Supplementary file 4_Decoding atherosclerosis through lactylation: multi-omics integration

Yirong Ma published this dataset on figshare in May 2026. It contains multi-omics data from a study investigating lactylation in atherosclerosis, integrating proteomic and transcriptomic analyses. The data underpins the identification of four hub genes (UAP1, NRP1, QPRT, NDST1) validated through machine learning and experimental models.

Use Cases

• Training diagnostic machine learning models based on the 25 candidate biomarker genes identified.
• Analyzing immune cell infiltration patterns in atherosclerosis based on CIBERSORT and single-cell data mentioned in the description.
• Prioritizing therapeutic compounds for atherosclerosis based on Connectivity Map (CMap) analysis results.
• Validating lactylation-related gene networks in vascular inflammation contexts.
• Conducting molecular docking and dynamics simulations based on the predicted NRP1–rivaroxaban interaction.

Strengths

• Data is integrated from multiple omics layers, including proteomics identifying 472 differentially expressed proteins and transcriptomics yielding 2,544 DEGs.
• Biomarker validation used an ensemble of 12 machine-learning algorithms, achieving a mean AUC of 0.979.
• Core findings were experimentally validated in Apoe−/− mouse models using RT-qPCR, Western blot, and immunofluorescence.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary file format is PDF (2.4 MB), which may require extraction to access structured data.

Provenance

Source

figshare

Collection Method

Generated from data-independent proteomics on HUVECs exposed to oxidised LDL and integration of public AS transcriptomes from GEO.

Time Range

Experimental timeframe not specified.

Freshness

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

Geography

Geographic scope of the study or source data is not specified.