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Citation: Ihlow, J.; Monjé, N.;
Hoffmann, I.; Bischoff, P.; Sinn, B.V.;
Schmitt, W.D.; Kunze, C.A.;
Darb-Esfahani, S.; Kulbe, H.; Braicu,
E.I.; et al. Low Expression of RGS2
Promotes Poor Prognosis in High-
Grade Serous Ovarian Cancer.
Cancers 2022,14, 4620. https://
doi.org/10.3390/cancers14194620
Academic Editors: Antonio Russo
and Charles Theillet
Received: 29 July 2022
Accepted: 19 September 2022
Published: 23 September 2022
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cancers
Article
Low Expression of RGS2 Promotes Poor Prognosis in High-Grade
Serous Ovarian Cancer
Jana Ihlow 1, † , Nanna Monjé1, †, Inga Hoffmann 1, Philip Bischoff 1,2,3 , Bruno Valentin Sinn 1,
Wolfgang Daniel Schmitt 1, Catarina Alisa Kunze 1, Sylvia Darb-Esfahani 4, Hagen Kulbe 5,6 ,
Elena Ioana Braicu 5,6, Jalid Sehouli 5,6 , Carsten Denkert 7, David Horst 1and Eliane Tabea Taube 1, *
1
Institute of Pathology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and
Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
2Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
3German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ),
69120 Heidelberg, Germany
4Institute of Pathology, Berlin-Spandau, Stadtrandstraße 555, 13589 Berlin, Germany
5
Department of Obstetrics and Gynecology with Center of Oncological Surgery, European Competence Center
for Ovarian Cancer, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and
Humboldt-Universität zu Berlin, Campus Virchow-Clinic, Augustenburger Platz 1, 13353 Berlin, Germany
6Tumorbank Ovarian Cancer Network, Berlin Institute of Health, Charité-Universitätsmedizin Berlin,
Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Campus Virchow-Clinic,
Augustenburger Platz 1, 13353 Berlin, Germany
7Institute of Pathology, Philipps-University Marburg, Baldingerstraße, 35043 Marburg, Germany
*Correspondence: eliane.taube@charite.de; Tel.: +49-30-450-536-033; Fax: +49-30-450-536-900
† These authors contributed equally to this work.
Simple Summary:
Recent advances in molecular medicine have indicated G-protein coupled recep-
tors (GPCRs) as possible therapeutic targets in ovarian cancer. The cellular effects of GPCRs are
determined by regulator of G protein signaling (RGS) proteins. Especially RGS2 has currently moved
into focus of cancer therapy. Therefore, we retrospectively analyzed RGS2 and its association with
the prognosis of high-grade serous ovarian cancer (HGSOC). Here, we provide in situ and in silico
analyses regarding the expression patterns and prognostic value of RGS2. In silico we found that
RGS2 is barely detectable in tumor cells on the mRNA level in bulk and single-cell data. Applying
immunohistochemistry in 519 HGSOC patients, we detected moderate to strong protein expression
of RGS2 in situ in approximately half of the cohort, suggesting regulation by post translational
modification. Furthermore, low protein expression of RGS2 was associated with an inferior overall-
and progression-free survival. These results warrant further research of its role and related new
therapeutic implications in HGSOC.
Abstract:
RGS2 regulates G-protein signaling by accelerating hydrolysis of GTP and has been identi-
fied as a potentially druggable target in carcinomas. Since the prognosis of patients with high-grade
serous ovarian carcinoma (HGSOC) remains utterly poor, new therapeutic options are urgently
needed. Previous
in vitro
studies have linked RGS2 suppression to chemoresistance in HGSOC, but
in situ data are still missing. In this study, we characterized the expression of RGS2 and its relation
to prognosis in HGSOC on the protein level by immunohistochemistry in 519 patients treated at
Charité, on the mRNA level in 299 cases from TCGA and on the single-cell level in 19 cases from
publicly available datasets. We found that RGS2 is barely detectable on the mRNA level in both bulk
tissue (median 8.2. normalized mRNA reads) and single-cell data (median 0 normalized counts), but
variably present on the protein level (median 34.5% positive tumor cells, moderate/strong expression
in approximately 50% of samples). Interestingly, low expression of RGS2 had a negative impact on
overall survival (p= 0.037) and progression-free survival (p= 0.058) on the protein level in lower FIGO
stages and in the absence of residual tumor burden. A similar trend was detected on the mRNA level.
Our results indicated a significant prognostic impact of RGS2 protein suppression in HGSOC. Due to
diverging expression patterns of RGS2 on mRNA and protein levels, posttranslational modification
Cancers 2022,14, 4620. https://doi.org/10.3390/cancers14194620 https://www.mdpi.com/journal/cancers
Cancers 2022,14, 4620 2 of 17
of RGS2 is likely. Our findings warrant further research to unravel the functional role of RGS2 in
HGSOC, especially in the light of new drug discovery.
Keywords: RGS2; G-protein signaling; high-grade serous ovarian cancer
1. Introduction
Regulator of G-protein signaling 2 (RGS2) is a member of the RGS R4 protein family
and controls G-coupled receptor signaling by binding the G
α
-subunit and consecutively
enhancing intrinsic GTPase activity in healthy cells [
1
]. Furthermore, RGS2 is able to
inhibit adenyl cyclase directly [
2
,
3
], to modulate muscarinic acetylcholine receptor signal-
ing [
4
] and to inhibit G
q
- and G
s
-signaling, thus affecting mitogen activation protein kinase
pathways (MAPK, ERK1/2) [
5
,
6
] and regulating the function of GPCRs [
7
]. In the past,
alterations in RGS2 have been linked to cardiovascular conditions [
8
–
10
], regulation of
insulin secretion [
11
], neurological disorders [
12
–
14
], and leukemogenesis [
15
,
16
]. More-
over, its role in solid cancer has emerged most recently, explicitly in breast carcinoma,
prostate carcinoma, and ovarian carcinoma [
17
–
22
]. Since RGS-protein domains have been
identified and discussed as druggable targets [
23
], it is relevant to analyze their biological
and prognostic impact in cancer patients. This applies particularly to patients with HGSOC,
whose long-term prognosis has improved with the invention of poly-ADP ribose poly-
merase (PARP)-inhibitors and anti-vascular endothelial growth factor (VEGF) antibodies,
but is still significantly limited by chemoresistance, lack of defined therapy targets and the
aggressive biology of the disease [
24
].
In vitro
, there is growing evidence that silencing
and consecutive suppression of RGS2 leads to chemoresistance in ovarian cancer cells by
modulating tumor cell growth [
21
,
22
]. In situ, expression, function, and prognostic impact
of RGS2 remain unknown. Since GPCRs are highly expressed in ovarian cancer [
25
–
27
], we
explored the expression and functional role of RGS2 in HGSOC, analyzed its associations
with clinicopathological characteristics, and determined the prognostic impact of RGS2
expression both in a large independent cohort of more than 500 patients on the protein
level by immunohistochemistry and in silico on the mRNA level.
2. Patients and Methods
2.1. Clinical Cohort
A total of 526 patients aged
≥
18 years were analyzed in this retrospective study. After
diagnosis of HGSOC, patients underwent cytoreductive surgery with or without previous
neoadjuvant chemotherapy at the Department of Gynecology, Charité-Universitätsmedizin
Berlin, Germany between 1 January 1991 and 31 December 2019. Histology type was
confirmed according to WHO criteria 2014 [
28
] by experienced board-approved gyneco-
logical pathologists (E.T.T., S.D.E., W.D.S., B.V.S, D.H). Apart from age < 18 years there
were no further exclusion criteria, especially none that were related to the clinical presenta-
tion of the tumor. Data on overall survival (OS) were available for 519 patients. Data on
progression-free survival (PFS) were available for 348 (67%) of these patients. The study was
performed in accordance with the Declaration of Helsinki and with local ethical guidelines
(ethic committee approval number EA1/051/18) and is supported by the TRANSCAN-2
project (grant no.: 2014-121). Clinical data were obtained from the Tumor Bank Ovarian
Cancer Network (www.toc-network.de) or the CharitéComprehensive Cancer Center
(https://cccc.charite.de) (accessed on 1 June 2022).
2.2. Tissue-Microarrays and Immunohistochemistry
Tissue microarrays (TMAs) with two tissue cores of each tumor were prepared from
formalin-fixed and paraffin-embedded HGSOC tissues. For the analysis, only primary
ovarian tumor tissue was used. The RGS2 antibody staining (Abcam ab36561, dilution
1:1000) was established on normal tissue using smooth muscle and colon tissue as positive
Cancers 2022,14, 4620 3 of 17
controls, and liver tissue as negative control, based on the manufacturer’s instructions.
HGSOC TMAs were stained immunohistochemically using a DISCOVERY XT autostainer
(Ventana Medical Systems, Inc., Tucson, AZ, USA). Briefly, 5
µ
m TMA sections were
deparaffinized, rehydrated, and subjected to heat-induced epitope retrieval followed by
endogenous blocking with H
2
O
2
. Subsequently, the slides were incubated for 60 min
with the RGS2 antibody (dilution 1:1000). A horseradish peroxidase (HRP)-conjugated
secondary antibody was then applied for 30 min. This was followed by chromogen 3,3
0
-
diaminobenzidine-tetrahydrochloride (DAB) application for 8 min and a counterstaining
with hematoxylin and bluing reagent for 12 min.
2.3. Digital Image Analysis
For digital image analysis, immunohistochemically stained TMA-slides were dig-
itized with a Panoramic Slide Scanner (3D Histech, Budapest, Hungary), and evalu-
ated using the open-source software platform QuPath (Version 0.2.3, available at https:
//github.com/qupath/qupath/releases, accessed on 05 April 2021) [
29
]. An automated
TMA dearrayer was applied to all cores in order to identify tumor areas (TMA grid man-
ually adjusted). After cell detection, cells were annotated as tumor cells and non-tumor
cells and a two-way random trees classifier was trained for automated classification. For
dichotomization, lymphocytes, macrophages, and fibroblasts were classified as non-tumor
cells. An intensity threshold was set to further classify cells as negative or positive based
on the mean cytoplasmic DAB density. Quality control was performed manually to ex-
clude artefacts. Digital image analysis yielded data on the median percentage of cells
with a positive staining result per total amount of tumor cells or non-tumor cells for each
two cores.
2.4. TCGA HGSOC Data Set Gene Expression Analysis
Gene expression data for mRNA (RNAseq V2) and survival data were available for
299 out of 585 HGSOC patients and downloaded from cBioportal (https://www.cbioportal.
org/, accessed on 5 May 2022). A ranked gene list was created by calculating Spearman’s
correlations of RGS2 mRNA expression and the mRNA expression of 18,870 genes within
the TCGA PanCancer Atlas dataset. Then, RGS2 and genes with the highest or lowest
co-expression were visualized in a heatmap using the OncoPrint tool on cBioportal [
30
,
31
].
Furthermore, heatmaps were created considering the co-expression of maker genes for
epithelial-mesenchymal transition, marker genes for methylation and genes for G-protein
mediated signaling. Since only 15 genes showed a strong correlation with RGS2, a gene set
enrichment analysis could not be performed.
2.5. Single-Cell Gene Expression Analysis in Three Publicly Available HGSOC Datasets
Single-cell analysis of RGS2 mRNA expression was performed in three different
publicly available datasets [
32
–
34
] using the open-source software “R” (version 4.1.1,
available at https://cran.r-project.org/bin/windows/base/old/4.1.1/, accessed on 10 May
2022) and the package “Seurat” (version 4.1.0, available at https://cran.r-project.org/web/
packages/Seurat/index.html, accessed on 10 May 2022) [
35
]. Single-cell count matrices and
metadata were downloaded for each dataset, and filtered for cells containing 500-6000 genes,
1000-60,000 reads, and <20% mitochondrial reads. Read counts were normalized using the
scTranform function. Cells were clustered by constructing shared nearest neighbor (SNN)
graphs based on the top 10 principal components at a resolution of 0.2. Uniform manifold
approximation and projection (UMAP) was used for visualization. Main cell types were
identified by scoring canonical cell type markers across clusters. In each dataset separately,
tumor cells with RGS2 mRNA levels > or
≤
median RGS2 expression in all tumor cells were
assigned as cells with high-expression of RGS2 (H-RGS2) and cells with low expression of
RGS2 (L-RGS2), respectively. Differentially expressed genes in H-RGS2 and L-RGS2 tumor
cells were computed using the FindAllMarkers function with the following parameters:
only positive markers, fraction of expressing cells inside the cluster
≥
0.15, difference
Cancers 2022,14, 4620 4 of 17
between fraction of expressing cells inside and outside the cluster
≥
0.15. For functional
analysis, cell cycle phases were scored as implemented in “Seurat v4”. The code used for
data analysis is available at https://github.com/bischofp/HGSOC_RGS2, accessed on
29 June 2022.
2.6. Statistical Analysis
Patients of our cohort were grouped in H-RGS2 and L-RGS2 based on their RGS2-
protein expression in tumor cells. Patients of the TCGA cohort were grouped based on
their RGS2 mRNA expression in tumor cells. Cut-offs for survival analysis were defined
using the digital cut-off finder of the University of Heidelberg [
36
]. Thresholds were
identified as 5.04% positive tumor cells for protein analysis and 9.66 normalized mRNA
reads for mRNA analysis. Statistical analysis was performed using IBM SPSS Statistics,
Version 23 (IBM 2015, Armonk, NY, USA). Patients’ characteristics were calculated using the
Mann–Whitney U test and chi square test followed by Bonferroni adjustment in multiple
subgroups. OS and PFS were analyzed using the Kaplan–Meier method. To specify median
follow-up, the reverse Kaplan–Meier method was applied [
37
]. A logrank test followed by
a univariate Cox proportional hazards model was used to determine independent survival
factors. To define a hazard ratio (HR), the variables were transformed into categorical
dichotomous data. Factors with a significant impact on OS and PFS were analyzed in a
stepwise multivariate Cox proportional hazards model. A p-value of <0.05 was considered
statistically significant.
3. Results
3.1. Staining Pattern of RGS2 in HGSOC
We examined the expression and distribution of RGS2 in primary HGSOC of a total of
519 patients. In the entire cohort, median RGS2 expression was 34.5% (IQR
6.4%–68.4%
)
in all tumor cells. Low RGS2 expression was revealed in the majority of tumor cells
(Figure 1). When expressed, RGS2 was intensively and homogenously stained within tumor
cells, particularly close to cell membranes. RGS2 staining intensity was approximately
equal within the tumor center and the tumor edge. Opposed to areas with more solid or
dissociated growth patterns, strongest and most frequent RGS2 expression was observed
in papillary tumor areas, respectively (p< 0.001, Figure 1A,B). Of all papillary tumor areas,
62% stained strongly positive (621/1007) and 38% were negative for RGS2 (368/1007). In
contrast, only 22% of all solid tumor areas were positive for RGS2 (104/455), whereas 88%
were negative or had barely detectable RGS2 staining (351/455). This difference was also
highlighted in samples that contained regions with a transition from papillary areas to solid
or disseminated areas (Figure 1A–C). Based on a threshold of 5.04% positive tumor cells,
HGSOC were then categorized as having low (L-RGS) or high (H-RGS) RGS2 expression.
3.2. Clinical Characteristics
Median follow-up was 89.1 months (95% CI 77.1-101.1 months) in the entire cohort. Of
all patients, the majority (88%) were diagnosed with tumor stages
≥
pT3. More advanced
FIGO stages, lymphatic invasion and venous invasion accumulated in the L-RGS2 group
(Table 1). Age (p= 0.926) and residual tumor burden (p= 0.699) did not differ between
the H-RGS2 and the L-RGS2 group. After the initial diagnosis, all patients had received
ovariectomy, radical hysterectomy, and tumor debulking. With regard to preceding neoad-
juvant chemotherapy, data were available in 35% of all patients only (n= 184/519). Of
these patients, 2 received neoadjuvant chemotherapy and 182 were untreated.
Cancers 2022,14, 4620 5 of 17
Cancers 2022, 14, x FOR PEER REVIEW 5 of 18
Figure 1. RGS2 protein expression pattern and distribution in HGSOC. (A) RGS2 by immunostain-
ing in primary HGSOC with regard to growth patterns. In solid or disseminated tumor areas, RGS2
staining was negative in the majority of cases. In papillary areas, RGS2 staining was predominantly
positive and distributed homogenously. In transition zones both positive and negative areas were
visible. (B) Detected cells were color-coded according to their classification: green = non-tumor cells,
blue = RGS2-negative tumor cells, red = RGS2-positive tumor cells. Panel images are magnifications
of boxed areas in upper panel images. Scale bars (left → right): Picture 1 and 2: 120 µm, Picture 3:
250 µm, Picture 4: 80 µm. (C) Distribution of RGS2-positive tumor cells in the entire cohort. Most
tumor cells were RGS2 negative or showed weak RGS2-expression; however, approximately half of
the patients showed detectable RGS2-expression in ≥50% of tumor cells.
3.2. Clinical Characteristics
Median follow-up was 89.1 months (95% CI 77.1-101.1 months) in the entire cohort.
Of all patients, the majority (88%) were diagnosed with tumor stages ≥ pT3. More ad-
vanced FIGO stages, lymphatic invasion and venous invasion accumulated in the L-RGS2
group (Table 1). Age (p = 0.926) and residual tumor burden (p = 0.699) did not differ be-
tween the H-RGS2 and the L-RGS2 group. After the initial diagnosis, all patients had re-
ceived ovariectomy, radical hysterectomy, and tumor debulking. With regard to preced-
ing neoadjuvant chemotherapy, data were available in 35% of all patients only (n =
184/519). Of these patients, 2 received neoadjuvant chemotherapy and 182 were untreated.
Figure 1.
RGS2 protein expression pattern and distribution in HGSOC. (
A
) RGS2 by immunostaining
in primary HGSOC with regard to growth patterns. In solid or disseminated tumor areas, RGS2
staining was negative in the majority of cases. In papillary areas, RGS2 staining was predominantly
positive and distributed homogenously. In transition zones both positive and negative areas were
visible. (
B
) Detected cells were color-coded according to their classification: green = non-tumor
cells, blue = RGS2-negative tumor cells, red = RGS2-positive tumor cells. Panel images are magni-
fications of boxed areas in upper panel images. Scale bars (left
→
right): Picture 1 and 2: 120
µ
m,
Picture 3: 250 µm
, Picture 4: 80
µ
m. (
C
) Distribution of RGS2-positive tumor cells in the entire cohort.
Most tumor cells were RGS2 negative or showed weak RGS2-expression; however, approximately
half of the patients showed detectable RGS2-expression in ≥50% of tumor cells.
Cancers 2022,14, 4620 6 of 17
Table 1.
Clinical characteristics and univariate survival in the HGSOC cohort with regard to RGS2
protein expression.
Characteristics Entire Cohort L-RGS2 H-RGS2 p-Value
n(%) 519 116 (22) 403 (78)
Age (years), median
(IQR) 61.5 (53–69) 62 (55–68) 61 (53–70) 0.926
RGS2-positive tumor
cells, median, % (IQR) 34.5 (6.4–68.4) 1.1 (0.3–2.4) 49.2 (24.2–75.4) <0.001
Primary tumor stage
(pT) 0.092
- pT1, n(%) 31 (6) 5 (4) 26 (6)
- pT2, n(%) 32 (6) 3 (3) 29 (7)
- pT3, n(%) 456 (88) 108 (93) 348 (87)
Lymph node stage (pN) 0.140
- pN0, n(%) 127 (24) 23 (20) 74 (18)
- pN1, n(%) 290 (56) 74 (64) 216 (54)
- pNX, n(%) 102 (20) 19 (16) 290 (72)
Distant metastasis (pM) 0.104
- pM0, n(%) 211 (41) 43 (37) 168 (42)
- pM1, n(%) 105 (20) 30 (26) 75 (18)
- pMX, n(%) 203 (39) 43 (37) 160 (40)
FIGO stage 0.185
- FIGO I, n(%) 24 (5) 5 (4) 19 (5)
- FIGO II, n(%) 22 (4) 2 (2) 20 (5)
- FIGO III, n(%) 373 (72) 80 (69) 293 (73)
- FIGO IV, n(%) 100 (19) 29 (25) 71 (17)
Residual tumor burden,
n(%) 0.699
- present, n(%) 133 (26) 32 (27.5) 101 (25)
- not present, n(%) 236 (45) 52 (45) 184 (46)
- n.A., n(%) 150 (29) 32 (27.5) 118 (29)
OS, median (95% CI) 41.2 (36.1–46.3) 30.6 (21.0–40.1) 43.0 (37.3–48.7) 0.037
PFS (n= 348 pts.),
median (95% CI) 19.3 (16.9–21.7) 15.9 (12.3–19.6) 19.3 (16.9–21.7) 0.058
Abbreviations: regulator of G-protein signaling (RGS2), low RGS2 expression (L-RGS2), high RGS2 expression (H-
RGS2), number of patients (n), interquartile range (IQR), Fédération Internationale de Gynécologie et d’Obstretique
(FIGO), not available (n.A.), overall survival (OS), progression free survival (PFS), confidence interval (CI), patients
(pts). Bold font indicates statistical significance.
3.3. Low RGS2 Protein Expression Is Partially Associated with a Poor Long-Term Survival
in HGSOC
On the protein level, lower expression of RGS2 was associated with an unfavorable
overall survival (p= 0.037) and showed a trend towards decreased progression-free survival
in the univariate analysis (p= 0.058). Five-year OS and PFS were 40% and 14% within
the H-RGS2 group as compared with 32% and 10% within the L-RGS2 group, respectively
(Figure 2C,E). Both risk of death and progression were elevated by 30% in the L-RGS2
subgroup (HR 1.3). A similar trend was observed on an mRNA level within the TCGA
cohort (Figure 2D,F). However, after including other relevant risk factors, such as residual
tumor burden, FIGO stage, and age, this effect translated into a clear but non-significant
trend towards an inferior progression-free survival (p= 0.193), whereas no differences in
OS were observed (p= 0.440, Table 2).
Cancers 2022,14, 4620 7 of 17
Cancers 2022, 14, x FOR PEER REVIEW 7 of 18
Figure 2. Low RGS2 expression indicates poor survival in HGSOC. (A) ROC curve determining best
discrimination thresholds of RGS2 protein expression (percent of tumor cells) in the HGSOC Char-
ité-cohort. (B) ROC curve determining best discrimination threshold of RGS2 mRNA Expression in
the HGSOC TCGA cohort. The arrow indicates chosen value for binary classification. AUC, area
under curve. (C,E) Overall- and progression-free survival for patients with low and high RGS2 pro-
tein expression within the Charité cohort. (D,F) Overall and progression-free survival for patients
with low and high RGS2 mRNA-expression within the TCGA cohort. p-value indicates log rank test
result, L-RGS2/H-RGS2: low/high expression of RGS2. HR: hazard ratio.
Table 2. Multivariate Cox regression model for OS and PFS in HGSOC patients with regard to im-
munohistochemical RGS2 protein expression in tumor cells and other factors with univariate signif-
icance.
OS (n = 519) PFS (n = 348)
Variable HR 95% CI p HR 95% CI p
Age > 60 1.16 0.96–1.51 0.233 1.02 0.78–1.33 0.912
FIGO > II 2.08 1.02–4.24 0.045 1.90 0.94–3.90 0.076
Figure 2.
