Integrated single-cell and bulk transcriptomics reveals STAB1 as a novel therapeutic target for ovarian cancer

Area of Science:

• Oncology
• Immunology
• Genomics

Background:

• Ovarian cancer (OC) poses significant mortality due to its immunosuppressive tumor microenvironment (TME) and intrinsic heterogeneity, leading to therapeutic resistance.
• Identifying novel therapeutic targets is crucial for overcoming these challenges in OC treatment.

Purpose of the Study:

• To integrate machine learning and single-cell transcriptomics to identify robust therapeutic targets within the OC ecosystem.
• To investigate the role of Stabilin-1 (STAB1) as a potential therapeutic target in OC.

Main Methods:

• Ensemble machine learning algorithms (LASSO, SVM-RFE) applied to multi-cohort bulk transcriptomic data for feature selection.
• High-resolution single-cell transcriptomics to determine STAB1 cellular localization within the OC microenvironment.
• In vitro loss-of-function assays in OC cell lines (A2780, SK-OV-3) to validate STAB1's functional role.

Main Results:

• Stabilin-1 (STAB1) was identified as a top-ranked prognostic determinant in ovarian cancer.
• Single-cell analysis revealed STAB1 enrichment in LYVE1+ macrophages and a hyper-aggressive, EMT-active tumor subpopulation.
• STAB1 silencing significantly inhibited OC cell proliferation, colony formation, and invasion in vitro.

Conclusions:

• STAB1 acts as a critical "dual-checkpoint" molecule, linking the immunosuppressive stroma and malignant epithelium in OC.
• STAB1 represents a promising novel therapeutic target for overcoming therapeutic resistance and dismantling the ovarian cancer ecosystem.