Introduction
Ovarian cancer (OC) is a highly aggressive malignant tumor, ranking fifth in the cancer-related death in [
1]. Patients with ovarian cancer have a poor prognosis, more than half of them having a survival time less than 5 [
2]. 90% of ovarian cancer are of epithelial origin, while the others are non-epithelial. Epithelial ovarian cancer includes many histological types, with respectively specific molecular changes, clinical behaviours, and treatment outcomes. Germ cell tumors and sex cord-stromal tumors are the main non-epithelial ovarian cancer. Germ cell tumors most often occur among younger women of childbearing age with good prognosis. Sex cord-stromal tumours affect all age groups, diagnosed with early stage, indolent [
3]. In addition, mutations in DNA repair pathways increase the risk of chemotherapy resistance.The synthetic lethality of poly (ADP-ribose) polymerase (PARP) inhibitors is directed against BRCA mutations, which can be novel therapeutic targets for epithelial ovarian cancer treatment. Several PARP inhibitors, including olaparib, rucaparib, and niraparib, gained clinical approval from FDA and/or EMA for the treatment epithelial ovarian cancer. Olaparib, rucaparib, and niraparib approximately are more efficient than veliparib in trapping [
4]. FIGO stage remains the most appropriate indicator for predicting the prognosis of OC, but it frequently fails to accurately predict the prognosis of a specific individual, as the survival of patients with the same disease stage varies [
5]. Therefore, identifying biomarkers related to high-risk patients with low survival rates is of great significance. Proteomics technologies and tools have made it possible to reveal molecular events and the proteomic characterization involved in tumor development. At this point, proteomic profiling of ovarian cancer, as well as their adaptive responses to therapy, can help develop new therapeutic strategies for reducing drug resistance and improving patient [
4].
Ovarian cancer cells evade immune surveillance and achieve tumor proliferation by shaping a highly immunosuppressive tumor microenvironment (TME)[
6]. According to reports, tumor-associated macrophages (TAM), regulatory T cells (Treg), myeloid-derived suppressor cells (MDSC), and tumor-associated dendritic cells (tDC) together make suppressive immune [
7]. TAMs with immunosuppressive M2 phenotype account for the majority of tumor microenvironments, and stimulate cancer [
8], metastasis, angiogenesis, immune [
9], and tumor [
10]. Therefore, exploring new strategies to identify molecules targets of the tumor microenvironment (TME), especially TAM, may improve the therapeutic effect and 5-year survival rate of OC patients.
Existence of cancer is often accompanied by changes in the complement system. On the one hand, due to its water-soluble properties, complement can often infiltrate into deeper tumors faster than immune cells. It recruits leukocytes through anaphylactoxin, opsonin and lysis, promotes tumor lysis and induces the release of pro-inflammatory mediators to enhance the anti-cancer [
11]. On the other hand, the complement system itself has the functions of angiogenesis, promotion of cell invasion and tissue [
12]. Therefore, complement also has a tumor-promoting effect. The encoding gene of C3AR1 (C3a anaphylatoxin chemotactic receptor) locates on chromosome 12 and is also known as AZ3B; C3R1 and C3AR. C3AR1 belongs to the G protein coupled receptor 1 family. Accumulating evidence confirms the role of C3AR1 in cancer. Increased C3AR1 suggests poor prognosis in patients with gastric adenocarcinoma, and leads to an increase in suppressive tumor immune cell [
13]. Pan-cancer analysis found that copy number variation and gene methylation are the main reasons for the increased expression of C3AR1 and are related to the dysfunctional T cell phenotype, which leads to tumor immune evasion and reduces the effect of [
14]. Studies have shown that ovarian cancer patients higher C3 level tend to have a shorter overall [
15]. C3 deficiency inhibits the formation of ovarian tumors in mice. In addition, down-regulating C3 expression reduces the growth rate of tumors in mouse ovarian cancer [
16]. However, the prognostic value of C3AR1 and its possible mechanism in ovarian cancer remains unclear. In our current research, we use comprehensive bioinformatics analysis to explore the expression and clinical value of C3AR1 in OC, and further explore its regulatory network and its role in shaping the tumor immune microenvironment.
