Investigating the mechanisms of drug resistance and prognosis in ovarian cancer using single-cell RNA sequencing and bulk RNA sequencing

Results

Ovarian cancer cell subpopulation classification

We obtained single-cell data from five ovarian cancer patients from the GEO database (GSM4675273-GSM4675277). We used the R package DoubletFinder to remove doublets from the samples (Supplementary Figure 1A–1E) and further filtered out low-quality single-cell data through R software, resulting in a clean dataset (Supplementary Figure 1F). After the removal of doublets, the number of cells was 46436. After the removal of low-quality cells, the number of cells was 45191. We observed that cells of different stages were relatively concentrated in the PCA plot (Supplementary Figure 1G), suggesting that cell staging had a minimal overall impact on our results. Based on gene expression and dispersion, we selected the top 2000 highly variable genes (Supplementary Figure 1H) and performed dimensionality reduction using RunPCA, choosing the top 30 dimensions for further analysis (Supplementary Figure 1I, 1J). We also explored the top ten highly variable genes for the first nine dimensions and presented them (Supplementary Figure 1K). Through clustering, we classified ovarian cancer cells into 27 clusters (Figure 1A) and further annotated them into eight major cell types using the R package singleR, Cellmarker database, and literature references (Figure 1B). These cell types included T_NK cells (14671), Endothelial Cells (ECs 838), Pericytes (729), Fibroblasts (7462), Epithelial Cells (EPCs 9683), B Cells (B 1113), Plasma Cells (1832), and Myeloid Cells (8863). Different samples showed variations in the distribution of cell types, possibly due to individual sample sources and the tissue of origin for the tumor cells (Figure 1C). We identified the top 5 MARKER genes for each of the eight major cell types and visualized them, along with G2M. Score, S.Score, and tumor treatment response (Figure 1D). We found that EPCs exhibited significantly elevated G2M.Score and S.Score, and their MARKER genes were highly correlated with drug resistance. Literature review revealed that EPCs are the primary tumor cells in ovarian cancer tissues, piquing our interest.

Figure 1. Ovarian cancer cell cluster classification. (A) A total of 28 clusters were identified after clustering 5 ovarian cancer patients. (B) Ovarian cancer cells were annotated into T_NK, ECs, Pericytes, Fibroblasts, EPCs, B Cells, Plasma, and Myeloid Cells based on distinct MARKER genes. (C) Distribution of cells from 5 different samples. (D) Expression patterns of the top 5 MARKER genes in each major cell cluster.

Tumor treatment response and cell staging of EPCs

Tumor treatment response can be categorized into three groups: Resistant, Refractory, and Sensitive. We studied the treatment response of various cell types in ovarian cancer tissues (Figure 2A) and found that the Resistant group was predominantly located within the EPCs cluster. Similarly, we observed that the G1 phase is a period after cell division and before DNA replication, the S phase is dedicated to DNA replication, and the G2M phase is the cell division preparation and mitotic phase. Abnormalities in these stages can lead to tumorigenesis. Combining cell staging with our findings, we noted that the G2M and S stages were significantly concentrated within the EPCs cluster (Figure 2B). To gain a more intuitive understanding of the distribution of treatment responses, we examined the proportions of different cell types in ovarian cancer (Figure 2C, 2G) and found that the Resistant group had the highest proportion of EPCs. Regarding tumor staging (Figure 2D, 2H), the proportion of EPCs in the G2M and S stages was significantly higher than in other cell types, while the G1 stage proportion was relatively lower. We also investigated the cell abundance of different cell groups in ovarian cancer, based on treatment response and cell cycle (Figure 2E, 2F). We observed that the Resistant group had the highest cell purity of EPCs. Similarly, in the G2M and S phases, the purity of EPCs was much higher than that of other types of ovarian cancer cells. This suggests that EPCs are highly resistant to treatment and exhibit more vigorous cellular activity, and there may be some underlying connections between these characteristics. Furthermore, we validated our findings using G2M.Score and S.Score, which showed that EPCs had significantly higher scores compared to other cell types, with the Resistant group exhibiting the highest scores among all groups (Figure 2I–2P). We also examined the number and types of DNA replications among different cell groups (Figure 2Q, 2R) and found that EPCs exhibited significantly higher scores. This might be related to the robust proliferative capacity of tumor cells.

Figure 2. Cell clustering by staging and drug treatment response level. (A) Distribution of UNMP drug treatment response levels. (B) Distribution of UNMP cell staging within major cell clusters. (C) Proportion of major cell clusters within the Resistant, Refractory, and Sensitive response groups. (D) Proportion of major cell clusters within the G2M, S, and G1 cell staging phases. (E, F) Abundance of major cell clusters concerning drug response groups and cell staging. (G) Proportion of major cell clusters within the Resistant, Refractory, and Sensitive response groups and their facets were shown. (H) Proportion of major cell clusters within the G2M, S, and G1 cell staging phases and their facets were shown. (I) Distribution of UNMP G2M.Score. (J–L) Violin plots for G2M.Score in major ovarian cancer cell clusters, cell staging, and drug response. (M) Distribution of UNMP S.Score. (N–P) Violin plots for S.Score in major ovarian cancer cell clusters, cell staging, and drug response. (Q, R) Distribution of UNMP nFeature and nCount.

Enrichment functional analysis of ovarian cancer cell subpopulations

We conducted differential gene expression analysis among various cell types in ovarian cancer tissues and presented the top 5 most significant genes (Figure 3A–3H). Using these differential genes, we performed GO enrichment functional analysis (Figure 3I) and found significant enrichment of pathways such as “Oxidative phosphorylation,” “Mitochondrial translation,” and “Aerobic respiration” in EPCs. Through GSEA enrichment functional analysis (Figure 3J–3S), we observed significant enrichment of pathways such as “Oxidative phosphorylation,” “Energy derivation by oxidation of organic compounds,” “Respiratory electron transport chain,” “Aerobic respiration,” and “Cellular respiration” in the high-expression group. In contrast, pathways including “T cell activation,” “Adaptive immune response,” “Inflammatory response,” “Positive regulation of the immune system process,” and “Antigen processing and presentation” were significantly enriched in the low-expression group. Oxygen-related pathways were prominently enriched in both GO and GSEA pathways. To further explore the underlying mechanisms, we conducted cellular metabolism analysis.

Figure 3. Enrichment analysis of major cell clusters. (A–H) Differential gene distribution in major ovarian cancer cell clusters compared to other cell types, displaying the top 5 upregulated and downregulated genes for each. (I) GO enrichment analysis for major cell clusters. (J–S) GSEA enrichment analysis for EPCs, displaying 5 pathways selected from both low and high expression groups.

Metabolism-related analysis of ovarian cancer cell subpopulations

Through cellular metabolism analysis (Figure 4A–4C), we found that Oxidative Phosphorylation, Glycolysis/Gluconeogenesis, and Pyruvate Metabolism scored significantly in various ovarian cancer cell subpopulations, treatment response levels, and cell staging. Among them, Oxidative Phosphorylation had a higher score in EPCs (Figure 4D–4G), suggesting that this metabolic pathway might be a key pathway in EPCs’ metabolism. The other two metabolic pathways, Glycolysis/Gluconeogenesis and Pyruvate Metabolism (Figure 4H–4O), scored higher not only in the EPCs group but also in the Resistant group and in cells in the G2M and S phases. We speculate that these two pathways may play a positive role in tumor progression.

Figure 4. Metabolic pathways of major cell clusters. (A) Top 20 metabolic pathways in major ovarian cancer cell clusters. (B) Top 20 metabolic pathways in G2M, S, and G1 cell staging. (C) Top 20 metabolic pathways in the Resistant, Refractory, and Sensitive response groups. (D–O) UNMP distribution and violin plots for the Oxidative Phosphorylation, Glycolysis / Gluconeogenesis, and Pyruvate Metabolism pathways in major ovarian cancer cell clusters, cell staging, and drug response.

Subpopulations of EPCs

To further study the composition and functions of EPCs, we re-clustered EPCs into five subpopulations (C0-C4). Based on the calculation of each subpopulation’s CNV (Figure 5A), we labeled the C4 group with the lowest CNV as Low Malignant EPCs, and the C1, C2, and C3 groups with the highest CNV as High Malignant EPCs. The intermediate group, C0, was labeled as Moderate Malignant EPCs. We selected a signature gene for each subpopulation based on the expression level and percentage of MARKER genes (Figure 5B). We also investigated the top 5 MARKER genes for each subpopulation, as well as their relationship with cell staging and treatment response levels (Figure 5C). We found that in EPCs, the expression of C0 subpopulation’s MARKER genes highly overlapped with the Sensitive group and was strongly correlated with G2M.Score and S.Score. This seemed contrary to the characteristics of the overall EPCs population being Resistant, which piqued our interest. We further explored the drug treatment response of each subpopulation (Figure 5D) and found that the C0 group exhibited higher drug sensitivity, while the C2 group showed higher drug resistance. Moreover, we observed the proportions of each subpopulation (Figure 5E, 5F), with C0 having the highest proportion of Sensitive cells, while C2 had the highest proportion of Resistant cells, and the C1 group showed a distribution between the two. Cell abundance calculations were consistent with these findings, with C0 having the highest purity in the Sensitive group, and C2 having the highest purity in the Resistant group (Figure 5K). We also calculated cell staging and proportions for each subpopulation (Figure 5G–5I) and found that in the cell cycle distribution, the G1 phase was most prevalent in C2, while the G2M and S phases were more common in C0 and C1 subpopulations. Cell abundance calculations corresponded to these results (Figure 5L). We further calculated G2M.Score, S.Score, nFeature, and nCount values for each subpopulation, finding that C0 and C1 subpopulations had higher proliferative activities (Figure 5J, 5M), while C2 had relatively higher metabolic activities (Figure 5N, 5O). We believe that C0 may represent a subpopulation of drug-sensitive cells within the overall Resistant EPCs population. In-depth exploration of the causes and mechanisms of drug sensitivity is crucial for ovarian cancer treatment.

Figure 5. Subgroup analysis of EPCs and related data. (A) Heatmap showing CNV status of subgroup cells. (B) UNMP distribution of subgroup cells. (C) Expression patterns of the top 5 signature genes in each subgroup. (D) UNMP distribution of drug responses in subgroup cells. (E) Proportion of subgroup cells within the Resistant, Refractory, and Sensitive response groups. (F) Proportion of subgroup cells within the Resistant, Refractory, and Sensitive response groups and their facets were shown. (G) UNMP distribution of cell staging in subgroup cells. (H) Proportion of subgroup cells within the G2M, S, and G1 cell staging phases. (I) Proportion of subgroup cells within the G2M, S, and G1 cell staging phases and their facets were shown. (J) UNMP distribution of G2M.Score. (K, L) Abundance of subgroup cells concerning drug response groups and cell staging. (M–O) UNMP distribution of S.Score, nFeature, and nCount.

Pseudotime analysis of subpopulation cells

We predicted the differentiation status of each subpopulation using Cytotrace and found that the C2 group had the lowest differentiation, followed by C1 and C0 (Figure 6A, 6B). To understand the temporal relationships between subpopulation cells, we calculated the relative temporal order of each subpopulation using monocle (Figure 6C) and divided all subpopulations into three time stages: State1 as the initial stage, State2 as the intermediate stage, and State3 as the terminal stage (Figure 6D). We found that G1, G2M, and S stages were present in all three states (Figure 6E), but C2 was almost exclusively in State1, while C0 was more prevalent in State2 (Figure 6F). We speculate that the C2 group is at the starting point of the trajectory, while C0 is at the center of the trajectory and progresses toward the surrounding subpopulations. We further validated the cell trajectory using slingshot and confirmed that C0 indeed occupies the intersection of the cellular trajectory (Figure 6G). This aligns with our previous research findings. Finally, we performed GO enrichment analysis based on the different gene expression order within each subpopulation and found that oxidative phosphorylation pathways were significantly enriched in the initial and intermediate stages, suggesting that this pathway plays a crucial role in the regulation of drug response, leading to the development of drug resistance and sensitivity (Figure 6H).

Figure 6. Pseudotemporal analysis of subgroup cells. (A, B) Assessment of Cytotrace scores in subgroup cells. (C) Pseudotemporal chart of subgroup cell UNMP based on Monocle analysis. (D) Temporal distribution of 3 states, with time progressing from State 1 to State 3. (E) Temporal distribution of subgroup cell staging. (F) Pseudotemporal distribution chart of subgroup cells. (G) Time trajectory of subgroup cells based on Slingshot analysis. (H) Enrichment analysis pathways based on the order of gene expression.

