Background: This study aims to screen, validate novel biomarkers and develop a user-friendly online tool for the detection of colorectal cancer (CRC).
Methods: Multi-omics approach, comprising proteomic analysis and single-cell transcriptomic analysis, was utilized to discover candidate tumor-associated antigens (TAAs). The presence of tumor-associated autoantibodies (TAAbs) in serum was subsequently assessed using enzyme-linked immunosorbent assays (ELISA) in 300 CRC patients and 300 healthy controls. Ten machine learning algorithms were utilized to develop diagnostic models, with the optimal one selected and integrated into an R Shiny-based GUI to enhance usability and accessibility.
Results: We identified twelve potential TAAs: HMGA1, NPM1, EIF1AX, CKS1B, HSP90AB1, ACTG1, S100A11, maspin, ANXA3, eEF2, P4HB, and HKDC1. ELISA results showed that five TAAbs including anti-CKS1B, anti-S100A11, anti-maspin, anti-ANXA3, and anti-eEF2 were potential diagnostic biomarkers during the diagnostic evaluation phase (all P < 0.05). The Random Forest model yielded an AUC of 0.82 (95% CI: 0.78-0.88) on the training set and 0.75 (95% CI: 0.68-0.82) on the test set, demonstrating the robustness of the results. Web-based implementations of CRC diagnostic tools are publicly accessible via weblink https://qzan.shinyapps.io/CRCPred/ .
Conclusions: A five biomarker panel can server as complementary biomarker to CEA and CA19-9 in CRC detection.
Keywords: Colorectal cancer; Immunodiagnostic; Machine learning; Multi-omics; Tumor-associated autoantibodies.
© 2025. The Author(s).