Materials and methods
Increased C3AR1 in OC and its clinical significance
Functional enrichment analysis of C3AR1 co-expression genes and differentially expressed genes
Co-expressed genes of C3AR1 were investigated by the R software and the Spearson correlation coefficient test. Genes with |R|≥0.6 and the p < 0.05 are considered as C3AR1 co-expressed. OC patients in the TCGA database was divided into two groups based on median C3AR1 expression. The differentially expressed genes (DEGs) were analyzed using the DESeq2.0 package, and criteria was |Log2FC|>2 and p < 0.05. Functional pathway analysis of GO and KEGG was conducted on DEGs and co-expressed genes individually, and visualized by the ggplot2 software package.
Correlation between C3AR1 and tumor immune infiltration
To explore the role of C3AR1 in regulating immune microenvironment of OC, we analyzed the relationship between the expression of C3AR1 and B cells, CD4 + T cells, CD8 + T cells, macrophages, neutrophil cell and dendritic cell infiltration in the TIMER database (
www.cistrome.shinyapps.io/timer). We also compiled the associations of 22 immune cell marker genes with C3AR1 mRNA levels in the TIMER, GEPIA and TCGA databases. The matrix score, immune score and ESTIMATE score between different C3AR1 expression groups are evaluated by the ESTIMATE algorithm. Infiltration level of immune cells in two C3AR1 groups was compared by the MCP counter algorithm. We further assessed the correlation between expression of C3AR1 and immune checkpoint.
Correlation between C3AR1 expression and m6A modified genes
Enhancing or inhibiting m6A methyltransferase (writer) or demethylase (eraser) alters tumor development, and function status of m6A RNA methylation is highly dependent on the cell microenvironment. We analyzed gene expression of m6A mediators in two C3AR1 groups in the R software package. The prognostic value of C3AR1 related m6A genes in OC were explored. The ggplot2 is used for data visualization.
Immunohistochemistry
Tissues of 5 OC patients and 5 healthy controls were stained by immunohistochemistry (IHC) after formalin fixation and paraffin-embedding. C3AR1 specific antibody (Proteintech) was diluted at 1:100.
Cell culture, plasmid transfection and C3AR1 expression detection
Human ovarian cancer cell line SKOV3 was purchased from NEWGAINBIO (China). We maintained cells at 37 °C in a humidified atmosphere containing 5% carbon dioxide in RPMI 1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA) and 1% penicillin/streptomycin solution (Gibco, USA). According to the manufacturer’s protocol, the plasmids (TSINGKE, China) with different concentrations were transfected into SKOV3 cells using jetPRIME transfection reagent (polyplus, China). After 48 h of transfection, Total RNA Extraction mini kit (Mabio,China) was used to extract total RNA from cultured cells for qRT-PCR. C3AR1 forward primer: CCCTACGGCAGGTTCCTATG; C3AR1 reverse primer: GACAGCGATCCAGGCTAATGG-3,‘ β-actin forward primer: GTGGGGCGCCCCAGGCACCAGGGC; β-actin reverse primer: CTCCTTAATGTCACGCACGATTTC. RIPA buffer (Solarbio) was used to lyse the cells and extract total proteins. Western blotting assay were conducted as described [
17], the antibody concentration of C3AR1 (1:1000 ThermoFisher) and GAPDH (1:3000 Proteintech).
EdU assay of cell proliferation rate
C3AR1 overexpression and control cells were treated with EdU reagent (Beyotime, C0075L) for 3 h. The cells were then immobilized with 4% paraformaldehyde and stained with fluorescent dye and Hoechst. Fluorescence detection was performed under an inverted fluorescence microscope.
