Machine learning integrations for development of a T-cell-tolerance derived signature to improve the clinical outcomes and precision treatment of hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is characterized by high rates of recurrence and metastasis and poor prognosis. A recently discovered concept of T cell tolerance (TCT) has become an entirely new target of cancer immunotherapy. Unfortunately, the effect of TCT on the outcomes of HCC has not been explored. In this study, 7 public datasets and one external clinical cohort, including 1716 HCC patients were explored. Through WGCNA analysis and differential analysis, we explored the key TCT-related modulates. A total of 95 machine learning integrations across all validation cohorts were compared and the optimal method with the highest average C-index value was selected to construct the TCT derived signature (TCTS). In all independent clinical cohorts, TCTS showed accurate prediction of the progno

SUBMITTER: Li J 

PROVIDER: S-EPMC9906086 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature