#3084 exploration of prognostic factors in diabetic nephropathy with nodular lesion using transcriptome analysis of human kidney tissues

Abstract Background and Aims Although the speed of eGFR decline is usually rapid in patients with diabetic nephropathy (DMN) and a nodular lesion (NL), some patients with NL experience a slow eGFR decline. The factors associated with the difference in prognosis remain unknown. In this study, we aimed to explore prognostic factors by performing transcriptome analysis on preserved residual renal biopsy specimens from patients with pathologically diagnosed and prognostically known DMN with nodules. Method Patients who had an annual eGFR drop rate > 5 mL/min/1.73 m2 were defined as rapid decliners (RD) and the others as slow decliners (SD). Twelve patients (RD = 6 and SD = 6) who underwent kidney biopsy, had no other renal comorbidities, and had been followed up for at least one year were included. Total RNA was isolated from renal biopsy tissues in formalin-fixed, paraffin-embedded blocks, and microarray analysis was done. Results At the time of renal biopsy, the eGFR, urine protein, and rate of sclerotic glomeruli were not statistically different between the RD and SD groups, at 49.7±10.1 mL/min/1.73 m2 and 57.5±12.4 mL/min/1.73 m2 (p = 0.258), 7.5±4.2 g/gCre and 4.0±2.7 g/gCre (p = 0.123), and 19.2±13.4% and 25.5±14.4% (p = 0.450), respectively. In microarray analyses (total 58,341 gene probes), genes with little expression (raw signals <50) and with little differences between RD and SD (processed signals <0.3) were excluded, leaving 14,227 genes for further analysis. A total of 1,496 differentially expressed genes (DEGs) were selected with p-values <0.05 and fold change ≥2 between the RD and SD group for further bioinformatic analysis. Using the Ingenuity Pathway Analysis system, 15 canonical pathways and 9 diseases or functions annotations were found to be enriched. In addition, upstream regulator analysis identified 73 upstream regulators. Among them, only two regulators, CBX5 and CCN5, showed p <0.05 and absolute z-score > = 2 as activated and inhibited regulators, respectively, in the RD group. We next performed regulator effects analysis, indicating one regulator network which identified the key upstream regulator CCN5. The regulator network also showed a small number of genes involved in the viral infection, which included CDH1, CD24, and ESR1. Additionally, IPA showed 25 interaction networks ranging from 11 to 38. The fifth-ranked network was associated with Cancer, Organismal Injury, and Abnormalities (score = 32), in which CD24 and ESR1 were included. We, therefore, performed qRT-PCR to compare the expression levels of CDH1, CD24, and ESR1 in the RD group with those in the SD group. The expression levels of CD24 and ESR1 in the RD group were downregulated compared with those in the SD group (p = 0.004 and p = 0.022, respectively) although there was little difference in the level of CDH1 between them (p = 0.638). Conclusion Transcriptomic analysis using renal biopsied tissue revealed that ESR1 and CD24 might be candidates for renoprotective factors in DMN with NL.