molecular docking simulations.
• Validating lactylation-related gene networks through consensus clustering and Weighted Gene Co-expression Network Analysis (WGCNA).

Strengths

• Integrates multiple data types, including proteomics (472 proteins) and transcriptomics (2,544 DEGs).
• Machine-learning validation achieved a high mean AUC of 0.979 across training and external sets.
• Includes experimental validation via RT-qPCR, Western blot, and immunofluorescence in a mouse model.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 3.8 MB size suggests a small dataset, potentially limited in scope.

Provenance

Source

figshare, authored by Yirong Ma.

Collection Method

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

Time Range

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Freshness

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

Geography

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