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Background <p>Lymphatic malformation (LM) is a rare vascular anomaly characterized by abnormal development of lymphatic vessels. LM imposes a significant burden on both affected individuals and society. Although several therapeutic approaches are currently available, the efficacy of existing pharmacological treatments remains limited. Therefore, this study aims to identify potential drug targets that may improve the treatment of LM.</p> Methods <p>We integrated two blood-based cis-eQTL datasets (eQTLGen, GTEx) and two plasma cis-pQTL datasets (UK Biobank Pharma Proteomics Project, deCODE Genetics) with LM genome-wide association study data from FinnGen. SMR was used to assess putative causal effects of gene and protein expression on LM risk, with HEIDI testing and COLOC-based colocalization analysis applied to evaluate locus-level signal sharing. Significant results were merged within each omics layer and intersected across transcriptomic and proteomic levels. Candidate genes were prioritized according to the strength of cross-dataset evidence, defined by the number of datasets showing significant SMR associations. Functional follow-up analyses included protein–protein interaction (PPI) network construction, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, drug-induced gene expression signature (perturbation) enrichment analysis, and molecular docking analyses performed for selected candidate targets.</p> Results <p>We identified 32 candidate genes supported by both transcriptomic and proteomic evidence. Among these, macrophage migration inhibitory factor (MIF) showed the strongest association with LM across four datasets. Eighteen additional genes, including MPO, DDT, CXCL16, and PI3, were supported by at least three datasets. PPI analysis revealed hub genes, such as CTSS, PXDNL, and MIF. GO and KEGG analyses highlighted processes related to immune cell migration, proteolysis regulation, membrane-associated complexes, tryptophan metabolism, transcriptional dysregulation in cancer, and complement/coagulation cascades. Drug enrichment and molecular docking analyses suggested potential compounds, including amikacin and fucose, as exploratory candidates for further study.</p> Conclusions <p>This integrative multi-omics study prioritized 32 candidate genes as potential therapeutic targets for LM based on convergent evidence from blood eQTL and plasma pQTL SMR analyses. Among these, MIF emerged as the top-ranked candidate. These findings provide valuable insights into the pathogenesis of LM and highlight promising targets for the development of novel immunotherapies or targeted interventions.</p>

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Exploratory identification of potential biomarkers and drug targets for lymphatic malformation by integrating plasma proteome and transcriptome

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月9日

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  • Jianke Wang,
  • Xixi Guo,
  • Kai Zheng,
  • Bin Ni,
  • Ma Bo,
  • Xueqiang Fan,
  • Qiangqiang Nie

摘要

Background

Lymphatic malformation (LM) is a rare vascular anomaly characterized by abnormal development of lymphatic vessels. LM imposes a significant burden on both affected individuals and society. Although several therapeutic approaches are currently available, the efficacy of existing pharmacological treatments remains limited. Therefore, this study aims to identify potential drug targets that may improve the treatment of LM.

Methods

We integrated two blood-based cis-eQTL datasets (eQTLGen, GTEx) and two plasma cis-pQTL datasets (UK Biobank Pharma Proteomics Project, deCODE Genetics) with LM genome-wide association study data from FinnGen. SMR was used to assess putative causal effects of gene and protein expression on LM risk, with HEIDI testing and COLOC-based colocalization analysis applied to evaluate locus-level signal sharing. Significant results were merged within each omics layer and intersected across transcriptomic and proteomic levels. Candidate genes were prioritized according to the strength of cross-dataset evidence, defined by the number of datasets showing significant SMR associations. Functional follow-up analyses included protein–protein interaction (PPI) network construction, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, drug-induced gene expression signature (perturbation) enrichment analysis, and molecular docking analyses performed for selected candidate targets.

Results

We identified 32 candidate genes supported by both transcriptomic and proteomic evidence. Among these, macrophage migration inhibitory factor (MIF) showed the strongest association with LM across four datasets. Eighteen additional genes, including MPO, DDT, CXCL16, and PI3, were supported by at least three datasets. PPI analysis revealed hub genes, such as CTSS, PXDNL, and MIF. GO and KEGG analyses highlighted processes related to immune cell migration, proteolysis regulation, membrane-associated complexes, tryptophan metabolism, transcriptional dysregulation in cancer, and complement/coagulation cascades. Drug enrichment and molecular docking analyses suggested potential compounds, including amikacin and fucose, as exploratory candidates for further study.

Conclusions

This integrative multi-omics study prioritized 32 candidate genes as potential therapeutic targets for LM based on convergent evidence from blood eQTL and plasma pQTL SMR analyses. Among these, MIF emerged as the top-ranked candidate. These findings provide valuable insights into the pathogenesis of LM and highlight promising targets for the development of novel immunotherapies or targeted interventions.