
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

39249551
1290
10.1007/s12672-024-01290-9
Analysis
Single-cell RNA sequencing reveals the change in cytotoxic NK/T cells, epithelial cells and myeloid cells of the tumor microenvironment of high-grade serous ovarian carcinoma
Meng Lingnan
Sun Shujuan 3579069620@qq.com

https://ror.org/05vy2sc54 grid.412596.d 0000 0004 1797 9737 Department of Oncology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150007 China
9 9 2024
9 9 2024
12 2024
15 41727 6 2024
29 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

The heterogeneity of high-grade serous ovarian carcinoma (HGSOC) has hindered the clinical treatment, and our current study aims to characterize the change in tumor microenvironment (TME) with the progression of HGSOC via single cell RNA sequencing (scRNA-seq).

Methods

The single-cell landscape in HGSOC was downloaded from the dataset GSE184880, which included 7 HGSOC and 5 normal samples and then applied for the filtering and annotation of cell clusters. The differentially expressed marker genes in these clusters were analyzed via “FindAllMarker” function in Seurat package and the functional enrichment analyses were implemented using clusterProflier package. Finally, the CellChat package was applied for the cell–cell communication analysis. Cellular experimental were determined Real-time Reverse Transcription Polymerase Chain Reaction (RT-qPCR).

Results

45,448 single cells were categorized into 10 cell clusters. The proportion of NK/T cells (49.5%), epithelial cells (15.3%) and myeloid cells (14%) was higher in the HGSOC samples. The heterogeneity and different enriched pathways of epithelial cells have been revealed with the progression of HGSOC from early to late stage, concurrent with the reduced activity of cytotoxic NK/T cells and the decreased capabilities of recruiting immune cells and presenting antigens in macrophages. Besides, the cell–cell communication analysis has revealed a strong communication of CXCL and CCL signal between M1 macrophages and cytotoxic NK/T cells in early stage of HGSOC. Moreover, RT-qPCR indicated that CCL4/5 and CCR1/5 levels were upregulated in tumor cell SK-OV-3.

Conclusion

The investigation using scRNA-seq has depicted the change in cytotoxic NK/T cells, epithelial cells and myeloid cells of the TME of HGSOC, which may provide another insight into the specific mechanisms underlying the progression of HGSOC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01290-9.

Keywords

High
Grade serous ovarian carcinoma
Single
Cell RNA sequencing
Tumor
Infiltrating lymphocytes
Immunosuppressive cells
Cell
Cell communication
issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Accumulating evidence has emerged focusing on cancer cell genetic and epigenetic alternations driving malignancy and unveiling the integral and indispensable role of the tumor microenvironment (TME) in determining tumor anatomy of physiology [1]. The direct effects of the TME on the growth, migration and differentiation of cancer cells have been already revealed, which therefore provides an opportunity for the diagnosis and treatment of cancers [2]. The immune system is a crucial determinant of the TME, as exemplified by the role of ongoing inflammation in creating a favorable microenvironment which favors the growth and progression of tumors and by the association of immune cell types with the survival of patients with various cancers [3]. The TME is documented to include a diversity of cell types including both immune and non-immune cell types which, with the factors they secrete, drive a chronic inflammatory, immunosuppressive and pro-angiogenic intra-tumoral environment and plays a crucial role in tumor development, progression and metastasis [1, 4]. The cellular composition and functional state of the TME can vary extensively according to the organ where tumor arises, the intrinsic features of tumor cells, the tumor stage and the patients’ characteristics [5]. With the gradual understanding of the TME, our knowledge on the number of biological molecules and mechanistic pathways possibly targetable for the management of cancer increases, which allows us to commence this study to investigate the specific mechanism underlying the carcinogenesis of high-grade serous ovarian carcinoma (HGSOC).

HGSOC is a heterogeneous, chronically unstable cancer and is the most prevalent and lethal type of ovarian cancer (OC), which accounts for 70–80% of OC mortality [6–8]. Due to the lack of typical pathological symptoms, most HGSOC patients have already developed into advanced stages when diagnosed [9]. The efficacious treatments for HGSOC are very limited, and currently debulking surgery integrated with platinum-based chemotherapy is widely accepted [10]. Although over 80% of patients with HGSOC respond well to the standard-of-care [11], the 5-year survival probability remains disappointing because almost all patients may experience the relapse following the diagnosis [12], which is usually associated with the development of drug resistance [13]. Overall, the difficulty of early diagnosis, high recurrence rate, and drug resistance are the main challenges in the treatment of HGSOC [14]. Some existing studies have already stressed the significance of understanding the factors within the TME which contribute to the immunogenicity of HGSOC in the development of immune therapies and the determination of more accurate patients’ prognosis, therefore highlighting the role of TME as a source of potential therapeutic targets and biomarkers [15–17].

