Nanoparticle–Protein Corona Boosted Cancer Diagnosis with Proteomic Transfer Learning

Keeping pace with the rapid growth of proteomic data, the integration of multiproteomic data can improve biomarker identification and cancer diagnosis. However, the data integration needs to overcome substantial challenges owing to considerable variability among diverse data set sources and the extensive range of protein expression levels. In this study, with serum and urine from the same individuals, we established two in-depth paired proteome databases, including 956 serum proteins and 4730 urine proteins. To integrate multiproteomic data, we developed a proteomic-based transfer learning neural network (ProteoTransNet) to enhance the accuracy of bladder cancer diagnosis and progression monitoring. Using random forest analysis on the integrated database, we selected two panels comprising the top 10 key proteins, achieving a diagnostic AUC of 0.996 and a stage classification AUC of 0.914. ProteoTransNet integrates serum and urine proteome databases with proteomic transfer learning, significantly enhancing the diagnostic accuracy through minimizing biases and errors caused by variations in proteomic data. Our study provides insights that transfer learning of sophisticated biological information may solve complicated biological problems in disease diagnosis, prognosis, and treatment.