Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Abstract

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.

Keywords: multi-omics; ovarian cancer; single-cell sequencing; spatial transcriptomics; transcriptomics; tumor heterogeneity; tumor immunology; tumor microenvironment.

Figure 1

Single-cell Sequencing Workflow for Ovarian Cancer. Single-cell suspensions are isolated from fresh ovarian cancer and metastatic cancer tissues. Barcoded Beads labeling technology is employed to obtain gene expression levels and profiles for each individual cell post-sequencing. Subsequent analysis, including cell clustering, differential gene expression, cell developmental trajectory, and differential gene clustering, generates diverse datasets, offering a comprehensive and updated understanding of ovarian cancer at the single-cell level.

Figure 2

Applications of Single-cell Sequencing Technology in Ovarian Cancer. Single-cell sequencing technology enables the identification of specific cell markers, facilitating the classification and characterization of diverse cell types and subtypes. Furthermore, analyzing differential gene expression among these distinct cell populations allows for a comprehensive understanding of their functional roles. Additionally, the identification of key gene expression signatures in different cell types helps elucidate specific cellular functions within the complex ovarian cancer microenvironment.

Figure 3

The Crucial Role of Single-cell Sequencing in Exploring Ovarian Cancer Heterogeneity. Heterogeneity Single-cell sequencing technology plays a vital role in unraveling the heterogeneity of ovarian cancer. It enables precise discrimination of tumor heterogeneity among different patients and distinguishes heterogeneity between distinct lesions within the same patient, such as primary and metastatic lesions. Importantly, given the diverse cellular composition within tumor tissues, single-cell sequencing also identifies cellular heterogeneity within the tumor microenvironment. Furthermore, the integration of multi-omicss data, including genomics, transcriptomics, proteomics, and epigenomics, provides a comprehensive understanding of tumor molecular characteristics and underlying developmental mechanisms.

Figure 4

Tumor Microenvironment in Ovarian Cancer. Based on the degree of immune cell infiltration, ovarian cancer can be categorized into immune-infiltrated, immune-excluded, and immune-desert types. The tumor microenvironment in ovarian cancer is complex, comprising various cell types whose interactions play a crucial role in tumor initiation and progression.

Figure 5

Single-cell Spatial Transcriptomics in Ovarian Cancer. The single-cell spatial transcriptomics technology allows for the spatial reconstruction of cells, mapping different cell types and their interactions onto HE-stained tissue sections. This innovative approach complements the limitations of traditional single-cell sequencing by providing crucial spatial information on cell-to-cell communication and interactions within the ovarian cancer microenvironment.