Comprehensive Analysis of ADCY Family Members in The Development, Immune Infiltration and Prognostic Value of Kidney Renal Clear Cell Carcinoma

Clinics of Surgery
Research Article ISSN: 2638-1451 Volume 7
Comprehensive Analysis of ADCY Family Members in The Development, Immune
Infiltration and Prognostic Value of Kidney Renal Clear Cell Carcinoma
Jingjing Lu1
, Yuanyuan Liu2
, Yan Zhang1
, Ping Li1*
and Minbin Chen1
1
Department of Oncology, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, Jiangsu, China
2
Clinical Research and Lab Center, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, Jiangsu, China
*
Corresponding author:
Ping Li,
Department of Oncology, Affiliated Kunshan
Hospital of Jiangsu University, Kunshan,
Jiangsu, China,
E-mail: pingli8702@outlook.com
Received: 26 Jan 2022
Accepted: 07 Feb 2022
Published: 14 Feb 2022
J Short Name: COS
Copyright:
©2022 Ping Li. This is an open access article distributed under
the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and build upon your work
non-commercially.
Citation:
Ping Li, Comprehensive Analysis of ADCY Family Members
in The Development, Immune Infiltration and Prognostic Value
of Kidney Renal Clear Cell Carcinoma.
Clin Surg. 2022; 7(3): 1-13
Keywords:
ADCY family; Kidney renal clear cell
carcinoma; Development; Immune infiltration
and prognostic
clinicsofsurgery.com 1
1. Abstract
Objective: Kidney renal clear cell carcinoma (KIRC) has a high
incidence worldwide. Adenylate cyclases (ADCYs) family is a
membrane-binding enzyme that passes through the GnRH signal-
ing pathway associated with the G protein system and participates
in epigenetic regulation. However, the role of different ADCYs
proteins in colorectal cancer is poorly understood.
1.1. Methods: Oncomine, gene expression profile interactive
analysis (GEPIA), Kaplan-Meier plotter, cBioPortal, GeneMA-
NIA and TIMER were used to analyze the differential expression,
pathological grade, prognostic value, gene changes and immune
cell infiltration of ADCYs in patients with renal cell carcinoma.
1.2. Results: Compared with normal control tissues, the expres-
sion levels of ADCY1/6 were decreased and the expression levels
ofADCY2/3/4/5/7/8/9 were increased in KIRC tissues. In addition,
except for ADCY7, the expression of other members of the ADCY
family tended to decrease with the increase of pathological grade
in KIRC patients. In the prognostic analysis, ADCY1/2/5/8/9 was
significantly associated with overall survival (OS) in KIRC pa-
tients, and ADCY1/4/5/6/9 was significantly associated with dis-
ease-free survival (DFS). ADCY1/5/9 may be a potential survival
prognostic biomarker in patients with KIRC. Functional analysis
indicated that the differentially expressed ADCYs might be related
to the activation of protein kinase activity, the regulation of phos-
pholipase activity and the activation of phosphodeoxylase activity.
Moreover, the expression of ADCYs were significantly correlated
with the infiltration of six immune cells in KIRC patients.
1.3. Conclusion: This study provides a new direction for potential
prognostic biomarkers of KIRC patients.
2. Introduction
The latest world cancer report shows that the incidence of kidney
cancer ranks ninth among male cancers and fourteenth among fe-
male cancers [1]. Although the mortality trend of kidney cancer in
many highly developed countries has stabilized or even declined,
in most countries with lower levels of development, the incidence
of renal cancer is still rising, and these gaps seem to be widening
[2]. Renal cell carcinoma (RCC) accounts for more than 90% of
kidney cancers, of which clear cell carcinoma accounts for 70%
and has a poor prognosis [3-5]. The treatment of renal cell carci-
noma has advanced from nonspecific immunotherapy to specific
immunotherapy. At present, research has found that many factors
affect the occurrence and development of kidney cancer, such as
VHL, NR1B2, METTL14, PTEN, PDL1, etc [6-10]. However,
poor treatment effect and drug resistance are still a major problem
in clinical treatment. Adenylate cyclases (ADCYs) are membrane
binding enzymes that include ADCY1-9 responsible for the con-

