Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas
Aim: To show that application of the systemic analysis may significantly improve comparison of different datasets. Different genes and proteins may converge on the same functional outputs. A comparison of 2 datasets by only identification names of affected molecules may miss that, leading to a concl...
Збережено в:
| Дата: | 2018 |
|---|---|
| Автор: | |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Інститут експериментальної патології, онкології і радіобіології ім. Р.Є. Кавецького НАН України
2018
|
| Назва видання: | Experimental Oncology |
| Теми: | |
| Онлайн доступ: | http://dspace.nbuv.gov.ua/handle/123456789/145590 |
| Теги: |
Додати тег
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|
| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas / S. Souchelnytskyi // Experimental Oncology. — 2018 — Т. 40, № 2. — С. 140–143. — Бібліогр.: 16 назв. — англ. |
Репозитарії
Digital Library of Periodicals of National Academy of Sciences of Ukraine| id |
irk-123456789-145590 |
|---|---|
| record_format |
dspace |
| spelling |
irk-123456789-1455902019-01-25T01:23:17Z Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas Souchelnytskyi, S. Short communications Aim: To show that application of the systemic analysis may significantly improve comparison of different datasets. Different genes and proteins may converge on the same functional outputs. A comparison of 2 datasets by only identification names of affected molecules may miss that, leading to a conclusion that there is nothing in common for these datasets. Systemic analysis may overcome this limitation, by focusing on functions represented by the identification names. Materials and Methods: Datasets were retrieved from open sources. Systemic analysis of vascularization features and angiogenesis signature was performed by using Cytoscape and its plugs-in. Results: In contrary to the initial statement of the lack of overlap between the vascularization features and the angiogenesis genes-signature in renal carcinomas, we observed an intersection on the functional level. Analysis of the networks built with identification names of vascularization and angiogenesis datasets showed an intersection, which included potent regulators of vessel formation and growth. Conclusion: Analysis of networks may expose functional links, which may be missed by a direct identification names comparison. Key Words: cancer, vascularization, angiogenesis, systemic analysis. 2018 Article Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas / S. Souchelnytskyi // Experimental Oncology. — 2018 — Т. 40, № 2. — С. 140–143. — Бібліогр.: 16 назв. — англ. 1812-9269 http://dspace.nbuv.gov.ua/handle/123456789/145590 en Experimental Oncology Інститут експериментальної патології, онкології і радіобіології ім. Р.Є. Кавецького НАН України |
| institution |
Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| language |
English |
| topic |
Short communications Short communications |
| spellingShingle |
Short communications Short communications Souchelnytskyi, S. Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas Experimental Oncology |
| description |
Aim: To show that application of the systemic analysis may significantly improve comparison of different datasets. Different genes and proteins may converge on the same functional outputs. A comparison of 2 datasets by only identification names of affected molecules may miss that, leading to a conclusion that there is nothing in common for these datasets. Systemic analysis may overcome this limitation, by focusing on functions represented by the identification names. Materials and Methods: Datasets were retrieved from open sources. Systemic analysis of vascularization features and angiogenesis signature was performed by using Cytoscape and its plugs-in. Results: In contrary to the initial statement of the lack of overlap between the vascularization features and the angiogenesis genes-signature in renal carcinomas, we observed an intersection on the functional level. Analysis of the networks built with identification names of vascularization and angiogenesis datasets showed an intersection, which included potent regulators of vessel formation and growth. Conclusion: Analysis of networks may expose functional links, which may be missed by a direct identification names comparison. Key Words: cancer, vascularization, angiogenesis, systemic analysis. |
| format |
Article |
| author |
Souchelnytskyi, S. |
| author_facet |
Souchelnytskyi, S. |
| author_sort |
Souchelnytskyi, S. |
| title |
Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas |
| title_short |
Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas |
| title_full |
Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas |
| title_fullStr |
Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas |
| title_full_unstemmed |
Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas |
| title_sort |
intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas |
| publisher |
Інститут експериментальної патології, онкології і радіобіології ім. Р.Є. Кавецького НАН України |
| publishDate |
2018 |
| topic_facet |
Short communications |
| url |
http://dspace.nbuv.gov.ua/handle/123456789/145590 |
| citation_txt |
Intersection between genes controlling vascularization and angiogenesis in renal cell carcinomas / S. Souchelnytskyi // Experimental Oncology. — 2018 — Т. 40, № 2. — С. 140–143. — Бібліогр.: 16 назв. — англ. |
| series |
Experimental Oncology |
| work_keys_str_mv |
AT souchelnytskyis intersectionbetweengenescontrollingvascularizationandangiogenesisinrenalcellcarcinomas |
| first_indexed |
2025-07-10T22:00:40Z |
| last_indexed |
2025-07-10T22:00:40Z |
| _version_ |
1837298982268698624 |
| fulltext |
140 Experimental Oncology 40, 140–143, 2018 (June)
INTERSECTION BETWEEN GENES CONTROLLING
VASCULARIZATION AND ANGIOGENESIS
IN RENAL CELL CARCINOMAS
S. Souchelnytskyi
College of Medicine, Qatar University, Doha 2713, Qatar
Aim: To show that application of the systemic analysis may significantly improve comparison of different datasets. Different genes
and proteins may converge on the same functional outputs. A comparison of 2 datasets by only identification names of affected
molecules may miss that, leading to a conclusion that there is nothing in common for these datasets. Systemic analysis may overcome
this limitation, by focusing on functions represented by the identification names. Materials and Methods: Datasets were retrieved
from open sources. Systemic analysis of vascularization features and angiogenesis signature was performed by using Cytoscape
and its plugs-in. Results: In contrary to the initial statement of the lack of overlap between the vascularization features and the
angiogenesis genes-signature in renal carcinomas, we observed an intersection on the functional level. Analysis of the networks
built with identification names of vascularization and angiogenesis datasets showed an intersection, which included potent regula-
tors of vessel formation and growth. Conclusion: Analysis of networks may expose functional links, which may be missed by a direct
identification names comparison.
Key Words: cancer, vascularization, angiogenesis, systemic analysis.
Systemic analysis of experimental data may pre-
vent erroneous conclusions when comparing different
datasets. Systemic analysis may unveil overlapping
patterns of genes, which can be missed by comparison
of genes name by name. Vascularization and angio-
genesis are crucial cancer hallmarks [1, 2]. Recently
there has been proposed a 14-genes vasculariza-
tion signature to separate good and poor prognosis
survivors with renal cell carcinomas [3]. The authors
concluded that there was no overlap with the previously
reported 48-genes angiogenesis signature [3, 4],
despite the fact that both signatures refer to angiogen-
esis. This conclusion may be the result of an identifica-
tion name for identification name comparison, which
could miss functional connections. In a living system,
different genes, transcripts as well as proteins work
in a cooperative way to ensure functional responses [5,
6]. Therefore, different genes and proteins may have
a similar functional output. Such functional connec-
tions would be unveiled by a systemic analysis [5–8].
Systemic analysis focuses on affected functions rather
than on identification names, and may lead to better
clinically relevant conclusions.
MATERIALS AND METHODS
The lists of the 14-genes and the 48-genes sig-
natures were used to build networks using the Cyto-
scape tool (version 3.6.0; www.cytoscape.org) [9].
Cytoscape allows building, visualization and analysis
of networks in different formats. Flexibility of Cyto-
scape in managing of different datasets, options for
retrieval of information from open source databases
and for comparison of different networks prompted
use of this tool in this study. UniProt database was used
to import a network of nodes and edges built with the
genes of the signatures. The 14-genes network con-
tained 178 nodes and 303 edges, and the 48-genes
network contained 836 nodes and 1763 edges. These
two networks were analyzed in Cytoscape for an in-
tersection. Intersection detected 62 nodes. Retrieved
shared nodes were further searched in Cytoscape
for regulators of angiogenesis, migration, vessel and
endothelial cells.
RESULTS AND DISCUSSION
Re-examination of the published 14-genes vas-
cularization [3] and the 48-genes angiogenesis [4]
signatures showed that there is an overlap between
these signatures on the networks level (Figure). An in-
tersection of the corresponding networks retrieved
62 shared nodes, with the use of the UniProt database.
