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Updated: May 16, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Harnessing single-cell and multi-omics insights: STING pathway-based predictive signature for immunotherapy response
Yang Ding1, Dingli Wang2, Dali Yan3
1Department of Pathology, Nanjing Drum Tower Hospital Group Suqian Hospital, Suqian, China.
Background:
Lung adenocarcinoma is the most prevalent type of small-cell carcinoma, with a poor prognosis. For advanced-stage patients, the efficacy of immunotherapy is suboptimal. The STING signaling pathway plays a pivotal role in the immunotherapy of lung adenocarcinoma; therefore, further investigation into the relationship between the STING pathway and lung adenocarcinoma is warranted.
Methods:
We conducted a comprehensive analysis integrating single-cell RNA sequencing (scRNA-seq) data with bulk transcriptomic profiles from public databases (GEO, TCGA). STING pathway-related genes were identified through Genecard database. Advanced bioinformatics analyses using R packages (Seurat, CellChat) revealed transcriptomic heterogeneity, intercellular communication networks, and immune landscape characteristics. We developed a STING pathway-related signature (STINGsig) using 101 machine learning frameworks. The functional significance of ERRFI1, a key component of STINGsig, was validated through mouse models and multicolor flow cytometry, particularly examining its role in enhancing antitumor immunity and potential synergy with α-PD1 therapy.
Results:
Our single-cell analysis identified and characterized 15 distinct cell populations, including epithelial cells, macrophages, fibroblasts, T cells, B cells, and endothelial cells, each with unique marker gene profiles. STING pathway activity scoring revealed elevated activation in neutrophils, epithelial cells, B cells, and T cells, contrasting with lower activity in inflammatory macrophages. Cell-cell communication analysis demonstrated enhanced interaction networks in high-STING-score cells, particularly evident in fibroblasts and endothelial cells. The developed STINGsig showed robust prognostic value and revealed distinct immune microenvironment characteristics between risk groups. Notably, ERRFI1 knockdown experiments confirmed its significant role in modulating antitumor immunity and enhancing α-PD1 therapy response.
Conclusion:
The STING-related pathway exhibited distinct expression levels across 15 cell populations, with high-score cells showing enhanced tumor-promoting pathways, active immune interactions, and enrichment in fibroblasts and IFI27+ inflammatory macrophages. In contrast, low-score cells were associated with epithelial phenotypes and reduced immune activity. We developed a robust STING pathway-related signature (STINGsig), which identified key prognostic genes and was linked to the immune microenvironment. Through in vivo experiments, we confirmed that knockdown of ERRFI1, a critical gene within the STINGsig, significantly enhances antitumor immunity and synergizes with α-PD1 therapy in a lung cancer model, underscoring its therapeutic potential in modulating immune responses.
Insights
This study reveals the STING pathway
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Lung adenocarcinoma has a poor prognosis, with suboptimal immunotherapy response in advanced stages.
- The STING signaling pathway is crucial for lung adenocarcinoma immunotherapy.
- Further research into the STING pathway's role in lung adenocarcinoma is warranted.
Purpose of the Study:
- To comprehensively analyze the STING pathway's role in lung adenocarcinoma using integrated single-cell and bulk transcriptomic data.
- To identify STING pathway-related genes and develop a prognostic signature (STINGsig).
- To validate the functional role of ERRFI1 in modulating antitumor immunity and its synergy with α-PD1 therapy.
Main Methods:
- Integrated analysis of scRNA-seq and bulk transcriptomic data (GEO, TCGA).
- Identification of STING pathway genes using the Genecard database.
- Bioinformatics analyses (Seurat, CellChat) for heterogeneity, communication, and immune landscape.
- Machine learning to develop the STINGsig.
- In vivo validation of ERRFI1 using mouse models and flow cytometry.
Main Results:
- Identified 15 distinct cell populations with unique marker profiles.
- Revealed elevated STING pathway activation in neutrophils, epithelial cells, B cells, and T cells.
- Demonstrated enhanced cell-cell communication in high-STING-score cells, particularly fibroblasts and endothelial cells.
- The STINGsig showed prognostic value and linked to immune microenvironment characteristics.
- ERRFI1 knockdown enhanced antitumor immunity and α-PD1 therapy response in vivo.
Conclusions:
- The STING pathway is differentially active across lung adenocarcinoma cell populations, influencing tumor-promoting pathways and immune interactions.
- A robust STINGsig was developed, highlighting prognostic genes and immune microenvironment links.
- ERRFI1 plays a critical role in enhancing antitumor immunity and shows therapeutic potential, synergizing with α-PD1 therapy for lung adenocarcinoma.