With the up-to-date advances in single-cell technologies like single-cell RNA sequencing (scRNA-seq), it’s feasible to carry out the systematic interrogation of the TME, which thus provides insights into the functional diversities of tumor-infiltrating immune cells [18]. An scRNA-seq of a HGSOC cell line OVCAR3 has unveiled that an enhancer of zeste homolog 2 inhibitor and a RAC1 GTPase inhibitor can promote the differentiation of subpopulations of HGSOC cells [19]. Recent studies have additionally established the role of scRNA-seq as a useful means to characterize tumor intercellular heterogeneity [20, 21]. When it comes to OC, the study of Gao et al. has identified the highly immunosuppressive TME in HGSOC and the exhausted subpopulation of CD8 + TNFRSF1B + T cells [22]. Another research has also heighted the intertumoral heterogeneity of HGSOC based on the intercellular ligand-receptor interactions of major cell types [23]. Another cohort which consists of scRNA-seq data from treatment-naïve and post-neoadjuvant chemotherapy pairs unveils how chemotherapy regulates cancer cell states and defines a cell state allowing the prediction based on relevant biomarkers and the targeting of chemoresistance [24]. Besides, an existing investigation of Xu et al., based on a total of 59,324 cells from HGSOC and the peri-tumor tissues sequenced by scRNA-seq, revealed the efficacy of four epithelial-to-mesenchymal transition (EMT) gene model in predicting the outcomes of HGSOC patients and characterized the tissue architecture within HGSOC [25]. These evidences therefore have built up our confidence to investigate the involvement of TME in HGSOC with the help of scRNA-seq. The relevant results, therefore, are reported as follows.

Methods

Download and pre-processing of scRNA-seq data

The single-cell landscape in HGSOC was downloaded from the dataset GSE184880 of the gene expression omnibus (https://www.ncbi.nlm.nih.gov/geo/), which included 12 samples (HGSOC = 7, Normal = 5) [25]. For filtering cells, each gene should be expressed in at least 3 cells and each cell expresses at least 200 genes. By the PercentageFeatureSet function, each cell expressed over 200 genes and fewer than 8000, with mitochondrial content < 15% (Supplementary Fig. 1A). The data of these 12 samples were then normalized by the NormalizeData function. All genes were subsequently scaled via the ScaleData function and principal component analysis (PCA) downscaling was implemented to reduce the dimension to find anchors. Harmony package was applied to remove the batch effects among the samples (Supplementary Fig. 1B) and the RunUMAP function was used for dimensionality reduction [26]. Finally, the functions FindNeighbors and FindClusters were applied for cell clustering at the parameters dim = 20 and resolution = 0.1 (Supplementary Fig. 1C). CellMarker2.0 database was employed for the annotation of the cell subpopulations using the relevant provided marker genes.

Differentially expressed marker genes analysis

The “FindAllMarker” function in Seurat package was applied to analyze the differentially expressed marker genes among these different cell subpopulations at the parameters as follows: logfc.threshold = 0.25, min.pct = 0.25, only.pos = T.

Functional enrichment analysis and AUCell analysis

The Hallmark gene sets and the gene ontology gene sets for biological processes (GO-BP) were downloaded from Human Molecular Signatures Database (MSigDB). The clusterProflier package was applied to conduct the functional enrichment analysis of interested genes at the threshold of P < 0.05 [27].

Cell–cell communication analysis

The cell–cell communication analysis in Normal and HGSOC tissues was implemented with CellChat package [28]. The objects were created using the createCellChat function and the overexpressed ligand-receptor pairs within different cell subpopulations were recognized using identifyOverExpressedGenes and identifyOverExepressedInteractions functions. The expression level of ligand-receptor pairs was then projected to the protein–protein interaction (PPI) network and the probability of interaction of ligand-receptor pairs in different cell subpopulations was inferred via computeCommunProb function. Finally, the netVisual_bubble function was adopted for the visualization.

Cell culture

Human ovarian epithelial cancer SKOV3 cells (BNCC310551) and human endothelial IOSE-80 cells (BNCC358126) were obtained from the BeNa Culture Collection (Beijing, China). Cells were grown in RPMI-1640 supplemented with 10%FBS under a humidified atmosphere with 5% CO2 at 37 °C.

Real-time reverse transcription polymerase chain reaction (RT-PCR)

Total RNA from cells was extracted using Trizol reagent. A cDNA was synthesized with a PrimeScript™ RT Reagent Kit (TakaRa, Shiga, Japan). Subsequently, mRNA expression levels were quantified by analysis of cDNA implemented with an iCycler iQ™ Real-Time PCR Detection System (Bio-Rad Laboratories, Hercules, CA, USA) using SYBR Green (TOYOBO, Osaka, Japan). mRNA expression levels were normalized to GAPDH and relative gene expression was calculated by 2−ΔΔCt. The primer sequences used in RT-qPCR were shown in Supplementary Table 1.

Statistical analysis

R software (version 4.3.1) was applied for the statistical analysis. The data between two groups were compared via Wilcoxon test. P < 0.05 were deemed to be statistically significant.

Results

Landscape of single cell in HGSOC

In the beginning, the single-cell landscape in HGSOC was downloaded from the dataset GSE184880. Hereafter, after the filtering, dimensionality reduction, cluster and annotation, 10 major cell lineages were identified from a total of 45,448 cells based on the classic marker genes (Fig. 1A, B): NK/T cells (NKG7, CD3D, CD8A), B cell (CD79A, MS4A1), Plasma cell (JCHAIN, IGLC2), Myeloid cells (C1QA, LYZ, CD14), proliferating T cells (MKI67, TOP2A, CD3D), fibroblast (COL1A1, DCN), epithelial cells (CD24, EPVAM, KRT8), endothelial cell (VWF, CLDN5, PECAM1), ovarian stromal cell (STAR) and SMC/myofibroblasts (ACTA2, TAGLN).Fig. 1 Landscape of single cell in HGSOC. A UMAP plot displaying the different cell lineages within the TME. B Bubble plot annotating expression levels of marker genes for each cell lineage. C Bar chart showing the relative abundance of different cell lineages in HGSOC and Normal tissues. D Pie chart depicting the percentage of different cell lineages in TME of HGSOC

We also compared the percentages of different cell lineages in HGSOC and Normal tissues (Fig. 1C) and discovered the higher percentage of T/NK cells, epithelial cells, myeloid cells, and plasma cell in HGSOC tissues. Besides, the percentage of each cell lineages in HGSOC tissue was determined and it was clear that the percentage of T/NK cells, epithelial cells and myeloid cells was relatively higher in the tissue (Fig. 1D). These results demonstrated that T/NK cells, epithelial cells and myeloid cells may play a crucial role in the occurrence and progression of HGSOC.

Heterogeneity of epithelial cells and differential enriched pathway in HGSOC

First of all, the characterization and re-cluster of epithelial cells of HGSOC were implemented and 3 subclusters were obtained and named as C1, C2, and C3 hereafter (Fig. 2A). The distribution of these three subclusters of epithelial cells of HGSOC in different stages was compared then (Fig. 2B). It was observable in these results that with the progression of HGSOC, the percentage of subclusters C1 and C2 was decreased, while that of subcluster C3 displayed an opposite trend, with a notably higher percentage in stage III (Fig. 2B). Subsequently, the enriched pathways of highly expressed genes specifical to these three subclusters were compared (Fig. 2C-E). It was visible that in the subcluster C1, the genes were mainly enriched in the pathways relevant to cell adhesion, while those in the subcluster C2 were evidently enriched in the pathways related to fission and cell cycle. Besides, genes in the subcluster C3 were revealed to be enriched in the extracellular matrix (ECM)-related pathways. The differences in percents and enriched pathways of the three subclusters (C1, C2, C3) in HGSOC suggested that the epithelial cells of HGSOC exhibited a high heterogeneity.Fig. 2 Heterogeneity of epithelial cells and differential enriched pathway in HGSOC. A UMAP plot showing three subclusters of epithelial cells (C1, C2, and C3). B Percentages of each subcluster of epithelial cells in different stages of HGSOC (Stage I, Stage II and Stage III). C-E Functional enrichment analysis displaying the enriched pathways of highly expressed genes specifical to these three subclusters (C1, C2, and C3). F Heat map unveiling the change in the activity of Hallmark pathways in these subclusters (C1, C2, and C3) during the different stages of HGSOC

