Comprehensive Single-Cell Characterization of LDL in the Ovarian Cancer Microenvironment and Its Prognostic...

Area of Science:

• Oncology
• Molecular Biology
• Immunology

Background:

• Low-density lipoprotein (LDL) is implicated in various cancers, but its role in the ovarian cancer tumor microenvironment (TME) is not well understood.
• Investigating LDL's distribution and function in ovarian cancer is crucial for understanding tumor progression.

Purpose of the Study:

• To comprehensively analyze LDL distribution, functional pathways, and prognostic significance in ovarian cancer using single-cell transcriptomics.
• To develop and validate an LDL-related prognostic signature for ovarian cancer risk stratification.

Main Methods:

• Single-cell RNA sequencing data from ovarian cancer patients were analyzed.
• AUCell, Gene Set Variation Analysis (GSVA), and CellChat were employed to assess LDL expression and cell-cell communication.
• Machine learning was used to construct the LDL-related ovarian cancer prognostic signature (LDLOCPS) from bulk RNA-seq data across eight cohorts.

Main Results:

• LDL was highly expressed in myeloid and stromal cells within the ovarian cancer TME.
• High LDL scores correlated with pathways like EMT, inflammation, coagulation, and angiogenesis, and enhanced cell-cell communication.
• The LDLOCPS model accurately stratified patients, with high LDLOCPS associated with worse overall survival and an immunosuppressive microenvironment.

Conclusions:

• This study elucidates LDL's role in ovarian cancer progression and its impact on the TME.
• The LDLOCPS model serves as a valuable tool for prognostic prediction in ovarian cancer.
• LDL-mediated immunosuppression suggests potential for targeted and immunomodulatory therapies.