Expression of “Hallmarks of Cancer” Genes in Cervical Carcinoma Is Differentially Affected by GPER1 Overexpression Depending on Histologic Entity

Abstract

Background/Aim

Cervical cancer (CC) remains the fourth most common malignancy in women worldwide. Current treatments primarily consist of surgery and combined radiochemotherapy, while targeted therapies, as seen in other malignancies, remain underdeveloped. The G-protein-coupled estrogen receptor (GPER1) is implicated in various cancers and can differentially influence tumor behavior, though its precise role in CC remains unclear, with both tumor-promoting and tumor-suppressive effects reported. We previously explored the impact of stable GPER1 overexpression (OE) in CC cell lines, SiHa (cervical squamous cell carcinoma, CSCC) and HeLa (cervical adenocarcinoma, CAC), analyzing proliferation, migration, invasion, apoptosis, and stem cell properties. GPER1-OE enhanced tumorigenic properties in CSCC cells but demonstrated tumor-suppressive effects in CAC cells. To investigate the underlying mechanisms, we conducted next-generation sequencing (NGS) analyses, which supported our earlier findings.

Materials and Methods

SiHa CSCC and HeLa CAC cells with stable GPER1-OE were generated. The effects of GPER1-OE on gene expression were then examined using next-generation sequencing (NGS) analyses.

Results

In CSCC cells, GPER1-OE upregulated genes involved in tumorigenic pathways, including epithelial-to-mesenchymal transition (EMT), mTOR-C1, Myc, p53, hypoxia, and angiogenesis signaling. In CAC cells, however, GPER1-OE downregulated these pathways, along with additional pathways such as KRAS, Hedgehog, TNFα (via NFκB), and Wnt/Beta-Catenin signaling.

Conclusion

The results highlight the divergent roles of GPER1-OE in CC cells, promoting oncogenesis in CSCC while exerting tumor-suppressive effects in CAC by modulating oncogenic signaling pathways.

Introduction

Cervical cancer (CC) is still the fourth most common malignancy in women worldwide, despite the introduction of effective HPV vaccination programs. Standard treatment typically involves primary surgical hysterectomy (1) or combined radiochemotherapy (2). In advanced stages, VEGF antibody bevacizumab is employed to inhibit tumor angiogenesis (3,4). However, unlike other malignancies such as breast cancer (5,6) or melanoma (7), no targeted molecular therapy is currently established for CC. This underscores the need for further research, particularly in the development of targeted therapeutic approaches. Estrogen receptors (ERs) have emerged as promising therapeutic targets and remain a focus of ongoing studies (8-10).

Histologically, CC is classified into cervical squamous cell carcinomas (CSCC), cervical adenocarcinomas (CAC), and less common subtypes (11). These two main subtypes differ significantly in etiology, pathology, and clinical presentation. While the incidence of CSCC is declining, the incidence of CAC is increasing (12,13). CSCC arises from the transformation of squamous epithelial cells lining the cervix and is strongly associated with persistent high-risk human papillomavirus (hrHPV) infection, particularly HPV type 16 (14). Progression from HPV infection to cervical intraepithelial neoplasia (CIN) and ultimately to invasive CSCC is a well-established pathophysiological process mediated by viral oncoproteins. CSCC is typically diagnosed at a less advanced stage than adenocarcinoma, resulting in a better prognosis (15). In contrast, CAC originates from the glandular epithelial cells of the endocervix. Although CAC accounts for only 10-20% of CC, its incidence is increasing, especially among younger women (12,16). While CAC is also linked to hrHPV, particularly HPV type 18, approximately 15% of cases are unrelated to HPV infection (17). CAC is more difficult to detect with conventional screening methods like Pap smears due to the higher location of glandular lesions in the cervical canal, which delays diagnosis and contributes to a poorer prognosis (18).

