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Article Open Access

Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing

  • Authors:
    • Wentao Yang
    • Wenwen Lai
    • Qian Hu
    • Jingsheng Ma
    • Hailang Liu
    • Zelong Li
    • Qigen Li
    • Xiaohui Guo
    • Lin Zhong
  • View Affiliations / Copyright

    Affiliations: Department of Organ Transplantation, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330000, P.R. China, Department of Liver Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200120, P.R. China
    Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 537
    |
    Published online on: September 22, 2025
       https://doi.org/10.3892/ol.2025.15283
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Abstract

Globally, primary liver cancer ranks as the sixth most prevalent cancer and was the third leading cause of cancer‑related deaths in 2022. Hepatocellular carcinoma (HCC) accounts for 75‑85% of all cases. In total, ~70% of patients with HCC experience recurrence within 5 years, which impacts their long‑term survival outcomes. Therefore, the development of a reliable predictive model for the probability of HCC recurrence represents a crucial clinical need. However, studies that integrate bulk RNA sequencing (RNA‑seq) and single‑cell (sc)RNA‑seq to construct prognostic models are lacking. The present study analyzed bulk RNA‑seq and scRNA‑seq datasets of patients with HCC to identify differentially expressed genes (DEGs) that affect HCC recurrence‑free survival (RFS). Subsequently, least absolute shrinkage and selection operator Cox penalized regression analysis was performed to construct a prognostic model. Enrichment analysis and immune infiltration analysis were applied to identify the underlying mechanisms involved. Univariate and multivariate Cox regression analyses were subsequently performed. Finally, independent dataset and reverse transcription‑quantitative PCR (RT‑qPCR) experiments were used to evaluate the prognostic model. A total of 5,586 DEGs were obtained from the bulk RNA‑seq dataset and 2,320 DEGs from the scRNA‑seq dataset. Moreover, 53 DEGs associated with the RFS of patients with HCC were identified. A total of 6 of these genes (cyclin‑dependent kinase inhibitor 2A, complement factor H‑related 3, cytochrome P450 family 2 subfamily C member 9, high mobility group box 2, immunoglobulin λ constant 2 and Jupiter microtubule‑associated homolog 1) were incorporated into the prognostic model. Patients in the high‑risk group had significantly worse RFS time than those in the low‑risk group. Furthermore, the cell cycle and immunosuppression were identified as possible factors affecting RFS. In addition, the prognostic signature retained independent predictive value for HCC RFS, and it was validated successfully by another publicly available dataset and RT‑qPCR experiments using patient tissues. In conclusion, the present study constructed a prognostic model for predicting RFS in patients with HCC via integrated analysis of scRNA‑seq and bulk RNA‑seq data that could serve as a valuable reference tool for clinicians.
View Figures

Figure 1

Processing of the GSE242889 dataset.
(A) Data from 57,219 cells were used for classification and
annotation analysis. (B) Correlation analysis of sequencing depth,
mitochondrial gene sequences and total intracellular sequences. (C)
Among the 21,625 genes analyzed, 1,500 had high variation and
20,125 had low intercellular variation. (D) A total of 17 PCs were
selected for further analysis. (E) Cells were divided into 30
separate clusters with a resolution of 0.8. (F) Heatmap displaying
the top 10 marker genes in each cluster. (G) UMAP plot
demonstrating the different cell types. CMP, common myeloid
progenitor; UMAP, Uniform Manifold Approximation and Projection; N,
normal; T, tumor. PCs, Principal Components; percent.mt, percentage
of the mitochondrial transcriptome.

Figure 2

Construction of the prognostic
signature. Difference in DEGs between (A) hepatocytes originating
from tumor samples and those originating from normal samples; and
(B) tumor and normal samples in the TCGA-LIHC cohort. (C) DEGs
associated with RFS. (D) Least absolute shrinkage and selection
operator coefficient profiles of DEGs associated with RFS. (E)
Partial likelihood deviance vs. log(λ). DEG, differentially
expressed gene; TCGA-LIHC, The Cancer Genome Atlas-Liver
Hepatocellular Carcinoma; RFS, recurrence-free survival.