The AUCell package was applied to determine the different activities of Hallmark pathways in these subclusters, and the changes in the activity of Hallmark pathways in these subclusters of different stages were compared as well (Fig. 2F). Notably, with the progression of HGSOC, the activity of both interferon response and fatty acid synthesis was gradually decreased in the subclusters C1 and C2, while that of glycolysis, hypoxia, epithelial-mesenchymal transition (EMT) and angiogenesis was increased in the subclusters C3.

Decreased activity of cytotoxic NK/T cells during the progression of HGSOC

Likewise, the characterization and re-cluster of NK/T cells were carried out and 3 different immunity-related subclusters were visible in the UMAP plot (Fig. 3A): cytotoxic NK/T cells, naïve T cells, and regulatory T cells (Treg). The percentages of these subclusters in different stage of HGSOC were also revealed, suggesting the relatively higher percentage of NK/T cells and the relatively lower percentage of Treg in all stages of HGSOC (Fig. 3B). Moreover, the infiltration levels of naive T cells and Tregs cells were the highest in the stage II of HGSOC.Fig. 3 Decreased activity of cytotoxic NK/T cells during the progression of HGSOC. A UMAP plot showing three subclusters of immune cells (cytotoxic NK/T cells, naïve T cells, and Treg). B Percentages of each subcluster of epithelial cells in different stages of HGSOC (Stage I, Stage II and Stage III). C Violin plots showing the expression levels of marker genes specific to the three subclusters (cytotoxic NK/T cells, naïve T cells, and Treg). D Heatmap showing the activity of cytotoxic NK/T cells-enriched pathways in different stages of HGSOC. E Bubble plot displaying the expression levels of specific genes during the different stages of HGSOC

The violin plots were then applied to determine the expression levels of specific marker genes within these subclusters (Fig. 3C). CD8A, GZMK and NKG7 were shown to express higher in cytotoxic NK/T cells, while CCR7 and TCF7 were found to express higher in naïve T cells. Besides, Tregs were demonstrated to express FOXP3, IL2RA and RTKN2.

A heat map then showed the activities of cytotoxic NK/T cells-relevant pathways in different stages of HGSOC (Fig. 3D), including interferon-α production, T cell receptor signaling pathway and type II interferon production. A gradual reduction in the activity of these pathways were seen based on a heatmap. Additionally, the expressions of genes specific to NK/T cells in different stages of HGSOC were determined and the corresponding results were seen in a bubble plot (Fig. 3E). Perforin 1 (PRF1) is a cytotoxic protein expressed in NK/T cells and serving as a definitive marker of immune cells with killing ability [29, 30]. According to the bubble plot, a gradual reduction on PRF1 level was seen with the progression of HGSOC from Stage I to Stage III.

An analysis on myeloid cells

3 immunity-related subclusters were categorized from the myeloid cells based on the UMAP map (Fig. 4A-B), namely, M0 Macrophage (marker: CD68), M1 Macrophage (marker: CD86) and Monocyte (marker: S100A8). The percentages of these subclusters in different stages of HGSOC were quantified as well, which revealed the gradual increase of M0 and M1 macrophages and the decrease of monocyte during the progression of HGSOC (Fig. 4C). The succeeding GO-BP enrichment analysis in these three subclusters has showed that monocytes and M0 macrophage were mainly enriched in regulation of inflammatory response, neutrophil activation (Fig. 4D-E). It was visible at the same time that M1 macrophage mainly enriched in regulation of lymphocyte activation and inflammatory response and antigen processing and presentation (Fig. 4F), which could process and present the antigens to T cells to further activate specific immune responses.Fig. 4 An analysis on myeloid cells. A-B UMAP plot showing three subclusters of myeloid cells (M0 Macrophage, M1 Macrophage and Monocyte) (A) and the expression levels of their marker genes B. C Percentages of each subcluster of myeloid cells in different stages of HGSOC (Stage I, Stage II and Stage III). D-F Functional enrichment analysis displaying the enriched pathways of highly expressed genes specifical to these three subclusters monocyte (D), M0 macrophage (E) and M1 macrophage (F). G Heat map showing the activity of M1 macrophage-enriched pathways in different stages of HGSOC. H Bubble plot displaying the expression levels of specific immunoregulation-related genes during the different stages of HGSOC

Hereafter, the expression changes of chemokines and genes related to antigen presenting in M1 macrophages were determined. Concurrent with the weakened T cell and myeloid leukocyte migration (Fig. 4G), the gradual decrease in the expression of these genes was also seen in M1 macrophage with the progression of HGSOC (Fig. 4H), hinting the loss of attraction to other immune cells, the reduced phagocytosis and cytotoxicity, and the decreased capability of recruiting immune cells and presenting antigens in macrophages of late stage of HGSOC.

