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Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma

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  • Linshan Zhou
  • Mu Yang
  • Jiadi Luo
  • Hongjing Zang
  • Songqing Fan
  • Yuting Zhan

Abstract

Background: Ras-GTPase-activating protein (GAP)-binding protein 1 (G3BP1) emerges as a pivotal oncogenic gene across various malignancies, notably including nasopharyngeal carcinoma (NPC). The use of automated image analysis tools for immunohistochemical (IHC) staining of particular proteins is highly beneficial, as it could reduce the burden on pathologists. Interestingly, there have been no prior studies that have examined G3BP1 IHC staining using digital pathology. Methods: Whole-slide images (WSIs) were meticulously collected and annotated by experienced pathologists. A model was intricately designed and rigorously tested to yield the quantitative data regarding staining intensity and extent. The collective output data was subjected multiplicative analysis, exploring its correlation with the prognosis. Results: The G3BP1 molecular marker scoring model was successfully established utilizing deep learning methodologies, with a calculated threshold staining scores of 1.5. Notably, patients with NPC exhibiting higher expression levels of G3BP1 proteins displayed significantly lower for overall survival rates (OS). Multivariate analysis further validated that positive expression of G3BP1 stood as an independent poorer prognostic factors, indicating a poorer prognosis for NPC patients. Conclusion: Computational pathology emerges as a transformative tool capable of substantially reducing the burden on pathologists while concurrently enhancing and diagnostic sensitivity and specificity. The positive expression of G3BP1 protein serves as valuable, independent biomarker, offering predictive insights into a poor prognosis for patients with NPC.

Suggested Citation

  • Linshan Zhou & Mu Yang & Jiadi Luo & Hongjing Zang & Songqing Fan & Yuting Zhan, 2025. "Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0315893
    DOI: 10.1371/journal.pone.0315893
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    References listed on IDEAS

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    1. Gil Shamai & Amir Livne & António Polónia & Edmond Sabo & Alexandra Cretu & Gil Bar-Sela & Ron Kimmel, 2022. "Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
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