Supplementary File 1: Multi-Omics Integration for Atherosclerosis Biomarker Discovery

25 candidate biomarkers were prioritized from proteomic and transcriptomic analyses of atherosclerosis. This 10.6 KB Excel file, authored by Yirong Ma and last updated in May 2026, contains results from a study integrating data-independent proteomics, public transcriptomes, and machine learning. The research validated four hub genes linked to lactylation and suggested a potential therapeutic interaction.

Use Cases

• Training diagnostic models based on the 25 prioritized candidate biomarkers mentioned in the study
• Analyzing lactylation-related gene profiles in vascular inflammation contexts described in the methodology
• Validating ensemble machine learning results (e.g., LASSO + GBM) for atherosclerosis classification
• Exploring immune cell infiltration patterns associated with hub genes like NRP1 and UAP1

Strengths

• Results are derived from integrated multi-omics data, including proteomics identifying 472 differentially expressed proteins and transcriptomics yielding 2,544 DEGs
• Biomarker prioritization involved an ensemble of 12 machine-learning algorithms with cross-validation and external validation
• Core findings were experimentally validated in an Apoe−/− mouse model 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 for large-scale analysis
• The dataset is very small (10.6 KB), indicating it likely contains summary results rather than raw experimental data

Provenance

Source

figshare, author Yirong Ma

Collection Method

Generated from a research study combining experimental proteomics on HUVECs, analysis of public GEO transcriptomes, consensus clustering, WGCNA, and machine learning.

Time Range

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Freshness

Last updated 2026-05-08 05:52:40; freshness should be verified

Geography

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