Cell–cell communication analysis

The potential cell–cell communication mediated by the ligand-receptor pairs in different stages of HGSOC was determined based on the scRNA-seq data (Fig. 5A-B). In our current study, we focused on the communication between cytotoxic NK/T cells and macrophages and discovered a strong communication of CXCL and CCL signal between M1 macrophages and cytotoxic NK/T cells in early stage of HGSOC. CXCL9, CXCL10, and CXCL11 chemokines can accelerate the infiltration of tumor-suppressive lymphocytes in TME through their receptor CXCR3. The expression of CXCL9 is related to the overall survival and T cell infiltration, which is a strong prognostic factor and a characteristic of immune reactive for HGSOC [31]. CCL4 and CCL5 are those powerful chemokines which can attract cells expressing CCR1 and CCR5 (including cytotoxic T lymphocytes (CTLs), helper T cells and NK cells), and affect their activation. The cytotoxicity of both CTLs and NK cells can therefore be strengthened and the production of interferon-γ (IFN-γ) can be increased, which leads to the enhanced capability of these cells to kill tumor cells. Through such mechanisms, the activation and proliferation of tumor-specific T cells can be strengthened to promote the immune surveillance and killing of tumor cells. Furthermore, we used RT-qPCR analysis to determine expressions of CCL4, CCL5, CCR1 and CCR5 in tumor cells, and results showed that all CCL4, CCL5, CCR1 and CCR5 mRNA levels were upregulated in tumor cells SK-OV-3 in comparison to IOSE-80 cells (Fig. 6A-D). These results demonstrated that CXCL and CCL chemokines may serve as the early diagnostic biomarkers and potential therapeutic targets for HGSOC.Fig. 5 Cell–cell communication analysis. A-B Heat maps displaying the interaction between ligands and receptors

Fig. 6 RT-qPCR analysis on CCL4/5 (A-B) and CCR1/5 (C-D) in tumor cells

Discussion

Increasing evidences have laid great emphasis on the application of scRNA-seq in a variety of cancer research, which can reveal the heterogeneity of tumor cells and monitor the progress of tumor development [32]. Our current study, based on the scRNA-seq data of the dataset GSE184880, unveiled the change in the TME of different stages of HGSOC. Notably, we confirmed the proportion of NK/T cells (49.5%), epithelial cells (15.3%) and myeloid cells (14%) was higher in the HGSOC samples, which prompts us to delve into the specific effects these cells played in HGSOC.

The epithelial malfunction, together with the primary dysfunction in the TME, is indicated to be crucial for the formation of metastasis and even carcinogenesis [33]. An existing study characterizing the TME of the metastatic OC via single-cell transcriptomics has recognized epithelial cells as the largest clusters of cells analyzed (which comprised approximately 50%) [34]. Based on the corresponding data summarized from scRNA-seq, the proportion of epithelial cells in HGSOC tissue was evidently higher than that in normal ovarian tissue (15.3% of all cells in HGSOC tissue). Subsequently, the characterization and re-cluster of epithelial cells of HGSOC were implemented and 3 subclusters were obtained and named as C1, C2, and C3 hereafter (Fig. 2A). The percentages of C1 and C2 subclusters were proved to be lower with the progression of HGSOC, while that of C3 subcluster showed an opposite trend. Then, the enriched pathways of the genes specifically highly expressed in these subclusters were compared (Fig. 2C-F). Relevant discoveries, accordingly, have revealed that the genes in the subcluster C1 were mainly enriched in the pathways relevant to cell adhesion, while those in the subcluster C2 were evidently enriched in the pathways related to fission and cell cycle. Besides, genes in the subcluster C3 were enriched in the ECM-related pathways. These discoveries have thus echoed the statement stressing the loss of cell–cell adhesion in tumors of epithelial origin [35]. It was reported that the epithelial cell-adhesion molecule-directed immunotherapy could applied for symptom management of the advanced OC [36]. An analysis on the differential activities of Hallmark pathways in these subclusters has suggested the decreased activities in interferon response and fatty acid synthesis in the subclusters C1 and C2 and the increased activities in glycolysis, hypoxia, EMT and angiogenesis was increased in the subcluster C3. EMT is a key process for epithelial cells to obtain mesenchymal phenotype, thereby promoting tumor metastasis [37]. Angiogenesis, as a marker of malignancies, could help malignant cells acquire nutrients and oxygen, supporting their growth and metastasis [38]. These discoveries, altogether, hinted the potential oncogenic role the subcluster C3 of epithelial cells play in HGSOC.

