Front. Genet.

Sec. Human and Medical Genomics

Volume 15 - 2024 | doi: 10.3389/fgene.2024.1516513

Provisionally accepted

The final, formatted version of the article will be published soon.

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Background: IgA nephropathy (IgAN) is a leading cause of renal failure, but its pathogenesis remains unclear, complicating diagnosis and treatment. The invasive nature of renal biopsy highlights the need for non-invasive diagnostic biomarkers. Bulk RNA sequencing (RNA-seq) of urine offers a promising approach for identifying molecular changes relevant to IgAN.We performed bulk RNA-seq on 53 urine samples from 11 untreated IgAN patients and 11 healthy controls, integrating these data with public renal RNA-seq, microarray, and scRNA-seq datasets. Machine learning was used to identify key differentially expressed genes, with protein expression validated by immunohistochemistry (IHC) and drug-target interactions explored via molecular docking.Results: Urine RNA-seq analysis revealed differential expression profiles, from which TYROBP and HCK were identified as key biomarkers using machine learning. These biomarkers were validated in both a test cohort and an external validation cohort, demonstrating strong predictive accuracy. scRNA-seq confirmed their cell-specific expression patterns, correlating with renal function metrics such as GFR and serum creatinine. IHC further validated protein expression, and molecular docking suggested potential therapeutic interactions with IgAN treatments.IgAN. Their predictive accuracy, validated through machine learning, along with IHC confirmation and molecular docking insights, supports their potential for both diagnostic and therapeutic applications in IgAN.

Keywords: IgA nephropathy, urine bulk RNA sequencing, non-invasive biomarkers, TYROBP, Hck, molecular docking

Received: 24 Oct 2024; Accepted: 03 Dec 2024.

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