Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures.

ABSTRACT:

Background

Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the need for personalized treatment strategies based on genomic data.

Methods

We analyzed whole-genome and RNA sequences from biopsy specimens of 65 advanced gastric cancer patients before their chemotherapy treatment. Using machine learning techniques, we developed a model with 123 omics features, such as immune signatures and copy number variations, to predict their chemotherapy outcomes.

Results

The model demonstrated a prediction accuracy of 70-80% in forecasting chemotherapy responses in both test and validation cohorts. Nota

SUBMITTER: Sasagawa S 

PROVIDER: S-EPMC11842519 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature