Bioinformatics Analysis of Gene Targets for Birt-Hogg-Dube

Syndrome Associated with Renal Cell Cancer using NetworkAnalyst

Mariemme Keilsy D. Martos and Marineil C. Gomez

School of Chemical, Biological, Material Engineering and Sciences, Mapua University,

Muralla St. Intramuros, Manila 1002, Philippines

Keywords: Renal Cell Cancer, Chromophobe Renal Cell Cancer Birt-Hogg-Dube Syndrome.

Abstract: CrRCC (chromophobe renal cell cancer) belongs to the group of non-clear cell cancer which accounts 4%-

5% of RCC. Birt-Hogg-Dube Syndrome (BHDS), a subtype for crRCC, occurs due to the germline mutation

of Folliculin (FLCN). Each disease has designated treatment and contrasting prognosis, but the histological

features of this syndrome may overlap with the other subtypes of RCC which makes it difficult to differentiate

and it has a limited amount of information available due to its uncommonness. This study aims to differentiate

the pathway and genes involved in BHDS disease through NetworkAnalyst. The dataset was gathered from

ArrayExpress and generated 395 significant DEGs in BHDS, which was then used to produce a pathway

enrichment network and protein-protein interaction (PPI). Cytoskeletal protein binding correlating with hub

genes KIT, RHOB, and UBC in BHDS indicates that this disease has a high risk for cell metastasis. This study

gives a new promising therapeutic target for the said disease.

1 INTRODUCTION

Every year, there are approximately 338,000 new

renal cell carcinoma releases in the world and about

30% of new renal cell carcinoma patients have

metastases at the time of diagnosis (Li et al., 2021).

Renal cell carcinoma (RCC) is a frequently diagnosed

cancer with high prevalence (Y. Y. Chen et al., 2020).

It is a heterogeneous tumor that derives from

epithelial cells of the renal tubular, which represents

a comprehensive 80% of all main RCC kidney tumors

(Singh, 2021). Obesity, hypertension, and cigarette

smoking are well-known risk factor for RCC although

their impact may be different depending on the

population. Renal cell carcinoma is more prone to

male gender than females and a high incidence is

generally seen from the sixth to eight decades of its

existence that proves gender, race, and age affects the

occurrence of RCC (Thompson et al., 2008). Genes

that are typically involved in renal cell carcinomas

such as VHL, MET, FLCN, SDH, TSC1, and TSC2

have an important role regarding with regulation of

cellular metabolic processes which suggest a

dysregulation of metabolic pathways involved in

oxygen, energy, and/or nutrient sensing as a key

feature of RCC carcinogenesis (Linehan, Srinivasan,

& Schmidt, 2010).

RCC has many histological subtypes with

different molecular drivers in which clear cell RCC is

the most prevalent subtype, approximately for about

75%. The remaining subtypes include papillary renal

cell cancer (pRCC), chromophobe renal cancer

(crRCC), MiT family translocation, and other rare

types (F. Chen et al., 2016). Most genomic alterations

in RCC were well defined until the World Health

Organization (WHO) in 2016 discovered

classifications of tumors included subtypes which

include Hereditary Leiomyomatosis and Renal Cell

Cancer (HLRCC), von Hippel-Lindau disease

(VHL), Birt-Hogg-Dube Syndrome (BHDS), and

Hereditary Papillary renal carcinoma (HPRCC)

(Moch, Cubilla, Humphrey, Reuter, & Ulbright,

2016).

Birt-Hogg-Dube Syndrome (BHDS) is a major

autosomal dominantly inherited syndrome. BHDS is

mostly involved with chromophobe renal cell

carcinoma (crRCC), which is the third common

subtype of RCC as it accounts 4%-5% of the incidence

rate. This syndrome is associated with other benign or

malignant tumors in other organs. Patients with BHDS

deal with the RCC subtype chromophobe cell RCC,

which is often considered as the counterpart of the

benign oncocytoma, own hybrid forms (oncocytoma-

chromophobe) (Murphy, Burns, Murtagh, Rooshenas,

Martos, M. and Gomez, M.

Bioinformatics Analysis of Gene Targets for Birt-Hogg-Dube Syndrome Associated with Renal Cell Cancer using NetworkAnalyst.

DOI: 10.5220/0010767400003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS, pages 73-80

ISBN: 978-989-758-552-4; ISSN: 2184-4305

& Caskey, 2021). This hereditary syndrome is

becoming more evident due to the advancement in

pathological and molecular characterization and since

there are many histological features are associated

with distinct RCC hereditary, overlapping of their

features is possible (Carlo et al., 2019).

It is important to identify patients at risk for

hereditary RCC, as it may influence care (e.g. radical

versus partial nephrectomy and surveillance type and

schedule) and family members at risk could be

offered specific screening to enable early detection.