Functional analysis of subpopulation cells

We calculated the core genes and their expression changes over time in each subpopulation (Figure 7A, 7B). We observed that NES and KDM5B were significantly expressed in the C2 group and decreased with time, while the genes KLF4 and MYC increased with time (Figure 7C–7F). In addition, to study the relationship between the top five transcription factors in C0 and C2 and their regulatory regions, we analyzed them (Figure 7G–7T) and found that the transcription factor ZNF580 played a significant regulatory role not only in the C0 subpopulation but also in the C2 subpopulation. By conducting GO enrichment analysis for subpopulation cells (Figure 8A), we found that oxidative phosphorylation pathways were significantly enriched in both C0 and C2 subpopulations, with a more concentrated presence of oxygen-related and cellular respiration pathways in C0. Based on this, we conducted GSEA enrichment analysis for the C0 group and found significant enrichment of pathways such as “Cytoplasmic Translation,” “Mitochondrial Gene Expression,” and “Mitochondrial Translation” in the high-expression group (Figure 8B–8D). Conversely, pathways like “Positive Regulation of T Cell Activation,” “Regulation of Leukocyte-Mediated Cytotoxicity,” and “Positive Regulation of Cell Adhesion” were significantly enriched in the low-expression group (Figure 8E–8G). This suggests that the C0 group is indeed associated with certain oxidative and respiratory functions.

Figure 7. Analysis of subgroup cell stemness genes and transcription factors. (A) Expression patterns of stemness genes in various subgroups. (B) Temporal order of stemness gene expression in subgroup cells. (C–F) Distribution of NES, KDM5B, KLF4, and MYC gene UNMP expression. (D–T) Distribution of UNMP for the top five transcription factors in both C0 and C2 and their regulatory regions.

Figure 8. Enrichment analysis of subgroup cells. (A) GO enrichment analysis of subgroup cells. (B–G) GSEA enrichment analysis of C0 subgroup cells.

Prognostic analysis

By intersecting the differential genes between ovarian cancer and C0 subpopulation signature genes in the TCGA database and using univariate Cox regression analysis, we identified 7 genes related to prognosis (Figure 9A). We verified these genes using LASSO Cox regression analysis, which showed stable and good performance (Figure 9B, 9C). Finally, through multivariate Cox regression analysis, we determined 6 prognostic genes, where RPL21, RPL23A, and RPL23 were high-risk genes, while SNRPD1, TMA7, and UBL5 were low-risk genes. We calculated the risk scores for each sample and divided them into high and low-risk groups based on the median score (Figure 9D). We assessed the relationship between the high and low-risk groups in terms of survival status and survival time (Figure 9E) and found that as the risk score increased, the number of deceased patients became more concentrated. We further studied the expression of prognostic genes in the high and low-risk groups (Figure 9F) and examined the coefficient values of prognostic genes (Figure 9G). Among them, SNRPD1 had the lowest coefficient, while RPL23 had the highest. Kaplan-Meier curves (Figure 9H) showed that the high-risk group had lower survival rates at different time points, with p < 0.001, indicating meaningful results. The ROC curve results for 1-year, 3-year, and 5-year survival were 0.61, 0.58, and 0.63, respectively (Figure 9I), indicating stable and excellent predictive performance. We further explored the correlation between prognostic genes, risk scores, and OS (Figure 9J–9P). The results were consistent with our previous research. We also investigated the correlation between different clinical factors and risk scores (Figure 10A, 10B) and constructed nomogram plots based on risk scores and clinical factors to predict the 1-year, 3-year, and 5-year survival rates of different patients (Figure 10C). We validated the accuracy of the 1-year, 3-year, and 5-year prognostic models (Figure 10D–10F). We also plotted ROC curves for each year’s survival (Figure 10G), with AUC values of 0.64, 0.60, and 0.66 for 1-year, 3-year, and 5-year survival, respectively, indicating good sensitivity and specificity and excellent predictive value.

