PURPOSE: The risk of vision loss from proliferative diabetic retinopathy (PDR) can be reduced with timely detection and treatment. We aimed to identify serum molecular signatures that might help in the early detection of PDR in patients with diabetes. METHODS: A total of 40 patients with diabetes were recruited at King Khaled Eye Specialist Hospital in Riyadh, Saudi Arabia, 20 with extensive PDR and 20 with mild non-proliferative diabetic retinopathy (NPDR). The two groups were matched in age, gender, and known duration of diabetes. We examined the whole genome transcriptome of blood samples from the patients using RNA sequencing. We built a model using a support vector machine (SVM) approach to identify gene combinations that can classify the two groups. RESULTS: Differentially expressed genes were calculated from a total of 25,500 genes. Six genes (CCDC144NL, DYX1C1, KCNH3, LOC100506476, LOC285847, and ZNF80) were selected from the top 26 differentially expressed genes, and a combinatorial molecular signature was built based on the expression of the six genes. The mean area under receiver operating characteristic (ROC) curve was 0.978 in the cross validation. The corresponding sensitivity and specificity were 91.7% and 91.5%, respectively. CONCLUSIONS: Our preliminary study defined a combinatorial molecular signature that may be useful as a potential biomarker for early detection of proliferative diabetic retinopathy in patients with diabetes. A larger-scale study with an independent cohort of samples is necessary to validate and expand these findings.