Objective  To identify the core targets driving renal fibrosis based on proteomics and identify potential natural product inhibitors using virtual screening technology.

Methods  A unilateral ureteral obstruction (UUO) model was established. Based on proteomics data of kidney tissue, weighted gene co-expression network analysis (WGCNA) was applied to identify gene modules highly correlated with the phenotype of renal fibrosis, and Lasso regression was combined to screen key genes. After validation by clinical datasets, natural small molecules targeting this protein were identified through virtual screening.

Results The expression levels of renal fibrosis-related markers in the renal tissues of UUO mice were significantly upregulated. WGCNA analysis showed that the Turquoise module containing 1,632 genes was significantly positively correlated with the renal fibrosis phenotype. Further Lasso regression was used to screen MYOF as the core gene. Public datasets indicate that the expression level of MYOF was significantly elevated in patients with chronic kidney disease, and it was positively correlated with the degree of renal interstitial fibrosis while negatively correlated with renal function. Molecular docking showed that platycodin D3, timosaponin BII, and tubuloside A exhibited a strong binding affinity for MYOF protein.

Conclusion MYOF may be a key pathogenic molecule driving renal fibrosis, and natural products such as platycodin D3 hold promise as anti-fibrotic drug candidates targeting this molecule.

1. Glassock RJ, Warnock DG, Delanaye P. The global burden of chronic kidney disease: estimates, variability and pitfalls[J]. Nat Rev Nephrol, 2017, 13(2): 104-114.

2. Kim KP, Williams CE, Lemmon CA. Cell-matrix interactions in renal fibrosis[J]. Kidney Dial, 2022, 2(4): 607-624.

3. Panizo S, Martínez-Arias L, Alonso-Montes C, et al. Fibrosis in chronic kidney disease: pathogenesis and consequences[J]. Int J Mol Sci, 2021, 22(1): 408.

4. Li L, Fu H, Liu Y. The fibrogenic niche in kidney fibrosis: components and mechanisms[J]. Nat Rev Nephrol, 2022, 18(9): 545-557.

5. Chianese M, Screm G, Salton F, et al. Pirfenidone and nintedanib in pulmonary fibrosis: lights and shadows[J]. Pharmaceuticals (Basel), 2024, 17(6): 709.

6. Lee J, Song JU. Safety and tolerability of combination treatment with pirfenidone and nintedanib in patients with idiopathic pulmonary fibrosis: a systematic review and Meta-analysis[J]. J Thorac Dis. 2023, 15(11): 5913-5921.

7. Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019[J]. J Nat Prod, 2020, 83(3): 770-803.

8. Barrera-Vázquez OS, Montenegro-Herrera SA, Martínez-Enríquez ME, et al. Selection of mexican medicinal plants by identification of potential phytochemicals with anti-aging, anti-inflammatory, and anti-oxidant properties through network analysis and chemoinformatic screening[J]. Biomolecules, 2023, 13(11): 1673.

9. Liu X, Zeng T, Zhang E, et al. Plant-based bioactives and oxidative stress in reproduction: anti-inflammatory and metabolic protection mechanisms[J]. Front Nutr, 2025, 12: 1650347.

10. Gong TT, Guo S, Liu FH, et al. Proteomic characterization of epithelial ovarian cancer delineates molecular signatures and therapeutic targets in distinct histological subtypes[J]. Nat Commun, 2023, 14(1): 7802.

11. Song C, Wang L, Zhang F, et al. DUSP6 protein action and related hub genes prevention of sepsis-induced lung injury were screened by WGCNA and Venn[J]. Int J Biol Macromol, 2024, 279(Pt 1): 135117.

12. Mantash S, Aboulouard S, Dakik H, et al. Uncovering injury-specific proteomic signatures and neurodegenerative risks in single and repetitive traumatic brain injury[J]. Signal Transduct Target Ther, 2025, 10(1): 195.

13. Pi R, Chen Y, Du Y, et al. Comprehensive analysis of myoferlin in human pancreatic cancer via bioinformatics[J]. Biomed Res Int, 2021, 2021: 2602322.

14. Murphy S, Zweyer M, Henry M, et al. Proteomic analysis of the sarcolemma-enriched fraction from dystrophic mdx-4cv skeletal muscle[J]. J Proteomics, 2019, 191: 212-227.

15. Peiffer R, Laverdeur E, Gaigneaux A, et al. Targeting myoferlin in ER/Golgi vesicle trafficking reprograms pancreatic cancer-associated fibroblasts[J]. EMBO J, 2025, 44(22): 6425-6465.

16. Barnhouse VR, Weist JL, Shukla VC, et al. Myoferlin regulates epithelial cancer cell plasticity and migration through autocrine TGF-β1 signaling[J]. Oncotarget, 2018, 9(27): 19209-19222.

17. Li M, Peng F, Wang G, et al. Coupling of cell surface biotinylation and silac-based quantitative proteomics identified myoferlin as a potential therapeutic target for nasopharyngeal carcinoma metastasis[J]. Front Cell Dev Biol, 2021, 9: 621810.

18. Rayego-Mateos S, Rodrigues-Diez R, Morgado-Pascual JL, et al. Role of epidermal growth factor receptor (EGFR) and its ligands in kidney inflammation and damage[J]. Mediators Inflamm, 2018, 2018: 8739473.

19. Turtoi A, Blomme A, Bellahcène A, et al. Myoferlin is a key regulator of EGFR activity in breast cancer[J]. Cancer Res, 2013, 73(17): 5438-5448.

20. Yan XC, Sanders JM, Gao YD, et al. Augmenting hit identification by virtual screening techniques in small molecule drug discovery[J]. J Chem Inf Model. 2020, 60(9): 4144-4152.

21. Pei H, Guo W, Peng Y, et al. Targeting key proteins involved in transcriptional regulation for cancer therapy: current strategies and future prospective[J]. Med Res Rev, 2022, 42(4): 1607-1660.

22. Gu H, Zhang T, Guan T, et al. Discovery of a highly potent and selective MYOF inhibitor with improved water solubility for the treatment of gastric cancer[J]. J Med Chem, 2023, 66(24): 16917-16938.