Figure 3

Evaluation of the prognostic
signature. (A) ROC curve in the training dataset. (B) Survival
analysis of the training dataset. (C) ROC curve in the validation
dataset. (D) Survival analysis in the validation dataset. ROC,
receiver operating characteristic.

Figure 4

Enrichment analysis and immune
infiltration analysis. (A) Kyoto Encyclopedia of Genes and Genomes
analysis. (B) Immune infiltration analysis. (C) Gene Ontology
analysis. (D) Stromal score, immune score and estimate score. BP,
Biological Process; CC, Cellular Component; MF, Molecular
Function.

Figure 5

Analysis of prognostic factors. (A)
Univariate and (B) multivariate Cox regression analysis. HR, hazard
ratio; CI, confidence interval; BMI, body mass index; AFP,
α-fetoprotein; T, tumor stage; N, lymph node stage; M, metastasis
stage.

Figure 6

Reverse transcription-quantitative
PCR of five pairs of tumor tissues and adjacent normal tissues from
patients with hepatocellular carcinoma. Relative gene expression of
(A) CDKN2A, (B) HMGB2, (C) JPT1, (D)
CFHR3, (E) CYP2C9 and (F) IGLC2. *P<0.05;
**P<0.01; ***P<0.001. CDKN2A, cyclin-dependent kinase
inhibitor 2A; CFHR3, complement factor H-related 3;
CYP2C9, cytochrome P450 family 2 subfamily C member 9;
HMGB2, high mobility group box 2; IGLC2,
immunoglobulin λ constant 2; JPT1, Jupiter
microtubule-associated homolog 1; ns, not significant.

Figure 7

Flow chart of the present study.
DEGs, differentially expressed genes; TCGA-LIHC, The Cancer Genome
Atlas-Liver Hepatocellular Carcinoma; RFS, recurrence-free
survival.
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Copy and paste a formatted citation
Spandidos Publications style
Yang W, Lai W, Hu Q, Ma J, Liu H, Li Z, Li Q, Guo X and Zhong L: Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing. Oncol Lett 30: 537, 2025.
APA
Yang, W., Lai, W., Hu, Q., Ma, J., Liu, H., Li, Z. ... Zhong, L. (2025). Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing. Oncology Letters, 30, 537. https://doi.org/10.3892/ol.2025.15283
MLA
Yang, W., Lai, W., Hu, Q., Ma, J., Liu, H., Li, Z., Li, Q., Guo, X., Zhong, L."Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing". Oncology Letters 30.5 (2025): 537.
Chicago
Yang, W., Lai, W., Hu, Q., Ma, J., Liu, H., Li, Z., Li, Q., Guo, X., Zhong, L."Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing". Oncology Letters 30, no. 5 (2025): 537. https://doi.org/10.3892/ol.2025.15283
Copy and paste a formatted citation
x
Spandidos Publications style
Yang W, Lai W, Hu Q, Ma J, Liu H, Li Z, Li Q, Guo X and Zhong L: Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing. Oncol Lett 30: 537, 2025.
APA
Yang, W., Lai, W., Hu, Q., Ma, J., Liu, H., Li, Z. ... Zhong, L. (2025). Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing. Oncology Letters, 30, 537. https://doi.org/10.3892/ol.2025.15283
MLA
Yang, W., Lai, W., Hu, Q., Ma, J., Liu, H., Li, Z., Li, Q., Guo, X., Zhong, L."Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing". Oncology Letters 30.5 (2025): 537.
Chicago
Yang, W., Lai, W., Hu, Q., Ma, J., Liu, H., Li, Z., Li, Q., Guo, X., Zhong, L."Prognostic model for predicting recurrence‑free survival in hepatocellular carcinoma using integrated analysis of single‑cell RNA‑sequencing and bulk RNA‑sequencing". Oncology Letters 30, no. 5 (2025): 537. https://doi.org/10.3892/ol.2025.15283
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