Dados do Trabalho

Título

Identifying non-invasive biomarkers for acute kidney allograft rejection: an in-silico study from publicly available cohorts

Resumo

Introduction: Acute allograft rejection is a major concern following kidney transplantation and can rapidly lead to the destruction of the graft. This condition has an incidence of 7.8% in the first year post-transplantation and is relevant for the transplant-specialized urologist in post-surgical follow-up. Acute allograft rejection is classically diagnosed with a renal allograft biopsy, but biomarkers in non-invasive exams, mainly blood and urine samples, have been studied as a novel diagnostic approach. Identifying such biomarkers can help predict allograft rejection and minimize graft damage by shortening its diagnosis. Objectives: This study aims to analyze gene expression profiling in cohorts from kidney transplant patients with urine and blood samples to identify biomarkers for acute allograft rejection. Methods: This is an in-silico study of cohort datasets with gene expression profiling analysis of urine or blood samples from transplanted patients, obtained from the GEO Datasets of the NCBI. The two selected datasets were extracted using Python and analyzed using GEO2R and the Prism10 software. Results: The first dataset comprised blood samples from a cohort of 59 patients, divided in no rejection (n = 40) and acute rejection (n = 19) groups after a kidney biopsy. The top 5 differentially expressed genes were ZBTB16 (p = 0.0099), FLT3 (p = 0.0133), ALOX15B (p = 0.0068), TPST1 (p = 0.0013), and IL1R2 (p = 0.0041), all of which were upregulated in patients with acute rejection. The second dataset comprised urine samples from a cohort of 56 patients, also divided in no rejection (n = 27) and acute rejection (n = 29) groups. The top 5 differentially expressed genes were H1-3 (p = 0.0018), PDCD1 (p = 0.0281), PSMB8 (p < 0.0001), FKBP5 (p = 0.01), and USP2 (p = 0.0126). USP2 was downregulated in transplant patients, while the other four genes were upregulated. Individually, the identified biomarkers display high positive predictive value for acute allograft rejection, but relatively low sensitivity. CD72, the ninth most differentially expressed gene in urinary pellets, has particularly high sensitivity, and CD72 dosage is promising as an individual biomarker. Conclusion: This study demonstrates that gene expression analysis of non-invasive samples can serve as predictors for acute kidney allograft rejection. Among the analyzed genes in the available datasets, IL1R2 and PSMB8 emerge as the most promising blood and urine biomarkers, respectively.

Palavras Chave

kidney transplantation; kidney transplant rejection; biomarkers