AUTHOR=Feng Chunxiang , Wang Zhixian , Liu Chang , Liu Shiliang , Wang Yuxi , Zeng Yuanyuan , Wang Qianqian , Peng Tianming , Pu Xiaoyong , Liu Jiumin 

TITLE=Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.909784

DOI=10.3389/fphar.2022.909784

ISSN=1663-9812

ABSTRACT=Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. And persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal disease. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify 3 different m6A subtypes of renal fibrosis based on the expression of 21 m6A regulators. We evaluated immune infiltration characteristics and the expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed WGCNA analysis using the expression data of 1611 drug targets to identify 474 genes associated with m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A 5 target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnosis renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC=0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC=0.755). These results indicate that the risk model has excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5 target gene model is significantly associated with many drugs and extracellular matrix activity. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by qRT-PCR and Western blot method.