Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework

ABSTRACT:

Background

The application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients’ prognosis and immunotherapeutic response.

Methods

NK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA–LIHC d

SUBMITTER: Feng Q 

PROVIDER: S-EPMC10466881 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature