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A serum proteomics platform enabling expression profiling in transplantation-associated clinical subsets gives an opportunity to identify non-invasive biomarkers that can accurately predict transplant outcome. In this study, we attempted to identify candidate serum biomarkers that could predict kidney allograft rejection/injury, regardless of its etiological and therapeutic heterogeneity. Using serum samples collected from kidney transplantation patients and healthy controls, we first employed Clontech-500 Ab microarrays to profile acute rejection (AR) and chronic graft injury (CGI) versus stable graft function (SF) and normal kidneys (NK). Using GenePattern analysis of duplicate arrays on pooled samples, we identified gender-independent biomarkers PARP1, MAPK1, SRP54, DP1, and p57 (FDR ≈ 25%), the concordant downregulation of which represented a detrimental profile common for both rejection/ injury types (AR-CGI). The reverse phase arrays qualified a 2-fold upregulation of PARP1 with an ROC of 0.87 in individual samples from patients with SF vs. AR-CGI rendering serum PARP1 as a biomarker for early prognosis. Ingenuity Pathways Analysis (IPA) connected PARP1 to some other markers (MAPK1), elucidating their possible interactions and connections to the immune response and graft-versus-host disease signaling. The downregulation of serum PARP1 in the damaged graft tissues, represents a perspective non-invasive marker, predicting the failing kidney graft, regardless of rejection/injury causes or gender. Thus, the successful identification of PARP1 as a biomarker in limited patient cohorts demonstrates that serum proteomics platform empowered by the GenePattern- and IPA-based Bioinformatics algorithm can guarantee a successful development of the clinically applicable prognostic biomarker panel.