Single-cell and machine learning approaches uncover intrinsic immune-evasion genes in the prognosis of hepatocellular carcinoma

Abstract

Background and aims: Hepatocellular carcinoma (HCC) is a tumor of high heterogeneity and complexity, which poses significant challenges to effective treatment and patient prognosis because of its immune evasion characteristics. To address these issues, single-cell technology and machine learning methods have emerged as a promising approach to identify genes associated with immune escape in HCC. This study aimed to develop a prognostic risk score model for HCC by identifying intrinsic immune-evasion genes (IIEGs) through single-cell technology and machine learning, providing insights into immune infiltration, enhancing predictive accuracy, and facilitating the development of more effective treatment strategies.

Materials and methods: The study utilized data from The Cancer Genome Atlas database to analyze gene expression profiles and clinical data related to intrinsic immune evasion in patients with HCC. Various tools, including the Human Protein Atlas, cBioPortal, single-cell analysis, machine learning, and Kaplan-Meier plot, were used to analyze IIEGs. Functional enrichment analysis was conducted to explore potential mechanisms. In addition, the abundance of infiltrating cells in the tumor microenvironment was investigated using single-sample gene set enrichment analysis, CIBERSORT, xCELL, and tumor immunophenotype algorithms. The expression of glycosylphosphatidylinositol anchor attachment 1 (GPAA1) was examined in the clinical sample of HCC by quantitative real-time polymerase chain reaction, Western blotting, and immunohistochemical staining.

Results: Univariate Cox analysis identified 63 IIEGs associated with the prognosis of HCC. Using random forest, least absolute shrinkage and selection operator regression analysis, and support vector machine, a risk score model consisting of six IIEGs (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), phosphatidylinositol glycan anchor biosynthesis class U (PIGU), endoplasmic reticulum membrane protein complex subunit 3 (EMC3), centrosomal protein 55 (CEP55), autophagy-related 10 (ATG10), and GPAA1) developed, which was validated using 10 pairs of HCC and adjacent non-cancerous samples. Based on the calculated median risk score, HCC samples were categorized into high- and low-risk groups. The Kaplan-Meier curve analysis showed that the high-risk group had a worse prognosis compared with the low-risk group. Time-dependent receiver operating characteristic analysis demonstrated the accurate predictive capability of the risk score model for HCC prognosis. Furthermore, immune infiltration analysis showed a positive correlation between the risk score model and 40 immune checkpoint genes as well as Th2 cells.

Conclusions: A prognostic risk score model was formulated by six IIEG signatures and showed promise in predicting the prognosis of patients diagnosed with HCC. The utilization of the IIEG risk score as a novel prognostic index, together with its significance as a valuable biomarker for immunotherapy in HCC, provides benefit for patients with HCC in determining therapeutic strategies for clinical application.

Keywords

Hepatocellular carcinoma (HCC) / Intrinsic immune-evasion genes (IIEGs) / The Cancer Genome Atlas (TCGA) / Machine learning / Single-cell analysis

Data availability statement

Data are contained in the article or supplementary material. The data generated and analyzed in this study are available on request from the corresponding authors. Some data were obtained from online databases, including TCGA, HPA, cBioPortal, UALCAN, TISCH2, and WebGestalt.

Authors’ contributions

Jiani Wang, Xiaopeng Chen, and Donghao Wu contributed equally to this work. Jiani Wang: Data collection, Bioinformatics analysis, Interpretation, Writing-original draft. Xiaopeng Chen: Data curation, Validation. Donghao Wu: Data curation, Validation. Changchang Jia: Conceptualization, Formal analysis, Funding acquisition. Qinghai Lian: Conceptualization, Formal analysis. Yuhang Pan: Resources, Supervision, Validation. Jiumei Yang: Conceptualization, Data analysis, Supervision, Project administra-tion, Writing-review & editing.

Declaration of competing interest

The authors declare that they have no conflict of interest.

Acknowledgements

This work was granted by the Natural Science Foundation of Guangdong Province (No. 2017A030310252, No. 2022A1515012650) and the Science and Technology Program of Guangzhou (No. 2024A03J0919).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.livres.2024.11.001.

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