The G-protein-coupled estrogen receptor (GPER1, formerly GPR30) (19) differs functionally from nuclear ERα and ERβ. GPER1, characterized by seven transmembrane domains, is localized in the plasma membrane and mostly intracellularly within the endoplasmic reticulum (20). Its non-genomic effects are initiated by ligand binding, typically 17β-estradiol, but GPER1 can also be targeted by many natural and synthetic molecules, either as selective or combined estrogen receptor agonists or antagonists (21,22). Ligand binding induces conformational changes and activation of G-proteins. This triggers signal transduction cascades (23) such as mitogen-activated protein kinases (MAPK)/extracellular signal-regulated kinases (ERK) and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT), crucial in both physiological and pathological processes (24). Through the Rho-ROCK pathway, GPER1 regulates transcription factors YAP and TAZ, impacting gene expression and proliferation. GPER1 also activates SRC kinases, inducing matrix metalloproteinases that cleave HB-EGF, leading to EGFR activation. GPER1 also enhances NOTCH and VEGF signaling, crucial for angiogenesis (23). By coordinating these pathways, GPER1 regulates processes such as tumor proliferation, angiogenesis, metastasis, and metabolism, playing roles in both normal physiology and cancer progression (23).

The role of GPER1 in tumor progression across various cancers is controversial, as it can either act as a tumor suppressor or an oncogene depending on the tissue type and tumor context (23). In breast cancer, GPER1 generally exhibits tumor-promoting effects (25-27) and has been implicated in resistance to tamoxifen (26,28,29) and to the CDK4/6 inhibitor palbociclib in breast cancer (30). In triple negative breast cancer cells, GPER1 knockdown enhances the antitumor efficacy of selective estrogen receptor β agonists (31). For ovarian and endometrial cancers, GPER1 has shown both tumor-promoting (32-34) and tumor-suppressive functions (35-37). Conversely, GPER1 appears to act as a tumor suppressor in malignant melanoma (38,39) and vulvar carcinoma (40,41). However, the precise mechanisms by which GPER1 contributes to carcinogenesis in various tissues remain incompletely understood and are the focus of current research (39,42,43).

GPER1 is frequently expressed in CC (44) and higher expression in early-stage CC has been associated with improved overall and recurrence-free survival (45). In vitro, GPER1 predominantly demonstrates tumor-suppressive effects in CC (46,47), including enhanced activation of cell death mechanisms with GPER1 overexpression (OE) (48). These findings support its tumor-suppressive role in CC. However, a clinical study (49) reported that elevated GPER1 expression in CC correlated with worse patient prognosis and invasive tumor growth, suggesting a potential oncogenic role for GPER1 in certain contexts. GPER overexpression (OE) resulted in divergent tumor behaviors in CC, depending on the histological subtype. In the SiHa CSCC cell line, GPER1-OE was associated with more aggressive tumor features, including increased proliferation, migration, and enhanced stem-like properties. In contrast, in the HeLa CAC cell line, GPER1-OE was linked to less aggressive tumor behavior, characterized by reduced proliferation and migration, increased apoptosis, and diminished stem-like properties.

To further investigate the role of GPER1 in CC, this study specifically analyzed the genetic background of stable GPER1-OE in CC cell lines aiming to uncover genetic alterations underlying the observed changes in cellular behavior. For this purpose, CC cell lines SiHa (CSCC) and HeLa (CAC) were transfected using the Sleeping Beauty transposon system to establish stable GPER1-OE. Additionally, gene expression in GPER1-overexpressing cells was analyzed through next-generation sequencing (NGS) to evaluate tumor biology-relevant signaling pathways. NGS results were correlated with functional cell characterization assays to elucidate the molecular mechanisms underlying the effects of GPER1-OE. Ultimately, this study aimed to assess the therapeutic relevance of GPER1 as a potential target for personalized CC therapies.

Materials and Methods

Cell culture. The human cervical cell lines SiHa (HPV16+) and HeLa (HPV18+) were obtained from the American Type Cell Collection (ATCC; Manassas, VA, USA) and cultured in minimum essential medium (MEM; L0416-500, Biowest, Nuaillé, France) supplemented with 10% fetal bovine serum (FBS; S181B-500, Biochrom, Berlin, Germany) and 1% Penicillin/Streptomycin (P/S; L0022-100, Biowest). To retain the identity of the cell lines, purchased cells were expanded and aliquots were frozen in liquid nitrogen. A new frozen stock was used every half year and mycoplasma testing of cultured cell lines was performed routinely using the polymerase chain reaction (PCR) Mycoplasma Test Kit I/C (D101-02, Vazyme, Düsseldorf, Germany). All cells were cultured in a humidified atmosphere with 5% CO₂ at 37˚C.

Transfection and establishment of stable GPER1 overexpression. Stable transfection of the cells was performed using the ‘Sleeping Beauty Transposon System’, a non-viral vector (SB vector) (50,51). This system is based on a transposase, an enzyme that enables the targeted and long-term integration of the transposon containing the desired gene into the genome of the target cells. Specifically, a plasmid containing the cDNA coding sequence of GPER1 under the control of a constitutive eukaryotic promoter (CMV promoter) was constructed by us (Figure 1) and manufactured by Vector Builder (Chicago, IL, USA). A cassette containing an IRES and a fluorescent reporter gene (GFP) was added downstream of the cDNA to allow selection of cells with integrated transgene by FACS sorting. For this purpose, 200,000 cells were seeded in a 6-well plate. The next day, the transfection mixture consisting of 1 µg plasmid, 1 µg transposase (Vector Builder), 12 µl Turbofect transfection reagent (Thermo Scientific, Darmstadt, Germany) and 200 µl Opti-MEM I medium (Gibco, Carlsbad, CA, USA) was prepared and added drop by drop to the cells in a 6-well plate after a 20-minute incubation and gently swirled. Incubation was carried out overnight for 8-10 hours to enable effective transfection. The following day, the transfection mixture was replaced with 2 ml Opti-MEM (Gibco).

Figure 1.

Structure of the G-protein-coupled estrogen receptor 1 (GPER1) stable overexpression plasmid. The plasmid contains the cDNA coding sequence of GPER1 under the control of a constitutive eukaryotic promoter (CMV promoter). A cassette containing an IRES and a fluorescent reporter gene (GFP) was added downstream of the cDNA to allow selection of cells with the integrated transgene by FACS sorting. Reproduced from Cancer Genomics & Proteomics, Vol. 22, No. 3, pp. 397-414, 2025

Cell separation to obtain a pure population of GPER1-overexpressing cells. Three days after transfection, the cells were first washed with DPBS (Pan Biotech, Aidenbach, Germany), then trypsinized and centrifuged in 15 ml tubes at room temperature at 1,300 rpm. After centrifugation, the cells were resuspended in DPBS (Pan Biotech), followed by another centrifugation and subsequent resuspension in DPBS (Pan Biotech) with 2% FCS (Biochrom). This cell mixture was transferred to sterile FACS tubes (5 ml) and then separated using the cell sorter based on the measured GFP signal. Since GPER1 in the SB vector are coupled to a GFP, negative and positive cells can be easily distinguished by flow cytometry using the GFP signal. After sorting, the cells were seeded on 24-well or 6-well plates to ensure a homogeneous culture of GPER1-overexpressing cells. Finally, ectopic expression of GPER1 was validated by qRT-PCR and Western blot in the pool of sorted cells or alternatively in single positive clones (52). Clones with the clearest GPER1-OE in qPCR and GFP signal in FACS were selected for further experiments.

Next generation sequencing (NGS). To compare the transcriptome of GPER1 overexpressing cells and control cells by NGS, total RNA was processed using the TruSeq RNA Library Prep Kit v2 according to the manufacturer's instructions (Illumina, San Diego, CA, USA) with half the volume. First, total RNA was isolated and then poly-A mRNA was captured with magnetic beads bound to poly-T oligos followed by fragmentation and synthesis of cDNA first and second strands. The cDNA was adenylated at the 3'-ends and indexed adapters were ligated to the cDNA fragments. Depending on the amount of starting material, the adapters were ligated with the corresponding stock concentrations, followed by an amplification step. Finally, a double size selection was performed in two steps with 0.6× and 0.9× NucleoMags (Macherey-Nagel, Düren, Germany), respectively, to enrich a DNA fragment size between 200 and 600 bp. The DNA concentration was determined using the Qubit fluorometer (Thermo Fisher) according to the instructions of the highly sensitive dsDNA assay (Thermo Fisher). The quality of the coding transcriptome and the average size were determined using a DNA bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Subsequently, NGS (paired-end 100 pb) was performed by the BGI Tech Solutions laboratory (BGI Europe, Copenhagen, Denmark) to compare the transcriptome of GPER1-overexpressing cells and control cells. The raw data (Fastq files) were processed using the Galaxy software v20.01 (https://galaxy.gwdg.de). After quality check using FastQC v0.6.5 (https://galaxy.gwdg.de), sequencing data were trimmed (FASTQ Trimmer tool v1.1.5; https://galaxy.gwdg.de) and aligned to the hg38 reference genome with the RNAstar tool v2.7.8a (https://galaxy.gwdg.de). Next, reads were assigned to their respective genomic features using FeatureCounts v2.0.8 (https://galaxy.gwdg.de). Finally, differential gene expression analyses were performed using DESeq2 v2.0.1 (https://galaxy.gwdg.de). Analyses of gene signature enrichment were performed using the gene set enrichment analysis (GSEA) tool v4.3.3 (https://www.gsea-msigdb. org/gsea) and the web-based Enrichr tool (https:// maayanlab.cloud/Enrichr/). The GSEA analyses were ran on a count matrix containing all genes harboring expression level over the background (basemean >15 normalized counts). The 'Hallmarks of cancer' gene sets database (H.all.v7.0) was used.

Statistical analysis. All experiments in this study were performed in biological duplicates. The findings were visualized through a gene set enrichment analysis (GSEA), which identified pathways that were either up-regulated or down-regulated by GPER1 overexpression. For each gene set, the enrichment score (ES) and normalized enrichment score (NES) are presented. Additionally, a heatmap was generated in GraphPad Prism (v. 8.4.3, GraphPad Software, Inc., San Diego, CA, USA) to illustrate the expression levels of relevant genes within these pathways.

Results

To investigate molecular differences in gene expression associated with GPER1-OE, NGS analysis was performed. Specific genes and signaling pathways, particularly those involved in tumor progression, were analyzed. The results were categorized into functional groups based on the ‘Hallmarks of Cancer’ (53-55) by GSEA to identify differences in gene expression between GPER1-overexpressing and control cells. For NGS analysis, the cell lines SiHa GPER1-OE, HeLa GPER1-OE, and control cells from the same cell line were used. The analysis focused on tumor related pathways.

Top differentially expressed genes in CC cell lines with stable GPER1 overexpression. The 30 top differentially expressed genes associated with “Hallmarks of cancer” in CC cell lines SiHa and HeLa with GPER1-OE compared to control cells in the NGS analysis are shown in Figure 2. In SiHa cells overexpressing GPER1, most genes are predominantly up-regulated, indicating increased gene expression in these cells. In contrast, gene expression in HeLa cells overexpressing GPER1 shows both up- and down-regulation.

Figure 2.

The 30 top differentially expressed genes from the NGS analysis of SiHa and HeLa cells with stable GPER1-OE. Normalized gene expression is shown as a heatmap, with control cells on the left and GPER1-overexpressing (GPER1-OE) SiHa (A) and HeLa cells (B) on the right. The values of the gene expression level of each gene are represented as colors, ranging from blue to red, based on the lowest (0) and highest (1.0) normalized relative quantities values of each gene, respectively. n=2.

Regulation of epithelial-mesenchymal transition pathway in CC cell lines with stable GPER1 overexpression. The signaling pathway of epithelial-mesenchymal transition showed differences in the GSEA profiles of the SiHa and HeLa cell lines with GPER1-OE. In SiHa cells, the genes of this gene set were down-regulated in control cells and up-regulated in the GPER1-overexpressing cells (SiHa GPER1-OE) (ES: -0.18865006, NES: -3.4380407, up-regulated in GPER1-OE) (Figure 3A). The genes shown in the heatmap also showed stronger up-regulation in GPER1-overexpressing SiHa cells (Figure 3B).

Figure 3.

Enrichment analysis of the epithelial-mesenchymal transition signaling pathway in SiHa and HeLa cells with stable GPER1-OE. Enrichment score of SiHa cells (A) with up-regulated genes normalized in a heatmap (B) and enrichment score of HeLa cells (C) with down-regulated genes normalized in a heatmap (D). The values of the gene expression level of each gene are represented as colors, ranging from blue to red, based on the lowest (0) and highest (1.0) normalized relative quantities values of each gene, respectively. NES SiHa: -3.4380407, HeLa: 0.8360039 (A, C). n=2.

However, in HeLa cells, genes associated to epithelial-mesenchymal transition pathway were more highly expressed in HeLa control cells, while they were less highly expressed in HeLa cells overexpressing GPER1, as shown by the GSEA profile (ES: 0.16452025, NES: 0.6935814, up-regulated in control) (Figure 3C). This can also be seen in the heat map, which shows a higher expression of genes shown in HeLa control cells compared to GPER1-OE cells (Figure 3D).

Regulation of mTOR-C1 signaling pathway in CC cell lines with stable GPER1 overexpression. The mTOR-C1 signaling pathway showed significant differences in the GSEA profiles of the SiHa and HeLa cell lines with GPER1-OE. In SiHa cells, genes of this gene set were down-regulated in SiHa control cells and up-regulated in SiHa GPER1-OE (ES: -0.12129349, NES: NaN, up-regulated in GPER1-OE) (Figure 4A). Genes shown in the heatmap also showed stronger up-regulation in GPER1-overexpressing SiHa cells (Figure 4B).

Figure 4.

Enrichment analysis of the mTORC1 signaling pathway in SiHa and HeLa cells with stable GPER1-OE. The figure shows the enrichment score of SiHa cells (A) with the up-regulated genes in a heatmap (B) and the enrichment score of HeLa cells (C) with the down-regulated genes in a heatmap (D). The values of the gene expression level of each gene are represented as colors, ranging from blue to red, based on the lowest (0) and highest (1.0) normalized relative quantities values of each gene, respectively. NES SiHa: NaN, HeLa: 0.8360039 (A, C). n=2.

However, in HeLa cells, mTOR-C1-associated genes were enriched in the control group, while their expression was lower in HeLa GPER1-OE cells, which is illustrated by the GSEA profile (ES: 0.19741023, NES: 0.8360039, up-regulated in the control) (Figure 4C). This is also reflected in the heatmap results, which show higher expression of genes in HeLa control cells compared to GPER1-OE cells (Figure 4D).

Regulation of MYC signaling pathway in CC cell lines with stable GPER1 overexpression. The MYC signaling pathway showed differences in the GSEA profiles of the SiHa and HeLa cell lines with GPER1-OE. In SiHa cells, genes of the MYC signaling pathway gene set were down-regulated in SiHa control cells and up-regulated in the GPER1-overexpressing cells (SiHa GPER1-OE) (ES: -0.24510589, NES: -2.6143346, up-regulated in GPER1-OE) (Figure 5A). The genes shown in the heatmap also showed stronger up-regulation in GPER1-overexpressing SiHa cells (Figure 5B).

Figure 5.

Enrichment analysis of the MYC signaling pathway in SiHa and HeLa cells with stable GPER1-OE. The figure shows the enrichment score of SiHa cells (A) with the up-regulated genes in a heatmap (B) and the enrichment score of HeLa cells (C) with the down-regulated genes in a heatmap (D). The values of the gene expression level of each gene are represented as colors, ranging from blue to red, based on the lowest (0) and highest (1.0) normalized relative quantities values of each gene, respectively. NES SiHa: -2.6143346, HeLa: 0.8360039 (A, C). n=2.

However, in HeLa cells, MYC-associated genes were more highly expressed in the control group, while they were less highly expressed in HeLa GPER1-OE cells, as shown by the GSEA profile (ES: 0.21148074, NES: 0.8852504, up-regulated in control) (Figure 5C). This can also be seen in the heat map, which shows a higher expression of genes shown in HeLa control cells compared to GPER1-OE cells (Figure 5D).

Regulation of angiogenesis pathway in CC cell lines with stable GPER1 overexpression. Angiogenesis signaling showed differences in the GSEA profiles of the SiHa and HeLa cell lines with GPER1-OE. In SiHa cells, genes of this gene set were down-regulated in SiHa control cells and up-regulated in the GPER1-overexpressing cells (SiHa GPER1-OE) (ES: -0.2213367, NES: -1.3548841, up-regulated in GPER1-OE) (Figure 6A). Genes shown in the heatmap also showed stronger up-regulation in GPER1-overexpressing SiHa cells (Figure 6B).

Figure 6.

