Identifying the molecular mechanism of blood stasis syndrome through the symptom phenotype–genotype association approach:

In traditional medicine (TM), blood stasis syndrome (BSS) is characterized by insufficient blood flow, resulting in a group of symptoms such as fixed pain, a dark complexion, bleeding, and an astringent pulse. While BSS pathology has been previously explored, its molecular mechanisms remain elusive owing to challenges in linking TM symptoms to genes. Our study aimed to elucidate the mechanisms underlying BSS using a phenotype-genotype association approach. We extracted BSS symptoms from various studies, linked them to medical terms using a Unified Medical Language System, and connected these terms to genes in the DisGeNET database. The molecular network patterns of BSS symptoms were revealed through analyzing protein-protein interactions and symptom-gene associations. Our findings revealed 1325 associations between 16 BSS symptoms comprising 32 concept-unified identifier terms and 937 genes. Network analysis highlighted the centrality of JAK2, ITGB3, and F2, associated with multiple BSS symptoms (≥5 concept-unified identifier terms) and numerous protein interactions (≥20 interactions). Enrichment analysis indicated the involvement of BSS genes in the immune system (P-value = 4.49e-14) and hemostasis (P-value = 1.28e-07) pathways. BSS symptoms were linked to genes regulating blood coagulation, immune responses, blood flow, and inflammatory reactions. This approach may be extended to establish genotype networks for understanding TM pattern identifications, which are composed of diverse groups of symptoms, for personalized diagnosis and treatment.

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Publication Details

Journal
Medicine
Published
2024-12-06
DOI
https://doi.org/10.1097/md.0000000000040717
Citations
2
Primary Topic
Traditional Chinese Medicine Studies
Type
article
Field-Weighted Citation Impact
1.43
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article

Identifying the molecular mechanism of blood stasis syndrome through the symptom phenotype–genotype association approach:

Hyeong Joon Jun, Minh Nhat Tran, Sanghun Lee
2 citations
Medicine
Traditional Chinese Medicine Studies
1.43
article

Identifying the molecular mechanism of blood stasis syndrome through the symptom phenotype–genotype association approach:

Hyeong Joon Jun, Minh Nhat Tran, Sanghun Lee
article en
2 citations

Abstract

In traditional medicine (TM), blood stasis syndrome (BSS) is characterized by insufficient blood flow, resulting in a group of symptoms such as fixed pain, a dark complexion, bleeding, and an astringent pulse. While BSS pathology has been previously explored, its molecular mechanisms remain elusive owing to challenges in linking TM symptoms to genes. Our study aimed to elucidate the mechanisms underlying BSS using a phenotype-genotype association approach. We extracted BSS symptoms from various studies, linked them to medical terms using a Unified Medical Language System, and connected these terms to genes in the DisGeNET database. The molecular network patterns of BSS symptoms were revealed through analyzing protein-protein interactions and symptom-gene associations. Our findings revealed 1325 associations between 16 BSS symptoms comprising 32 concept-unified identifier terms and 937 genes. Network analysis highlighted the centrality of JAK2, ITGB3, and F2, associated with multiple BSS symptoms (≥5 concept-unified identifier terms) and numerous protein interactions (≥20 interactions). Enrichment analysis indicated the involvement of BSS genes in the immune system (P-value = 4.49e-14) and hemostasis (P-value = 1.28e-07) pathways. BSS symptoms were linked to genes regulating blood coagulation, immune responses, blood flow, and inflammatory reactions. This approach may be extended to establish genotype networks for understanding TM pattern identifications, which are composed of diverse groups of symptoms, for personalized diagnosis and treatment.

MedicineVol. 103(49)
Chungnam National University (KR), Korea Institute of Oriental Medicine (KR), Hue University (VN), Korea University of Science and Technology (KR)
Good health and well-being
Openalex Percentile: Top 15%
Traditional Chinese Medicine Studies
1.43
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