Machine learning-based integration develops a neutrophil-derived signature for improving outcomes in hepatocellular carcinoma.

ABSTRACT:

Introduction

The heterogeneity of tumor immune microenvironments is a major factor in poor prognosis among hepatocellular carcinoma (HCC) patients. Neutrophils have been identified as playing a critical role in the immune microenvironment of HCC based on recent single-cell studies. However, there is still a need to stratify HCC patients based on neutrophil heterogeneity. Therefore, developing an approach that efficiently describes "neutrophil characteristics" in HCC patients is crucial to guide clinical decision-making.

Methods

We stratified two cohorts of HCC patients into molecular subtypes associated with neutrophils using bulk-sequencing and single-cell sequencing data. Additionally, we constructed a new risk model by integrating machine learning analysis from 101 predic

SUBMITTER: Gong Q 

PROVIDER: S-EPMC10419218 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature