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.