Volume 7 Issue 3 -2022 Research Article
version of adenosine triphosphate (ATP) to the second messenger
cAMP [11]. They are involved in the GnRH signaling pathway
associated with the G protein system and are involved in many
cellular processes, including the promotion of tumor cell growth
[12]. According to the similarity of gene sequence and common
regulation pattern, these 9 genes were divided into 4 categories.
ADCY1, 3 and 8 are characterized by being activated by Ca2+/
calmodulin. ADCY2, 4 and 7 are ineffective against Ca2+, but can
be activated by G protein βγ subunits. Low concentration of Ca2+
can inhibit ADCY5 and ADCY6, but this group is not sensitive
to the regulation of βγ subunits. And ADCY9 can be specifically
inhibited by Ca2+/calmodulin, so it is listed as a separate group
[13,14]. Several studies have reported the correlation between
ACDYs and tumors, such as melanoma, NSCLC, glioblastoma,
esophageal carcinoma, acute myeloid leukemia, prostate cancer,
colorectal cancer, hepatocellular carcinoma (HCC), cervical can-
cer and pancreatic cancer [12,15,22]. Nevertheless, the role of
ADCYs in the occurrence and progression of cancer has not been
specifically clarified. And the mechanism of abnormal expression
of ADCYs in renal carcinoma and its effect on prognosis have not
been fully elucidated. Further study of its bioinformatics to guide
clinical treatment and improve the prognosis is imminent. In this
study, a comprehensive bioinformatics analysis of the expression
of ADCYs in KIRC was performed using public data to evaluate
its prognostic value in KIRC patients and explore possible mecha-
nisms of action to provide a potential target for clinical treatment.
3. Results
3.1. Transcription of ADCYs in KIRC
We analyzed the expression of ADCY family members in various
tumors and normal tissue controls using the Oncomine database.
By comparison, several studies have shown that ADCYs is highly
expressed in renal tumors (Figure 1). As shown in multiple data-
sets showed that mRNAexpression ofADCY2/3/4/5/7 was signifi-
cantly elevated in KIRC tissues. In Gumz Renal statistics dataset,
the fold change of ADCY2 in KIRC tissues compared with normal
tissues was 4.9, and 1.883-fold in Beroukhim Renal statistics data-
set of Non-Hereditary Clear Cell Renal Cell Carcinoma, 1.734-
fold in Beroukhim Renal statistics dataset of Hereditary Clear Cell
Renal Cell Carcinoma, 2.141-fold in Jones Renal statistics dataset,
2.78-fold in Yusenko Renal statistics dataset. In Lenburg Renal
statistics, a 1.541-fold increase in ADCY3 expression was detect-
ed in KIRC as compared to normal control tissues. Similar increas-
es of ADCY3 expression in KIRC were also seen in Gumz Renal
statistics dataset (2.132-fold), Beroukhim Renal statistics dataset
of Hereditary Clear Cell Renal Cell Carcinoma (1.942-fold), Ber-
oukhim Renal statistics dataset of Hereditary Clear Cell Renal Cell
Carcinoma (1.623-fold), Yusenko Renal statistics dataset (1.407-
fold) and Jones Renal statistics dataset (1.165-fold). In addition, in
Lenburg Renal statistics, a 1.215-fold increase in ADCY4 expres-
sion was also detected in KIRC patients compared to the control
group. Similarly, in Lenburg Renal statistics and Yusenko Renal
statistics, ADCY5 expression was also 1.377-fold and 1.661-fold
increased in the KIRC patients compared with the control group,
respectively. ADCY7 over-expression was also detected in KIRC
tissues compared with normal tissues with a 2.897-fold in Higgins
Renal statistics, 2.418-fold in Beroukhim Renal statistics dataset
of Non-Hereditary Clear Cell Renal Cell Carcinoma, 2.608-fold in
Beroukhim Renal statistics dataset of Hereditary Clear Cell Renal
Cell Carcinoma, 2.879-fold in Gumz Renal statistics, 2.660-fold
in Jones Renal statistics and 4.259-fold in Yusenko Renal statis-
tics. GEPIA dataset was further used to compare the expression
level of ADCYs in KIRC and normal control tissues. As shown
in Figure 2A and 2B, the expression levels of ADCY2/3/4/5/7/9
in KIRC tissues were higher than those in normal tissues. These
results tend to be consistent with the results of Oncomine. To ex-
plore the possible role of ADCYs in the development of KIRC, we
evaluated the correlation between ADCYs expression and patho-
logical staging in patients with KIRC. As can be seen in Figure 3,
ADCY1/2/3/4/5/6/8/9 are significantly correlated with the patho-
logical staging of KIRC and participate in its occurrence and de-
velopment, while ADCY7 has no significant correlation with the
pathological staging of KIRC.
3.2. Prognostic value of ADCYs mRNA expression in KIRC
patients
Gepia analysis was used to develop overall survival (OS) and dis-
ease-free survival (DFS) curves to explore and analyze the cor-
relation between different ADCYs and clinical outcomes, so as to
further evaluate the prognostic value of differential expression of
ADCYs in KIRC. Figure 4A shows a significant correlation be-
tween ADCY1/2/5/8/9 and OS in patients with KIRC, and it can
be seen that the higher expression level of ADCY1/2/5/8/9 has a
better prognosis. The expression levels of ADCY3/4/6/7 were not
statistically significant with OS (Figure 4A). In the DFS curve,
ADCY1/4/5/6/9 were significantly correlated with DFS in KIRC
patients, indicating that the high expression group had a better prog-
nosis (Figure 4B). The expression levels of ADCY2/3/7/8 in DFS
were not statistically significant (Figure 4B). We further analyzed
the prognostic value of ADCYs in patients with KIRC using Ka-
plan-Meier plotter. High mRNA expression of ADCY1/2/4/5/6/8/9
was significantly associated with OS in KIRC patients (Figure 5).

