RNA‐seq and ATAC‐seq analysis of CD163 ⁺ macrophage‐induced progestin‐insensitive endometrial cancer cells

2.1. Ethics statement

This study complied with the principles of the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Obstetrics and Gynecology Hospital of Fudan University (approval No.2021–131). All patients signed an informed consent form before participating in the study.

2.2. Patients and specimens

A total of 22 endometrial tissues were collected from AEH or EEC patients who received fertility‐preserving treatment at the Obstetrics and Gynecology Hospital of Fudan University between January 2017 and August 2019. All patients were pathologically diagnosed by endometrial biopsy through dilation and curettage under a hysteroscope. Pathologic diagnosis was independently confirmed by two experienced gynecological pathologists, according to the World Health Organization pathological classification (2014). If their opinions differed, a seminar was held in the pathology department to determine the final diagnosis.

The inclusion and exclusion criteria for fertility‐sparing treatment followed National Comprehensive Cancer Network guidelines. ¹⁰ , ¹¹ The inclusion criteria were as follows: (1) histologically‐proven AEH or well‐differentiated EEC G1 without myometrial invasion; (2) no signs of suspicious extrauterine involvement on enhanced magnetic resonance imaging, enhanced computed tomography or ultrasound; (3) patients younger than 45 years old; (4) strong willingness to preserve fertility; (5) no contraindications for progestin treatment or pregnancy; (6) not pregnant; and (7) good compliance for treatment. Written informed consent was obtained from all patients before initiating treatment. The exclusion criteria were as follows: (1) use of local or systematic progestins with more than 1 month before hysteroscopic evaluation; (2) recurrent AEH or EEC; (3) evidence of myometrial invasion; and (4) loss of follow‐up. All endometrial tissues in this study were obtained before fertility‐preserving treatment or within 1 month of treatment.

We defined progestin insensitivity as meeting one of the following criteria ¹¹ : (1) presented progressive disease at any time during conservative treatment, (2) maintained stable disease after 7 months of first‐line treatment, or (3) did not achieve CR after 10 months of first‐line treatment. The conditions above indicated progestin insensitive (PIS), whereas other conditions were considered progestin sensitive (PS). The clinical characteristics of all enrolled patients are shown in Table 1.

2.3. Cell lines and cell culture

The human EC cell line Ishikawa was kindly provided by Dr. Yu Yinhua (MD Anderson Cancer Center), and the human EC cell line ECC‐1 was purchased from American Type Culture Collection (Manassas, VA, USA). The human monocyte cell line (THP‐1) was kindly provided by the Stem Cell Bank, Chinese Academy of Sciences. All cell lines were authenticated by short tandem repeat profiling and were routinely tested for no mycoplasma contamination. ECC‐1 and THP‐1 cells were cultured in RPMI 1640 medium, and Ishikawa cells were cultured in DMEM/F12 medium, both supplemented with 10% fetal bovine serum (FBS), 1% penicillin, and streptomycin. All cells were maintained at 37°C in a humidified incubator containing 5% CO2.

2.4. Preparation of human peripheral blood monocyte‐derived macrophages

First, peripheral blood mononuclear cells (PBMCs) were isolated from the buffy coats of healthy donors by density gradient centrifugation with lymphocyte isolation solution (Serumwerk Bernburg AG). Then, CD14⁺ cells were isolated from mononuclear cells by CD14 immunomagnetic beads through positive magnetic selection (Miltenyi Biotec). CD14⁺ monocytes were cultured in RPMI 1640 with 10% FBS and in 6‐well flat‐bottom culture plates at 5 × 10⁵ cells/mL.

2.5. Drug intervention

The following drugs were used for cell interventions as indicated: phorbol‐12‐myristate‐13‐acetate (PMA) (Sigma Aldrich, 793,416), recombinant human IL4 (Sigma Aldrich, srp3093), recombinant human IL13 (Sigma Aldrich, srp3274), recombinant human IL10 (Novoprotein, CX04), recombinant human TGFβ (Novoprotein, CA59), recombinant human IL10 neutralizing antibody (Biolegend, 501,427), recombinant human TGFβ neutralizing antibody (Biolegend, 947,303), and medroxyprogesterone acetate (MPA) (Selleck, NSC‐26386). Vehicles included dimethyl sulfoxide (DMSO), phosphate buffer saline (PBS), or PBS supplemented with 0.1% BSA.

THP‐1 cells and PBMCs were first treated with 100 ng/mL PMA for 24 h, respectively, to generate M0 macrophages, which are differentiated but unpolarized macrophages. Then, M0 cells were treated with IL4 (20 ng/mL)/IL13 (20 ng/mL) for 48 h to induce CD163⁺ macrophage polarization. The cell culture supernatant was collected as conditioned medium (CM) after culturing THP‐1 or PBMC‐derived CD163⁺ macrophages for 24 h and used in further studies as indicated. Prior to MPA or CM treatment, cells were cultured in phenol red‐free medium supplemented with 10% charcoal‐stripped FBS.

2.6. Enzyme‐linked immunosorbent assay (ELISA)

The levels of IL10 and TGFβ in cell culture supernatants of THP‐1 cells and CD163⁺ macrophages were measured by an ELISA kit (R&D Systems) according to the manufacturer's instructions.

2.7. Flow cytometry

Macrophages were incubated with fluorescent‐tagged antibodies for flow cytometry as follows: anti‐Human CD163‐PE, anti‐Human IL10‐APC, and anti‐Human TGFβ‐BV421 (all Biolegend, USA). Then, macrophages were detected by flow cytometry (Beckman). ECC‐1 and Ishikawa cells (300,000 cells/well) were seeded in 6‐well plates and allowed to adhere to culture plates overnight. Cells were then treated with MPA, CM, and IL10/TGFβ for 48 h. The cells were then trypsinized with EDTA‐free trypsin and washed twice with cold PBS. The cells were re‐suspended in 100 μl of binding buffer and stained with 1 μl of annexin V‐FITC and 1 μl of PI working solution for 15 min at room temperature in the dark (Dojindo, Japan). Finally, the cell apoptotic level was determined using a flow cytometer (Beckman). As negative controls, isotype‐matched antibodies with corresponding fluorescent labels were used.

2.8. Real‐time quantitative PCR (RT‐qPCR)

Total RNAs were extracted using an RNA Purification Kit (EZBioscience, B0004). After removing genomic DNAs with a DNA remover, total RNAs were reverse transcribed into cDNAs using the Reverse Transcription Kit (EZBioscience, A0010GQ). cDNA amplification was performed using the TB Green® Premix Ex Taq™ II (TaKaRa, RR820A). The 2^−ΔΔCt method was used to calculate gene expression levels relative to GAPDH. Primers were listed in Table S1.

2.9. Western blotting analysis

Western blotting analysis was conducted as previously described. ¹² The following primary antibodies were used: PR (Santa Cruz Biotechnology, sc‐166,169), cyclin D1 (Cell Signaling Technology, E3P5S), p21 (Cell Signaling Technology, 12D1), p27 (Cell Signaling Technology, D69C12), and β‐actin (Huabio, M1210).

2.10. Cell viability assay

ECC‐1 and Ishikawa cells (3000 cells/well) were seeded in 96‐well plates, allowed to adhere overnight, and treated with MPA, CM, or cytokines for 48 h. Cell viability was detected using Cell Counting Kit‐8 (CCK‐8) in accordance with the manufacturer's instructions (DOJINDO, Japan).