Enrichment analysis of angiogenesis signaling in SiHa and HeLa cells with stable GPER1-OE. The figure shows the enrichment score of SiHa cells (A) with the up-regulated genes in a heatmap (B) and the enrichment score of HeLa cells (C) with the down-regulated genes in a heatmap (D). The values of the gene expression level of each gene are represented as colors, ranging from blue to red, based on the lowest (0) and highest (1.0) normalized relative quantities values of each gene, respectively. NES SiHa: -1.3548841, HeLa: 1.433516 (A, C). n=2.

In contrast, genes associated with angiogenesis signaling were up-regulated in HeLa control cells, whereas they were less expressed in HeLa GPER1-OE cells, as shown by the GSEA profile (ES: 0.41423646, NES: 1.433516, up-regulated in control) (Figure 6C). This can also be seen in the heatmap, which shows a higher expression of genes shown in HeLa control cells compared to GPER1-OE cells (Figure 6D).

Regulated hypoxia pathway in CC cell lines with stable GPER1 overexpression. Additionally, the hypoxia signaling pathway showed differences in the GSEA profiles of the SiHa and HeLa cell lines with stable GPER1-OE. In SiHa cells, genes of this gene set were down-regulated in the control group and increasingly up-regulated in GPER1-overexpressing cells (SiHa GPER1-OE) (ES: -0.112475306, NES: -2.1287837, up-regulated in GPER1-OE) (Figure 7A). Genes shown in the heatmap also showed stronger up-regulation in GPER1-overexpressing SiHa cells (Figure 7B).

Figure 7.

Enrichment analysis of hypoxia signaling in SiHa and HeLa cells with stable GPER1-OE. The figure shows the enrichment score of SiHa cells (A) with up-regulated genes in a heatmap (B) and the enrichment score of HeLa cells (C) with down-regulated genes in a heatmap (D). The values of the gene expression level of each gene are represented as colors, ranging from blue to red, based on the lowest (0) and highest (1.0) normalized relative quantities values of each gene, respectively. NES SiHa: -2.1287837, HeLa: 0.9313551. n=2.

However, in HeLa cells, hypoxia-associated genes were enriched in HeLa control cells, while they were less expressed in HeLa GPER1-OE cells, which is illustrated by the GSEA profile (ES: 0.2146684, NES: 0.9313551, up-regulated in the control) (Figure 7C). This can also be seen in the heatmap, which shows higher expression of genes shown in HeLa control cells compared to GPER1-OE cells (Figure 7D).

Regulation of TNFα- (via NFκB) signaling pathway in CC cell lines with stable GPER1 overexpression. Moreover, genes of the TNFα- (via NFκB) signaling were down-regulated in HeLa GPER1-overexpressing cells, while they were enriched in HeLa control cells, which is illustrated by the GSEA profile (ES: 0.23743154, NES: 1.0579472, up-regulated in the control) (Figure 8A). This can also be seen in the heatmap, which shows higher expression of genes shown in HeLa control cells compared to GPER1-OE cells (Figure 8B).

Figure 8.

Enrichment analysis of the TNFα- (via NFκB) signaling pathway in HeLa cells with stable GPER1-OE. The figure shows the enrichment score (A) and the increased down-regulated genes associated to the TNFα- (via NFκB) signaling pathway of GPER1-overexpressing cells in a heatmap (B). The values of the gene expression level of each gene are represented as colors, ranging from blue to red, based on the lowest (0) and highest (1.0) normalized relative quantities values of each gene, respectively. NES HeLa: 1.0579472. n=2.

Additional regulated signaling pathways in CC cell lines with stable GPER1 overexpression. In addition to the results shown, NGS analysis also revealed an increased up-regulation of genes associated to p53, NOTCH- and IL6-JAK-STAT3 signaling pathways in SiHa cells with stable GPER1-OE compared to control cells of this cell line [NES (p53): -1.1848131, NES (NOTCH): 0.7713486, NES (IL6-JAK-STAT3): -0.4999823].

In contrast, HeLa cells with stable GPER1-OE exhibited a pronounced down-regulation of genes involved in p53, NOTCH and IL6-JAK-STAT3 signaling pathways [NES (p53): 1.9359634, NES (NOTCH): 0.44311014, NES (IL6-JAK-STAT3): 0.7277576].

Moreover, in HeLa cells with stable GPER-OE, genes associated with KRAS, myogenesis and PI3-Akt-mTOR signaling pathways, as well as Hedgehog, inflammatory response and Wnt/β-Catenin signaling pathways, were more strongly down-regulated compared to HeLa control cells [NES (KRAS): 1.3972607, NES (myogenesis): 2.0867476, NES (PI3-Akt-mTOR): 0.7108444, NES (Hedgehog), NES (inflammatory response): 1.8515202, NES (Wnt/β-Catenin): 0.6550231].

Discussion

GPER1 is a seven-transmembrane receptor and is activated by 17β-estradiol (19,20). It is known to regulate and activate multiple signaling pathways, including cAMP, MAPK/ERK, and EGFR signaling cascades (21,44,56). GPER1 influences various neoplasms and can exhibit either tumor-suppressive or oncogenic properties depending on the tumor entity. In cervical cancer (CC), GPER1 primarily functions as a tumor suppressor (45,46,48); however, GPER1 overexpression (OE) resulted in a worse prognosis and survival, reported by a clinical study (49). Upon GPER1-OE in vitro, oncogenic properties were predominantly observed in cervical squamous cell carcinoma (CSCC), whereas tumor-suppressive effects were more apparent in cervical adenocarcinoma (CAC) (52).

These findings highlight the importance of distinguishing between histological subtypes when assessing the role of GPER1 in CC. To investigate the contrasting effects of GPER1-OE in CC cells, genome-wide transcriptome analyses were performed using next-generation sequencing (NGS) on SiHa CSCC and HeLa CAC cell lines with stable GPER1-OE, compared to their respective control cells.

NGS has provided critical insights into the genetic basis of GPER1-OE and its impact on the transcriptomic landscapes of CC cells, revealing the regulation of oncogenic signaling pathways associated with cell proliferation, survival, and tumor progression in a histological subtype-dependent manner (57-59). GPER1, known for its capacity to activate diverse and complex signaling pathways (23), demonstrates altered pathway activation under OE conditions compared to low expression levels. NGS data further underscore the distinct and often opposing effects of GPER1-OE on signaling pathways in SiHa and HeLa cells, emphasizing the pivotal role of histological subtype in GPER1-mediated modulation. In SiHa CSCC cells, genes in pathways such as EMT, MYC, mTORC1, p53, angiogenesis, and hypoxia, were up-regulated. Conversely, in HeLa CAC cells, GPER1-OE led to down-regulation of genes within the same pathways - EMT, MYC, mTORC1, p53, angiogenesis, and hypoxia - as well as additional suppression of KRAS, Hedgehog, TNFα (via NFκB), and Wnt/β-catenin signaling pathways. The up-regulation of these pathways in SiHa CSCC cells, correlates with their more aggressive tumor behavior, whereas the down-regulation observed in HeLa CAC cells aligns with their comparatively less aggressive phenotype in the form of increased apoptosis, reduced proliferation, and diminished migration capacity (52).

These contrasting behaviors in up- and down-regulation of oncogenic pathways between the two CC cell lines can be attributed to their intrinsic differences, including origins and etiology as well as genetic and epigenetic landscapes (60,61) and therefore influence the effect of stable GPER1-OE but also therapy and prognosis.

CSCC and CAC represent the two predominant histological entities in CC, accounting for 80% and 10-20% of cases, respectively (13). Notably, the incidence of CAC has been increasing in recent years, while the incidence of CSCC is decreasing (12,13).

CSCC arises from the squamous epithelial cells of the ectocervix. In contrast, CAC originates from glandular epithelial cells of the endocervix (16). This distinction reflects differences in cellular morphology and protein expression, with squamous cells typically expressing higher levels of keratins (e.g., CK5/6) and epithelial adhesion markers like E-cadherin, while glandular cells are characterized by mucin expression (e.g., MUC1, MUC5AC) and secretory profiles (62,63).

Key mutations differ significantly between the two predominant histological subtypes of CC, reflecting distinct oncogenic pathways involved in their tumorigenesis. KRAS mutations are predominantly detected in CAC, with a frequency of 17.5%, but are absent in CSCC, highlighting their specific role in CAC development (64). Conversely, EGFR amplifications, observed exclusively in CSCC at a frequency of 10.2%, have been linked to poorer overall survival (65). Mutations and amplifications of PIK3CA are present in 23-36% of CC cases, with a higher prevalence in SCC compared to CAC, indicating subtype-specific involvement of the PI3K/AKT/mTOR pathway in tumor progression (64). Furthermore, differential activation of oncogenic pathways, including EGFR signaling, immune response pathways, and PI3K/AKT/mTOR signaling can be detected between the CC entities, which underscores the molecular heterogeneity between SCC and CAC (66).

The two subtypes also differ etiologically. While high-risk HPV (hrHPV) infection is the primary risk factor for CC, approximately 15% of CAC cases are not linked to HPV infection (17). SiHa CSCC cells, are characterized by the presence of HPV type 16. This HPV subtype is typically associated with slower cellular proliferation and reduced invasiveness compared to HeLa cells, which are derived from CAC and harbor HPV type 18 (67). HPV16 and HPV18 exhibit distinct genomic features that influence their oncogenic potential and association with CC subtypes. HPV18 demonstrates higher promoter activity in its long control region (LCR), leading to increased expression of the E6 and E7 oncogenes, which more effectively inactivate tumor suppressors p53 and Rb compared to HPV16 (68). Additionally, HPV18 shows delayed and less efficient expression of late genes (L1, L2), facilitating immune evasion and persistent infections (69).

Epigenetic differences such as variations in promoter methylation and chromatin accessibility also play a role in the difference of genetic changes by stable GPER1-OE in CC cell lines. For example, RASSF1A promoter methylation is detected twice as high in CAC than in CSCC, indicating that RASSF1A silencing may contribute to adenocarcinoma development (70,71). Similarly, hypermethylation of the CDH13 gene, encoding H-cadherin, is predominantly observed in CAC (72). Furthermore, APC promoter hypermethylation occurs in 60% of CAC cases but is much less frequent in CSCC with only 13% (72-74). CAC and CSCC also exhibit immunological differences, such as increased microsatellite instability in CAC and increased CD4+ T-cell infiltration in CSCC (75), which may influence the therapy response and the selection of specific therapeutic agents.

Such distinctions in genetic, epigenetic and immuno-logical features also reflect broader patterns in tumor subtype biology, including variations in growth rates, invasive capacities, and therapeutic resistance. CAC is, compared to CSCC, associated with a more advanced stage at time of diagnosis, resulting in a poorer prognosis of patients (18). These differences highlight the complex interplay of genetic, epigenetic and histological factors in shaping the unique properties of CC subtypes which contribute to a different influence of stable GPER1-OE on CC cells.

Conclusion

The findings demonstrate that GPER1, when overexpressed, can exhibit both tumor-promoting and tumor-suppressing properties in CC, depending on the histological subtype. These observations align with previous studies showing that the function of GPER1 is highly dependent on tumor type and cellular context (76,77). GPER1 emerges as a promising therapeutic target in CC due to its central role in regulating essential cell processes as well as its interaction with key signaling pathways, including EMT, mTOR, MYC, p53, and Wnt/β-catenin. Dysregulation of these pathways is frequently observed in cancer, including CC, and significantly contributes to tumor progression (55,78).

Further research is required to fully understand the therapeutic potential of GPER1 in CC and its subtypes. Future studies should focus on molecular analyses, such as qPCR and Western blotting, to validate the NGS-identified pathways regulated by GPER1-OE. Additionally, the mechanisms by which GPER1 modulates these pathways should be thoroughly characterized, with particular attention to interactions between GPER1 and the tumor microenvironment, as well as potential synergies with established therapies like radiation or chemotherapy.

Understanding GPER1's function in specific molecular and clinical contexts is crucial, especially across different histological subtypes of CC, as these variations significantly contribute to tumor heterogeneity and therapeutic response. Ultimately, such insights could enable targeted modulation of GPER1, establishing it as a candidate for personalized therapy approaches in CC that enhance efficacy while minimizing side effects. Achieving these advancements will require a multidisciplinary approach, integrating fundamental molecular research with clinical studies.

Conflicts of Interest

The Authors declare no conflicts of interest.

Authors’ Contributions

Conceptualization, C.G.; investigation, L.H.; writing – original draft preparation, L.H. and C.G.; writing – review and editing, J.G.; project administration, C.G. All Authors have read and agreed to the published version of the manuscript.

Acknowledgements

The Authors thank Sonja Blume and Matthias Läsche for excellent technical assistance.

Funding

This research received no external funding.