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Biomarker LEPRE1 induces pelitinib‑specifc drug responsiveness by regulating ABCG2 expression and tu…

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작성자 관리자 작성일2022-02-24 조회2,112회

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A‑Ram Lee[1] Sunho Lee[2], JeeYoon Shin[2] , Ji‑Young Kim[1] , Kyoung‑Sik Moon[1,3] & Joungsun Jung[2,3]*  

 

 

[1] Department of Advanced Toxicology, Korea Institute of Toxicology (KIT), Daejeon 34114, Republic of Korea.

[2] Genome Data Integration Centre, Syntekabio Inc., Daejeon 34025, Republic of Korea.

[3] These authors jointly supervised this work: Kyoung-Sik Moon and Jongsun Jung 

 

 

 

 

Abstract

Biomarkers for treatment sensitivity or drug resistance used in precision medicine include prognostic and predictive molecules, critical factors in selecting appropriate treatment protocols and improving survival rates. However, identifcation of accurate biomarkers remain challenging due to the high risk of false-positive fndings and lack of functional validation results for each biomarker. Here, we discovered a mechanical correlation between leucine proline-enriched proteoglycan 1 (LEPRE1) and pelitinib drug sensitivity using in silico statistical methods and confrmed the correlation in acute myeloid leukemia (AML) and A549 lung cancer cells. We determined that high LEPRE1 levels induce protein kinase B activation, overexpression of ATP-binding cassette superfamily G member 2 (ABCG2) and E-cadherin, and cell colonization, resulting in a cancer stem cell-like phenotype. Sensitivity to pelitinib increases in LEPRE1-overexpressing cells due to the reversing efect of ABCG2 upregulation. LEPRE1 silencing induces pelitinib resistance and promotes epithelial-to-mesenchymal transition through actin rearrangement via a series of Src/ERK/coflin cascades. The in silico results identifed a mechanistic relationship between LEPRE1 and pelitinib drug sensitivity, confrmed in two cancer types. This study demonstrates the potential of LEPRE1 as a biomarker in cancer through in-silico prediction and in vitro experiments supporting the clinical development of personalized medicine strategies based on bioinformatics fndings. 

 

 

 

[open link] https://www.nature.com/articles/s41598-022-06621-w.pdf