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

European Journal of Medical Research

Research

Le Wang¹  Qian Feng²  Lei Song³  Zhihao Huang⁴  Linquan Wu⁴  Hongcheng Lu⁴ 

[1] Department of Blood Transfusion, The Second Affiliated Hospital of Nanchang University, 330000, Nanchang, China;Department of Emergency, The Second Affiliated Hospital of Nanchang University, 330000, Nanchang, China;Department of General Practice, The Second Affiliated Hospital of Nanchang University, 330000, Nanchang, China;Department of General Surgery, The Second Affiliated Hospital of Nanchang University, 1st min de Road, 330000, Nanchang, China;

关键词: Hepatocellular carcinoma;    Prognosis;    Immunotherapy;    Single-cell sequencing;    Machine learning;   

DOI  :  10.1186/s40001-023-01300-6

 received in 2023-03-26, accepted in 2023-08-18,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsNK 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 data set, GSE14520 data set, GSE76427 data set and ICGC–LIRI–JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set.ResultsWith the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients’ prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS–PD1 blocker, IPS–CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group.ConclusionsOverall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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