Progress in understanding primary glomerular disease: insights from urinary proteomics and in-depth analyses of potential biomarkers based on bioinformatics

Abstract

Chronic kidney disease (CKD) has become a global public health challenge. While primary glomerular disease (PGD) is one of the leading causes of CKD, the specific pathogenesis of PGD is still unclear. Accurate diagnosis relies largely on invasive renal biopsy, which carries risks of bleeding, pain, infection and kidney vein thrombosis. Problems with the biopsy procedure include lack of glomeruli in the tissue obtained, and the sampling site not being reflective of the overall lesion in the kidney. Repeated renal biopsies to monitor disease progression cannot be performed because of the significant risks of bleeding and kidney vein thrombosis. On the other hand, urine collection, a noninvasive method, can be performed repeatedly, and urinary proteins can reflect pathological changes in the urinary system. Advancements in proteomics technologies, especially mass spectrometry, have facilitated the identification of candidate biomarkers in different pathological types of PGD. Such biomarkers not only provide insights into the pathogenesis of PGD but also are important for diagnosis, monitoring treatment, and prognosis. In this review, we summarize the findings from studies that have used urinary proteomics, among other omics screens, to identify potential biomarkers for different types of PGD. Moreover, we performed an in-depth bioinformatic analysis to gain a deeper understanding of the biological processes and protein–protein interaction networks in which these candidate biomarkers may participate. This review, including a description of an integrated analysis method, is intended to provide insights into the pathogenesis, noninvasive diagnosis, and personalized treatment efforts of PGD and other associated diseases.

Keywords:

• Primary glomerular disease
• urine
• proteomics
• biomarker
• diagnosis

Acknowledgments

We thank Figdraw (https://www.figdraw.com/) and Untitled (https://app.biorender.com/) for providing us with a drawing platform.

Disclosure statement

The research was conducted in the absence of any commercial or financial relationships that might be construed as potential conflicts of interest. No potential conflict of interest was reported by the author(s).

Additional information

Funding

Our study was supported by “345 Talent Project” of Shengjing Hospital of China Medical University [2022146], Liaoning Province Central Government’s special project to guide local scientific and technological development [2019JH6/1], and National Key Research and Development Program of China [2018YFE0207300].