Figure 1: Transcription of ADCYs in various tumor types (Oncomine).
Figure 2. The expression level of ADCYs in KIRC and normal control tissues (GEPIA).

Figure 2 (A): Comparison of expression levels of ADCYs in KIRC versus normal tissue samples. (B) TPM values of ADCYs in KIRC and normal
tissue samples. T represents the KIRC sample and N represents the normal tissue sample.

Figure 3: Association of ADCY expression with tumor stage in patients with KIRC (GEPIA).
Figure 4: Prognostic value of ADCYs mRNA expression in KIRC patients (GEPIA).
(A) Correlation between ADCYs expression and OS in KIRC patients.

Figure 4(B): Correlation between ADCYs expression and DFS in KIRC patients.
Figure 5: Prognostic value of ADCYs mRNA expression in KIRC patients (Kaplan-Meier plotter) (OS).

3.3. Analysis of gene Alteration, Expression, and Interaction of
ADCYs in KIRC patients
We used the Cbioportal to analyze the molecular characteristics in
ADCYs in patients with KIRC.
The data showed that ADCYS changed in 174(34%) of 510 KIRC
patients. Among them, 7%, 6%, 8%, 8%, 7%, 5%, 8%, 2.9% and
8% mutations occurred in ADCY1-9, respectively (Figure 6A.6B).
Among the most frequent mutations are low mRNA, high mRNA,
mutation multiple changes, amplification and deep deletion (Fig-
ure 6B). Next, we used String to perform protein-protein interac-
tion PPI network analysis on the differentially expressed ADCYs
to find their possible interactions. We selected the 5 most closely
related molecules, Prkacg, mtnr1b, grm3, gpr83 and gnas, with
a total of 14 nodes and 86 edges (Figure 6C). Further function-
al exploration was conducted using Genemania, and the results
showed that the differentially expressed functional ADCYs and its
related molecules (such as GUCY1A2, GUCY1A3, GUCY1B3,
GUCY2C, GUCY2F, NPR1, NPR2, GUCY2D, PLCB2, GNA11,
RGS2, NPR3, NPPB, TRAPPC5, TRAPPC6B, GUCA2B, TRAP-
PC3L, NPPA, TRAPPC6A ) are associated with phosphorus-ox-
ygen lyase activity, cyclase activity, activation of protein kinase
A activity, regulation of phospholipase activity, response to glu-
cagon, cellular response to glucagon stimulus, activation of phos-
pholipase C activity and so on(Figure 6D).
Figure 6: Mutation and expression analysis of ADCYs gene in KIRC (cBioPortal and STRING) (A, B). Protein-protein interaction networks with
different expressions of ADCYs (C, D).
Figure 6B: Mutation and expression analysis of ADCYs gene in KIRC (cBioPortal and STRING) (A, B). Protein-protein interaction networks with

Figure 6C: Mutation and expression analysis of ADCYs gene in KIRC (cBioPortal and STRING) (A, B). Protein-protein interaction networks with
Figure 6D: Mutation and expression analysis of ADCYs gene in KIRC (cBioPortal and STRING) (A, B). Protein-protein interaction networks with
3.4. Correlation Between Differential Expression of Adcys and
Immune Cell Infiltration in Tissues of Patients with KIRC
A growing number of studies have found that immune cell infil-
tration is an important factor affecting the prognosis of cancer
patients, including patients with KIRC23
. Therefore, we used the
TIME database of 6 kinds of immune cell infiltration, including B
cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and
dendritic cells, to analyze the correlation of immune infiltration of
ADCYs in KIRC (Figure 7). The analysis showed that the expres-
sion of ADCY1 was significantly correlated with the infiltration of
6 kinds of immune cells. ADCY5/6/9 were significantly correlat-
ed with B cells, CD8+ T cells, CD4+ T cells, macrophages, neu-
trophils and dendritic cells. ADCY2 was significantly correlated
with macrophages, neutrophils, and dendritic cells. ADCY3 was
significantly correlated with CD8+ T cells, CD4+ T cells, macro-
phages, neutrophils, and dendritic cells. ADCY4 was significantly
correlated with B cells, CD8+ T cells, CD4+ T cells, macrophages
and neutrophils. ADCY6 was significantly correlated with CD4+
T cells, macrophages and neutrophils. ADCY8 was significantly
correlated with CD4+ T cells. Among them, except the expression
of ADCY1 and 4 were negatively correlated with the infiltration of
B cells, the others were positively correlated.

Figure 7: Correlations between ADCYs expression and immune cell infiltration (TIMER).

4. Materials and Methods
4.1. Oncomine
ADCY gene expression data were obtained from Oncomine web-
site (www.oncomine.org). Student’s t test was used to compare the
expression of ADCY in clinical tumor samples and matched nor-
mal control samples. Check the following options “Analysis Type:
Cancer vs. Normal Analysis” “Cancer Type: Clear Cell Renal Cell
Carcinoma” and “Data Type: mRNA”. The threshold defaults to
P-VALUE 0.05, FOLD CHANGE 1.5, GENE RANK top 10%.
4.2. Gene Expression Profiling Interactive Analysis (GEPIA)
GEPIA used standard processing pipelines to analyze RNA se-
quencing expression data from the TCGA and GTEX projects
[24]. In this study, GEPIA was used to analyze the correlation be-
tween the differential expression of ADCYs in KIRC tumor and
normal tissues, pathological staging analysis and related prognosis
analysis. Student's T-test was used for expression and pathologi-
cal staging analysis. Survival analysis was performed using Ka-
plan-Meier curves.
4.3. Kaplan-Meier Plotter
Application of Kaplan Meier - plotter (http://kmplot.com/analy-
sis/) analysis different ADCYs mRNA expression to the value of
the prognosis of patients with KIRC [25]. The difference was sta-
tistically significant when P < 0.05.
4.4. CBioPortal
cBioPortal (http://cbioportal.org) is a web site that can visualize
multidimensional cancer genomic data for clinical data analysis
[26]. ADCYs expression alterations in KIRC patients based on
TCGA database was analyzed by cBioPortal.
4.5. String
String (https://string-db.org/) is a website that analyzes the inter-
actions between proteins and proteins in order to fully understand
cellular mechanisms [27]. Through PPI network analysis, the po-
tential interactions of ADCYs and other genes can be collected and
integrated in String.
4.6. GeneMANIA
Genemania (http://www.genemania.org) is a Web interface for
generating assumptions about gene function, analyzing gene lists,
and sequencing genes for functional analysis [28]. We use it to
predict the potential predictive value of ADCYs.
4.7. Timer
TIMER (https://cistrome.shinyapps.io/timer/) is a web server that
provides a comprehensive analysis of 6 types of immune cell in-
filtrates including B cells, CD8+ T cells, CD4+ T cells, macro-
phages, neutrophils, and dendritic cells [29]. We used TIMER to
visualize the relationship between the expression of ADCYs and
the level of KIRC immune infiltration.
5. Discussion
The role of ADCY family members in several cancers has been
demonstrated [12,15-22], but its unique role in KIRC remains to
be further elucidated. This study comprehensively analyzed the
ADCYs in KIRC from the aspects of expression level, patholog-
ical grade, survival prognostic value, gene mutation and immune
cell infiltration.
Analysis of the above results found that both the TCGA database
and the Genotype-Tissue Expression (GTEx) databases showed
that ADCY2/3/4/5/7/8/9 are highly expressed in tumor tissues,
while ADCY1/6 are low in tumor tissues. In addition, the analysis
suggested that, except for ADCY7, other ADCY factors were sig-
nificantly correlated with the pathological stage of KIRC, and the
results were statistically significant. They may play an important
role in the occurrence and development of KIRC patients. OS sur-
vival analysis showed that the high expression of ADCY1/2/5/8/9
had a better prognosis. The prognostic analysis of DFS showed
that the high expression of ADCY1/4/5/6/9 in tumor tissues
had a better prognosis. K-M survival analysis also showed that
ADCY1/2/4/5/6/8/9 high expression had better OS. Comparing
the results, it is found that the highly expressed of ADCY1, 5 and
9 have better OS and DFS at the same time. Combined with the
above expression level analysis, ADCY1 is low in KIRC, and the
prognosis of OS and DFS is poor. Analysis from a genetic per-
spective shows that the differentially expressed ADCYs in KIRC
often undergo genetic changes, especially low mRNA expression.
These genes are considered to have the potential to activate pro-
tein kinase activity, regulate phospholipase activity and activate
phosphor-oxygen lyase activity, which play important roles in
regulating the proliferation, invasion, survival prognosis and even
immune disorders of various cancer cells [30-33]. The role of tu-
mor microenvironment(TME) in promoting tumor occurrence and
development is increasingly emphasized [34,35]. CD4+ T cells
and CD8 + T cells can enhance the anti-tumor activity of CTLs,
enhance the penetration of CTLs into the core of tumors, and par-
ticipate in promoting the killing of cancer cells [36-38]. Immu-
notherapy also plays an important role in the current and future
treatment of kidney cancer [39]. Consistently, our analysis results
show that the expression of ADCYs may be significantly related to
the infiltration of the above 6 immune cells, reflecting the immune
status of KIRC patients, and providing new potential directions for
clinical treatment. As ACYD1,5,9, which are significantly related
to prognosis in both OS and DFS survival analyses, deserve atten-
tion. Adcy1 is an important regulator that catalyses the produc-
tion of cyclic adenosine 3', 5' -monophosphate (cAMP) from ATP,
and then participates in cell growth, differentiation, proliferation,
apoptosis , metabolism and other cellular reactions [40,41]. Stud-
ies have reported that ADCY1 is associated with the survival prog-
nosis of melanoma, non-small cell lung cancer, and glioma [15-
17]. Yang Lx et al. pointed out that the high expression of ADCY1