Meanwhile, T cells, a crucial part of the immune system, have been suggested to be a component of the TME [39]. Also, an existing study has already addressed the major components of intra-tumoral T cells, including naïve, effector, memory, Treg and exhausted or dysfunctional T cells [40]. According to the relevant UMAP plot of this study, T cells were categorized into three sub-clusters: cytotoxic NK/T cells, naïve T cells and Treg cells. Further determination on the percentage of these sub-clusters has suggested the relatively higher percentage of cytotoxic NK/T cells in all stages of HGSOC (late stage in particular) and the lowest percentage of Treg in stage III. The interaction between epithelial cells and NK/T cells plays an important role in the generation and development of NK/T cells [41, 42]. Epithelial cells, by potentially expressing interleukin 2, is crucial for NK cell-mediated immunosurveillance in mammary tumors [43]. Previous studies have indicated that the tumor cell subpopulations with dual-characterized of "epithelial-immune" exert strong immunosuppressive and regulatory functions on T cells in the TME [44]. In addition, the infiltration levels of naive T cells and Tregs cells were the highest in the stage II of HGSOC. Tregs could contribute to immune escape for numerous tumors, promoting tumor development [45]. A previous study indicated that, in HGSOC, ACTB + Tregs exhibited higher expression of metastasis-related genes and were closely related to oncogenic pathways [46]. Meanwhile, a heatmap demonstrating the activities of cytotoxic NK/T cells-relevant pathways in different stages of HGSOC, including interferon-α production, T cell receptor signaling pathway and type II interferon production. A gradual reduced activities of these pathways were seen in our study, which further proved the anti-tumor role of interferon-α [47], T cell receptor signaling [48], and type II interferon [49].

Myeloid cells are the most abundant cells within the TME which can be recruited and modulated by the tumors to a variety of cells in order to maintain the immunosuppressive microenvironment [50]. In OC, the immunosuppressive myeloid cells have been proved to raise stem cells, facilitate angiogenesis, and affect the efficacy of immunotherapy [51, 52]. Three sub-clusters, namely, monocytes, M0 macrophage and M1 macrophage, were visible based on the UMAP plot of our study, and a gradual increased percentage of macrophage yet decreased percentage of monocyte was seen with the progression of HGSOC. Schematically, there are three major subpopulations of macrophages within human tissue, including naïve macrophage (M0), pro-inflammatory M1 macrophage and anti-inflammatory M2 macrophage [53]. The GO-BP enrichment analysis has revealed the enrichment of these subclusters in regulation of inflammatory response, modulation of neutrophil and the recruitment of immune cells, respectively. M1 macrophage, generally, plays a pro-inflammatory role via enhancing tumor antigen-presenting ability or indirectly promoting the proliferation of immune cells like CD8+ T cells and NK cells [54]. Similar results were seen in our study. In detail, M1 macrophage mainly enriched in regulation of lymphocyte activation and inflammatory response and antigen processing and presentation (Fig. 4F), which could process and present the antigens to T cells to further activate specific immune responses. Additionally, the expression of genes related to immunoregulation was also gradually decreased in M1 macrophage with the progression of HGSOC (Fig. 4H), hinting the loss of attraction to other immune cells, the reduced phagocytosis and cytotoxicity, and the decreased capability of recruiting immune cells and presenting antigens in macrophages of late stage of HGSOC.

Given the essential role of cell–cell communication of different cell populations within the TME in primary tumor growth, metastasis evolution and immune escape, we finally shift our attention to the cell–cell communication in TME of HGSOC [55]. Here, we focused on the communication between cytotoxic NK/T cells and macrophages and discovered a strong communication of CXCL and CCL signal between M1 macrophages and cytotoxic NK/T cells in early stage of HGSOC (Fig. 5). CXCL and CCL are important chemokines involved in the occurrence, development, and metastasis of multiple cancers, including OC [56]. CXCL9 can encode some secreted proteins playing a crucial role in inflammation, immune regulation, and angiogenesis processes in tumors [57]. CXCL9 and its two family members, CXCL10 and CXCL11, can facilitate the infiltration of tumor-suppressive lymphocytes in TME through their receptor CXCR3 [58]. In preclinical models of OC, the expression level of CXCL9 showed notably positive correlations with T cell infiltration and overall survival [59]. CCL4 is a CC chemokine which can promote tumor development and progression via recruiting Treg and pro-tumorigenic macrophages [60]. CCL5 is another CC chemokine mainly expressed by T cells and monocytes, which shows the highest affinity to CCR5 (a G-protein-coupled receptor (GPCR) which can also combine to CCL4 with a N-terminal extracellular tail) and can induce the activation and proliferation of particular NK cells together with certain cytokines released by T cells like IFN-γ [61, 62]. Further, it should not be dismissed that CCL5 and CCL4 levels are positively correlated with the extent of T lymphocytes infiltration in OC [62]. The strong communication of CXCL and CCL signal between M1 macrophages and cytotoxic NK/T cells in early stage of HGSOC has contributed to the strengthened cytotoxicity of both CTLs and NK cells and the increased production of IFN-γ, which leads to the enhanced capability of these cells to kill tumor cells. Hereby, the activation and proliferation of tumor-specific T cells can be strengthened to promote the immune surveillance and killing of tumor cells. RT-qPCR results also showed CCL4/5 and CCR1/5 levels were increased tumor cells, implied there may be presented cell–cell communication between M1 macrophages and cytotoxic NK/T cells. Overall, these outcomes revealed the complicated intercellular interactions in the immune microenvironment of HGSOC, and CXCL and CCL chemokines may serve as auxiliary biomarkers for early diagnosis of HGSOC, which provided a new insight for the early detection of HGSOC, enhancing patient survival rates, improving prognosis, and targeted therapy.

Some limitations, however, should be noticed and addressed in this study. First, the sample size for our scRNA-seq analysis is relatively small, more larger cohorts are required to verify the findings in our current study. Second, this article only explored the communication between cytotoxic NK/T cells and macrophages, yet the interaction among other cell types has not been researched, considering their important roles in cancer development, which will be focus on subsequently. Third, the specific mechanisms of single-cell subclusters in HGSOC progression and immune regulation, as well as the therapeutic modulation of the identified signaling axes (such as CCL4/5 and CCR1/5) have not been expounded. In the future, we will further explore these unsolved mysteries through in vivo and in vitro experiments, such as cell-based, animal experiments, and clinical trials.

In conclusion, our investigation using scRNA-seq has depicted the change in the cytotoxic NK/T cells, epithelial cells and myeloid cells of TME of HGSOC, which, we hope, may provide another insight into the specific mechanisms underlying the progression of HGSOC.

Supplementary Information

Additional file 1.

Additional file 2.

Additional file 3.

Abbreviations

TME Tumor microenvironment

HGSOC High-grade serous ovarian carcinoma

OC Ovarian cancer

scRNA-seq Single-cell RNA sequencing

EMT Epithelial-to-mesenchymal transition

TRM Tissue-resident memory

PCA Principal component analysis

GO-BP Gene ontology biological process

MSigDB Molecular Signatures Database

PPI Protein–protein interaction

ECM Extracellular matrix

CTLs Cytotoxic T lymphocytes

IFN-γ Interferon-γ

GPCR G-protein-coupled receptor

Acknowledgements

None

Author contributions

All authors contributed to this present work: [LNM] designed the study. [SJS] and [LNM] collected and analyzed data. [LNM] and [SJS] drafted the manuscript. [LNM] and [SJS] reviewed and revised the manuscript. The manuscript has been approved by all authors for publication.

Funding

The authors declare that they have receive no funding.

Data availability

The datasets generated and/or analyzed during the current study are available in the [GSE184880] repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE184880].

Declarations

Ethics approval and consent to participate

Not applicable.

Patient Consent Statement

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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