Moreover, 17 of these nodes are involved in regula-
tion of angiogenesis, migration and endothelial cell
functions (the Figure and the Table). The network
built with the list of 14 genes generated a network
with 178 nodes and 303 edges. The network built
with the list of the 48 genes generated a network with
846 nodes and 1763 edges. The shared 62 nodes and
the 17 genes of relevance to angiogenesis, migration
and endothelial cells functions are listed in the Table.
It has to be noted that there are many systems
biology tools for building and analyzing of networks.
Here is reported an analysis with the use of Cytoscape,
as this tool has a well-developed plugs-in (Apps) and
allows easy retrieval of data from different databases.
Other tools, such as FunCoup or String, may also
be used, as long as they retrieve complete datasets.
To evaluate a completeness of a dataset, the author
did cross-checks with published reports. This cross-
check shows whether in the network would be retrieved
interactions described earlier. It would indicate com-
prehensiveness of the coverage of interactions and
whether the network represents available knowledge.
If a tool does not retrieve such interactions, it would
Submitted: February 07, 2018
Correspondence: E-mail: serhiy@qu.edu.qa
Exp Oncol 2018
40, 2, 140–143
Experimental Oncology 40, 140–143, 2018 (June) 141
Figure. Systemic analysis identifies links of the vascularization signature with the angiogenesis genes-signature, which were not
detectable by a direct comparison. A direct comparison of the 14-genes and 48-genes signatures of vascularization and angioge-
nesis did not show an intersection [3, 4]. The analysis of the corresponding networks detects 62 nodes common for the networks.
There are nodes (in yellow) with relevance to regulation of angiogenesis, migration, vessel formation and endothelial cells. The
nodes are annotated in the Table
142 Experimental Oncology 40, 140–143, 2018 (June)
be advised to change settings of a search or even
change a tool.
Accessibility and type of databases is another im-
portant point in selection of a tool for network analysis.
Use of open source databases is advised, as proprie-
tary databases may have restrictions on access to their
data. Open source databases are often specialized and
may focus on certain types of molecules (DNA, RNA,
proteins, metabolites, small molecules), interactions
(direct, functional, correlations) or species (H. sa-
piens, M. musculus, D. melanogaster, C. elegance,
D. rerio, etc) [10]. Therefore, it is advised to ensure that
the databases used for a network building represent
types of data which are relevant to a study.
The detected with Cytoscape shared 62 nodes rep-
resent proteins with reported roles in tumorigenesis,
angiogenesis and stroma formation. Examples are
Her2, Her4, pp60c-src, stat3, p94-fer and Dapk3 ki-
nases (see Table). Thus, detection of shared nodes
confirms that the vascularization includes regulators
of angiogenesis.
Detection of the shared nodes emphasizes the power
of systemic analysis in unveiling of hidden links. As the old
saying goes “all roads lead to Rome”, the same is valid
for cancer — many mechanisms may lead to the same
result, i.e. tumor vascularization [1, 2]. These cancer-
roads may be traced with systems biology tools, which
may identify common functional targets. The shared
nodes represent well-known regulators of angiogenesis
and vascularization of tumors. For example, Her2, Her4,
STAT3, SIRT-1 and Src have been reported to regulate
tumor angiogenesis [11–15]. It has to be noted that these
shared nodes show functional links to well-known genes
of relevance for development of renal carcinoma, e.g.
VHL, VEGFR, HIF, mTOR [11–15]. Moreover, the shared
nodes represent regulators of functions targeted in treat-
ment of renal carcinoma, such as anti-VEGFR treat-
ments [16]. This confirms an importance of the systemic
analysis for further clinical applications, as it allows linking
of diagnostic and prognostic marker signatures with
pathways targeted in treatments of patients. Thus, the
signatures of angiogenesis and vascularization do have
a functional overlap, but to detect it, a network analysis
has to be applied.
CONCLUSIONS
Network-based analysis may detect functional
similarities between seemingly not intersecting data-
sets. A network-based analysis is rooted in the sys-
temic character of tumorigenesis, and it unveils how
different genes and proteins may regulate the same
functions. This study may be used as a template for
an analysis of intersections for other datasets.