may be related to chemotherapy resistance of esophageal cancer
cells [18,42]. ADCY5 is a hub gene in Colorectal cancer and may
be involved in the development of triple-negative breast cancer
through hormone-related pathways [43,44]. ADCY9 was differen-
tially expressed in endometrial cancer [45]. Meanwhile, Hua Yi
et al. found that ADCY9 is highly expressed in colon cancer tu-
mor tissues and is related to the TNM staging [46,47]. The present
study also showed that ADCY1/5/9 was significantly associated
with prognosis, pathological grade, and immune infiltration in pa-
tients with KIRC. Low expression of ADCY1/5/9 may be a poor
prognostic factor for KIRC survival. In the meantime,ADCY2 and
8 showed significant correlations with OS in both GEPIA and K-M
Plotter databases, ADCY4 and 6 were significantly correlated with
DFS in GEPIA, which are also noteworthy. ADCY2 was differen-
tial expression in oral cancer, prostate cancer and metastatic colon
cancer [48-50]. ADCY8 mutations was associated with a signif-
icant decrease in tumor PD-L1 expression and may affect early
lung squamous cell carcinoma clinical outcomes and personalized
targeted immunotherapy strategies [51]. Nicole M Warrington et
al. believe that ADCY8 has genetic polymorphism, which is relat-
ed to the risk of NF1 glioma in a gender-specific manner, which
increases the risk for women and reduces the risk for men [52]. It
has also been reported that ADCY8 is differentially expressed in
endometrial cancer and is involved in the occurrence and develop-
ment of HCC [45,53]. ADCY4 is differentially expressed in breast
cancer, lung squamous cell carcinoma, lung adenocarcinoma and
hepatocellular carcinoma [54-57]. Weijing Li et al. found that
ADCY6 can be used as a prognostic factor related to the immune
process regulated by DNA methylation in luminal-like breast can-
cer, and patients with ADCY6 down-regulation and hypermethyl-
ation have a better prognosis [58]. At the same time, ADCY6 is
also differentially expressed in lung cancer and laryngeal cancer
[16,59]. ADCY2/8/4/6 also have the potential to be a reliable prog-
nostic factor in KIRC.
In this study, ADCY3 and 7 were not significantly associated with
the prognosis of KIRC, but were differentially expressed in KIRC
and correlated with pathological grade and multiple immune cell
infiltrates. Which may be involved in the progression of KIRC.
Several studies showed that ADCY3 overexpression may play a
tumor-promoting effect through the cAMP/PKA/CREB pathway.
By increasing the levels of cAMP and phosphorylated cAMP re-
sponse element binding protein (CREB), it increases the mRNA
levels and activities of matrix metalloproteinase 2 (MMP2) and
MMP9 [60-62]. Chunling Li et al. demonstrated that the expres-
sion of ADCY7 promotes the development of leukemia by reduc-
ing the apoptosis of AML cells, which is inversely correlated with
the overall survival of AML patients [19]. ADCY7 can be mediat-
ed by miR-192 to affect intracellular cAMP levels, and affect the
differentiation of APL cells induced by ATRA, further affecting
its recurrence [63]. The differential expression of ADCY3 and 7
in pathological grade and immune infiltration may also be related
to the occurrence and development of KIRC, which needs further
exploration and discussion. Our research still has some limitations.
The data analyzed in this research are obtained from online da-
tabases. Extensive cell biology experiments and clinical studies
are still needed to validate our findings and explore the potential
mechanisms and roles of different ADCYs in KIRC to guide clin-
ical practice.
6. Funding
This project was supported by National Natural Science Founda-
tion of China [81773192, 82072712], Jiangsu University Clini-
cal Medical Science and Technology Development Fund 2018
(JLY20180012) and Suzhou Science and Technology plan project
(KJXW2020072).
References
1. Sanchez DJ. Celeste SMJBeBA-RoC. Genetic and metabolic hall-
marks of clear cell renal cell carcinoma. 2018.
2. Znaor A, Lortet-Tieulent J, Laversanne M, Jemal AJEU. Internation-
al variations and trends in renal cell carcinoma. 2015; 87: 519-530.
3. Eble J, Sauter G, Epstein J. Pathology and Genetics of Tumours of
the Urinary System and Male Genital Organs. IARC Press. 2004.
4. Gonzales MJJoCN. The 2000 World Health Organization classifica-
tion of tumours of the nervous system. 2001; 8(1): 1-3.
5. Rini BI, Campbell SC, Rathmell WK. Renal cell carcinoma. Current
opinion in oncology. 2006; 18(3): 289-296.
6. Nickerson ML, Jaeger E, Shi Y, Durocher JA, Moore LEJCCR.
Improved Identification of von Hippel-Lindau Gene Alterations in
Clear Cell Renal Tumors. 2008; 14(15): 4726-4734.
7. Yin L, Li W, Wang G. NR1B2 suppress kidney renal clear cell carci-
noma (KIRC) progression by regulation of LATS 1/2-YAP signaling.
2019; 38.
8. Wang Q, Zhang H, Chen Q, Wan Z, Qian WJDm. Identification of
METTL14 in Kidney Renal Clear Cell Carcinoma Using Bioinfor-
matics Analysis. 2019; 2019: 1-11.
9. Que WC, Qiu HQ, Chen Y, Liu MB, Wu CYJCCA. PTEN in kidney
cancer: A review and meta-analysis. 2018:92-98.
10. Kelsey RJNRU. Kidney cancer: PDL1 as a biomarker in high-risk
RCC. 2018; 15(4).
11. Tong T, Shen Y, Lee HW, Yu R, Park TJSR. Adenylyl cyclase 3 hap-
loinsufficiency confers susceptibility to diet-induced obesity and in-
sulin resistance in mice. 2016; 6: 34179.
12. Yu SJ, Yu JK, Ge WT, Hu HG, Yuan Y, Zheng SJWJoG. SPARCL1,
Shp2, MSH2, E-cadherin, p53, ADCY-2 and MAPK are progno-
sis-related in colorectal cancer. 2011; 17(015): 2028-2036.
13. Ludwig MG, Seuwen KJJoR, Research ST. CHARACTERIZA-
TION OF THE HUMAN ADENYLYL CYCLASE GENE FAMILY:
cDNA, GENE STRUCTURE, AND TISSUE DISTRIBUTION OF
THE NINE ISOFORMS. 2002; 22(1-4): 79.