Statistical analysis
All data were statistically analyzed using GraphPad Prism (version 9.1.0). The student t test, one-way ANOVA, and Chi-square test were used to assess differences in variables between groups. The log-rank test evaluated the statistical significance of Kaplan-Meier survival curves. Spearman and statistical significance were used to analyze gene expression correlations. |R| > 0.3 is considered to be relevant, P values < 0.05 was considered statistically significant.
Discussion
In this study, the expression of C3AR1 in OC was analyzed and verified from both mRNA and protein levels by bioinformatics and experimental methods. Correlation analysis between the methylation and the expression of C3AR1 showed that low methylation levels partly contribute to the high C3AR1 mRNA level. Previous studies have found that GBM, LGG, and COAD patients with high C3AR1 expression tend to have a poor [
14].We also confirmed that the prognosis (OS, PFS, PPS) of OC patients with high C3AR1 expression is poor. Morever, it was found that C3AR1 expression was related to tumor grade, stage, recurrence, lymph node metastasis and survival status. In vitro experiment indicates that increased C3AR1 promotes the proliferation of ovarian cancer cells. These evidence suggests that C3AR1 can be used as a potential prognosis biomarker for evaluating the prognosis of OC patients and targeting C3AR1 may help improve the treatment of OC patients.
Current researches on C3AR1 in tumors mainly focuses on stomach adenocarcinomas (STAD), [
18] and kidney renal clear cell carcinoma (KIRC)[
19]. There are few studies regarding the biological and molecular functions of C3AR1 in OC.
We analyzed the related genes of C3AR1 and found that the expression of LAPTM5, CD53, CYBB, LAIR1, MS4A6A, HAVCR2, CD86, FCCR2A and FCCR2C in OC has the strongest correlation with C3AR1. Chen et al. found that down-regulation of LAPTM5 induces cell cycle arrest in G0/G1 phase to inhibit the proliferation and viability of bladder cancer [
20]. HAVCR2 inhibits anti-tumor immunity by interacting with its ligand Galectin 9 (Gal9), phosphatidylserine (Ptdser) high mobility group box 1 (HMGB1), and carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1)[
21]. GO and KEGG analysis of C3AR1 co-expressed genes showed that C3AR1 was mainly correlates with T cell, leukocyte and lymphocyte activation and cytokine and chemokine activity. KEGG pathway analysis showed that C3AR1 was mainly related to cytokine-cytokine receptor interaction and chemokine signaling pathway. Enrichment analysis results of DEGs confirmed this result again. A number of studies have shown that multiple chemokines and cytokines are involved in the progression of ovarian [
22,
23].
Immune cell infiltration affects tumor metastasis, chemotherapy resistance, and prognosis of OC patients. We analyzed data from multiple databases and confirmed that C3AR1 is closely related to immune cell infiltration, including macrophages. In addition, immune cell markers from 3 data sets also confirmed these results. Among them, C3AR1 has the strongest correlation with TAM, M2 macrophage and monocyte markers, suggesting that C3AR1 may affect the immune microenvironment of OC mainly by regulating macrophage infiltration. The role of M2 and TAM in promoting tumor progression in ovarian cancer has been widely recognized. In ovarian cancer, M2 macrophages promote the formation of ascites by reducing the expression of VLA4 in their cell membranes and reducing the level of VCAM1 in endothelial [
24]. Mechanistically speaking, the down-regulation of VLA4 or VCAM1 results in the inhibition of the RAC1 / ROS / PYK2 (p-PYK2) /p-VE-cad cascade, thereby enhancing cell adhesion. In addition, targeting the VLA4/VCAM1 axis can enhance the vascular barrier and inhibit the formation of ascites in the body. At the same time, studies have found that EGF derived from TAMs promoting the early transluminal metastasis. Specifically, TAM in ovarian cancer activates EGFR through secreted EGF and up-regulates VEGF/VEGFR signals in peripheral tumor cells, supporting tumor cell proliferation and migration. Blocking EGFR or ICAM-1 in TAM by drugs or antibodies inhibits tumor tissue formation and disease progression in mouse models of ovarian [
