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Title: SARS-CoV-2 receptor ACE2 identifies immuno-hot tumors in breast cancer
Running title: ACE2 identifies immuno-hold tumors
Author list:
Jie Mei 1,2,*, Yun Cai 1,2,*, Rui Xu 2,*, Xinqian Yu 3,*, Lingyan Chen 1, Tao Ma 4, Tianshu
Gao 2, Fei Gao 2, Yichao Zhu 3,5,#, Yan Zhang 1,#
Note: Jie Mei, Yun Cai, Rui Xu and Xinqian Yu contributed equally to this work
Authors’ affiliations:
1 Department of Oncology, Wuxi Maternal and Child Health Hospital Affiliated to
Nanjing Medical University, Wuxi 214000, China.
2 Wuxi Clinical Medical College, Nanjing Medical University, Wuxi 214000, China.
3 Department of Physiology, Nanjing Medical University, Nanjing 211166, China.
4 Department of Breast Surgery, Wuxi Maternal and Child Health Hospital Affiliated
to Nanjing Medical University, Wuxi 214000, China.
5 State Key Laboratory of Reproductive Medicine, Nanjing Medical University,
Nanjing 211166, China.
E-mails for Authors:
Jie Mei: [email protected]
Yun Cai: [email protected]
Rui Xu: [email protected]
Xinqian Yu: [email protected]
Lingyan Chen: [email protected]
Tao Ma: [email protected]
Tianshu Gao: [email protected]
Fei Gao: [email protected]
Yichao Zhu: [email protected]
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Yan Zhang: [email protected]
# Corresponding author:
Yan Zhang, Ph. D
Department of Oncology
Wuxi Maternal and Child Health Hospital Affiliated to Nanjing Medical University
No. 48 Huaishu Rd., Wuxi 214000, China
E-mail: [email protected]
# Corresponding author:
Yichao Zhu, Ph. D
Department of Physiology
Nanjing Medical University
No. 101 Longmian Av., Nanjing 211166, China
E-mail: [email protected]
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Abstract
Angiotensin-converting enzyme 2 (ACE2) is known as a host cell receptor for Severe
Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is identified to be
dysregulated in multiple tumors. Although the characterization of abnormal ACE2
expression in malignancies has been preliminarily explored, in-depth analysis of ACE2
in breast cancer (BRCA) has not been elucidated. A systematic pan-cancer analysis was
conducted to assess the expression pattern and immunological role of ACE2 based on
RNA-sequencing (RNA-seq) data downloaded from The Cancer Genome Atlas
(TCGA). Next, correlations between ACE2 expression immunological characteristics
in the BRCA tumor microenvironment (TME) were evaluated. Also, the role of ACE2
in predicting the clinical features and the response to therapeutic options in BRCA was
estimated. These findings were subsequently validated in another public transcriptomic
cohort as well as a recruited cohort. ACE2 was lowly expressed in most cancers
compared with adjacent tissues. ACE2 was positively correlated with
immunomodulators, tumor-infiltrating immune cells (TIICs), cancer immunity cycles,
immune checkpoints, and tumor mutation burden (TMB). Besides, high ACE2 levels
indicated the triple-negative breast cancer (TNBC) subtype of BRCA, lower response
to endocrine therapy and higher response to chemotherapy, anti-ERBB therapy,
antiangiogenic therapy and immunotherapy. To sum up, ACE2 correlates with an
inflamed TME and identifies immuno-hot tumors, which may be used as an auxiliary
biomarker for the identification of immunological characteristics in BRCA.
Key words: ACE2, breast cancer, tumor immunity, tumor microenvironment
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Introduction
Coronavirus disease 2019 (COVID-19) is infectious pneumonia caused by Severe
Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection [1]. At the end
of 2019, COVID-19 was originally reported in China in December 2019. Just over a
year, COVID-19 has transmitted fastly to almost all countries, which leads to a serious
global public health problem. Based on the latest statistics released by World Health
Organization, a total of 146,054,107 confirmed cases and 3,092,410 deaths have been
reported as of 25 April, 2021 (https://covid19.who.int/). Angiotensin-converting
enzyme 2 (ACE2) is recognized as a host cell receptor for SARS-CoV and SARS-CoV-
2 [2, 3]. Emerging research reported that the expression and distribution of ACE2 were
tissue-specific to some extent, which is enriched in the lung, esophagus, kidney, bladder,
testis, stomach and ileum using a single-cell RNA sequencing (RNA-seq) technique [4].
However, ACE2 is expressed in all organs, excepting for the prostate and brain,
although some organs exhibit low expression [5]. Thus, organs expressing high ACE2
appear to be more impressionable to SARS-CoV-2 infection in healthy individuals.
Breast cancer (BRCA) is a multifactorial disease, which has the highest incidence
in the world. In 2020, a total of 2,261,419 new cases and 684,996 deaths have been
reported according to the latest statistics [6]. Be a multifactorial disease, dysregulation
of the immune landscape acts as a significant role in the oncogenesis and development
of BRCA, which lays the molecular foundation for immunotherapy [7]. ACE2 is known
as a tumor suppressor and is lowly expressed in most cancers [8-10]. Encouragingly,
several analyses reveal that ACE2 correlates with the abundances of a number of tumor-
infiltrating immune cells (TIICs) in multiple cancers [11, 12]. However, indiscriminate
pan-cancer analysis neglects in-depth research on dominant tumor species, which may
lead to ignoring the great value of ACE2 in regulating tumor immunity and acting as a
indicator for the stratification of tumor immunogenicity.
Tumors are complex masses consisting of malignant as well as normal cells. The
multiple interplays between these cells via cytokines, chemokines and growth factors
constitute the tumor microenvironment (TME) [13]. TME could be crucial for the
response to several therapies and the prognosis. Tumors can be simply classified into
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cold or hot depending on their TME. Cold tumors are characterized as
immunosuppressive TME and insensitive to either chemotherapy or immunotherapy,
and hot tumors represent higher response rates to these therapies, which is featured by
T cell infiltration and immunosuppressive TME [14]. In principle, the hot tumors
exhibit a good response to immunotherapy, such as anti-PD-1/PD-L1 therapy [15].
Thus, distinguishing hot and cold tumors is a critical strategy to demarcate the response
to immunotherapy.
In this research, we first conducted a pan-cancer analysis of the expression and
immunological features of ACE2. We discovered that ACE2 exhibited the tightest
correlation with immunological factors in BRCA, which may be a dominant tumor
species for the in-depth analysis of the immunological role of ACE2. We also revealed
that ACE2 indicated an inflamed TME and identified immuno-hot tumors in BRCA,
and had the potential to estimate the molecular subtype of BRCA.
Methodology
Public datasets retrieval
The Cancer Genome Atlas (TCGA) data: The pan-cancer normalized RNA-seq datasets,
copy number variant (CNV) data processed by GISTIC algorithm, 450K methylation
data, mutation profiles, the activities of transcription factor (TF) calculated by RABIT,
and clinical information were obtained from UCSC Xena data portal
(https://xenabrowser.net/datapages/). The somatic mutation data were obtained from
TCGA (http://cancergenome.nih.gov/) and then used to calculate the tumor mutation
burden (TMB) by R package “maftools”. The abbreviations for TCGA cancer types are
shown in Table S1.
METABRIC data: The normalized RNA-seq dataset, CNV data processed by
GISTIC algorithm, mutation profiles and clinical data of BRCA patients in
METABRIC cohort were downloaded from cBioPortal data portal
(http://www.cbioportal.org/datasets) [16].
Prognostic analysis using PrognoScan
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PrognoScan database (http://dna00.bio.kyutech.ac.jp/PrognoScan/) was applied to
assess the prognostic value of ACE2 in BRCA across a large cohort of public
microarray datasets [17]. All the results were exhibited in the study.
Linked Omics database analysis
The Linked Omics database (http://www.linkedomics.org/login.php) is a web-based
tool to analyze multi-dimensional datasets [18]. The functional roles of ACE2 in BRCA
was predicted using the Linked Omics tool in term of Gene Ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathways by the gene set enrichment
analysis (GSEA). Default options were used for all parameters.
Assessment of the immunological features in TME of BRCA
The immunological features of TME in BRCA contain immunomodulators, the
activities of the cancer immunity cycle, infiltration levels of TIICs, and the expression
of inhibitory immune checkpoints.
The information of 122 immunomodulators including major histocompatibility
complex (MHC), receptors, chemokines, and immunostimulators was collected from
the research of Charoentong et al. [19]. Considering the cancer immunity cycle which
contains seven stages reflects the anti-cancer immune response and the activities of
each step decide the fate of tumor cells, we subsequently calculated the activation
scores of each step by single sample gene set enrichment analysis (ssGSEA) according
to the expression level of specific signatures of each step [20]. Moreover, in order to
avoid calculation errors resulted from various algorithms which were developed to
explore the relative abundance of TIICs in TME, we comprehensively estimated the
infiltration levels of TIICs using the following independent algorithms: TIMER [21],
EPIC [22], MCP-counter [23], quanTIseq [24] and TISIDB [25]. ESTIMATE
algorithm was also performed to calculate Tumor Purity, ESTIMATE Score, Immune
Score and Stromal Score [26]. Furthermore, we also collected several well-known
effector genes of TIICs, and computed the T cell inflamed score according to the
expression levels and weighting coefficient of 18 genes reported by Ayer et al. [27].
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To verify the role of ACE2 in mediating cancer immunity in BRCA, we grouped
the patients into the high ACE2 and the low ACE2 group with the 50% cutoff based on
the expression levels of ACE2, and then analyzed the difference of the immunological
features of TME concerning the above aspects between the high and low ACE2 groups.
Immunophenoscore analysis
As previously reported, a patient’s immunophenoscore (IPS) can be calculated without
bias using machine learning by consideration of the 4 major categories of components
that measure immunogenicity: effector cells, immunosuppressive cells, MHC
molecules, and immunomodulators [19]. The IPS values of BRCA patients were
obtained from the Cancer Immunome Atlas (TCIA) (https://tcia.at/home).
Calculation of the enrichment scores of various gene signatures
According to previous research [28], we collected several gene-sets positively
associated with therapeutic response to immunotherapy, targeted therapy and
radiotherapy, and specific markers of biological process correlated with anti-tumor
immunity such as genes involved in DNA replication. The enrichment scores of these
signatures were obtained using the GSVA R package [29].
Prediction of therapeutic response
The role of ACE in predicting the response to chemotherapy was also evaluated. First,
BRCA-related drug-target genes were screened using the Drugbank database
(https://go.drugbank.com/). Besides, we predicted the response to anti-therapy for each
patient based on the Cancer Genome Project (CGP) database
(https://www.sciencedirect.com/topics/neuroscience/cancer-genome-project) as well.
Several common therapeutics, including vinblastine, cisplatin, doxorubicin, etoposide,
gefitinib, gemcitabine, paclitaxel, parthenolide, sunitinib and vinorelbine were selected.
The prediction process was performed by R package “pRRophetic” where the samples’
half-maximal inhibitory concentration (IC50) was calculated by ridge regression and
the prediction accuracy was assessed by 10-fold cross-validation according to the CGP
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training set. Default options were used for all parameters [30].
Clinical samples
Two tissue microarrays (TMAs, HBreD050Bc01 and HBreD090Bc03) were obtained
from Outdo Biotech (Shanghai, China). The HBreD050Bc01 microarray contained 40
BRCA and 10 adjacent samples. The HBreD090Bc03 microarray contained 85 BRCA
and 5 adjacent samples. Thus, a total of 125 BRCA samples and 15 adjacent samples
were involved in the current research.
Immunohistochemistry and semi-quantitative evaluation
Next, Immunohistochemistry (IHC) staining was conducted on these tissue slides. The
primary antibodies used in the research were as following: anti-ACE2 (1:3000 dilution,
Cat. ab15348, Abcam, Cambridge, UK), anti-CD8 (Ready-to-use, Cat. PA067, Abcarta,
Suzhou, China) and anti-PD-L1 (Ready-to-use, Cat. GT2280, GeneTech, Shanghai,
China). Antibody staining was visualized with DAB and hematoxylin counterstain, and
stained sections were scanned using Aperio Digital Pathology Slide Scanners. All
stained sections were independently evaluated by two independent pathologists. For
semi-quantitative evaluation of ACE2 and PD-L1 staining, the percentage of positively
stained cells was scored as 0-4: 0 (< 1%), 1 (1-5%), 2 (6-25%), 3 (26-50%) and 4 (>
50%). The staining intensity was scored as 0-3: 0 (negative), 1 (weak), 2 (moderate)
and 3 (strong). The immunoreactivity score (IRS) equals to the percentages of positive
cells multiplied with staining intensity. For CD8 staining, infiltration level was assessed
by estimating the percentage of cells with strong intensity of membrane staining in the
stroma cells.
Statistical analysis
Statistical analysis and figure exhibition performed using R language 4.0.0. The
statistical difference of continuous variables between the two groups was evaluated by
Wilcoxon rank sum test or Mann-Whitney test and chi-square test was used when the
categorical variables were assessed. Pearson’s correlation was used to evaluete the
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correlation between two variables. Receiver-operating characteristic (ROC) analysis
was plotted to assess the specificity and sensitivity of the candidate indicator, and the
area under the ROC curve (AUC) was generated for diagnostic biomarkers. Prognostic
values of categorical variables were assessed by log-rank test. For all analyses, P value
≤ 0.05 were deemed to be statistically significant.
Results
Expression and immunological roles of ACE2 across human cancers
After a systematic pan-cancer analysis of the expression of ACE2 in the TCGA
database, we discovered that ACE2 was lowly expressed in a fraction of cancers,
including BRCA, KICH, and LUAD. However, ACE2 also shown to be overexpressed
in CESE, KIRP, LIHC and UCEC (Figure 1A). Next, we conducted a pan-cancer
survival analysis about overall survival (OS), progression-free survival (PFS) using
Kaplan-Meier analysis. ACE2 emerged as a prognostic risk factor for both OS and PFS
in KIRC, LIHC and UCS (Figure S1A-B). However, these results need further
verification, especially based on recruited cohorts.
We next conducted a pan-cancer analysis aimed to examine the immunological
features of ACE2 in various tumors. The results uncovered that ACE2 was positively
correlated with most immunomodulators in BRCA (Figure 1B). We also calculated the
infiltrating levels of TIICs in the TME by the ssGSEA method. Similarly, ACE2 was
positively correlated with most TIICs in BRCA (Figure 1C). Moreover, we revealed
that the expression of ACE2 was positively related to the expression of several immune
checkpoints, including LAG3, TIGIT, CTLA4 and PD-L1 in BRCA (Figure 1D).
Although these positive correlations between ACE2 and immunological features were
found in other tumors, such as CESC, KIRC and PRAD, the highest correlation was
observed in BRCA. Moreover, according to previous research, ACE2 was not expressed
in immune cells, and the expression of ACE2 in bulk RNA-seq data was derived from
non-immune cells in all probability, such as tumor cells in the tissues.[31] Besides,
considering the positive correlation between ACE2 and PD-L1, an immune checkpoint
expressed on tumor cells, we conducted the TFs network analysis, and found that a
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mass of shared TFs that potentially regulated ACE2 and PD-L1 (Figure S2, Table S2).
Collectively, the expression pattern of ACE2 is TME- characteristic, which illustrates
the potential of ACE2 as an immune-related biomarker and therapeutic target in BRCA.
Potential regulatory factors, prognostic value and functions of ACE2 in BRCA
Mutations in ACE2 gene were rare (0.30%, Figure S3A), so the mutations seemed to
not be a dominating factor for ACE2 expression. The CNV pattern of ACE2 was shown
in Figure S3B. Remarkably, copy number amplification of the ACE2 upregulated the
expression of ACE2 mRNA (Figure S3B). Besides, methylation level was positively
correlated with ACE2 expression (Figure S3C). These findings suggest that epigenetic
modifications of the ACE2 gene might be essential for the regulation of expression.
Considering that ACE2 expression was not related to survival outcome in the
TCGA database, we next assess its prognostic role using PrognoScan tool. However,
the prognostic role of ACE2 in BRCA was inconsistent (Table S3). We speculated that
the prognostic value of ACE2 may be associated with subtypes and therapeutic
regimens in BRCA, which needed to be further studied.
Moreover, the functions of ACE2 in BRCA was analyzed using LinkedOmics tool.
GO enrichment analysis assessed the functions of ACE2 in term of three aspects,
including biological processes, cellular components and molecular functions. Plentiful
of statistically significant terms were found and the top 5 terms positively correlated
with ACE2 expression of each analysis were retained. As shown in Table S4, the most
critical terms were associated with immune-related processes. These results reveal that
ACE2 may act as a critical role in regulating anti-tumor immunity in BRCA.
ACE2 shapes an inflamed TME in BRCA
Considering that ACE2 was positively related to a great proportion of
immunomodulators in BRCA, we next explored in-depth immunological role of ACE2
in BRCA. Most MHC molecules were upregulated in the high ACE2 group, which
suggested that the ability of antigen presentation and processing was upregulated in the
high ACE2 group (Figure 2A). Besides, most chemokines and paired receptors were
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upregulated in the high ACE2 group (Figure 2A). These chemokines and receptors
facilitate the recruitment of effector TIICs, including CD8+T cells, TH17 cells and
antigen-presenting cells. Next, we calculated the infiltration level of TIICs using five
independent algorithms. Similar to the previous results, ACE2 was positively related to
the infiltration levels of the majority of immune cells using various algorithms (Figure
2B). The ESTIMATE method was next applied to estimate Tumor Purity, ESTIMATE
Score, Immune Score and Stromal Score. Compared with the low ACE2 group, the high
ACE2 group had enhanced ESTIMATE Score, Immune Score and Stromal Score but
decreased Tumor Purity (Figure 2C). In addition, ACE2 was negatively correlated with
the marker genes of immune cells, including CD8+T cell, dendritic cell, macrophage,
NK cell and Th1 cell (Figure 2D). Besides, the activities of the cancer immunity cycle
are a direct integrated manifestation of the functions of the chemokines and other
immunomodulators. In the high ACE2 group, activities of the most steps in the cycle
were revealed to be upregulated (Figure 2E). The expression of immune checkpoints
such as PD-1/PD-L1 was uncovered to be high in the inflamed TME [32]. In our
research, ACE2 was suggested to be positively related to most immune checkpoints,
including VTCN1, PD-L1, PD-1, CTLA4 and so on (Figure 2F). Totally, ACE2 is
tightly correlated with the development of an inflamed TME, which may act as a critical
role in identifying the immunogenicity of BRCA.
ACE2 predicts immune phenotype in BRCA
Theoretically, BRCA patients with high ACE2 expression should have a higher
response to immunotherapy because ACE2 identifies an inflamed TME. Immune-
related target expression levels commonly reflect the response to immunotherapy. As
expected, the expression levels of most immunotargets such as CD19, PD-1 and PD-
L1, were upregulated in the high ACE2 group (Figure 3A). T cell inflamed score is
developed using IFN-γ-related mRNA profiles to predict clinical response to PD-1
blockade [27], and BRCA patients in the high ACE2 group exhibited higher T cell
inflamed scores (Figure 3B). Besides, TMB level is another biomarker for the
prediction of the response to immunotherapy [33]. Foreseeably, in the low ACE2 group,
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the frequency of mutant genes and TMB were both lower compared with the high ACE2
group (Figure 3C-E). Given that the TMB levels were most enriched in the range of 0-
1200 (Figure S4), the comparison between the two groups was limited to the range of
1-1200 to avoid the effect of extremum. More importantly, TP53 exhibited incredibly
high mutation rates in the high ACE2 group (Figure 3C-D), which was a biomarker for
better response to immunotherapy [34, 35]. According to previous research, patients
with high levels of microsatellite instability (MSI-H) tend to be sensitive to
immunotherapy [33]. We next assess the status of mismatch repair (MMR) proteins and
ACE2 expression. The proportion of MSI-H in BRCA varied largely, from 0.2% to
18.6% [36], but in most the proportion of MSI-H in BRCA less than 5%. We set the
low 5% as the threshold of MMR protein deficiency. As expected, the proportion of
MLH1 and PMS2 deficiency in the high ACE2 group was higher than that in the low
ACE2 group, which indicated that BRCA patients with high ACE2 expression may
have a higher MSI-H proportion (Figure 3F). Using IPS as a surrogate of the response
to immunotherapy, we discovered that patients with high ACE2 expression had notably
higher IPS (Figure 3G). In summary, immunotherapy may be carried out in BRCA
patients with high ACE2 expression as they tend to be sensitive to immunotherapy.
ACE2 predicts molecular subtypes and therapeutic options in BRCA
We next evaluated the ACE2 expression and clinic-pathologic features of BRCA. As
Figure 4A exhibited, ACE2 expression was significantly associated with age,
histological type, molecular type, ER status and PR status, but not related to other
features. Specifically, ACE2 was upregulated in ER-negative, PR-negative and the
triple-negative breast cancer (TNBC) tissues (Figure S5A), and ROC analysis indicated
a notable diagnostic value in identifying these molecular subtypes (Figure S5B). TNBC
has been identified as a subtype with high aggressiveness and PD-L1 expression.
However, in the TCGA cohort, the prognosis of TNBC patients showed no notable
difference compared with non-TNBC patients (Figure S6), which may be due to various
therapies. This may account for why ACE2 was upregulated in TNBC subtype but not
related to prognosis.
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In addition, the enrichment scores, such as IFN-γ signature, APM signal, cell cycle,
DNA replication and et al., were higher in the high ACE2 group (Figure 4B). Thus,
targeted therapy suppressing these oncogenic pathways could be applied for the
treatment of BRCA with high ACE2 expression. Moreover, findings from the
Drugbank database revealed a remarkably higher response to chemotherapy, anti-
ERBB therapy (excluding Afatinib), antiangiogenic therapy and immunotherapy in the
high ACE2 group (Figure 4C). This shows that chemotherapy, anti-ERBB therapy,
antiangiogenic therapy and immunotherapy can be applied, either alone or in
combination, for the therapy of BRCA with high expression. However, BRCA with
lower ACE2 expression was possibly sensitive to endocrine therapy and Afatinib.
Moreover, IC50 of anti-cancer drugs in patients from the TCGA database according to
the pRRophetic algorithm was estimated. The results showed patients with high ACE2
expression were more sensitive to common anti-cancer drugs (Figure 4D). To sum up,
ACE2 is an indicator for the subtype of TNBC and resistance to endocrine therapy in
BRCA, but patients with high ACE2 expression tend to be sensitive to more therapeutic
opportunities, including chemotherapy, anti-ERBB therapy, antiangiogenic therapy and
immunotherapy.
ACE2 predicts immune phenotypes and clinical subtypes in independent cohorts
The above findings were next validated in the METABRIC database. Similar to the
evidence from the TCGA database, the infiltration levels of the majority of immune
cells using various algorithms were increased in the high ACE2 group (Figure 5A).
Besides, the enrichment scores of chemokine immunomodulator, MHC and receptor
were also higher in the high ACE2 group (Figure 5B). ACE2 was positively related to
the marker genes of immune cells (Figure 5C). As expected, immunotargets and T cell
inflamed scores were increased in the high ACE2 group as well (Figure 5D-E). We also
analyzed the association between TMB level and ACE2 expression. Although the TMB
levels were not remarkably different in the two groups (Figure S7), the notably various
mutant feature of TP53 was observed in the METABRIC database (Figure 5F-G). The
deficient frequency of MLH1 was higher, which implied the MSI-H may be common
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in the high ACE2 group (Figure S8). Besides, ROC analysis validated the notable
diagnostic value in identifying ER, PR status and TNBC subtype (Figure 5H). However,
similar to the result in the TCGA database, the prognosis of TNBC patients showed no
remarkable difference compared with non-TNBC patients (Figure S9). Furthermore,
we also performed IC50 prediction of anti-cancer drugs in patients from the
METABIRC database using the pRRophetic algorithm. As expected, patients with high
ACE2 expression were more sensitive to these anti-cancer drugs which were mentioned
in the previous analysis (Figure 5I).
To further validate above results, we also obtained a TMA cohort for verification,
which included 125 BRCA samples and 15 adjacent samples. As Figure 6A shown,
ACE2 was notably decreased in BRCA tissues in comparison to normal tissues (Figure
6A-B). In accord with the above results, ACE2 was overexpressed in ER-negative, PR-
negative and the TNBC tissues (Figure 6C). In addition, the current BRCA cohort was
divided into the low and high expression groups according to the median level of ACE2
expression (IRS ≤ 3 vs. IRS > 3). As Table 1 exhibited, ACE2 expression was
associated with age, ER status, PR status and molecular type (Table 1). Moreover, the
infiltrating level of CD8+T cell and PD-L1 expression were higher in the high ACE2
group (Figure 6D-F). In conclusion, ACE2 expression is related to clinical features and
immune phenotypes in BRCA. However, due to unavailable therapeutic information,
we are unable to assess the association between ACE2 expression and the response to
various therapies.
Discussion
COVID-19 is becoming a global concern and the major public threat in the last two
years. Cancer patients are more impressionable to COVID-19 infection due to the
underlying disease, which may be due to systemic reduced immunity or anticancer
therapy [37]. The situation could be more terrible in lung cancer patients as they already
have chronic pulmonary inflammation and the lung TME supports for SARS-CoV-2
and accelerates infection [38]. Besides, cancer patients have a worse prognosis after
SARS-CoV-2 infection. For example, lung cancer patients were reported to suffer more
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from severe events, including increased death rate and ICU admission rate [39].
Moreover, emerging studies suggest that SARS-CoV-2 infection could affect cancer
progression. Dormant cancer cells tend to survive after successful therapy of primary
tumors and localize in particular microanatomical sites of metastasis-prone organs [40].
Acute lung inflammation and neutrophil extracellular traps have been exhibited to
activate the exit from dormancy of breast dormant cancer cells respectively, leading to
distant metastasis [41].
However, an isolated case report has revealed that SARS-CoV-2 infection induced
complete spontaneous remission in a patient with lymphoma [42]. This may be because
of the SARS‐CoV‐2 infection activating an anti‐tumor immune response, as has been
previously reported, concurrent infections induced complete remission of diffuse large
B-cell lymphoma independent of any interventions [43]. Recently, oncolytic
virotherapy has developed as an encouraging anti-cancer therapy through virus self-
replication. On the other side, oncolytic virus enhances anti-tumor immunity responses
by increasing the immune infiltration and turn the tumor to be sensitive to
immunotherapy and chemotherapy [44]. Thus, we speculated SARS‐CoV‐2 infection
induced spontaneous remission of tumor has parallels with oncolytic virotherapy to
some extent. Whatever, the complicated interplay between the immune system and
cancer after SARS‐CoV‐2 infection needs to be further highlighted.
As crosstalk between COVID-19 and the tumor, ACE2 act as a significant role in
both SARS‐CoV‐2 infection and cancer. ACE2 has been found to be as a tumor
suppressor in various cancers. It was reported that ACE2 exerts anti-tumor roles by
suppressing tumor angiogenesis [8]. Dai et al. [11] reported that upregulated ACE2
expression was correlated with a favorable clinical outcome in hepatocellular
carcinoma. Besides, the associations between ACE2 with anti-tumor immunity and
immunotherapy were also explored. Yang et al. [12] reported that the overexpression
of ACE2 was significantly associated with enhanced immune infiltration in endometrial
cancer and renal papillary cell cancer. Bao et al. [31] uncovered tight correlations
between ACE2 expression and immune gene signatures in multiple cancers. However,
the crucial values of ACE2 in the identification of tumor immune status have not been
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evaluated.
In this research, we first conducted a pan-cancer analysis of the immunological
features of ACE2. We discovered that ACE2 exhibited the tightest correlation with
immunological factors in BRCA, and in-depth analysis of ACE2 in BRCA was
subsequently conducted. We found that ACE2 was positively related to the expression
of important immunomodulators, such as CCL5, CXCL10 and CXCR4. Besides, ACE2
was also positively related to increased TIICs and cancer immunity cycles. Namely, the
recruitment of effector TIICs was enhanced, thereby facilitating the formation of an
inflamed TME. Besides, we discovered that high ACE2 expression was correlated with
the enhanced response to immunotherapy by check the difference of immune
checkpoints expression, T cell inflamed score, TMB, MMR protein deficiency status
and IPS scores in the ACE2 high and low groups. Another important finding was that
high ACE2 levels indicated the negativity of hormone receptors, including ER, PR and
HER2 receptors. More importantly, ACE2 expression was increased in the TNBC
subtype of BRCA. TNBC is summarized by deadly aggressiveness and lacked
treatment [45]. However, PD-L1 was often overexpressed in TNBC [46], and its
response to immune checkpoint inhibition was encouraging.
Conclusions
In the current study, we revealed that ACE2 shaped an inflamed TME according to the
evidence that ACE2 positively related to the immunological patterns of TME in BRCA.
Besides, we uncovered that BRCA had the potential to estimate the response of
immunotherapy, the molecular subtypes and the response to several therapeutic
strategies. Overall, ACE2 may be used as a promising biomarker for the identification
of immunological features in BRCA.
Acknowledgments
This work was supported by the Natural Science Foundation of China (82073194), the
Major project of Wuxi Science and Technology Bureau (N20201006) and the Wuxi
Double-Hundred Talent Fund Project (BJ2020076).
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Availability of data and materials
All data supported the results in this study are showed in this published article and its
supplementary files. Besides, original data for bioinformatics analysis could be
downloaded from corresponding platforms.
Authors’ contributions
Y Zhang and Y Zhu conceived the study and participated in the study design,
performance, coordination and project supervision. JM, YC, RX, XY, LC, TM, TG and
FG collected the public data and performed the bioinformatics analysis. JM performed
the IHC staining. Y Zhang and Y Zhu revised the manuscript. All authors reviewed and
approved the final manuscript.
Ethics approval and consent to participate
Ethical approval for the study of tissue microarray slide was granted by the Clinical
Research Ethics Committee, Outdo Biotech (Shanghai, China).
Competing interests
The authors have no competing interests.
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Figure legends
Figure 1. The relationship between ACE2 and immunological features in pan-cancers
(A) Pan-cancer analysis of ACE2 expression in tumor and paracancerous tissues in the
TCGA database. Ns: no significant difference; *P < 0.05; **P < 0.01; ***P < 0.001;
****P < 0.0001. (B) Correlation between ACE2 and 122 immunomodulators
(chemokines, receptors, MHC and immunostimulators). The color reveals the
correlation coefficient. The asterisks reveal statistical difference assessed by Pearson
analysis. (C) Correlation between ACE2 and 28 TIICs calculated with the ssGSEA
algorithm. The color reveals the correlation coefficient. The asterisks reveal statistical
difference assessed by Pearson analysis. (D) Correlation between ACE2 and four
immune checkpoints, LAG3, TIGIT, CTLA4, PD-L1. The dots symbolize cancer types.
Figure 2. ACE2 shapes an inflamed TME in BRCA
(A) Expression levels of 122 immunomodulators between the high and low ACE2
groups in BRCA. (B) Differences in the levels of TIICs calculated using five algorithms
between the high and low ACE2 groups. (C) Differences in Tumor Purity, ESTIMATE
Score, Immune Score, and Stromal Score estimating by ESTIMATE method between
the high and low ACE2 groups. (D) Differences in the gene markers of the common
TIICs between the high and low ACE2 groups. (E) Correlation between ACE2 and
common inhibitory immune checkpoints. The color reveals the Pearson correlation
coefficient. (F) Differences in the various steps of the cancer immunity cycle between
the high and low ACE2 groups. Ns: no significant difference; *P < 0.05; **P < 0.01;
***P < 0.001; ****P < 0.0001.
Figure 3. Correlation between ACE2 and the immune phenotype in BRCA
(A) Differences in expression levels of immune-related targets the high and low ACE2
groups in BRCA. (B) Differences in T cell inflamed scores between the high and low
ACE2 groups. The T cell inflamed score is positively related to the response to cancer
immunotherapy. (C, D) Mutational landscape in the high and low ACE2 groups. (E)
Differences in TMB levels between the high and low ACE2 groups. (F) Differences in
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deficiency rates of MMR proteins between the high and low ACE2 groups. (G)
Differences in IPS scores between the high and low ACE2 groups.
Figure 4. ACE2 predicts the molecular subtype and the therapeutic options in BRCA
(A) Correlations between ACE2 and clinic-pathological features in BRCA. (B)
Correlations between ACE2 and the enrichment scores of several therapeutic signatures.
(C) Correlation between ACE2 and the drug-target genes extracted from the Drugbank
database. (D) Differences in IC50 of common anti-cancer drugs between the high and
low ACE2 groups. ****P < 0.0001.
Figure 5. Roles of ACE2 in predicting immune and clinical phenotypes in the
METABIRC cohort
(A) Differences in the levels of TIICs calculated using five algorithms between the high
and low ACE2 groups in BRCA. (B) Differences in enriched scores of chemokines,
receptors, MHC and immunostimulators between the high and low ACE2 groups. (C)
Differences in the gene markers of the common TIICs between the high and low ACE2
groups. (D) Differences in expression levels of immune-related targets between the high
and low ACE2 groups. (E) Differences in T cell inflamed scores between the high and
low ACE2 groups. (F, G) Mutational landscape in the high and low ACE2 groups. (H)
Diagnostic values of ACE2 expression in identifying ER, PR and triple-negative
subtypes. (I) Differences in IC50 of common anti-cancer drugs between the high and
low ACE2 groups. **P < 0.01; ****P < 0.0001.
Figure 6. Roles of ACE2 in predicting clinical and immune phenotypes in the recruited
TMA cohort
(A) Representative images revealing ACE2 expression in tumor and para-tumor tissues
using anti-ACE2 staining. Magnification, 200X; (B) Expression levels of ACE2 in
tumor and para-tumor tissues. (C) Expression levels of ACE2 in various molecular
subtypes. (D) Representative images revealing CD8+T cell infiltration and PD-L1
expression in the high and low ACE2 groups. Magnification, 200X; (E) Differences in
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CD8+T cell infiltration between the high and low ACE2 groups. (F) Differences in PD-
L1 expression between the high and low ACE2 groups.
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Table 1. Associations between ACE2 expression and clinico-pathological features in BRCA. Features Cases ACE2 expression χ2 value P value
low high Age
≤60 94 48 46 4.561 0.033 >60 31 9 22
T stage
≤2cm 47 18 29 1.979 0.159 >2cm 76 39 37
unknown 2a
N stage
N0 63 27 36 0.275 0.600 N1-N3 61 29 32
unknown 1b
M stage
N0 124 56 68 1.203 0.456c M1 1 1 0
Clinical stage
0-2 89 41 48 0.027 0.869 3-4 36 16 20
Grade
I-II 53 28 25 1.755 0.185 II-III 71 29 42
unknown 1
ER status
negative 63 18 45 14.847 <0.001 positive 62 39 23
PR status
negative 70 21 49 15.607 <0.001 positive 55 36 19
HER2 status
negative 78 35 43 0.102 0.750 positive 46 22 24
unknown 1
Molecular type
non-TNBC 88 47 41 6.758 0.009 TNBC 36 10 26
unknown 1
Note: a: T1-T3, not T4; b: The patient had distant metastasis; c: P value was calculated by Fisher test.
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*****
****************
***
**
*
**
****
*******
**
**
***
*
*
*
**************
*
*************
***
****
*********
*********
****************
***
****
****
*
*
*
*
*********
*
****
*
*****
***
**
**
*
*
**
*
**
***
*
************
*
**
***
*
***
***************************
*********
**
*
**
*
***
**
*
**
***
*
*
*
*
*
******
*
*
**
**
*
**
*
****
*
*
*
ACC
BLCA
BRC
AC
ESCC
HO
LC
OAD
DLBC
ESCA
GBM
HN
SCKIC
HKIR
CKIR
PLAM
LLG
GLIH
CLUADLU
SCM
ESOO
VPAADPC
PGPR
ADR
EADSAR
CSKC
MS TADTG
CT
THC
ATH
YMU
CEC
UC
SU
VM
CCL1CCL11
CCL14CCL15CCL16CCL17CCL18CCL19CCL2
CCL21CCL22
CCL24CCL25CCL26CCL27CCL28
CCL4CCL5CCL7CCL8
CXCL1
CXCL11CXCL12
CXCL14CXCL16CXCL17CXCL2
CXCL5CXCL6CXCL9XCL1XCL2BTNL2CD27CD276CD28
CD48
CD86ENTPD1HHLA2ICOSICOSLGIL2RAIL6IL6RKLRC1KLRK1LTAMICBNT5EPVRRAET1ETMIGD2
TNFRSF14TNFRSF17TNFRSF18TNFRSF25TNFRSF4TNFRSF8TNFRSF9
TNFSF14TNFSF15TNFSF18TNFSF4TNFSF9ULBP1B2M
OA
PA1
TAP1TAP2TAPBPCCR1
CCR2
CCR4CCR5CCR6CCR7CCR8CCR9
CXCR1CXCR2
CXCR4CXCR5CXCR6XCR1
Type
Chemokine
Immunostimulator
MHC
Receptor
1
*
*
*
*
*
***
*
****
**
*
*
*
**
********
******
*
**
***
***
**
*********
****************
**
**
****
********************************************************************
***************************************
*
********
**
******
***********
***
*****************
******
************************
*
*
**
*
*
****
*
*
*
*********
*****
**
***
*****
***
***
**
*****************
*
**
*
*
*
*
*
*
**
**
***
****
*
*
****
***
**
*
***
**
***
****
*****
******
*
****
**
**
*
*
*
**
*
*
**
*
***
**************
**
**
*****
**
*
**
************
*********
**
******
**
*
*
*
*
*
*
*
*
*
*
**********
******************
****************
**
***
**
*******************************
*******
****
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**
**
****
**
*
*****
**
**
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*
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****
***
*
**
*
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*
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***
*
**
**
*
*
***
*********
*
*
*
*
**
**
*
***
**
*
**
***
**
**
*
***
**
**************
******
****
*****
****************
*
**
*
*
**
**
**
*******
******
****
****
*****
***
*
****
********************
*******************
***
*
*
***
**
***
******
*
**
***
*
*
*****
*
**
**
***
*
**
*
*
*
***
*
**
****
***
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*
***
*
*
**
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***
******
*
**
****
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*****
**********
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*
**
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*
*
**
***************
****
*****
*
*
*****
****************
*****************
************
*
******
*
*
**
************
************************************************************************************************
***
**
***
***
**
**
*
**
*
*
*****
*
*
*
*
*
****
**
*
**
*
**
*
**
*
*
*
*
*
*
*
**
*
**
*
**
***
******
*
***
*
*
*
***
*
***
*
***
***
***
*
*
*****
*****
*
**
**
**
*******
*****
**
***
***********
**
******
*****
****************
***
**
*
**
****
*******
**
**
***
*
*
*
**************
*
*************
***
****
*********
*********
****************
***
****
****
*
*
*
*
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*
****
*
*****
***
**
**
*
*
**
*
**
***
*
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*
**
***
*
***
***************************
*********
**
*
**
*
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**
*
**
***
*
*
*
*
*
******
*
*
**
**
*
**
*
****
*
*
*
CCL1CCL11
CCL14CCL15CCL16CCL17CCL18CCL19CCL2
CCL21CCL22
CCL24CCL25CCL26CCL27CCL28
CCL4CCL5CCL7CCL8
CXCL1
CXCL11CXCL12
CXCL14CXCL16CXCL17CXCL2
CXCL5CXCL6CXCL9XCL1XCL2BTNL2CD27CD276CD28
CD48
CD86ENTPD1HHLA2ICOSICOSLGIL2RAIL6IL6RKLRC1KLRK1LTAMICBNT5EPVRRAET1ETMIGD2
TNFRSF14TNFRSF17TNFRSF18TNFRSF25TNFRSF4TNFRSF8TNFRSF9
TNFSF14TNFSF15TNFSF18TNFSF4TNFSF9ULBP1B2M
OA
PA1
TAP1TAP2TAPBPCCR1
CCR2
CCR4CCR5CCR6CCR7CCR8CCR9
CXCR1CXCR2
CXCR4CXCR5CXCR6XCR1
1
BRCA
5
15
Correlation
valu
e)
log Pa
aaa
5
15
Correlation
Correlation Between ACE2 and LA
BRCA
5
15
Correlation
valu
e)
Correlation
log Pa
aa
5
15
Correlation Between ACE2 and TIGIT
BRCA
Correlation
valu
e)
Correlation
log Pa
aaaa
5
15
25
Correlation Between ACE2 and CTLA4
BRCA
5
15
Correlation
valu
e)
Correlation
log Paaa
5
15
Correlation Between ACE2 and PD-L1
A
B C
D
OV
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint
CCR1CCR10CCR2CCR3CCR4CCR5CCR6CCR7CCR8CCR9CX3CR1CXCR1CXCR2CXCR3CXCR4CXCR5CXCR6XCR1B2M
OA
PA1
TAP1TAP2TAPBPBTNL2
HHLA2ICOS
IL2RAIL6IL6RKLRC1KLRK1LTAMICB
PVR
ULBP1CCL1CCL11CCL13CCL14CCL15CCL16CCL17CCL18CCL19CCL2CCL20CCL21CCL22CCL23CCL24CCL25CCL26CCL27CCL28CCL3CCL4CCL5CCL7CCL8CX3CL1CXCL1CXCL10CXCL11CXCL12CXCL13CXCL14CXCL16CXCL17CXCL2CXCL3CXCL5CXCL6CXCL9XCL1XCL2
ACE2
Group
GroupHigh ALow A
ACE212
0
TypeChemokineImmunostimulatorMHCReceptor
0
2
4CCR1CCR10CCR2CCR3CCR4CCR5CCR6CCR7CCR8CCR9CX3CR1CXCR1CXCR2CXCR3CXCR4CXCR5CXCR6XCR1B2M
OA
PA1
TAP1TAP2TAPBPBTNL2
HHLA2ICOS
IL2RAIL6IL6RKLRC1KLRK1LTAMICB
PVR
ULBP1CCL1CCL11CCL13CCL14CCL15CCL16CCL17CCL18CCL19CCL2CCL20CCL21CCL22CCL23CCL24CCL25CCL26CCL27CCL28CCL3CCL4CCL5CCL7CCL8CX3CL1CXCL1CXCL10CXCL11CXCL12CXCL13CXCL14CXCL16CXCL17CXCL2CXCL3CXCL5CXCL6CXCL9XCL1XCL2
ACE212
0
TypeChemokineImmunostimulatorMHCReceptor
0
2
4 B cellCancer associated fibroblast
MacrophageNK cell
racterized cellT cell
Cytotoxic lymphocyteB lineageNK cellMonocytic lineageMyeloid dendritic cellNeutrophil
blastsB cellMacrophage M1Macrophage M2MonocyteNeutrophilNK cell
ry)
T cell regulator Tregs)Myeloid dendritic celluncharacterized cellB cell
NeutrophilMacrophage
ritic cellActiv T cellCentral memor T cell
fector memeor T cellActiv T cellCentral memor T cell
fector memeor T cellT follicular helper cell
T cellType 1 T helper cellType 17 T helper cellType 2 T helper cellRegulatory T cellActivated B cellImmature B cellMemory B cellNK cell
right NK cellcell
Myeloid derived suppressor cellNK T cellActivated dendritic cellPlasmacytoid dendritic cellImmature dendritic cellMacrophage
Mast cellMonocyteNeutrophil
GroupHigh ALow A
ACE212
0
Algorithm
MCPquanTIseq
0
1
2
3
0
1000
2000
Low A
High A
StromalScore
0
2000
4000
Low A
High A
ImmuneScore
0
2500
5000
Low A
High A
A
Low A
High A
TumorPurity
ns ns **** **** **** **** **** **** **** **** **** **** **** **** **** **** * **** **** **** **** **** ****
0
3
6
T ce
ll re
crui
ting
T ce
ll re
crui
ting
T ce
ll re
crui
ting
Th1
cell
recr
uitin
g
ritic
cel
l rec
ruiti
ng
Th22
cel
l rec
ruiti
ng
Mac
roph
age
recr
uitin
g
Mon
ocyt
e re
crui
ting
Neu
troph
il re
crui
ting
NK
cell
recr
uitin
g
ruiti
ng
Baso
phil
recr
uitin
g
Th17
cel
l rec
ruiti
ng
B ce
ll re
crui
ting
Th2
cell
recr
uitin
g
Treg
cel
l rec
ruiti
ng
ruiti
ng
variable
valu
e
High A Low A
Rel
ease
of c
ance
r cel
l ant
igen
s
Can
cer a
ntig
en p
rese
ntat
ion
Prim
ing
and
activ
atio
n
Infil
tratio
n of
imm
une
cells
into
tum
or
Rec
ogni
tion
of c
ance
r cel
ls b
y T
cells
Killin
g of
can
cer c
ells
CXCL10CXCR3CCL5
L
TBX21
CRTAMIL7RNCR1PRR5LSPON2
CYBBLILRA2MARCOMMP8MS4A6ACCL4CTLA4
IL21R
SLC15A3
GroupHigh ALow A
ACE212
0
Type
ritic cellMacrophageNK cellTh1 cell
0
2
4
Type
0
1
A VTC
N1
CTL
A4H
AVC
R2
LAIR
1PV
R ACAM
1BT
LA
KLR
C1
LA
AVTCN1
CTLA4HAVCR2
LAIR1PVR
ACAM1BTLA
KLRC1
LA
ACE2
Group
ACE2
Group
A B C
D F
E
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint
P = 0.170
6
8
10
Low ACE2
High ACE2
IPS
CTL
A4-n
eg-P
D1-
neg
IPS CTLA4-neg-PD1-neg
P < 0.0001
3
5
7
9
11
Low ACE2
High ACE2
IPS
CTL
A4-n
eg-P
D1-
pos
IPS CTLA4-neg-PD1-pos
P = 0.0001
6
8
10
Low ACE2
High ACE2
IPS
CTL
A4-p
os-P
D1-
neg
IPS CTLA4-pos-PD1-neg
P < 0.0001
3
6
9
Low ACE2
High ACE2
IPS
CTL
A4-p
os-P
D1-
pos
IPS CTLA4-pos-PD1-pos
P < 0.0001
7.5
10.0
12.5
High ACE2
Low ACE2
Panc
ance
r T c
ell i
nfla
med
sco
re
P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001
0
5
10
15
CD19 PDCD1 CD274 CTLA4 IDO1 TNFRSF17 PVR
Rel
ative
exp
ress
ion
leve
l
Group High ACE2 Low ACE2
High ACE2 (92.81%)
0
5366
SPTA1RYR3
RUNX1FAT3DMD
SYNE2MUC4PTEN
USH2AFLG
MAP2K4NEB
SYNE1RYR2
ZFHX4NCOR1HMCN1MUC16KMT2C
MAP3K1CDH1
TTNGATA3
TP53PIK3CA
4%4%4%4%4%4%4%5%5%5%5%5%5%5%6%6%6%10%12%12%13%16%17%19%38%
0 194
Frame_Shift_InsNonsense_MutationMissense_MutationFrame_Shift_Del
In_Frame_DelSplice_SiteMulti_Hit
Low ACE2 (88.67%)
P < 0.0001
0
500
1000
High ACE2
Low ACE2
TMB
leve
l
Deficiency Normal
0
4161
LYSTCSMD1
DNAH17PKHD1L1
NEBMUC17
MAP3K1DMD
OBSCNLRP2
CSMD3HMCN1SPTA1SYNE1
PTENRYR2
USH2AKMT2CGATA3
FLGMUC16
CDH1TTN
PIK3CATP53
5%5%5%5%5%6%6%6%6%6%6%7%7%7%7%7%8%8%8%9%12%13%22%31%50%
0
Missense_MutationSplice_SiteNonsense_MutationFrame_Shift_Ins
Frame_Shift_DelIn_Frame_DelIn_Frame_InsMulti_Hit
251
Deficiency Normal
Deficiency Normal
Deficiency Normal
High ACE2
Low ACE2
A B
F
DC E
G
95.23%
4.77%
94.68%
5.32%
0
25
50
75
100
perc
enta
ge
MSH2 P = 0.782
95.05%
4.95%
94.86%
5.14%
0
25
50
75
100
perc
enta
ge
MSH6 P = 1.000
92.48%
7.52%
97.43%
2.57%
0
25
50
75
100
perc
enta
ge
MLH1 P < 0.001
92.84%
7.16%
97.06%
2.94%
0
25
50
75
100
perc
enta
ge
PMS2 P = 0.002
High ACE2
Low ACE2
High ACE2
Low ACE2
High ACE2
Low ACE2
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint
Vinblastine$TUBBMethotrexate$TYMSGemcitabine$TYMSCisplatin$TFGemcitabine$CMPK1Docetaxel$MAP2Vinblastine$TUBE1
Group
ACE2
12
0
Type
Chemotherapy
0
1
2
Afatinib$ERBB4Cetuximab$C1QACetuximab$C1QBCetuximab$C1QCCetuximab$FCGR2BCetuximab$FCGR2CTrastuzumab$EGFRCetuximab$EGFRCetuximab$C1RCetuximab$C1S
ERBB therapy
Bevacizumab$VEGFASorafenib$KITPaclitaxel$MAP2Sunitinib$CSF1RPazSorafenib$FLT1Sunitinib$FLT1Sunitinib$FLT4Sorafenib$FLT4
Antiangiogenic therapyAtezolizumab$CD274Durvalumab$CD274Avelumab$CD274
Immunotherapy
age
subdivision
ER status
PR status
HER2 status
molecular type
histological type
T stage
N stage
M stage
clinical stage
recurrence status
vital status
vital status ns
living
deceased
recurrence status ns
tumor free
with tumor
unknown
clinical stage ns
Stage I
Stage II
Stage III
Stage IVunknown
M stage ns
M0
M1
unknown
N stage ns
N0
N1
N2
unknown
T stage ns
T1
T2
T4
unknown
histological type ***Infiltrating Carcinoma NOS
Infiltrating Ductal Carcinoma
Infiltrating Lobular Carcinoma
Medullary Carcinoma
Metaplastic Carcinoma
Mixed Histology (please specify)
Mucinous Carcinoma
Other, specify
unknown
molecular type ****TNBC
unknown
HER2 status ns
negative
positive
unknown
PR status ****negative
positive
unknown
ER status ****negative
positive
unknown
subdivision ns
left
right
age ****90
ACE2
Group
Low ACE2
High ACE2
Group
ACE2
12
0
Low ACE2
High ACE2
ACE2
Group
e
APM signal
Cell cycle
DNA replication
Progesterone-mediated oocyte maturation
Proteasome
Spliceosome
EGFR ligands
WNT work
Hypoxia
0
1
2
0
1
2ACE2
Group
Group
ACE2
12
0
Low ACE2
High ACE2
Endocrinotherapy
A
B C
D
**** **** **** **** **** **** **** **** **** **** ****
0
4
Vinblastine Cisplatin Docetaxel Doxorubicin Etoposide Gefitinib Gemcitabine Paclitaxel Parthenolide Sunitinib Vinorelbine
pred
icte
dPty
pe
Group High ACE2 Low ACE2
ER antagonist$ESR1PR antagonist$PGR
way
MicroRNAs in cancer
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint
CD8ACD8BCXCL10CXCR3CCL5GZMAGZMBIFNGPRF1TBX21C1QBCRTAMIL7RNCR1PRR5LSPON2C1QACLEC5ACYBBLILRA2MARCOMS4A6ACCL4CTLA4SLAMF1FGL2IL21RSIGLEC1SLC15A3
ACE2
Group Group
Low ACE2
High ACE2
ACE2
12
5
CD8+T cell
DC
Macrophage
NK cell
Th1 cell
0
2
4
CD8ACD8BCXCL10CXCR3CCL5GZMAGZMBIFNGPRF1TBX21C1QBCRTAMIL7RNCR1PRR5LSPON2C1QACLEC5ACYBBLILRA2MARCOMS4A6ACCL4CTLA4SLAMF1FGL2IL21RSIGLEC1SLC15A3
ACE2
Group
12
5
Type
0
2
4P < 0.0001
0.0
0.4
0.8
Low ACE2
High ACE2
Expr
essi
on
ChemokineP < 0.0001
0.0
0.3
0.6
0.9
Low ACE2
High ACE2
Expr
essi
on
Immunostimulator
P < 0.0001
0.0
0.4
0.8
Low ACE2
High ACE2
Expr
essi
on
ReceptorP < 0.0001
0.0
0.4
0.8
Low ACE2
High ACE2
MHC
Expr
essi
on
P < 0.0001 P < 0.0001 P = 0.059 P < 0.0001 P < 0.0001 P < 0.0001 P < 0.0001
6
8
10
CD19 PDCD1 CD274 CTLA4 IDO1 TNFRSF17 PVR
Rel
ativ
e ex
pres
sion
Group High ACE2 Low ACE2
0
81
NCOR2LAMA2AKAP9
CBFBTG
BIRC6HERC2ARID1A
TBX3USH2A
RYR2DNAH5
MLL2DNAH2
DNAH11CDH1
AHNAKSYNE1KMT2C
MAP3K1AHNAK2
MUC16GATA3
TP53PIK3CA
5%6%6%6%6%6%6%6%6%7%7%7%7%7%8%9%9%10%13%13%15%16%16%24%46%
0 426
Missense_MutationFrame_Shift_DelFrame_Shift_InsSplice_SiteNonsense_Mutation
In_Frame_DelIn_Frame_InsNonstop_MutationMulti_Hit
Low ACE2 (94.17%)
4370
46
COL12A1BIRC6
NOTCH1COL6A3
UTRNTG
AKAP9DNAH5HERC2
PDE4DIPRYR2MLL2
MAP3K1USH2AGATA3
DNAH2CDH1
AHNAKDNAH11
KMT2CSYNE1
AHNAK2MUC16PIK3CA
TP53
5%5%5%6%6%6%7%7%7%7%8%8%8%8%9%9%9%10%11%12%15%18%19%40%47%
0
Missense_MutationFrame_Shift_InsSplice_SiteFrame_Shift_DelNonsense_Mutation
In_Frame_DelIn_Frame_InsNonstop_MutationTranslation_Start_SiteMulti_Hit
High ACE2 (96.77%)
P < 0.0001
7
8
9
10
11
High ACE2
Low ACE2
Panc
ance
r T c
ell i
nfla
med
sco
re
B cellCancer associated fibroblastCD4+T cell CD8+T cell Endothelial cellMacrophageNK celluncharacterized cellT cellCD8+T cellCytotoxic lymphocytesB lineageNK cellMonocytic lineageMyeloid dendritic cellNeutrophilEndothelial cellFibroblastB cellMacrophage M1Macrophage M2MonocyteNeutrophilNK cellCD4+T cell (non.regulatory)CD8+T cell T cell regulatory (Tregs)Myeloid dendritic celluncharacterized cell
Activated CD8+T cellCentral memory CD8+T cellEffector memeory CD8+T cellActivated CD4+T cellCentral memory CD4+T cellEffector memeory CD4+T cellT follicular helper cellGamma delta T cellType 1 T helper cellType 17 T helper cellType 2 T helper cellRegulatory T cellActivated B cellImmature B cellMemory B cellNK cellCD56bright NK cellCD56dim NK cellMyeloid derived suppressor cellNK T cellActivated dendritic cellPlasmacytoid dendritic cellImmature dendritic cellMacrophageEosinophilMast cellMonocyteNeutrophil
ACE2
Group
GroupLow ACE2
High ACE2
ACE2
12
5
AlgorithmEPICMCPquanTIseq
TISIDB
0
1
2
3A B C
D
E F G
Sens
itivi
ty
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
ER status : 0.816PR status: 0.666TNBC : 0.779
**** **** ** ** **** **** ** **** **** **** ****
0
4
Vinblastine Cisplatin Docetaxel Doxorubicin Etoposide Gefitinib Gemcitabine Paclitaxel Parthenolide Sunitinib Vinorelbine
pred
icte
dPty
pe
Group High ACE2 Low ACE2H I
TIMER
B cellCD4+T cell CD8+T cell NeutrophilMacrophageDendritic cell
.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 10, 2021. ; https://doi.org/10.1101/2021.05.10.443377doi: bioRxiv preprint
A B C
D E F
Para
-tum
orTu
mor
Low
Hig
h
anti-ACE2 anti-CD8 anti-PD-L1
anti-ACE2
Low ACE2
High ACE2
Low ACE2
High ACE2
The
pro
porti
on o
f CD
8+T
cell
PD-L
1 IR
S
ACE2
IRS
Para-tu
mor
Tumor ER-
ER+PR-
PR+HER2-
HER2+TNBC
non-T
NBC
P = 0.031 P < 0.001
P = 0.007 P = 0.042
P < 0.001 P = 0.282 P = 0.035
ACE2
IRS
0
2
4
6
8
0
5
10
15
20
25
0
2
4
6
8
0
2
4
6
8
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Supplemental Figure legends
Figure S1. Forest plots of prognostic value of ACE2 in predicting (A) OS and (B) PFS
in pan-cancer analysis.
Figure S2. TFs that correlated with (A) ACE2 and (B) PD-L1 calculated by RABIT
algorithm in BRCA.
Figure S3. Potential regulatory factors of ACE2 in BRCA.
(A) Mutations in ACE2 gene. (B) The associations between CNV pattern and ACE2
expression in BRCA. (C) The correlation between methylation level and ACE2
expression.
Figure S4. Mutational density curve in BRCA. The TMB levels were most enriched in
the range of 0-1200.
Figure S5. ACE2 predicts the molecular subtype in BRCA
(A) Expression level of ACE2 in different molecular subtypes in BRCA. (B) Diagnostic
values of ACE2 expression in identifying ER, PR and triple-negative subtypes.
Figure S6. Survival analysis of patients with TNBC compared with that with non-
TNBC (TCGA)
Figure S7. Differences in TMB levels between the high and low ACE2 groups in BRCA
(METABIRC).
Figure S8. Differences in deficiency rates of MMR proteins between the two groups
(METABIRC).
Figure S9. Survival analysis of patients with TNBC compared with that with non-
TNBC (METABIRC).
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KIRC
LIHC
UVM
MESO
UCS
LGG
KIRP
ACC
GBM
HNSC
LUSC
COAD
ESCA
OV
KICH
LAML
PRAD
READ
CESC
SARC
SKCM
LUAD
THYM
STAD
PAAD
BRCA
PCPG
DLBC
THCA
BLCA
CHOL
UCEC
TGCT
P value
<0.0001
0.013
0.021
0.045
0.048
0.071
0.082
0.087
0.097
0.106
0.126
0.239
0.239
0.287
0.293
0.385
0.386
0.396
0.451
0.455
0.464
0.466
0.471
0.513
0.538
0.556
0.598
0.608
0.653
0.814
0.823
0.921
0.991
HR
1.70
1.56
3.00
1.62
2.04
0.73
1.73
1.95
0.75
1.24
1.24
1.34
0.76
1.17
2.10
1.19
1.76
1.52
1.20
1.16
0.90
0.90
0.60
1.11
0.88
1.10
1.48
0.69
0.80
1.04
1.11
1.04
1.01
95%CI
0 1 2 3 4 5 6 7 8
KIRC
LIHC
UCS
KICH
MESO
UVM
LUSC
HNSC
COAD
GBM
ACC
OV
BLCA
CESC
TGCT
SARC
LUAD
READ
LGG
CHOL
KIRP
STAD
DLBC
UCEC
BRCA
PCPG
THCA
PRAD
PAAD
SKCM
ESCA
THYM
P value
<0.0001
0.003
0.005
0.036
0.050
0.063
0.086
0.119
0.155
0.167
0.171
0.185
0.254
0.420
0.431
0.466
0.486
0.528
0.580
0.595
0.620
0.631
0.673
0.678
0.854
0.896
0.912
0.958
0.964
0.965
0.967
0.998
HR
1.98
1.56
2.65
5.15
1.70
2.10
1.33
1.25
1.37
0.78
1.54
1.20
1.19
1.21
0.78
1.13
0.91
1.34
0.93
0.79
1.14
0.92
0.77
0.88
1.03
0.95
1.03
0.99
1.01
1.00
0.99
1.00
95%CI
0 2.5 5 7.5 10 12.5 15 17.5 20 22.5
A B
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HSF1
MAFK
CCNT2MAFF
ZNF143BHLHE40
SIX5
BCLAF1
MYC
SP1
SP2
SP4
CREB1
NR2C2
GATA2
GTF2F1
RFX5MAZTCF7L2
ZKSCAN1
RUNX3
ELK4
ZNF274
KDM5A
CTCF
FOXP2NFATC1
HMGN3
RBBP5ESRRA
EGR1GTF2B
ZBTB33SUPT20H
TCF3
FOS
IRF3
IRF1
TAF1
ESR1
TBP
HNF4G
MEF2C
MEF2A
PRDM1
PHF8
HDAC1
YY1
HDAC6
FOXM1
IKZF1
SPI1
STAT3
STAT2
STAT1
FOSL1
FOSL2
RXRA
EP300
IRF4
SREBF1
NRF1
WRNIP1
EZH2
ELF1 TAL1
MYBL2
CEBPB
TFAP2C
UBTF
ETS1
SUZ12
MTA3
ZBTB7A RAD21REST
EBF1
E2F6
E2F4
E2F1MBD4
POU2F2
ATF2
0
100
200
300
Correaltion
2v
HSF1
MAFK
CCNT2
MAFFZNF143 BHLHE40SIX5
MYC
SP1
SP2
SP4
CREB1
GATA3
GTF2F1
MAZ
ZNF274CTCF
HMGN3
FOXA1
SAP30CBX3
GTF2B
TCF3
FOS
ZEB1
IRF3
IRF1
TAF1
ESR1
TBP
HNF4G
MEF2C
PHF8
HDAC1HDAC2
YY1
FOXM1IKZF1
SPI1
STAT3
SRF
RXRA
MAX
SREBF1
NRF1
EZH2
ELF1
TAL1
MYBL2
TEAD4
CEBPB
TFAP2C
MTA3
NR3C1
REST
EBF1
E2F6
E2F4
E2F1
MBD4
POU2F2
ATF2
0
50
100
150
Correaltion
2v
A B
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0.5
1.0
1.5
2.0
0 5 10ACE2 expression
ACE2
met
hyla
tion
CNV -2 CNV 0 CNV 1 CNV 2
02
46
810
1214
ACE2
exp
ress
ion
P = 0.048 R = 0.410, P <0.0001BA
L162F
Peptidase_M2Peptidase_M2
0 805aa200 400 600
0
5
AC
E2
Mut
atio
ns
A
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0 1000 2000 3000 4000 5000
0.00
00.
005
0.01
00.
015
Den
sity
TMB level
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P < 0.0001 P < 0.0001 P < 0.0001 P = 0.120
0
5
10
non-TNBC
ER negat
ive
PR negat
ive
HER2 nega
tive
Relat
ive A
CE2
expr
essio
n
TNBC
ER positi
ve
PR positi
ve
HER2 posi
tive
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
ER status: 0.856PR status: 0.766
r
A
B
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++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++ ++++++ + +
++ ++
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ ++
++ +
P = 0.120
0.00
0.25
0.50
0.75
1.00
0 100 200 300Time
Surv
ival
pro
babi
lity
Strata + +Tr Tr
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P = 0.230
0
20
40
60
80
High ACE2 Low ACE2
TMB
leve
l
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Deficiency Normal
Deficiency Normal
Deficiency Normal
Deficiency Normal
High ACE2
Low ACE2
High ACE2
Low ACE2
High ACE2
Low ACE2
High ACE2
Low ACE2
94.75%
5.25%
95.17%
4.83%
0
25
50
75
100
perc
enta
ge
MSH2 P = 0.753
95.80%
4.20%
94.12%
5.88%
0
25
50
75
100
perc
enta
ge
MSH6 P = 0.116
93.38%
6.62%
96.53%
3.47%
0
25
50
75
100
perc
enta
ge
MLH1 P = 0.002
0
94.22%
5.78%
95.69%
4.31%
25
50
75
100
perc
enta
ge
PMS2 P = 0.173
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+++++++
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+++++++
+
P = 0.630
0.00
0.25
0.50
0.75
1.00
0 100 200 300Time
Surv
ival
pro
babi
lity
Strata + +Tr Tr
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Table S1. Table of abbreviations. Abbreviation Full name ACC Adrenocortical carcinoma BLCA Bladder urothelial carcinoma BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangio carcinoma COAD Colon adenocarcinoma DLBC Lymphoid neoplasm diffuse large B-cell lymphoma ESCA Esophageal carcinoma GBM Glioblastoma multiforme HNSC Head and neck squamous cell carcinoma KICH Kidney chromophobe carcinoma KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma LAML Acute myeloid leukemia LGG Brain lower grade glioma LIHC Liver hepatocellular carcinoma LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and paraganglioma PRAD Prostate adenocarcinoma READ Rectum adenocarcinoma SARC Sarcoma SKCM Skin cutaneous melanoma STAD Stomach adenocarcinoma TGCT Testicular germ cell tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine corpus endometrial carcinoma UCS Uterine carcinosarcoma UVM Uveal melanoma
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Table S2. TFs that correlate with ACE2 and PD-L1. Group Intersect TFs ACE2 negative & PD-L1 negative HSF1, ZNF143, SIX5, MYC, SP1, SP2, SP4,
GTF2F1, MAZ, ZNF274, HMGN3, GTF2B, IRF3, ESR1, HNF4G, PHF8, YY1, RXRA, SREBF1, NRF1, EZH2, ELF1, MYBL2, REST
ACE2 negative & PD-L1 positive CTCF, FOXM1, TAL1, MTA3, E2F1, ATF2 ACE2 positive & PD-L1 negative N.A. ACE2 positive & PD-L1 positive MAFK, CCNT2, MAFF, BHLHE40, CREB1,
TCF3, FOS, IRF1, TAF1, TBP, MEF2C, HDAC1, IKZF1, SPI1, STAT3, CEBPB, TFAP2C, EBF1, E2F6, E2F4, MBD4, POU2F2
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Table S3. Prognostic values of ACE2 in BRCA across public microarray datasets. Dataset Probe ID Cases Endpoint HR 95% CI P value GSE7378 219962_at 54 DFS 0.26 0.01 - 8.86 0.175 GSE7378 222257_s_at 54 DFS 0.41 0.02 - 7.59 0.154 GSE4922-GPL96 222257_s_at 249 DFS 0.71 0.49 - 1.04 0.043 GSE4922-GPL96 219962_at 249 DFS 1.03 0.85 - 1.24 0.145 GSE1456-GPL96 222257_s_at 159 DSS 1.41 0.89 - 2.26 0.007 GSE1456-GPL96 219962_at 159 DSS 1.04 0.74 - 1.46 0.186 E-TABM-158 222257_s_at 117 DSS 1.22 0.66 - 2.26 0.124 E-TABM-158 219962_at 117 DSS 1.48 0.80 - 2.74 0.118 GSE3494-GPL96 219962_at 236 DSS 1.11 0.87 - 1.43 0.096 GSE3494-GPL96 222257_s_at 236 DSS 0.77 0.47 - 1.27 0.062 GSE19615 219962_at 115 DMFS 1.32 0.72 - 2.41 0.038 GSE19615 222257_s_at 115 DMFS 1.47 0.92 - 2.35 0.024 GSE6532-GPL570 219962_at 87 DMFS 3.76 0.91 - 15.59 0.065 GSE6532-GPL570 222257_s_at 87 DMFS 3.36 1.21 - 9.30 0.168 GSE9195 219962_at 77 DMFS 0.30 0.01 - 12.36 0.109 GSE9195 222257_s_at 77 DMFS 0.44 0.05 - 4.21 0.192 GSE12093 219962_at 136 DMFS 0.93 0.62 - 1.39 0.197 GSE12093 222257_s_at 136 DMFS 1.08 0.71 - 1.66 0.306 GSE11121 219962_at 200 DMFS 0.81 0.62 - 1.04 0.059 GSE11121 222257_s_at 200 DMFS 0.73 0.53 - 1.02 0.013 GSE2034 219962_at 286 DMFS 0.93 0.79 - 1.10 0.080 GSE2034 222257_s_at 286 DMFS 0.92 0.72 - 1.16 0.068 E-TABM-158 219962_at 117 DMFS 1.36 0.73 - 2.54 0.133 E-TABM-158 222257_s_at 117 DMFS 1.41 0.87 - 2.29 0.060 GSE2990 219962_at 125 DMFS 1.55 1.02 - 2.34 0.070 GSE2990 222257_s_at 125 DMFS 1.54 0.86 - 2.76 0.082 GSE2990 219962_at 54 DMFS 0.81 0.25 - 2.61 0.100 GSE2990 222257_s_at 54 DMFS 0.79 0.41 - 1.49 0.102 GSE7390 222257_s_at 198 DMFS 1.19 1.04 - 1.36 0.005 GSE7390 219962_at 198 DMFS 1.16 1.03 - 1.30 0.003 GSE9893 10330 155 OS 0.83 0.61 - 1.11 0.027 GSE1456-GPL96 219962_at 159 OS 1.05 0.78 - 1.40 0.247 GSE1456-GPL96 222257_s_at 159 OS 1.28 0.84 - 1.93 0.029 E-TABM-158 219962_at 117 OS 1.39 0.78 - 2.47 0.232 E-TABM-158 222257_s_at 117 OS 1.19 0.69 - 2.07 0.204 GSE7390 222257_s_at 198 OS 1.23 1.08 - 1.41 0.001 GSE7390 219962_at 198 OS 1.18 1.05 - 1.33 0.002 GSE12276 222257_s_at 204 RFS 1.11 1.00 - 1.22 0.005 GSE12276 219962_at 204 RFS 1.12 1.02 - 1.22 0.003 GSE6532-GPL570 219962_at 87 RFS 3.76 0.91 - 15.59 0.065 GSE6532-GPL570 222257_s_at 87 RFS 3.36 1.21 - 9.30 0.168 GSE9195 219962_at 77 RFS 0.49 0.05 - 4.52 0.107
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GSE9195 222257_s_at 77 RFS 0.60 0.13 - 2.70 0.130 GSE1378 21336 60 RFS 1.38 1.06 - 1.79 0.001 GSE1379 21336 60 RFS 1.36 1.08 - 1.73 0.000 GSE1456-GPL96 219962_at 159 RFS 1.04 0.77 - 1.39 0.239 GSE1456-GPL96 222257_s_at 159 RFS 1.19 0.77 - 1.83 0.075 E-TABM-158 222257_s_at 117 RFS 1.19 0.69 - 2.07 0.204 E-TABM-158 219962_at 117 RFS 1.39 0.78 - 2.47 0.232 GSE2990 222257_s_at 62 RFS 0.79 0.47 - 1.31 0.031 GSE2990 219962_at 125 RFS 1.35 0.88 - 2.06 0.113 GSE2990 222257_s_at 125 RFS 1.32 0.75 - 2.30 0.078 GSE2990 219962_at 62 RFS 0.71 0.27 - 1.86 0.145 GSE7390 219962_at 198 RFS 1.06 0.95 - 1.17 0.034 GSE7390 222257_s_at 198 RFS 1.09 0.96 - 1.24 0.033
DFS: Disease Free Survival; DSS: Disease Specific Survival; DMFS: Distant Metastasis Free Survival; OS: Overall Survival; RFS: Relapse Free Survival.
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Table S4. GO and KEGG pathway enrichment analyses of ACE2 in BRCA. Gene Set Description ES NES P value Biological process GO:0002250 adaptive immune response 0.592 1.974 <0.001 GO:0002237 response to molecule of bacterial origin 0.576 1.900 <0.001 GO:1990868 response to chemokine 0.655 1.895 <0.001 GO:0060326 cell chemotaxis 0.568 1.867 <0.001 GO:0031349 positive regulation of defense response 0.559 1.865 <0.001 Cell component GO:0005790 smooth endoplasmic reticulum 0.717 1.749 <0.001 GO:0001533 cornified envelope 0.689 1.898 <0.001 GO:0097038 perinuclear endoplasmic reticulum 0.682 1.544 0.020 GO:0001891 phagocytic cup 0.671 1.533 0.019 GO:0042611 MHC protein complex 0.666 1.452 0.023 Molecular function GO:0016701 oxidoreductase activity, acting on single
donors with incorporation of molecular oxygen
0.774 1.779 <0.001
GO:0016917 GABA receptor activity 0.755 1.670 <0.001 GO:0042287 MHC protein binding 0.725 1.707 <0.001 GO:0001530 lipopolysaccharide binding 0.720 1.689 0.003 GO:0070003 threonine-type peptidase activity 0.688 1.563 0.024 KEGG hsa04650 Natural killer cell mediated cytotoxicity 0.654 1.987 <0.001 hsa04060 Cytokine-cytokine receptor interaction 0.599 1.969 <0.001 hsa05340 Primary immunodeficiency 0.741 1.889 <0.001 hsa00601 Glycosphingolipid biosynthesis 0.803 1.875 <0.001 hsa00380 Tryptophan metabolism 0.726 1.843 <0.001
ES: Enrichment score; NES: Normalized enrichment score.
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