14. Qi C, Sorrentino S, Medalia O, Korkhov VMJS. The structure of
a membrane adenylyl cyclase bound to an activated stimulatory G
protein. 2019; 364(6438): 389-394.
15. Chen J, Wu F, Shi Y, Yang D, Liu YJMMR. Identification of key
candidate genes involved in melanoma metastasis. 2019; 20(2).
16. He RQ, Li XJ, Liang L. The suppressive role of miR-542-5p in NS-
CLC: the evidence from clinical data and in vivo validation using a
chick chorioallantoic membrane model. 2017.
17. Ma J, Hou X, Li M, Ren H, He CJJoCR. Therapeutics. Genome-wide
methylation profiling reveals new biomarkers for prognosis predic-
tion of glioblastoma. 2015; 11(6): 212.
18. Yang LX, Li BL, Liu XH, Yuan Y, Zhao JJIJoC, Pathology E. RNA-
seq reveals determinants of sensitivity to chemotherapy drugs in
esophageal carcinoma cells. 2014; 7(4): 1524-1533.
19. ADCY7 supports development of acute myeloid leukemia %J Bio-
chem Biophys Res Commun. 2015; 465(1): 47-52.
20. Masugi Y, Tanese K, Emoto K. Overexpression of adenylate cy-
clase-associated protein 2 is a novel prognostic marker in malignant
melanoma. 2016; 65(12): 627-634.
21. The role of high cell density in the promotion of neuroendocrine
transdifferentiation of prostate cancer cells %J Molecular Cancer.
2014; 13(1): 113.
22. Alessia, Bortoluzzi, Giulio. Positive cardiac inotropic effect of albu-
min infusion in rodents with cirrhosis and ascites: molecular mech-
anisms. 2013.
23. Ghatalia P, Gordetsky J, Kuo F, Dulaimi E, Plimack EJJfIoC. Prog-
nostic impact of immune gene expression signature and tumor in-
filtrating immune cells in localized clear cell renal cell carcinoma.
2019; 7.
24. Zefang T, Chenwei L, Boxi K, Ge G, Cheng L, Zemin ZJNAR. GE-
PIA: a web server for cancer and normal gene expression profiling
and interactive analyses. 2017.
25. ádám N, András L, Otília M, Reports GrBJS. Validation of miRNA
prognostic power in hepatocellular carcinoma using expression data
of independent datasets. 2018; 8(1): 9227.
26. Gao J, Aksoy BA, Dogrusoz U. Integrative analysis of complex
cancer genomics and clinical profiles using the cBioPortal. Science
signaling. 2013; 6(269): pl1.
27. Damian S, Gable AL, David L. STRING v11: protein-protein as-
sociation networks with increased coverage, supporting functional
discovery in genome-wide experimental datasets. 2018(D1): D607.
28. David WF, Donaldson SL, Ovi C. The GeneMANIA prediction
server: biological network integration for gene prioritization and
predicting gene function. 2010; 38(Web Server issue): 214-220.
29. Li T, Fan J, Wang B. TIMER: A Web Server for Comprehensive
Analysis of Tumor-Infiltrating Immune Cells. 2017; 77(21): e108.
30. Shuo, Cai, Ping-Hui. Expression of phospholipase C isozymes in
human breast cancer and their clinical significance. 2017; 37(3).
31. Obeng EO, Rusciano I, Marvi MV, Fazio A, Cocco LJIJoMS. Phos-
phoinositide-Dependent Signaling in Cancer: A Focus on Phospho-
lipase C Isozymes. 2020; 21.
32. Lattanzio R, Piantelli M, Falasca MJAiBR. Role of phospholipase C
in cell invasion and metastasis. 2013; 53(3): 309-318.
33. Koss H, Bunney TD, Behjati S, Katan M. Dysfunction of phospho-
lipase Cγ in immune disorders and cancer. Trends in biochemical
sciences. 2014; 39(12): 603-611.
34. Roma-Rodrigues C, Mendes R, Baptista P, Fernandes AJIJoMS. Tar-
geting Tumor Microenvironment for Cancer Therapy. 2019; 20(4).
35. Hinshaw DC, Shevde LAJCR. The Tumor Microenvironment In-
nately Modulates Cancer Progression. 2019; 79(18).
36. Bagher, Farhood, Masoud, Najafi, Keywan, physiology MJJoc.
CD8+ cytotoxic T lymphocytes in cancer immunotherapy: A review.
2018.
37. Jannie, Borst, Tomasz. CD4+ T cell help in cancer immunology and
immunotherapy. 2018.
38. Brightman SE, Naradikian MS, Miller AM, Schoenberger SPJJoLB.
Harnessing neoantigen specific CD4 T cells for cancer immunother-
apy. 2020; 107(4).
39. Hammers HJCOiU. Immunotherapy in kidney cancer: the past, pres-
ent, and future. 2016; 26(6): 543-547.
40. Arakawa H, Akkentli F, Erzurumlu RSJE. Region-Specific Disrup-
tion of Adenylate Cyclase Type 1 Gene Differentially Affects So-
matosensorimotor Behaviors in Mice(1,2,3). 2014; 1(1): 7-14.
41. Zou T, Liu J, She L, Chen J, Liu ZJJoC. A perspective profile of
ADCY1 in cAMP signaling with drug-resistance in lung cancer.
2019; 10(27): 6848-6857.
42. Li S, Dickson DW, Iacobuziodonahue CA, Wang DJIJoC, Pathology
E. The Launch of International Journal of Clinical and Experimental
Pathology. 2008; 21.
43. Liang B, Li C, Zhao JJMO. Identification of key pathways and genes
in colorectal cancer using bioinformatics analysis. 2016; 33(10):
111.
44. Chen F, Li Y, Qin N, Wang F, Shen HJJoBR. RNA-seq analysis iden-
tified hormone-related genes associated with prognosis of triple neg-
ative breast cancer. 2020; 34(2): 129-138.
45. Orchel J, Witek L, Kimsa M, Strzalka-Mrozik B, Mazurek UJIJoGC.
Expression Patterns of Kinin-Dependent Genes in Endometrial Can-
cer. 2012; 22(6): 937-944.
46. Yi H, Wang K, Jin JF, Jin H, Liu XJMSMIMJoE. Research C. Ele-
vated Adenylyl Cyclase 9 Expression Is a Potential Prognostic Bio-
marker for Patients with Colon Cancer. 2018; 24: 19-25.
47. Li H, Liu Y, Liu J. Assessment the ADCY9 polymorphisms on col-
orectal cancer risk in the Chinese Han population. 2021.
48. Abbas SZ, Qadir MI, Muhammad SA. Systems-level differential
gene expression analysis reveals new genetic variants of oral cancer.
Scientific reports. 2020; 10(1): 14667.
49. Zhang X, Yao X, Qin C, Luo P, Zhang JJE, Medicine T. Investigation
of the molecular mechanisms underlying metastasis in prostate can-
cer by gene expression profiling. 2016.
50. Jablons, Oncology DJIJo. Comprehensive genomic analyses of a
metastatic colon cancer to the lung by whole exome sequencing and
gene expression analysis. 2014.

