Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer.

ABSTRACT: Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then valid

SUBMITTER: Ning J 

PROVIDER: S-EPMC10148812 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature