City of Hope Medical Center
This study aims to develop a model to predict response to chemotherapy in gastric cancer using RNA splicing information from tumor tissue. By analyzing genetic patterns and applying machine learning, the study seeks to identify patients who are less likely to benefit from treatment, helping guide clinical decision-making.
This multicenter observational study aims to develop and validate an alternative splicing (AS)-based model to predict response to 5-FU-based adjuvant chemotherapy in stage II/III gastric cancer. AS events were identified using TCGA SpliceSeq and UCSC Xena data, and selected candidates were quantified by RT-qPCR. A predictive model was constructed using Elastic Net-based feature selection and XGBoost, and evaluated in independent training and validation cohorts. An integrated model incorporating clinicopathological factors was also developed. The primary endpoint is treatment response define…
Inclusion Criteria: * Pathologically confirmed stage II or III gastric cancer * Underwent curative surgical resection * Received 5-FU-based adjuvant chemotherapy * Availability of tumor tissue samples for analysis Exclusion Criteria: * History of other malignancies * Inadequate or poor-quality tissue samples (e.g., contamination)
This is an observational study without assigned interventions. All patients received standard-of-care 5-FU-based adjuvant chemotherapy, and no experimental intervention was performed.
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