51. Choi M, Kadara H, Zhang J. Mutation profiles in early-stage lung
squamous cell carcinoma with clinical follow-up and correlation
with markers of immune function. Annals of oncology: official jour-
nal of the European Society for Medical Oncology. 2017; 28(1): 83-
89.
52. Warrington NM, Sun T, Luo J. The cyclicAMP pathway is a sex-spe-
cific modifier of glioma risk in type I neurofibromatosis patients.
Cancer research. 2015; 75(1): 16-21.
53. Dong-Yue W, Peng L, Hai-Wei L. Up-regulation of CTD-2547G23.4
in hepatocellular carcinoma tissues and its prospective molecular
regulatory mechanism: a novel qRT-PCR and bioinformatics analy-
sis study. 2018; 18(1): 1-17.
54. Fan Y, Mu J, Huang M, Imani S, Wen QJE. Epigenetic identification
of ADCY4 as a biomarker for breast cancer: An integrated analysis
of adenylate cyclases. 2019; 11(1).
55. Yu Y, Tian XJM. Analysis of genes associated with prognosis of lung
adenocarcinoma based on GEO and TCGA databases. 2020; 99.
56. Hu J, Xu L, Shou T, Chen QJTlcr. Systematic analysis identifies
three-lncRNA signature as a potentially prognostic biomarker for
lung squamous cell carcinoma using bioinformatics strategy. 2019;
8(5): 614-635.
57. Zhang L, Luo B, Dang YW. The clinical significance of endothelin
receptor type B in hepatocellular carcinoma and its potential molec-
ular mechanism. 2019.
58. Gene expression and DNA methylation analyses suggest that im-
mune process-related ADCY6 is a prognostic factor of luminal-like
breast cancer %J Journal of Cellular Biochemistry. 2020; 121(7).
59. Colombo J, Fachel AA, Calmon MDF. Gene expression profiling
reveals molecular marker candidates of laryngeal squamous cell car-
cinoma. 2009; 21(3): 649.
60. Jun Z, Kun W, Chao L, Zhi-Gang JJAjopG. physiology l. LINC00319
acts as a microRNA-335-5p sponge to accelerate tumor growth and
metastasis in gastric cancer by upregulating ADCY3. 2020.
61. Hong SH, Goh SH, Lee SJ, Hwang JA, Lee YSJO. Upregulation of
adenylate cyclase 3 (ADCY3) increases the tumorigenic potential of
cells by activating the CREB pathway. 2013; 4(10).
62. Quinn S, Graves S, Dains-Mcgahee C. Adenylyl cyclase 3/adenylyl
cyclase-associated protein 1 (CAP1) complex mediates the anti-mi-
gratory effect of forskolin in pancreatic cancer cells. 2017.
63. Bing, Yanyan, Chang. Adenylate cyclase 7 regulated by miR-192
promotes ATRA-induced differentiation of acute promyelocytic leu-
kemia cells. 2018.

Comprehensive Analysis of ADCY Family Members in The Development, Immune Infiltration and Prognostic Value of Kidney Renal Clear Cell Carcinoma

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Comprehensive Analysis of ADCY Family Members in The Development, Immune Infiltration and Prognostic Value of Kidney Renal Clear Cell Carcinoma