Low RGS2 expression indicates poor survival in HGSOC. (
A
) ROC curve determining
best discrimination thresholds of RGS2 protein expression (percent of tumor cells) in the HGSOC
Charité-cohort. (
B
) ROC curve determining best discrimination threshold of RGS2 mRNA Expression
in the HGSOC TCGA cohort. The arrow indicates chosen value for binary classification. AUC, area
under curve. (
C
,
E
) Overall- and progression-free survival for patients with low and high RGS2
protein expression within the Charitécohort. (
D
,
F
) Overall and progression-free survival for patients
with low and high RGS2 mRNA-expression within the TCGA cohort. p-value indicates log rank test
result, L-RGS2/H-RGS2: low/high expression of RGS2. HR: hazard ratio.
Cancers 2022,14, 4620 8 of 17
Table 2.
Multivariate Cox regression model for OS and PFS in HGSOC patients with re-
gard to immunohistochemical RGS2 protein expression in tumor cells and other factors with
univariate significance.
OS (n= 519) PFS (n= 348)
Variable HR 95% CI pHR 95% CI p
Age > 60 1.16 0.96–1.51 0.233 1.02 0.78–1.33 0.912
FIGO > II 2.08 1.02–4.24 0.045 1.90 0.94–3.90 0.076
Residual
tumor 2.10 1.61–2.72 <0.001 1.64 1.22–2.20 0.001
L-RGS2 1.24 0.84–1.51 0.440 1.24 0.90–1.71 0.193
Abbreviations: overall survival (OS), progression-free survival (PFS), number of patients (n), hazard ratio (HR),
confidence interval (CI), Fédération Internationale de Gynécologie et d’Obstretique (FIGO), low expression of
RGS2 (L-RGS2). Bold font indicates statistical significance.
3.4. RGS2 mRNA Expression in Ovarian Cancer Cells Is Weak on the Single-Cell Level
To evaluate mRNA expression in HGSOC tumor cells precisely, we analyzed RGS2
mRNA expression in silico on the single-cell level in three publicly available HGSOC
data sets [
32
–
34
] that included data of therapy-naïve patients, patients with neoadjuvant
chemotherapy and patients with metastatic disease (Figure 3A). In these datasets, a total of
19 patients were included. Nearly all tumor cells showed either weak or no RGS2 mRNA
expression at all, while strong RGS2 mRNA expression was more common in stromal and
immune cells, especially in macrophages (Figure 3B–D). Median RGS2 mRNA expression
was 0 normalized mRNA reads per tumor cell (IQR 0.00–00) in all patients. This applied to
both primary and metastatic tumor cells. In the few tumor cells with proof of RGS2-mRNA
expression, RGS2 did not translate substantially into proliferative activity as it is shown by
cell cycle diagrams in Figure 3C.
3.5. RGS2 mRNA-Suppression Is Linked to Tumor Cell Plasticity on a Single-Cell Level
Regarding the functional role of RGS2 in HGSOC, gene expression analysis was con-
ducted in all three single-cell datasets (Figure 3E). Gene expression levels and co-expressed
genes were highly variable between all datasets. Certain subtypes of L-RGS2 HGSOC held
co-expression of genes coding for elements of tumor-cell integrity and tumor cell plasticity
such as KLK8 (p= 1.13
×
10
−5
), KLK6 (p= 8.07
×
10
−3
) or SLPI (
p= 2.94 ×10−8
), CLDN7
(p= 5.16
×
10
−5
), SMIM22 (p= 1.82
×
10
−8
) and genes associated with the innate immune
system pathway such as VAMP8 (p= 3.59
×
10
−9
), IFI27 (p= 2.43
×
10
−13
), and LGALS3
(p= 8.94 ×10−8).
3.6. TCGA Gene Expression Analysis Reveals an Association between RGS2 Expression and
Protein Synthesis and Co-Dependence on Methylation
In the TCGA cohort (n= 299), genes showing the strongest positive and negative
co-expression with RGS2 were identified and co-expression of other functional relevant
genes was explored. Median RGS2 mRNA expression was only 8.19 normalized mRNA
reads (IQR 7.30–9.11) in bulk tissue. As expected, moderate co-expression of genes asso-
ciated with G-Protein signaling, such as RGS1 (p= 1.44
×
10
−29
), GTP-binding protein
GEM (
p= 1.55 ×10−26
), and cAMP-specific 3
0
,5
0
-cyclic phosphodiesterase 4B (PDE4B,
p= 3.79 ×10−22
) was observed (Table 3, Figure 4A). In general, RGS2 showed only mild
correlations with other co-expressed genes with a maximum Spearman’s correlation coeffi-
cient of 0.6. Therefore, a detailed gene set enrichment analysis could not be performed. As a
result of the staining patterns detected in immunohistochemistry, we examined the correla-
tion between RGS2 expression and expression of marker genes for epithelial-mesenchymal
transition (EMT, Figure 4B, Table 3). Four different expression patterns emerged: (I) RGS2
barely detectable with downregulation of classical EMT hallmark genes (SNAI1, SNAI2,
ZEB1, ZEB2, VIM, FN1, and TWIST1), downregulation of CDH1 and upregulation of
alternative genes for EMT (ERBB2, ERBB3, and DDR1), (II) H-RGS with downregulation of
Cancers 2022,14, 4620 9 of 17
CDH1 and upregulation of EMT hallmark genes, (III) L-RGS with downregulation of CDH1
and downregulation of EMT hallmark genes, (IV) RGS2 barely detectable, downregulation
of CDH1, and variable regulation of both classical and alternative EMT hallmark genes.
Cancers 2022, 14, x FOR PEER REVIEW 9 of 18
Figure 3.
Single cell analysis with regard to RGS2 mRNA expression in three pre-existing datasets.
(
A
) Composition of each HGSOC study cohort and tissue sampling sites, each manikin represents
Cancers 2022,14, 4620 10 of 17
one patient. CHT: chemotherapy [
32
–
34
] (
B
) Number and composition of cell types within each
HGSOC study cohort. Each dot represents one cell. (
C
) RGS2 mRNA expression within HGSOC
tumor cells and associated cell cycle phases. Each dot represents one tumor cell. Most of the tumor
cells show low RGS2 expression and cell cycle phases do not differ substantially between cells with
low or high RGS2 expression (L-/H-RGS2). (
D
) mRNA expression within each cell type that was
present per dataset. As compared with surrounding non-malignant cells, RGS2 mRNA expression
is markedly low within the tumor cell compartment. (
E
) Differential mRNA expression of genes
showing strongest or weakest correlation with RGS2 in the tumor cell compartment within each
dataset. Each row represents one cell.
Table 3.
Correlation between RGS2 mRNA-Expression and mRNA expression of associated genes
in the TCGA cohort (n= 299). A: TOP 15 genes that are positively correlated with RGS2. B: TOP
15 genes
that are negatively correlated with RGS2. C: Co-expression of hallmark-genes for epithelial,
mesenchymal translation. D: Hallmark genes for methylation. E: Genes that are associated with
downstream signaling of RGS2 and hypoxia. Bold font indicates statistical significance.
Gene Localization Spearman’s Correlation p-Value
A
C5AR1 19q13.32 0.591 1.24 ×10−29
RGS1 1q31.2 0.590 1.44 ×10−29
NR4A3 9q22 0.587 3.64 ×10−29
GPR183 13q32.3 0.584 8.27 ×10−29
DUSP1 5q35.1 0.576 6.98 ×10−28
OSM 22q12.2 0.574 1.24 ×10−27
IL6 7p15.3 0.564 1.28 ×10−26
GEM 8q22.1 0.563 1.55 ×10−26
ADAMTS4 1q23.3 0.550 4.03 ×10−25
C11ORF96 11p11.2 0.547 7.71 ×10−25
SERPINE1 7q22.1 0.547 7.82 ×10−25
CCN1 1p22.3 0.538 7.01 ×10−24
GLIPR1 12q21.2 0.521 3.10 ×10−22
PDE4B 1p31.3 0.520 3.79 ×10−22
SLC2A3 12p13.31 0.514 1.38 ×10−21
B
ZNF3 7q22.1 −0.329 5.15 ×10−9
C9ORF163 9q34.3 −0.315 2.40 ×10−8
ZBTB3 11q12.3 −0.309 4.83 ×10−8
ZSCAN25 7q22.1 −0.301 1.11 ×10−7
MARVELD2 5q13.2 −0.300 1.15 ×10−7
NPAS3 14q13.1 −0.292 2.63 ×10−7
RSPH1 21q22.3 −0.284 5.88 ×10−7
SLC52A3 20p13 −0.283 6.28 ×10−7
DYDC1 10q23.1 −0.280 8.02 ×10−7
KLLN 10q23 −0.278 9.90 ×10−7
PGAP2 11p15.4 −0.276 1.12 ×10−3
SOX17 8q11.23 −0.274 1.48 ×10−3
SRGAP3 3p25.3 −0.273 1.57 ×10−3
TUT1 11q12.3 −0.269 2.18 ×10−3
ACP6 1q21.2 −0.269 2.19 ×10−3
Cancers 2022,14, 4620 11 of 17
Table 3. Cont.
Gene Localization Spearman’s Correlation p-Value
C
VIM 10p13 0.293 2.47 ×10−7
ZEB2 2q22.3 0.424 1.55 ×10−14
ZEB1 10p11.22 0.409 1.59 ×10−13
FN1 2q35 0.402 4.54 ×10−13
SNAI2 8q11.21 0.343 1.05 ×10−9
TWIST1 7p21.1 0.389 2.65 ×10−12
SNAI1 20q13.13 0.427 1.06 ×10−14
CDH2 18q12.1 −0.019 0.749
ERBB2 17q12 −0.055 0.343
DDR1 6p21.33 −0.090 0.118
ERBB3 12q13.2 −0.153 7.92 ×10−3
CDH1 16q22.1 −0.104 0.072
D
HDAC6 Xp11.23 −0.142 0.014
HDAC11 3p25.1 −0.135 0.019
HDAC7 12q13.11 −0.135 0.020
HDAC4 2q37.3 −0.111 0.054
HDAC1 1p35.2-p35.1 −0.083 0.154
HDAC3 5q31.3 −0.078 0.179
HDAC5 17q21.31 0.077 0.186
HDAC10 22q13.33 0.054 0.355
HDAC2 6q21 0.051 0.379
HDAC9 7p21.1 −0.015 0.796
HDAC8 Xq13.1 −0.007 0.900
DNMT1 19p13.2 −0.010 0.085
DNMT3A 2p23.3 0.048 0.410
DNMT3B 20q11.21 −0.028 0.625
E
EIF2B3 1p34.1 0.137 0.018
VEGFA 6p21.1 0.109 0.061
HIF1A 14q23.2 0.039 0.503
PLCG2 16q24.1 0.081 0.164
PLCG1 20q12 9.46 ×10−30.870
ITPR1 3p26.1 0.112 0.053
ITPR3 6p21.31 −0.085 0.142
ITPR2 12p11.23 0.049 0.398
ADCY1 7p12.3 −0.028 0.634
PRKCA 17q24.2 0.116 0.148
Bold font indicates statistical significance.
Due to the discrepancies between RGS2 mRNA expression and immunohistochemical
RGS2 expression, further pathways of interest were evaluated. These included downstream
signaling of RGS2, protein synthesis, and methylation. Interestingly, an upregulation of
methylation associated marker genes was present in 60% of cases with low expression of
RGS2, whereas this was not observed in the remaining cases (Figure 4C). Regarding protein
synthesis, an upregulation of translation initiation factor EIF2B3 was visible in a subset of
L-RGS2 patients (Figure 4D). Concerning downstream signaling of RGS2, an upregulation
of PLCG1/2, PKA, and IP3 was found in certain subsets of the L-RGS2 group (Figure 4D).
Cancers 2022,14, 4620 12 of 17
Cancers 2022, 14, x FOR PEER REVIEW 11 of 18
Figure 4. Bulk mRNA expression data from the TCGA HGSOC cohort (n = 299) for functional rele-
vant genes in the context of RGS2 expression obtained from cBioportal. (A) Differential expression
of the TOP15 genes with strongest and weakest co-expression with RGS2. RGS2 shows moderate
co-expression of genes associated with signaling or pro-inflammatory states and negative correla-
tion with genes coding for zinc finger domains. (B) RGS2 and co-expression of maker genes for
epithelial-mesenchymal transition (EMT) reveals four different clusters: I. RGS2 barely detectable,
downregulation of classical EMT hallmark genes and upregulation of alternative genes for EMT
(blue boxes on the left and right), II. H-RGS and upregulation of EMT hallmark genes (yellow box),
III. L-RGS and downregulation of EMT hallmark genes (grey box), IV. RGS2 barely detectable and
variable regulation of both classical and alternative EMT hallmark genes (pink box). (C) RGS2 and
co-expression marker genes for methylation reveals increased methylation activity more than 50%
of patients with low RGS2 expression. (D) RGS2 and co-expression of genes associated with RGS2
downstream signaling, hypoxic cell stress and alternative signaling mechanisms demonstrates post-
translational modification (EIF2B3), hypoxic cell stress (HIFα) and compensatory upregulation of
downstream targets in nearly half of the patients with low RGS2 expression and underlines the
positive feedback loop between RGS2 suppression and phospholipase C (PLCG1 and 2) mediated
phosphorylation of RGS2.
Figure 4.
Bulk mRNA expression data from the TCGA HGSOC cohort (n= 299) for functional
relevant genes in the context of RGS2 expression obtained from cBioportal. (
A
) Differential expression
of the TOP15 genes with strongest and weakest co-expression with RGS2. RGS2 shows moderate
co-expression of genes associated with signaling or pro-inflammatory states and negative correla-
tion with genes coding for zinc finger domains. (
B
) RGS2 and co-expression of maker genes for
epithelial-mesenchymal transition (EMT) reveals four different clusters: I. RGS2 barely detectable,
downregulation of classical EMT hallmark genes and upregulation of alternative genes for EMT
(blue boxes on the left and right), II. H-RGS and upregulation of EMT hallmark genes (yellow box),
III. L-RGS and downregulation of EMT hallmark genes (pink box), IV. RGS2 barely detectable and
variable regulation of both classical and alternative EMT hallmark genes (grey box). (
C
) RGS2
and co-expression marker genes for methylation reveals increased methylation activity more than
50% of patients with low RGS2 expression. (
D
) RGS2 and co-expression of genes associated with
RGS2 downstream signaling, hypoxic cell stress and alternative signaling mechanisms demonstrates
post-translational modification (EIF2B3), hypoxic cell stress (HIF
α
) and compensatory upregulation
of downstream targets in nearly half of the patients with low RGS2 expression and underlines the
positive feedback loop between RGS2 suppression and phospholipase C (PLCG1 and 2) mediated
phosphorylation of RGS2.
Cancers 2022,14, 4620 13 of 17
4. Discussion
In this study, we provide novel insight into the expression patterns, functional role,
and prognostic impact of RGS2 (a regulator of GPCRs), investigating it deliberately in
a large cohort of HGSOC patients. We showed that RGS2 suppression has a negative
impact on long-term survival on the protein level and found that RGS2 expression is lost in
HGSOC with solid growth pattern. In addition, we found that RGS2 mRNA expression is
related to tumor cell integrity and protein synthesis and differs substantially from RGS2
protein expression.
In our cohort, patients with RGS2 protein suppression in primary HGSOC had a
substantially decreased OS and PFS. Risk of disease progression or death was increased by
30% in the univariate analysis. This effect was not observed in the multivariate analysis
in the presence of residual tumor burden and advanced FIGO stages which also included
metastasis. Although not significant, there was also a clear trend towards an inferior OS
and PFS with RGS2 suppression on the mRNA level. Of note, AUC values were only
moderate. Pre-existing data about the prognostic role of RGS2 in different cancers appear
contradictory. In prostate cancer, a survival benefit has been reported in patients with
low expression of RGS2 [
20
] similar to observations in pulmonary adenocarcinoma [
38
].
On the contrary, RGS2 repression seems to be linked to reduced OS in breast cancer [
39
],
bladder cancer [
40
], and stage II/III colorectal cancer [
41
]. Due to its distinct isoforms,
RGS2 has various modes of (inter)actions in these cancers [
7
,
17
–
20
,
38
–
42
]. It is mainly
detected within the tumor tissue rather than in the peripheral blood. Although not essential
for its evaluation as a tissue biomarker, these aspects might dampen the role of RGS2 for
specific cancer monitoring.
RGS2 protein expression controls G-protein signaling in healthy cells by activating
intrinsic GTPase activity [
1
]. Thus, our observation that RGS2 mRNA and protein expres-
sion are barely detectable in HGSOC tumor cells is reasonable from a biological point of
view. Low expression of RGS2 has previously been described in chemo-resistant HGSOC
cell lines. Therein promotor methylation has been revealed as the main cause for RGS2
suppression [
22
]. Indeed, data obtained from the TCGA cohort in our analysis showed
that upregulation of methylation/acetylation-associated genes is present in approximately
75% of L-RGS2 HGSOC patients. In the remaining 25% of patients, alternative mechanisms
leading to downregulation of RGS2 gene expression (such as hypoxic cell stress) are likely
as it has been described in prostate cancer earlier [
20
]. Previous analyses indicate that RGS2
gene expression correlates directly with RGS2 mRNA expression in non-cancerous cells [
43
].
In contrast, our study shows a major discrepancy between RGS2 mRNA- and RGS2 protein
expression, indicating post-translational modification and protein turnover as one possible
mechanism of RGS2 protein regulation in HGSOC. Post-translational modification might
either enhance a protective function of RGS2 in papillary HGSOC [
44
] or minimize it in HG-
SOC subtypes with solid growth, e.g., via protein kinase C dependent phosphorylation [
45
].
This warrants further investigations of RGS2 on the protein level, particularly in the light of
recent drug discovery attempts that include targeting non-canonical mechanisms of RGS2
expression [
23
]. In this context, therapeutical RGS2 enhancement via selective inhibition of
its proteasomal degradation might be of special interest [44,46,47].
Previous studies described the impact of RGS2 alterations on tumor cell proliferation
and hormone receptor related tumor cell growth [
17
,
19
,
20
,
40
]. For HGSOC, our results
indicate a multimodal function of RGS2 that is primarily related to tumor cell preservation
rather than cell proliferation. In the single-cell data, proliferation and cell cycle markers
were not elevated. Interestingly, we identified an association between low RGS2 expression
and sustained tumor cell plasticity on the single-cell level (Figure 3D). In the H-RGS2
group, no clear expression pattern emerged. However, the single-cell data were of limited
representativeness since they included only a very small number of patients (n= 19) which
prevents from any conclusions with regard to statistical correlations. On bulk RNA levels,
we recognized an activation of G-protein downstream signaling genes and an association
between RGS2 expression and protein synthesis via upregulation of EIF2B3 in the L-RGS2
Cancers 2022,14, 4620 14 of 17
TCGA cohort. This supports the previous finding that RGS2 decreases global mRNA
translation and protein synthesis, cellular stress response, and tumor growth by binding
EIF2B3 and disrupting the EIF2-EIF2B GTPase cycle [
14
,
48
,
49
]. This might ultimately
promote tumor cell survival and cause chemoresistance. Unlike protein synthesis, EMT
seems to play a minor role in patients with RGS2 suppression, since we observed ambiguous
expression patterns of EMT hallmark genes in both L-RGS and H-RGS patients on the
bulk RNA level in the TCGA cohort. Yet, mechanisms for EMT might be enhanced on a
protein level, since immunohistochemistry revealed a loss of RGS2 expression in tumor
areas with solid growth in our own cohort. This assumption has been described previously
in prostate cancer [
20
]. However, data from the TCGA cohort are less specific than single
cell data and might be biased by the minor presence of other intratumoral cell types such as
lymphocytes. Furthermore, there are several other mechanisms that additionally alter EMT
and chemoresistance in HGSOC that were detected by high-throughput proteomic methods
but not evaluated within the scope of this study, such as upregulation of Annexin A3 or
MiR181A [
50
–
53
]. Additionally, clinical data on neoadjuvant or adjuvant chemotherapy
(e.g., HiPEC) were only available in one quarter of all patients. This is a significant
limitation of our study since both are known to modulate outcome and gene expression in
HGSOC [
54
,
55
]. Therefore, future studies in larger cohorts should include these additional
clinical data in the multivariate model. This might alter multivariate results and increase
HR and significance levels.
In conclusion, RGS2 seems to be part of a multimodal protein-interaction that is
associated with decreased long-term survival in primary HGSOC and might serve as a
potential druggable target itself or in combination with GPCR-directed therapies, e.g.,
by modulating endosomal internalization of GPCR-directed nanoparticles [
21
,
56
]. So far,
little is known about the RGS2-related protein interactions, the metabolomic effects of
RGS2, and about its impact on cancer stem cell capacity in HGSOC. Future research on
RGS2-related protein interactions should also take (neoadjuvant) chemotherapy, BRCA 1/2
mutational status, and homologue recombination deficiency (HRD) into account and may
thereby shed more light onto the functional and clinical implications of RGS2 in HGSOC.
This might ultimately lead to new personalized treatment options that include RGS2 as a
druggable target in therapy resistant HGSOC, which remains hard to treat up until this day.
The determination of tissue biomarkers in general is not sufficient to predict the clinical
outcome or the presence of residual disease in patients with HGSOC. Until today, surgical
effort, chemotherapy response, and center experience remain the main determining factors
that improve patients’ survival [57].
Author Contributions:
E.T.T., J.I. and N.M. designed the study. E.T.T. supervised the study. E.T.T.,
J.I., B.V.S., W.D.S., C.A.K., S.D.-E., C.D., D.H., E.I.B. and J.S. performed clinical and diagnostic workup
required for this study. N.M., H.K. and E.T.T. performed stainings and laboratory work. N.M., J.I.
and E.T.T. performed digital image analysis. I.H., N.M., J.I. and E.T.T. performed statistical analysis
of the immunohistochemical results and the TCGA cohort. P.B. and J.I. performed analysis and
interpretation of single-cell data. J.I. and E.T.T. wrote the manuscript draft. All authors critically
revised the manuscript and approved the final version. All authors have read and agreed to the
published version of the manuscript.
Funding:
The project is supported by the TRANSCAN-2 project (grant no.:2014-121). Furthermore,
we acknowledge support from the German Research Foundation (DFG) and the Open Access Publi-
cation Fund of Charité-Universitätsmedizin Berlin. P.B. is participant in the BIH CharitéClinician
Scientist Program funded by the Charité-Universitätsmedizin Berlin and the Berlin Institute of Health.
Institutional Review Board Statement:
The study was conducted according to the guidelines
of the Declaration of Helsinki and approved by the Institutional Ethics Committee of Charité-
Universitätsmedizin Berlin (protocol code EA1/051/18, date of approval 2018).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
Data are available from the corresponding author upon
reasonable request
.
Cancers 2022,14, 4620 15 of 17
Acknowledgments:
We gratefully thank Ines Koch and Sylwia Handzik for their excellent technical
support with the laboratory workup required for this study. We owe thanks to Blathnáid Ryan for
language correction.
Conflicts of Interest: The authors declare no conflict of interest.
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