25]. We speculate that the overexpression of C3AR1 promotes the infiltration of TAM cells and M2 macrophages in OC, thereby accelerating tumor progression. Finally, we found that 8 immune checkpoints including CD274 (PD-1) are closely related to C3AR1. Patients with OC who respond to immune checkpoint blockade treatment will have long-term benefits. An anti-PD-1/PD-L1 therapy’s efficacy is partly determined by the expression of PD-L1 on tumour cells. Compared with patients expressing low PD-L1, those with high PD-L1 expression have higher response rate. This can be attribute to the strong immunosuppression and low mutation burden in tumor microenvironment. Several studies have suggested defective DNA repair machinery is associated with higher levels of neoantigens in OC patients with BRCA mutations and homologous recombination defects. The most effective immunotherapy relies in newly diagnosed OC and overcome the exhausted immune system. Several strategies have been develop to enhance sensitivity to immunotherapy in OC, including the dual immune checkpoint blockade, as well as a combination therapies involving immune checkpoint and PARP inhibitors (PARPi), cytotoxic drugs, radiotherapy, and/or angiogenesis [
26]. Studies have shown that CDK4/6 inhibition combined with PD-1 blockade treatment has higher CXCL10 and CXCL13 levels and CD8 + and CD4 + T cell activity than monotherapy-treated ovarian cancer [
27]. We speculate that the combined use of small molecule inhibitors targeting C3AR1 and ICB may be a promising strategy for the treatment of ovarian cancer. However, our hypothesis on the relationship between C3AR1 and macrophage infiltration and immune checkpoint blocking therapy still needs more research evidence to verify.
Several studies have confirmed m6A mediators in promoting ovarian cancer proliferation, angiogenesis, chemotherapy resistance and tumor microenvironment regulation. Wang et al. found that knocking down YTHDF1 in cisplatin-resistant ovarian cancer cells can inhibit cancer stem cell-like characteristics by overexpression of TRIM29 [
28]. Liu et al. confirmed that YTHDF1 is up-regulated in high-grade serous ovarian cancer and is related to tumor grade, FIGO staging, and overall [
29]. And through multi-omics studies, it is determined that its direct target in ovarian cancer cells is the translation initiation factor EIF3C, and YTHDF1 controls the translation of EIF3C in a m6A-dependent manner. In addition, many m6A-related factors such as YTHDF2, LETM1, METL3 and ALKBH5 have also been confirmed to be associated with the malignancy of ovarian cancer. This study found that C3AR1 was significantly correlated with ALKBH5, IGFBP3, METL14, RBM15, WTAP, YTHDC2, and YTHDF3. It was also revealed that the expression levels of RBMX, IGFBP2, WTAP, VIRMA, YTHDF3, METL14, FTO, HNRNPC, and YTHDC2 in the C3AR1 high expression group were significant changed. Finally, survival curve analysis shows that patients with ovarian cancer with high WTAP expression have a worse prognosis. We speculate that C3AR1 may be involved in m6A-related gene-mediated tumor regulation, and its tumor-promoting effect may be achieved through the modulation of WTAP.
This study demonstrates that C3AR1 is up-regulated and promotes cancer cell proliferation in ovarian cancer, as well as associated with various pathological features. In particular, C3AR1 overexpression is associated with immune cell infiltration, mediating the tumor immune microenvironment, and is associated with poor prognosis of ovarian cancer patients. Patients with high C3AR1 are more likely to benefit from immunotherapy as a result of elevated immune checkpoints expression levels. Taken together, these results indicate that C3AR1 may be a potential new immunotherapeutic target for ovarian cancer.
Authors’ contributions.
Kaixian Deng conceptualized the study design and supervised the analysis. Jinfa Huang collected the data, performed the statistical analysis, and wrote the paper. Lei Zhou conducted the experiment and polished the draft. All authors read and approved the final manuscript.
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