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Construction of a hepatocytes-related and protein kinase–related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm

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Abstract

With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase–related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature.

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Data availability

GSE149614: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149614; TCGA-LIHC: https://portal.gdc.cancer.gov/projects/TCGA-LIHC; ICGC-LIRI-JP: https://dcc.icgc.org/projects/LIRI-JP; Protein kinase-related genes (PKRGs): GeneCards Database (https://www.genecards.org/).

Abbreviations

HCC :

hepatocellular carcinoma

PKRGs :

protein kinase–related genes

OS :

overall survival

RS :

risk score

DEGs :

differentially expressed genes

TMB :

tumor mutation burden

GSEA :

gene set enrichment analysis

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Author information

Author notes

  1. Zhuoer Zhang and Lisha Mou contributed equally to this work.

Authors and Affiliations

  1. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China

    Zhuoer Zhang

  2. Shenzhen Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, NO. 3002 Sungang Road, Shenzhen, 518035, Futian District, China

    Lisha Mou & Zuhui Pu

  3. Department of Gastroenterology, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510000, China

    Xiaoduan Zhuang

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  1. Zhuoer Zhang
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  2. Lisha Mou
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  4. Xiaoduan Zhuang
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Contributions

Z.P. and X.Z. designed the study and revised the manuscript. Z.Z. and L.M. performed the analysis and wrote the manuscript. The authors declare that all data were generated in-house and that no paper mill was used.

Corresponding authors

Correspondence to Zuhui Pu or Xiaoduan Zhuang.

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The studies were approved by the institutional research ethics committee of Shenzhen Second People’s Hospital (No. 20170822007).

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Highlights

1. For the first time, we constructed a protein kinase–related genes (PKRG) prediction model for hepatocellular carcinoma patients by single-cell RNA-sequencing and the machine learning algorithm.

2. The PKRG signature and competing-risk nomogram established in this study demonstrate high predictability and accuracy, offering valuable tools for clinical decision-making.

3. With the ability to precisely predict the prognosis of HCC, our PKRG model has the potential to guide clinical predictions, preventions, and personalized medicine strategies for HCC patients.

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Zhang, Z., Mou, L., Pu, Z. et al. Construction of a hepatocytes-related and protein kinase–related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm. J Physiol Biochem 79, 771–785 (2023). https://doi.org/10.1007/s13105-023-00973-1

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  • DOI: https://doi.org/10.1007/s13105-023-00973-1

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