Journal of cardiothoracic surgery · 2026
Screening of atherosclerosis diagnostic markers based on lymphangiogenesis-related genes and analysis of immunological characteristics.
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Zhu Ning, Bi Junjie, Tong Guoxin
Аннотация
BACKGROUND: Atherosclerosis (AS) was a major cause of cardiovascular disease, and traditional diagnostic methods often fail to detect AS promptly and accurately. Formation of new lymphatic vessels, or lymphangiogenesis, is a crucial process in the development of many diseases. However, the value of lymphangiogenesis-related genes (LRGs) as potential diagnostic markers for AS remained incompletely elucidated.
METHODS: This study analyzed AS transcriptome data from comprehensive gene expression omnibus databases. A systematic screening for potential AS biomarkers was performed using the integration of three machine learning techniques. Single-gene enrichment analysis explored the potential biological processes of diagnostic genes in AS. Diagnostic gene features served as the basis for constructing a nomogram, whose predictive efficacy was then assessed and confirmed. Immune infiltration analysis was employed to investigate the immune microenvironment characteristics of AS. To screen for potential drugs targeting diagnostic genes, drug prediction was performed using the DSigDB database. Separately, consensus clustering analysis of AS samples revealed molecular subtypes that differ in biological features.
RESULTS: This study identified three key LRGs as diagnostic biomarkers for AS and constructed a diagnostic model. The model demonstrated robust diagnostic performance in both training and validation sets. Further enrichment analysis indicated that all three diagnostic genes participate in immune-related biological processes. Immune cell infiltration analysis revealed higher infiltration levels of CD8 + T cells, NK cells, and neutrophils in the AS group. Butein and pimaric acid emerged as potential therapeutic agents targeting these diagnostic genes. Additionally, AS was successfully classified into three molecular subtypes, each exhibiting distinct molecular mechanisms and immune characteristics.
CONCLUSION: This study established a diagnostic model centered on lymphangiogenesis to elucidate the complex immune response characteristics in AS and their associated molecular mechanisms.
METHODS: This study analyzed AS transcriptome data from comprehensive gene expression omnibus databases. A systematic screening for potential AS biomarkers was performed using the integration of three machine learning techniques. Single-gene enrichment analysis explored the potential biological processes of diagnostic genes in AS. Diagnostic gene features served as the basis for constructing a nomogram, whose predictive efficacy was then assessed and confirmed. Immune infiltration analysis was employed to investigate the immune microenvironment characteristics of AS. To screen for potential drugs targeting diagnostic genes, drug prediction was performed using the DSigDB database. Separately, consensus clustering analysis of AS samples revealed molecular subtypes that differ in biological features.
RESULTS: This study identified three key LRGs as diagnostic biomarkers for AS and constructed a diagnostic model. The model demonstrated robust diagnostic performance in both training and validation sets. Further enrichment analysis indicated that all three diagnostic genes participate in immune-related biological processes. Immune cell infiltration analysis revealed higher infiltration levels of CD8 + T cells, NK cells, and neutrophils in the AS group. Butein and pimaric acid emerged as potential therapeutic agents targeting these diagnostic genes. Additionally, AS was successfully classified into three molecular subtypes, each exhibiting distinct molecular mechanisms and immune characteristics.
CONCLUSION: This study established a diagnostic model centered on lymphangiogenesis to elucidate the complex immune response characteristics in AS and their associated molecular mechanisms.