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pmid:38457593
Machine learning-based cell death signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.
Fan Li · Qian Feng · Ran Tao
Medicine · 2024 doi:10.1097/MD.0000000000037314
Paper
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Experiment
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Data
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Analysis
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Execution
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Complete file
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Using TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets, PCD-related signature (PRS) was constructed with an integrative procedure including 10 machine learning methods. The role of PRS in predicting the immunotherapy benefits was evaluated by several predicting score and 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210). The model developed by Lasso + CoxBoost algorithm having a highest average C-index of 0.66 was considered as the optimal PRS. As an independent risk factor for STAD patients, PRS had a good performance in predicting the overall survival rate of patients, with an AUC of 1-, 3-, and 5-year ROC curve being 0.771, 0.751 and 0.827 in TCGA cohort. High PRS score demonstrated a lower gene set score of some immune-activated cells and immune-activated activities. Patient with high PRS score had a higher TIDE score, higher immune escape score, lower PD1&CTLA4 immunophenoscore, lower TMB score, lower response rate and poor prognosis, indicating a less immunotherapy response. The IC50 value of some drugs correlated with chemotherapy and targeted therapy was higher in high PRS score group. Our investigation developed an optimal PRS in STAD and it acted as an indicator for predicting the prognosis, stratifying risk and guiding treatment for STAD patients.", "claims": [{"id": "claim-method", "statement": "A total of 101 kinds prediction models fitted via the LOOCV framework in TCGA cohort, and the C-index of each model across all GEO cohorts was further calculated (Fig. 2A). 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