FUNDING
Support by NPRP9-453-3-089, QUST-SPR-2017-12,
QUST-SPR-2017-11, HMC-MCR-RP16354 and HMC-
MCR-RP-iTRI research grants is acknowledged.
REFERENCES
1. Wang Z, Dabrosin C, Yin X, et al. Broad targeting of an-
giogenesis for cancer prevention and therapy. Semin Cancer
Biol 2015; 35: Suppl: S224–S43.
2. Johannessen TC, Wagner M, Straume O, et al. Tumor
vasculature: the Achilles’ heel of cancer? Expert Opin Ther
Targets 2013; 17: 7–20.
Table. Annotations of the 14-genes signature of vascularization [3], the 48-genes signature of angiogenesis [4], 62 shared nodes between the networks
and 17 nodes which were retrieved from the shared 62-nodes by a search for “angiogenesis AND vessel AND migration AND endothelial”. Fourteen and
48 genes are annotated by their GO terms. Sixty-two and 17 nodes are annotated by the accession numbers and names in parentheses
14-genes signature
of vascularization [3]
48-genes signature
of angiogenesis [4]
62 shared nodes between networks
of the 14- and 48-genes
17 nodes retrieved with search
terms: angiogenesis, vessels,
migration and endothelial cells
ADH5, NLRC4, RPL36A,
RPLP2, SLC16A4,
TNFSF8, ZNF16, SOCB,
GOSR2, IFNA13, CMYA5,
STAT3, KCNJ12, MED10
ACVRL1, AGGF1, AMOT,
ANG, ANGPTL3, ANGPTL4,
ATPIF1, BTG1, C1GALT1,
CANX, CDH13, CHRNA7,
COL4A2, COL4A3, EGF,
EMCN, EPGN, ERAP1,
FOXO4, HTATIP2, IL17F,
IL18, IL8, MYH9, NCL, NF1,
NOTCH4, NPPB, NPR1, PF4,
PLG, PML, PROK2, RHOB,
RNH1, ROBO4, RUNX1,
SCG2, SERPINF1, SHH,
SPHK1, SPINK5, STAB1,
TGFB2, THY1, TNFSF12,
TNNI3, VEGFA
P16591 (p94-Fer kinase), O54784 (Dapk3), O43293
(DAP-like kinase), Q9BUH7 (WD repeat-contain-
ing protein antisense to TP73 gene), Q63584 (Tmp21),
P11520 (Tis11a), Q86UE5 (KIAA1582), Q96EB6 (Sirt1),
Q9WU01 (Slm1), Q2XNF6 (SIR2L1), Q5VYR3 (NIRF),
Q9Y2L0 (CNOT1), Q9UF92 (CNOT6L), Q9UEG1 (UBA52),
Q9R1S0 (Eppb9), Q9NU89 (MAGE-D3), Q9H9A5 (CNOT10),
Q9H5A8 (Proto-oncogene c-Src), Q9H3E0 (HSPC131),
Q96IX4 (RQCD1), Q8VEG6 (Cnot6l), Q8TDQ0 (TIMD3),
Q8TCR0 (CAGH26), Q8CJ65 (Slamf7), Q7L3L1 (ZXDC),
Q6P4S0 (AGO1), Q6P2M9 (C2orf29), Q60735
(Src-associated in mitosis 68 kDa protein), Q2PNI9 (LAK1),
Q15303 (HER4), Q14469 (HES1), P35233 (Ptpn2),
Q5SV78 (Dioxin-inducible factor 3), P06733 (Enolase 1),
P02452 (Alpha-1 type I collagen), O00257-3 (CBX4),
Q9NZN7 (LENG2), P78543 (BTG family
member 2), O88609 (LIM/homeobox protein LMX1B),
Q9UPQ9 (TNRC6B), Q2HR82 (K8), P45973 (CBX5),
Q13547 (HDAC1), P70338 (Gfi1), O43846 (ErbB3-bind-
ing protein 1), P68104 (EEF1A1), P50616 (Transduc-
er of erbB-2, 1), P31384 (Carbon catabolite repressor
protein 4), Q8K3P5 (Cytoplasmic deadenylase, CCR4),
P52907 (CAPZA1), Q9ULM6 (CCR4a), Q60809 (Caf1),
Q9UIV1 (BTG1-binding factor 1), P31607 (B-cell transloca-
tion gene 1 protein), Q96FG0 (HZF1), P04626
(Proto-oncogene Neu), Q9UKV8 (AGO2), Q4VAB3 (Eif2c2),
Q923E1 (Dab2), O14916 (APRF), P42227 (Stat3),
P22449 (Hnf4a)
P12931 (pp60c-src),
