Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

Abstract

Background: There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.

Methods: Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.

Results: The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.

Conclusion: This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

Keywords: Diabetes Mellitus; Immunotherapy; Monocyte marker genes; Ovarian cancer; Single-cell RNA sequencing.

Fig. 1

Flow chart of study design. PPI, protein‒protein interaction; TF, transcription factor; miRNA, microRNA; RT-qPCR, real time quantitative polymerase chains reaction

Fig. 2

Identification of OC Subtypes. (A) t-SNE plot of 12 samples from the GSE184880 dataset after dimensionality reduction and batch correction; (B) top 10 differentially expressed genes in ovarian cancer; (C) Seurat clustering results with resolutions ranging from 0 to 3, where different colors represent different resolutions and larger dots indicate a higher number of cells in the subgroups; (D) t-SNE plot of the 29 cell clusters classified based on scRNA-seq data; (E) heatmap showing the relative expression levels of marker genes in the 29 cell clusters; (F) t-SNE plot indicating the identification of various cell subtypes; (G) heatmap displaying the relative expression levels of marker genes in different cell subtypes

Fig. 3

PBMC Gene Expression Profiles of Monocytes in Diabetic Peripheral Blood. (A) Seurat clustering results with a resolution ranging from 0 to 3. Different colors represent different resolutions, and larger dots indicate subpopulations with a higher number of cells. (B) t-SNE plots illustrating the classification of 15 cell clusters based on scRNA-seq data. (C) t-SNE plots used for identifying distinct cell subtypes. (D) Heatmap displaying the relative expression of marker genes in various cell subtypes

Fig. 4

Simultaneous involvement of mononuclear cells in both DM and OC pathogenesis. (A) Immune cell composition analysis using CIBERSORT in GSE40595; (B) immune cell composition analysis using CIBERSORT in GSE29142. The x-axis represents immune cell types, while the y-axis represents the relative abundance of different samples; (C) Venn diagram demonstrating the overlapping genes among differentially expressed genes in monocytes between GSE184880 and GSE165816. A total of 119 common genes were identified. (D) Heatmap depicting the intersection of differentially expressed genes in GSE40595 with the 119 common genes; (E) heatmap illustrating the intersection of differentially expressed genes in GSE29142 with the 119 common genes. Each column represents a specific gene, while each row corresponds to a sample or condition. Data were presented as the mean ± SD. *p < 0.05, **p < 0.01, *** p < 0.001

Fig. 5

Identification and functional enrichment analysis of hub genes. (A) Visualization of the PPI network using Cytoscape 3.8.2 software. Each node represents a protein, and each edge represents the relationship between two proteins. The size and color intensity of a node indicate its importance in the network. (B) Bar plot showing the intersection of differentially expressed genes in GSE29142 with the 119 shared genes in monocytes. (C) Bar plot depicting the intersection of differentially expressed genes in GSE40595 with the 119 shared genes in monocytes. (D) GO enrichment analysis of the 119 shared genes, including biological processes. (E) GO enrichment analysis of the 119 shared genes, focusing on molecular functions. (F) GO enrichment analysis of the 119 shared genes, highlighting cellular components. (G) KEGG enrichment analysis of the 119 common genes involved. BP, biological process; MF, molecular function; CC, cellular component. * p < 0.05

Fig. 6

Validation of hub gene expression and survival. (A) mRNA expression of hub genes in ovarian cancer tissues compared to normal tissues, indicated by asterisks. (B) Kaplan‒Meier survival analysis of 10 hub genes in mononuclear cells of OC patients, showing high and low expression groups. (C) Pearson correlation analysis of hub genes in the GSE40595 dataset; (D) pearson correlation analysis of HUB genes in the GSE29142 dataset. The color indicates the strength of the correlation. Correlation coefficients between 0 and 1 represent positive correlation, between − 1 and 0 represent negative correlation. The larger the absolute value of the coefficient, the stronger the correlation. * p < 0.05

Fig. 7

Network Analyst generated an interconnected regulatory interaction network of TF genes, in which blue circular nodes represent TFs and genes interacting with TFs are depicted as red circular nodes

Fig. 8

NetworkAnalyst generated an interconnected regulatory interaction network of miRNA-gene. The square nodes represent miRNAs, while the genes that interact with the miRNAs are depicted as circles. The size of the node area and the darkness of the color indicate their relative importance within the network

Fig. 9

Expression of Core Proteins and Transcription Factors in Ovarian Tumors. (A) Immunohistochemical staining of HSP90AA1 and HSPA8 in age-matched high-grade serous ovarian tissues and non-malignant ovarian tumor tissues (n = 4). (B) Expression changes of core proteins HSP90AA1, HSPA8, and SOD1, as well as transcription factors SREBF2 and GTAT2 (n = 4). Control, Non-malignant ovarian tumor group; Control + DM, Non-malignant ovarian tumor group combined with DM; OC, High-grade serous ovarian cancer group; OC + DM, High-grade serous ovarian cancer group combined with DM. *p < 0.05, **p < 0.01, ***p < 0.001