Single-cell RNA sequencing in ovarian cancer: decoding the tumor microenvironment for personalized therapy

Area of Science:

• Oncology
• Genomics
• Immunology

Background:

• Ovarian cancer (OC) is a leading cause of gynecological cancer mortality.
• Tumor heterogeneity and the tumor microenvironment (TME) significantly influence OC progression, metastasis, and treatment resistance.

Purpose of the Study:

• To review recent applications of single-cell technologies in ovarian cancer research.
• To highlight the role of single-cell RNA sequencing (scRNA-seq) in understanding TME biology.
• To explore future directions for clinical translation and personalized therapeutic strategies in OC.

Main Methods:

• Utilized single-cell RNA sequencing (scRNA-seq) to analyze cellular heterogeneity in OC.
• Integrated scRNA-seq data with spatial transcriptomics, multi-omics, and artificial intelligence.
• Reviewed existing literature on single-cell technologies in OC research.

Main Results:

• scRNA-seq provides high-resolution insights into rare cell populations, transcriptional dynamics, and intercellular communication in OC.
• This technology facilitates the discovery of prognostic biomarkers, immune signatures, and potential therapeutic targets.
• Integration with other advanced platforms enhances the capture of cellular complexity and patient stratification.

Conclusions:

• scRNA-seq is a powerful tool for decoding the complex TME in ovarian cancer.
• Despite challenges like cost and technical variability, scRNA-seq drives progress in precision oncology.
• Future applications promise improved patient stratification and personalized treatment strategies for OC.