Data-driven patient stratification and drug target discovery by using medical information and serum proteome data of idiopathic pulmonary fibrosis patients
Abstract Medical information is valuable information obtained from humans regarding the phenotype of diseases. Omics data is informative to understand diseases at biomolecular level. We aimed to detect patient stratification patterns in a data-driven manner and identify candidate drug targets by investigating biomolecules that are linked to phenotype-level characteristics of a targeted disease. Such data integration is challenging because the data types of them are different, and these data contain many items that are not directly related to the disease. Hence, we developed an algorithm, subset binding, to find inter-related attributes in heterogeneous data. To search for potential drug targets for intractable IPF (idiopathic pulmonary fibrosis), we collected medical information and proteome data of serum extracellular vesicles from patients with interstitial pneumonia including IPF. Our approach detected 20 proteins linked with IPF characteristics, whose expression intensities were confirmed to be high in fibrotic areas of human lung tissues. Furthermore, ponatinib, which inhibits these proteins, suppressed EMT (epithelial mesenchymal transition) in vitro. This workflow paves the way for data-driven drug target discovery even for intractable diseases whose mechanisms of pathogenesis are not fully understood..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
ResearchSquare.com - (2023) vom: 10. Aug. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Natsume-Kitatani, Yayoi [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| doi: |
10.21203/rs.3.rs-405195/v2 |
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| 520 | |a Abstract Medical information is valuable information obtained from humans regarding the phenotype of diseases. Omics data is informative to understand diseases at biomolecular level. We aimed to detect patient stratification patterns in a data-driven manner and identify candidate drug targets by investigating biomolecules that are linked to phenotype-level characteristics of a targeted disease. Such data integration is challenging because the data types of them are different, and these data contain many items that are not directly related to the disease. Hence, we developed an algorithm, subset binding, to find inter-related attributes in heterogeneous data. To search for potential drug targets for intractable IPF (idiopathic pulmonary fibrosis), we collected medical information and proteome data of serum extracellular vesicles from patients with interstitial pneumonia including IPF. Our approach detected 20 proteins linked with IPF characteristics, whose expression intensities were confirmed to be high in fibrotic areas of human lung tissues. Furthermore, ponatinib, which inhibits these proteins, suppressed EMT (epithelial mesenchymal transition) in vitro. This workflow paves the way for data-driven drug target discovery even for intractable diseases whose mechanisms of pathogenesis are not fully understood. | ||
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| 700 | 1 | |a Hirata, Haruhiko |4 aut | |
| 700 | 1 | |a Miyake, Kohtaro |4 aut | |
| 700 | 1 | |a Shiroyama, Takayuki |4 aut | |
| 700 | 1 | |a Shirai, Yuya |4 aut | |
| 700 | 1 | |a Noda, Yoshimi |4 aut | |
| 700 | 1 | |a Adachi, Yuichi |4 aut | |
| 700 | 1 | |a Enomoto, Takatoshi |4 aut | |
| 700 | 1 | |a Amiya, Saori |4 aut | |
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| 700 | 1 | |a Narumi, Ryohei |4 aut | |
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| 700 | 1 | |a Cheng, Fei |4 aut | |
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