GENETIC BIOMARKERS IN PREDICTING SURGICAL OUTCOMES: A TRANSLATIONAL MEDICINE APPROACH

Abstract

Subclinical acute rejection (SCAR) is a clinically relevant post-transplant complication and can have a negative impact on graft survival in the long-term, even in the absence of overt clinical signs. The need to detect molecular changes linked with SCAR early on is crucial in enhancing the prediction of post-surgical outcomes in kidney transplantation. This study aimed to identify candidate transcriptomic biomarkers associated with SCAR by comparing gene expression profiles between SCAR and histologically normal kidney transplant biopsy samples. A retrospective in silico transcriptomic analysis was performed using the GEO dataset GSE294632, comprising 24 renal biopsy samples (12 SCAR and 12 No SCAR) profiled on the Agilent SurePrint G3 Human Gene Expression v3 microarray platform. Differential expression analysis was conducted using the limma framework with empirical Bayes moderation, followed by gene annotation and integrative visualization, including volcano plots, principal component analysis, hierarchical clustering, and biomarker-specific boxplots. Although no genes met the adjusted false discovery rate threshold, exploratory analysis identified a coherent candidate gene signature associated with SCAR. Key biomarkers included POMT2, LAMA2, CSAD, RECK, HCG8, and lnc-IL32-1, reflecting a transcriptional pattern characterized by reduced expression of genes involved in extracellular matrix integrity and cellular maintenance, alongside increased expression of immune-related transcripts. These findings indicate that SCAR is associated with a biologically structured molecular signature capturing early graft injury processes. The results support the translational potential of transcriptomic biomarkers for improving post-transplant monitoring and predicting surgical outcomes.