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

The role of N-methyladenosine modification in acute and chronic kidney diseases

N6-methyladenosine (m6A) modification is a kind of RNA modification in which methylation occurs at the sixth N position in adenosine in RNA, which can occur in various RNAs such as mRNAs, lncRNAs and miRNAs. This is one of the most prominent and frequent posttranscriptional modifications within organisms and has been shown to function dynamically and reversibly in a variety of ways, including splicing, export, attenuation and translation initiation efficiency to regulate RNA expression. There are three main enzymes associated with m6A modification: writers, readers and erasers. Increasing evidence has shown that m6A modification is associated with the onset and development of kidney disease. In this article, we address the important physiological and pathological roles of m6A modification in kidney diseases (uremia, ischemia-reperfusion kidney injury, drug-induced kidney injury, and diabetic nephropathy) and its molecular mechanisms to provide reference for the diagnosis and clinical management of kidney diseases.

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria

Background

A lot of studies have revealed that chronic urticaria (CU) is closely linked with COVID-19. However, there is a lack of further study at the gene level. This research is aimed to investigate the molecular mechanism of COVID-19-related CU via bioinformatic ways.

Methods

The RNA expression profile datasets of CU (GSE72540) and COVID-19 (GSE164805) were used for the training data and GSE57178 for the verification data. After recognizing the shared differently expressed genes (DEGs) of COVID-19 and CU, genes enrichment, WGCNA, PPI network, and immune infiltration analyses were performed. In addition, machine learning LASSO regression was employed to identify key genes from hub genes. Finally, the networks, gene-TF-miRNA-lncRNA, and drug-gene, of key genes were constructed, and RNA expression analysis was utilized for verification.

Results

We recognized 322 shared DEGs, and the functional analyses displayed that they mainly participated in immunomodulation of COVID-19-related CU. 9 hub genes (CD86, FCGR3A, AIF1, CD163, CCL4, TNF, CYBB, MMP9, and CCL3) were explored through the WGCNA and PPI network. Moreover, FCGR3A, TNF, and CCL3 were further identified as key genes via LASSO regression analysis, and the ROC curves confirmed the dependability of their diagnostic value. Furthermore, our results showed that the key genes were significantly associated with the primary infiltration cells of CU and COVID-19, such as mast cells and macrophages M0. In addition, the key gene-TF-miRNA-lncRNA network was constructed, which contained 46 regulation axes. And most lncRNAs of the network were proved to be a significant expression in CU. Finally, the key gene-drug interaction network, including 84 possible therapeutical medicines, was developed, and their protein-protein docking might make this prediction more feasible.

Conclusions

To sum up, FCGR3A, TNF, and CCL3 might be potential biomarkers for COVID-19-related CU, and the common pathways and related molecules we explored in this study might provide new ideas for further mechanistic research.