Figure 9. Independent prognostic analysis. (A) Seven differentially expressed genes associated with prognosis. (B) Distribution of LASSO analysis coefficient spectrum for six prognostic genes. (C) Parameter selection in the optimal cross-validation LASSO regression. (D) Patients categorized into high- and low-risk groups based on their risk scores. (E) Distribution of patients in the high- and low-risk groups. (F) Heatmap showing the distribution of prognosis-related genes. (G) Cofe values of prognosis-related genes. (H) Kaplan-Meier survival analysis curves for high- and low-risk groups. (I) Time-dependent ROC curves with area under the curve (AUC) values of 0.61, 0.58, and 0.63 for 1-year, 3-year, and 5-year intervals. (J–P) Correlation analysis of genes with risk scores and overall survival (OS).

Figure 10. Clinical relevance analysis. (A, B) Analysis of the correlation between risk scores and age as well as ethnicity factors. (C) Survival curves for ovarian cancer patients at 1-year, 3-year, and 5-year intervals. (D–F) Nomo model charts for 1-year, 3-year, and 5-year OS tests. (G) Time-dependent ROC curve graphs with AUC values of 0.64, 0.60, and 0.66 for 1-year, 3-year, and 5-year intervals, respectively.

Immune-related analysis and enrichment analysis

We calculated the TIDE scores and found that the high-risk group had higher scores (Figure 11A), indicating a higher likelihood of immune escape in high-risk group tumors. We screened immune checkpoint genes in the high and low-risk groups and found that TNFRSF14 and C10orf54 were significantly expressed in the low-risk group, while LAG3 was expressed at higher levels in the high-risk group (Figure 11B–11D). To further study the immune relevance of high and low-risk groups, we calculated immune cell infiltration (Figure 11E, 11F) and explored the relationships between risk scores, immune cells, and prognostic genes (Figure 11G, 11H). We found that M1 macrophages were negatively correlated with risk scores and RPL23, RPL21, while M2 macrophages were positively correlated with risk scores. Furthermore, T cells follicular helper exhibited strong correlations with SNRPD1 and risk scores. We hypothesized that M1 macrophages and T cells follicular helper were crucial for prognosis and immunotherapy, while M2 macrophages played an opposing role in this context. We also performed GO and KEGG enrichment analysis based on the differential genes between high and low-risk groups (Figure 12A, 12B) and found significant enrichment of pathways such as “Feeding Behavior” and “Neuropeptide Signaling Pathway” in GO, and “Neuroactive Ligand−Receptor Interaction" and “Glycerolipid Metabolism” in KEGG.

Figure 11. Immune-related analysis of C0 subgroup. (A) Violin plot of TIDE scores for high- and low-risk groups. (B–D) Immune checkpoint scores for high- and low-risk groups. (E) Heatmap showing differences in predictive genes, tumor microenvironment, and immune cells between high- and low-risk groups. (F) Proportion of immune cells in high- and low-risk groups. (G) Correlation between immune cells and risk scores. (H) Correlation between immune cells, prognosis-related genes, risk scores, and overall survival (OS).

Figure 12. Enrichment analysis of prognostic genes. (A) Functional enrichment analysis of prognostic genes using GO terms. (B) Functional enrichment analysis of prognostic genes using KEGG pathways.

Author Contributions

Conceptualization, Pengfei Liu and Jinbao Liu; Data curation, Pengfei Liu and Jinbao Liu; Formal analysis, Pengfei Liu and Jinxing Liu; Funding acquisition, Xiao Yu; Investigation, Pengfei Liu and Jinbao Liu; Methodology, Pengfei Liu, Jinbao Liu and Jinxing Liu; Project administration, Pengfei Liu and Jinbao Liu; Resources, Pengfei Liu and Jinbao Liu; Software, Pengfei Liu, Jinbao Liu and Xiao Yu; Supervision, Xiao Yu; Writing – original draft, Pengfei Liu and Jinbao Liu; Writing – review and editing, Pengfei Liu, Jinbao Liu, Jinxing Liu and Xiao Yu.

Acknowledgments

Thanks to everyone who have contributed to this article.

Conflicts of Interest

The authors declare that the research is conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This work was supported by the National Natural Science Foundation of China (82104917); the Natural Science Foundation of Shandong Province (ZR2021MH079).

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