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Updated: Jan 16, 2026
Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
SPP1+ Macrophage-Associated Prognostic Signature in Hepatocellular Carcinoma via Integrated Single-Cell and Bulk
Suyang Yue1,2, Qin Ding3, Shanzhong Tan1
1Department of Integrated TCM and Western Medicine, Nanjing Hospital Affiliated to Nanjing University of Chinese Medicine, Nanjing, China.
Insights
Hepatocellular carcinoma (HCC) prognosis is improved by a new seven-gene signature linked to SPP1+ macrophages. This biomarker aids in predicting patient survival and guiding personalized immunotherapy strategies for liver cancer.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Hepatocellular carcinoma (HCC) presents a significant global health challenge due to high mortality and limited therapeutic avenues.
- Tumor-associated macrophages, particularly the SPP1+ subset, are increasingly recognized as critical modulators of the tumor immune microenvironment and HCC progression.
Purpose of the Study:
- To identify key immune cell subsets involved in HCC progression.
- To develop a prognostic gene signature associated with SPP1+ macrophages for HCC patient stratification.
- To evaluate the predictive accuracy of the developed signature for patient survival.
Main Methods:
- Integration of single-cell RNA sequencing (scRNA-seq) with bulk transcriptomic data (TCGA, ICGC).
- Identification of immune cell subsets via clustering and ligand-receptor interaction analysis.
- Construction and validation of a prognostic risk model using Cox regression, Lasso, survival analysis, ROC curves, and an AI framework.
Main Results:
- Twelve distinct immune cell types were identified, highlighting significant interactions of SPP1+ macrophages with tumor and immune cells.
- A robust seven-gene signature (SNX5, YBX1, GNPD1, RAB32, TPM3, ATP6V0B, RAB7A) was established, effectively stratifying patients based on survival risk across independent cohorts.
- The prognostic model demonstrated high predictive accuracy, evidenced by strong AUC values and a significant correlation between gene expression and risk scores.
Conclusions:
- SPP1+ macrophages are pivotal in modulating the immune response and driving progression in Hepatocellular Carcinoma.
- The developed seven-gene signature serves as a reliable prognostic biomarker for HCC.
- This SPP1+ macrophage-associated signature holds potential for informing personalized treatment strategies and precision immunotherapy in HCC.
Abstract:
Background: Hepatocellular carcinoma (HCC) is a major cause of cancer mortality, with limited treatment options due to its high heterogeneity. SPP1+ tumor-associated macrophages are emerging as key regulators of the tumor immune microenvironment and disease progression. Methods: We integrated scRNA-seq data from the GEO database with bulk transcriptomic data from TCGA and ICGC. Immune cell subsets were identified through clustering and ligand-receptor interaction analyses. Prognostic genes associated with SPP1+ macrophages were screened using univariate Cox and Lasso regression. A risk model was built and validated using survival analysis and ROC curves. A multialgorithm AI framework was applied to enhance model performance. Results: Twelve immune cell types were identified, with SPP1+ macrophages showing strong interactions with tumor and immune cells. A seven-gene signature (SNX5, YBX1, GNPD1, RAB32, TPM3, ATP6V0B, and RAB7A) was constructed, effectively stratifying patients by survival risk in both TCGA and ICGC cohorts. The model showed strong predictive power with high AUC values and a significant correlation between gene expression and risk scores. Conclusion: SPP1+ macrophages play a crucial role in HCC immune modulation and progression. The gene signature developed provides a reliable tool for prognosis and may inform personalized treatment. This SPP1+ macro-associated signature offers a novel and robust biomarker for prognosis and may guide precision immunotherapy strategies in HCC.
