Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.

ABSTRACT: The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17-gene prognosis-related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine-learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C-index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an indep

SUBMITTER: Tang P 

PROVIDER: S-EPMC11558266 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature