Identifying Biomarkers to Predict the Prognosis of Biliary Atresia by Weighted Gene Co-Expression Network Analysis

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Abstract

The prognosis of children with biliary atresia (BA) after Kasai operation remains difficult to predict, and liver fibrosis is closely related to the prognosis of children with BA. We aimed to find biomarkers for native liver survival (NLS) prediction by weighted gene co-expression network analysis (WGCNA). The biological processes and signal pathways that biomarkers involved in were further analyzed by bioinformatics. Quantitative Real-time PCR, Western blot and immunohistochemistry was performed to detect biomarkers expression. The relationship of biomarkers with clinicopathological characteristics of BA was also investigated. LECT2 was overexpressed or knockdown in LX-2 cells, and the expression of fibrogenic genes such as a-SMA and COL1A1 was quantified. We found that LECT2 mRNA expression was higher in BA liver tissues compared with normal liver tissues. Bioinformatics showed that LECT2 mainly played a fibrosis-promoting role in the development in BA by regulating bile acid metabolism and promoting inflammatory response. LECT2 immunohistochemistry scores of BA children were higher than control group (p = 0.001). Survival analysis revealed that LECT2 high expression is an unfavorable prognostic factor for native liver survival in BA patients. Additionally, the high LECT2 expression was an independent prognostic factor affecting native liver survival (HR 3.702, 95%CI:2.085–6.575, p = 0.001). LECT2 modulates TGF-β mediated a-SMA and COL1A1 expression in LX-2 cells. siRNA-LECT2 inhibits the expression of a-SMA and COL1A1 in LX-2 cells. Overexpression of LECT2 resulted in an increase in a-SMA and COL1A1 expression. Knockdown of LECT2 inhibits the proliferation and increase apoptosis in activated LX-2 cells. LECT2 may act as a new prognostic biomarker for native liver survival in BA patients.

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Kong, M., & Xiang, B. (2021). Identifying Biomarkers to Predict the Prognosis of Biliary Atresia by Weighted Gene Co-Expression Network Analysis. Frontiers in Genetics, 12. https://doi.org/10.3389/fgene.2021.760182

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