Q15303 (HER4), Q14469 (HES1),
Q5SV78 (Dioxin-inducible factor 3),
P02452 (Alpha-1 type I collagen),
O88609 (LIM/ homeobox pro-
tein LMX1B), P62324 (B-cell
translocation gene 1 protein),
P04626 (HER2), P98078 (Dif-
ferentially expressed in ovari-
an carcinoma 2), P40763 (STAT3),
P42227 (Aprf), P22449 (Transcrip-
tion factor HNF-4), P16591 (FER ty-
rosine kinase), O54784 (Dapk3),
Q96EB6 (SIRT-1), Q9R1So (B9d1),
O43293 (DAP-like kinase)
Experimental Oncology 40, 140–143, 2018 (June) 143
3. Ing N, Huang F, Conley A, et al. A novel machine
learning approach reveals latent vascular phenotypes predictive
of renal cancer outcome. Sci Rep 2017; 7: 13190.
4. Liberzon A, Birger C, Thorvaldsdottir H, et al. The
molecular signature database hallmark gene set collection.
Cell Systems 2015; 1: 417–25.
5. Califano A, Alvarez MJ. The recurrent architecture
of tumour initiation, progression and drug sensitivity. Nat Rev
Cancer 2017; 17: 116–30.
6. Yan W, Xue W, Chen J, Hu G. Biological networks
for cancer candidate biomarkers discovery. Cancer In-
form 2016; 15: 1–7.
7. Attarha S, Andersson S, Mints M, Souchelnytskyi S. In-
dividualised proteome profiling of human endometrial tumours
improves detection of new prognostic markers. Br J Can-
cer 2013; 109: 704–13.
8. Zakharchenko O, Cojoc M, Dubrovska A, Souchel-
nytskyi S. A role of TGFβ1 dependent 14-3-3σ phosphoryla-
tion at Ser69 and Ser74 in the regulation of gene transcription,
stemness and radioresistance. PLoS One 2013; 8: e65163.
9. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a soft-
ware environment for integrated models of biomolecular inter-
action networks. Genome Res 2017; 13: 2498–504.
10. Miryala SK, Anbarasu A, Ramaiah S. Discerning mo-
lecular interactions: A comprehensive review on biomolecular
interaction databases and network analysis tools. Gene 2018;
642: 84–94. doi: 10.1016/j.gene.2017.11.028.
11. Jomrich G, Schoppmann SF. Targeting HER 2 and
angiogenesis in gastric cancer. Expert Rev Anticancer Ther
2016; 16: 111–22.
12. Alameddine RS, Otrock ZK, Awada A, Shamsed-
dine A. Crosstalk between HER2 signaling and angiogenesis
in breast cancer: molecular basis, clinical applications and
challenges. Curr Opin Oncol 2013; 25: 313–24.
13. Gao P, Niu N, Wei T, et al. The roles of signal trans-
ducer and activator of transcription factor 3 in tumor angio-
genesis. Oncotarget 2017; 8: 69139–61.
14. Wang J, Zhuang S. Src family kinases in chronic kidney
disease. Am J Physiol Renal Physiol 2017; 313: F721–8.
15. Kunhiraman H, Edatt L, Thekkeveedu S, et al.
2-Deoxy glucose modulates expression and biological activity
of VEGF in a SIRT-1 dependent mechanism. J Cell Biochem
2017; 118: 252–62.
16. Zarrabi K, Fang C, Wu S. New treatment options for
metastatic renal cell carcinoma with prior anti-angiogenesis
therapy. J Hematol Oncol 2017; 10: 38.
Copyright © Experimental Oncology, 2018
|