Abstract
Background: A growing body of evidence has pointed to the prognostic value of immune infiltration in gastrointestinal stromal tumors (GISTs). Thus, we aimed to develop a novel immune-based prognostic (IP) nomogram for better prognosis prediction of GISTs. Methods: Gene expression profiles of 22 immune features of GISTs were extracted from the GEO dataset. The immunoscore (IS) was constructed using the LASSO Cox regression model and validated in 54 GISTs via immunohistochemistry. The IP nomogram integrating IS and clinicopathological factors was constructed. The performance of the IP nomogram was evaluated and compared with conventional risk prognostic criteria. Results: The IS was established based on 4 features: CD8, CD8/CD3, CD68, CD163/CD68. Significant differences were observed between the low- and high-IS groups in the 5-year RFS (93.9% vs. 33.3%, p <0.001). The IS (6.27, 1.09-35.98), tumor size (1.14, 1.00-1.30), mitosis (1.07, 1.02-1.12), and tumor rupture (6.42, 1.12-36.88) were found to be independent predictors of GISTs and subsequently used to build the IP nomogram. The performance of this nomogram (AUC 0.96) was superior to that of IS (0.84) alone and conventional models including the MSKCC nomogram (0.86), heat maps by Joensuu (0.93), and the modified NIH (0.76). The decision curve analysis demonstrated the highest net benefit of the nomogram. Conclusion: The IS was demonstrated to be an independent predictor of GISTs, adding prognostic value to routine clinical prognostic criteria. The novel IP nomogram showed a gratifying prediction accuracy. However, further studies are needed to validate the analytical accuracy and practicability of the nomogram. Funding: This work was supported by Chinese Medical Board Grant on Evidence-Based Medicine, New York, USA (No. 98-680), National Natural Science Foundation of China (No. 30901427). Declaration of Interests: No potential conflicts of interest were disclosed. Ethics Approval Statement: The institutional review board approved the retrospective analysis of anonymous data.
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