2.11. Immunohistochemical (IHC) staining

IHC staining was performed as previously described. ¹³ Primary antibodies used in IHC staining included CD163 (Abcam, ab182422) and PR (Abcam, ab32085). Semi‐quantitative optical analysis was performed as previously described. ¹⁴

2.12. ATAC‐seq and RNA‐Seq analyses

Ishikawa cells were harvested from 6‐cm dishes in 1 ml Trizol (Invitrogen, Carlsbad, CA, USA) in accordance with the manual after a 24 h induction with 20 μM MPA or DMSO in the presence or absence of CM. RNAs were subjected to RNA‐Seq analysis with a BGISEQ‐500 system by Beijing Institute (BGI), China. RNA integrity was examined using a NanoDrop spectrophotometer (Thermo Fisher) and Bioanalyzer 2100 (Agilent). RNAs were sheared and reverse transcribed into cDNAs using random primers for library construction. Subsequently, sequencing was performed using the prepared library. ¹⁵ All generated raw sequencing reads were filtered to obtain clean reads stored in the FASTQ format. Bowtie2 and HISAT were applied to align the clean reads to the reference gene and genome, respectively. ¹⁶ , ¹⁷ The expression level (FPKM) of genes was calculated by RSEM. ¹⁸ Read counts for each gene were determined using the SubRead package. ¹⁹ Normalization and differential expression analysis were performed using DeSeq2. ²⁰

To investigate chromatin accessibility, Ishikawa cells in 10‐cm dishes were collected after 24 h of treatment with MPA or DMSO in the presence or absence of CM. ATAC‐seq was performed as described previously. ²¹ Libraries were pooled in equimolar ratios with barcodes and sequenced on the BGISEQ‐500 platform (BGI‐Shenzhen, China).

For RNA‐seq analysis, genes with |Log2 Fold change| > 0.5849 and adjusted p < 0.05 were selected as differentially expressed genes (DEGs). For ATAC‐seq analysis, opening or closing peaks with |Log2 Fold change| > 0.5849 and non‐adjusted p < 0.05 were selected according to MAnorm analysis. Raw and processed data are available from the corresponding author on reasonable request.

2.13. Bioinformatic analysis

To investigate the biological relationship between PGR (PR coding gene) and M1/M2‐like macrophage‐secreted cytokine genes, Ingenuity Pathway Analysis software (IPA, Ingenuity System; http://www.ingenuity.com) was used for the analysis of functional biological networks. Network analysis was performed by uploading gene IDs into IPA, carefully ensuring that each gene was uniquely and accurately recognized by the software. Gene interaction networks were then generated automatically (i.e., independent of investigators). Pathways and networks were ranked according to the number of molecules with a cutoff value (p < 0.05) for significantly enriched pathways/networks involving PGR, and cytokine genes were identified using the “Compare” module in IPA.

The bioinformatics analyses used in integration analysis of ATAC‐Seq and RNA‐Seq included REACTOME pathways, transcription factor (TF) prediction, and Motif enrichment. (1) Based on overlapping DEGs by ATAC‐Seq and RNA‐Seq integrated analysis, top ten REACTOME pathways were enriched, and DEGs in the pathways potentially regulating progestin insensitivity were first screened out; (2) potential TFs that regulate the expression of the overlapping DEGs were enriched by HOMER Software, and DEGs‐encoding TFs with p value <0.05 were screened out; and (3) Motif enrichment was performed to identify important TFs by using homer peak analysis software.

We employed deepTools ²² to plot the heatmap of the read coverage. Briefly, the sorted bam files were used to calculate the scores per genome regions by computeMatrix with scale‐regions mode, and the heatmap was generated based on the score matrix by plotHeatmap with default options.

2.14. Statistics analysis

Statistical analyses were performed using SPSS statistical software (version 23.0, IBM). All experiments were repeated at least three times. Student's t‐test, one‐way or two‐way ANOVA, and Spearman's correlation analysis were used for further statistical analyses. The clinical characteristics of patients were analyzed using the Mann–Whitney U test, chi‐square test, or Fisher's exact test as appropriate. Statistical significance was determined as p < 0.05 in two‐sided tests.

3.1. Increased number of infiltrating CD163 ⁺ macrophages in AEH and EEC patients with progestin insensitivity

To determine whether CD163⁺ macrophages regulate progestin sensitivity in patients with AEH/EEC, we first analyzed the relationship between CD163⁺ macrophage infiltration and progestin sensitivity. CD163 was used to identify M2‐like macrophages in AEH or EEC specimens with PS (n = 13) and PIS (n = 9). The clinical characteristics of PS and PIS groups were comparable (Table 1). IHC staining analysis revealed that CD163 was mainly expressed in tumor stromal regions in AEH/EEC tissues (Figure 1A). The number of CD163⁺ macrophages in AEH/EEC specimens was counted and analyzed by semi‐quantitative optical analysis. We found that the number of infiltrating CD163⁺ macrophages in the tumor stroma of patients with PIS was significantly higher than that in the PS group (Figure 1B). Furthermore, infiltrating CD163⁺ macrophages increased gradually with the time to achieve CR (Figure 1C). The increased number of infiltrating CD163⁺ macrophages indicated their important roles in mediating progestin insensitivity in AEH/EEC patients.

3.2. CD163 ⁺ macrophages decreased the sensitivity of EC cells to progestin therapy

Based on the clinical evidence above, we assumed that infiltrating CD163⁺ macrophages might contribute to progestin sensitivity in AEH/EEC. Accordingly, CD163⁺ macrophages were successfully induced from THP‐1 cells (Figure S1) and PBMCs, respectively (Figure S2B). Macrophages were terminally differentiated after treatment with PMA and IL4/IL13 and showed higher levels of M2 type macrophage markers, including CD163, IL10, and TGFβ. First, we co‐cultured EC cells with different amounts of CD163⁺ macrophage‐derived CM diluted with normal medium (NM). As shown in Figure S2A, cells cultured with diluted CM (CM:NM = 1:2) showed the similar cell viability compared with cells treated with complete NM. Therefore, diluted CM (CM:NM = 1:2) was applied for the following study.

We next explored the response of EC cells to MPA in the presence or absence of CM. We found that CM stimulated the proliferation of ECC‐1 and Ishikawa cells, whereas the inhibitory rate of MPA was significantly decreased in Ishikawa cells (59.18 ± 0.7975% vs. 30.67 ± 2.050%, p < 0.0001) and ECC‐1 (70.03 ± 0.2963% vs. 30.29 ± 0.9188%, p < 0.0001) (Figure 2A,B). PBMC‐derived CD163⁺ macrophages were employed to further confirm our findings. CM from CD163⁺ macrophages could induce a significant decline of the inhibitory rate of MPA in Ishikawa (31.20 ± 0.6673%vs. 57.20 ± 0.2406%, p < 0.0001) and ECC‐1 (22.20 ± 1.477% vs. 68.39 ± 0.2782%, p < 0.0001) cells compared with CM from M0 macrophages (Figure S2C,D). Progestins induce the cell cycle arrest of EC cells by downregulating cyclin D1 and upregulating p21 and p27 expression. ²³ Cyclin D1 is an essential factor for cell cycle G1/S transition, whereas p21 and p27 bind to cyclin‐CDK complexes and induce cell cycle arrest. ²⁴ As shown in Figure 2C,D, MPA treatment alone significantly decreased cyclin D1 expression and increased p21 and p27 expression in EC cells. CM of CD163⁺ macrophages generated from THP‐1 cells significantly diminished the MPA‐induced cell cycle inhibition. Consistent results were obtained when EC cells treated with CM of CD163⁺ macrophages generated from PBMCs (Figure S2E,F). Another important indicator of effective progestin treatment is an increase in apoptotic EC cells. We next evaluated whether CM affects progestin‐induced EC cell apoptosis using an annexin‐V and PI assay. Compared with the MPA alone group, the proportion of early and late apoptotic cells was significantly decreased in the MPA and CM combination group of ECC‐1 cells (20.78 ± 0.5485% vs. 11.94 ± 1.075%, p < 0.0001) and Ishikawa cells (20.78 ± 0.5485% vs. 11.94 ± 1.075%, p < 0.0001) (Figure 2D). Collectively, these results demonstrated that CD163⁺ macrophages decreased the progestin sensitivity of EC.

3.3. CD163 ⁺ macrophages and secreted cytokines decreased PR protein in EC cells

It is well established that the effects of progestin on the endometrium are mediated by interactions with the PR and PR‐mediated signaling pathway. ²⁵ We asked whether PR expression was downregulated in CD163⁺ macrophage‐induced PIS EC cells. First, IHC staining of serial endometrial tissue sections showed increased infiltrating CD163⁺ macrophages in the stroma and decreased PR expression levels in adjacent epithelial and stroma regions (Figure 3A). We further analyzed the correlation between the number of CD163⁺ macrophages and epithelial PR expression using Pearson's correlation test, and the results demonstrated that the number of CD163⁺ macrophages was significantly negatively correlated with the PR IHC score (Figure 3B). To determine the PR expression status after treatment with CM from CD163⁺ macrophages, we analyzed the changes in PR expression in EC cells. Real‐time PCR showed that treatment with CM derived from CD163⁺ macrophages decreased the PR mRNA level in a dose‐dependent manner (Figure 3C). Similarly, Western blotting analysis demonstrated that CM from both THP‐1 and PBMC‐derived CD163⁺ macrophages reduced PRA and PRB protein expression in ECC‐1 and Ishikawa cells dose‐dependently (Figure 3D and Figure S2G). This suggested that PR downregulation was an important event for the CD163⁺ macrophage‐induced desensitization of EC cells to progestins.

Our above findings showed that CM derived from CD163⁺ macrophages induced progestin insensitivity and downregulated PR expression. We predicted that cytokines secreted by CD163⁺ macrophages might be involved in the CM‐mediated progestin insensitivity. To test this, IPA was used to determine whether a potential regulating network existed between CD163⁺ macrophage‐related cytokine genes and PGR. As shown in Figure S3A, the IPA network illustrated possible molecular interactions among several M1/M2 cytokines and PGR, and IL10 and TGFβ were identified as potential cytokines regulating PGR in EC cells. ELISA further confirmed that THP‐1‐derived CD163⁺ macrophages exhibited a significantly increased secretion of IL10 and TGFβ than the THP‐1 cells (Figure S2H). Next, IL10 or TGFβ could inhibit PGR transcription and PR protein expression in EC cells (Figure 3E,F). Furthermore, CM‐induced PR downregulation effects could be antagonized by IL10/TGFβ neutralizing antibodies in EC cells (Figure 3G,H). IL10/TGFβ neutralizing antibodies could also reverse the progestin insensitivity induced by CM in ECC‐1 cells (Figure 3I).

Taken together, these findings indicate that CD163⁺ macrophages decreased PR expression and desensitized EC cells to progestins.

3.4. CD163 ⁺ macrophages antagonized PR signaling, driving EC cell unresponsiveness to progestins

The data above showed that CD163⁺ macrophages desensitized EC cells to progestins, but the molecular/biological profile changes in EC cells remain unclear. Next, RNA‐seq analysis of Con, MPA, CM, and MPA_CM treatment groups was performed. To confirm the quality of RNA‐seq data, Pearson's correlation was calculated based on the read counts (Figure S4A), and principal component analysis was performed according to the amount of data variability (Figure S4B). Both analyses showed clustered samples by group. The mRNA expression profiles were presented in Figure S4C. The numbers of upregulated and downregulated DEGs were calculated according to the Con or MPA group (Figure S4D). To determine how CD163⁺ macrophage‐derived CM affects MPA‐regulated DEGs, an expression heat map of 1180 MPA‐upregulated and ‐downregulated DEGs in all samples was generated (Figure 4A). Here, the MPA_CM group tended to show decreased MPA‐upregulated DEGs and increased MPA‐downregulated DEGs compared with the MPA group. Among 744 MPA‐upregulated DEGs, the expression of 42.5% was reversed, 51.6% remained unaffected, and only 5.9% were increased in the MPA_CM group (Figure 4B). Similarly, among 436 MPA‐downregulated DEGs, the expression of 37.8% was reversed, 58.0% remained unaffected, and only 4.1% were decreased in the MPA_CM group (Figure 4C). Taken together, we concluded that CM derived from CD163⁺ macrophages was likely to antagonize PR signaling in EC cells by blocking or even reversing the expression of MPA‐regulated DEGs.

3.5. CD163 ⁺ macrophage‐induced profile alterations determined by RNA‐seq and ATAC‐seq

To further explore the mechanism by which CD163⁺ macrophages antagonize PR signaling in EC cells, DEGs from CM versus Con and MPA_CM versus MPA were merged and 211 overlapping upregulated DEGs and 124 overlapping downregulated DEGs were generated (Figure 5A). To understand the functional alterations in the 335 dysregulated DEGs, gene ontology (GO) terms were generated, and the top ten terms were shown in Figure 5B–D. The GO terms for the biological process (BP) category included extracellular matrix organization, positive regulation of cell migration, and cell adhesion (Figure 5B). Similarly, the GO terms for the cellular component (CC) category were significantly enriched in extracellular space, extracellular exosome, plasma membrane, integral component of plasma membrane, cell surface, external side of plasma membrane, extracellular region, proteinaceous extracellular matrix, endoplasmic reticulum lumen, and extracellular matrix (Figure 5C). In addition, the molecular function category included protein binding, fibronectin binding, heparin binding, receptor binding, cytokine activity, protease binding, integrin binding, semaphorin receptor binding, extracellular matrix binding, and chemorepellent activity (Figure 5D). Furthermore, KEGG pathway analysis was performed on these overlapping DEGs, and the enriched pathways included ECM‐receptor interaction, TNF signaling pathway, focal adhesion, PI3K‐AKT signaling pathway, cytokine‐cytokine receptor interaction, pathway in cancer, NF‐kappa B signaling pathway, cell adhesion molecules, and others (Figure 5E). Based on the GO and KEGG analyses, extracellular matrix‐related signaling was significantly enriched in CD163⁺ macrophage‐induced EC cells. Taken together, these findings suggested that extracellular matrix (ECM) related mechanisms might be involved in the CD163⁺ macrophage‐mediated desensitization of EC cells to progestin.

To accurately obtain transcriptional regulatory sequence information based on chromatin accessibility, ATAC‐seq was performed (Figure S5A). The M‐A plot after normalization showed opening and closing peaks in genes relative to the corresponding control (Figure S5B). Then, the opening genes in ATAC‐seq and upregulated DEGs in RNA‐seq were overlapped. Similarly, the closing genes in ATAC‐seq and downregulated DEGs in RNA‐seq were overlapped. The number of overlapped genes was shown in Figure S5C. The targeted DEGs with chromatin open or closed regions were obtained from merged CM versus Con and MPA_CM versus MPA groups based on the integrated analysis of ATAC‐seq and RNA‐seq (Figure 5F). Based on the targeted DEGs, 10 candidate transcription factors, including PAX4, GATA1, MYOD1, PITX2, FOXF2, POU1F1, RUNX1, SRF, TBP, and CBFA2T3, were identified by homer peak analysis (Figure 5F). Within them, FOXF2, POU1F1, and RUNX1 are also involved in regulating ECM‐related signaling, which is consistent with GO and KEGG pathway enrichment analyses. In summary, through RNA and ATAC‐seq analysis, ECM signaling and ECM‐related transcription factors, FOXF2, POU1F1, and RUNX1, were identified to be the critical molecular factors underlying IL10/TGFβ induced progestin desensitization.