Candidate Peripheral Blood Transcriptomic Features Associated with Clinical Risk Status in Corneal Transplant Recipients: A Machine-Learning Analysis

Corneal transplantation (CT) is the primary treatment for blindness caused by corneal disease, but graft rejection remains a critical determinant of long-term prognosis. Conventional rejection assessments are typically retrospective and may miss opportunities for early intervention. This study used machine-learning algorithms to examine peripheral blood gene-expression profiles in corneal transplant recipients before and after transplantation. Publicly available transcriptomic data comprised 240 pre-transplant samples (102 high-risk and 138 low-risk) and 332 post-transplant samples (193 high-risk and 139 low-risk). Each sample was represented by expression measurements for 20,522 genes. Nine feature-selection methods, two classification algorithms, incremental feature selection, and the Synthetic Minority Over-sampling Technique were used to analyze the before-CT and after-CT datasets separately for biomarker screening. Intersection analysis of the optimal feature subsets identified ABCD1, LOC100129307, SF3B4, and RAMP2 as important genes in both the before-CT and after-CT settings. Risk groups were defined clinically before transplantation and were not based on rejection events; acute rejection was not analyzed as an outcome. These findings constitute exploratory feature discovery and identify candidate peripheral blood transcriptomic features associated with clinical risk status for further study; they do not establish independently validated biomarkers or generalizable predictive performance.