Each subtype is endowed with its unique risk factors,

prognosis, prevalence, survival rate, responsiveness

to diverse therapeutic agents, and clinical outcomes.

Furthermore, the main treatment is surgery combined

with chemotherapy and immunotherapy, but the

therapeutic effect is limited (Fisher, Gore, & Larkin,

2013). Therefore, it is necessary to further study the

pathogenesis of BHD syndrome to find possible early

diagnostic markers and therapeutic targets.

The objectives of the study are identifying the

main gene/s concern in differentiating the

pathogenesis on different classifications of RCC

specifically on BHDS disease and then integrating it

on web-tool based mainly named NetworkAnalyst

that will enable the user to construct a protein-protein

interaction network that will aid classification of the

pathophysiological pathways of this subtype of RCC.

This study focuses on the genes and pathways that

are needed to differentiate the specific subtype of

RCC therefore aimed at one organ, which is the

kidney. As the NetworkAnalyst is used as the main-

tool-based software, it also limits the resource of

collected microarray data by choosing the

ArrayExpress as the library resource for the dataset of

BHDS (E-GEOD-21816). It is also noted that

research on human tissues is used in conducting this

study on gene expressed table.

2 METHODOLOGY

2.1 Data Collection

The gene expression dataset of BHDS [E-GEOD-

21816 (GPL10175 Platform)] was manually searched

and gathered from ArrayExpress database. The

ArrayExpress (https://ebi.ac.uk/arrayexpress) is an

open-source platform for the storage of genetic data.

The E-GEOD-21816 dataset includes 6 normal kidney

tissues and 6 Birt-Hogg-Dube syndromes associated

with renal tumors patients. These microarray datasets

(BHDS kidney tissues vs. normal kidney tissues and

HLRCC kidney tissues vs normal kidney tissues) are

inputted in text file (.txt) and are uploaded in

NetworkAnalyst. Table 1 consists of the inclusions

and exclusions criteria for the microarray dataset.

Table 1: Inclusions and Exclusions criteria for microarray

datasets.

Inclusion Exclusion

Kidney tissues No other organ tissues

Homo sa

ien or

anis

Non-human or

anism

BHDS No other subtypes of

disease

2.2 Data Preprocessing, Quality Check,

and Normalization

In uploading, the gene expression table, data should

be specified according to their specific organism, data

type, ID type, and gene-level summarization. Both

datasets are specified as homo sapien, microarray

data, Entrez ID, and mean, accordingly. After the

datasets are successfully uploaded, a quality check

and normalization of the data are done to enable to

have more refined data analysis. Diagnostic plots

such as box plots and density plots are included in

both the quality check category and the normalization

category. These diagnostic plots give different

perspectives on the data. The distribution of gene

expression values can be seen through these

diagnostic plots and the results of different

normalization methods on sample clustering can be

visualized using PCA plots (G. Zhou et al., 2019).

Box plots are applied to examine the normalization

status. Log scale is applied if all data values are 20

while quantile normalization is used if all samples

have identical distribution (Xia, Gill, & Hancock,

2015). The two datasets are filtered and further

normalized to quantile normalization.

2.3 Identification of DEGs

NetworkAnalyst may also be used to distinguish

DEGs between renal tumor tissue and normal kidney

tissue samples. If one probe set does not contain the

homologous gene, or if one gene has numerous probe

sets, the data are removed (Li et al., 2021). Fold

change of the genes present in BHDS tumor

compared to normal kidney tissue were analyzed

using the LIMMA package. The comparison of

interest is set to its specific comparison (control vs.

infected). To determine the genes that are

significantly expressed on both datasets, the FDR

adjusted p-values were kept to less than 0.05. Based

on the fold change, genes were categorized into two

classes, up-regulated genes (log2FC > 2) and down-

BIOINFORMATICS 2022 - 13th International Conference on Bioinformatics Models, Methods and Algorithms

regulated genes (log2FC < -2), the cut-off statistic

criteria were based in the study of L. Zhou et al. (L.

Zhou, Li, Li, & Huang, 2020). Genes that were

commonly up-regulated and down-regulated in both

datasets were used to further analysis.

2.4 Pathway Enrichment Analysis

The pathway and process enrichment analysis were

performed in all the common DEGs in both datasets.

NetworkAnalyst allows users to perform functional

enrichment analysis for highlighted nodes using

different databases such as GO, KEGG, PANTHER,

and Reactome pathway databases (Xia, Benner, &

Hancock, 2014). KEGG, PANTHER, and Reactome

are commonly used for biological information

databases worldwide. The GO resource includes three

aspects of biology which are biological process (BP),

cellular component (CC), and molecular function

(MF), and it is also commonly used in bioinformatics.

The rule of significant is that P-value < 0.05 (Li et al.,

2021).

2.5 Networking Mapping and Visual

Analytics

This step deals with constructing PPI networks,

heatmaps, volcano plots, and other visualization

steps. The summary-level data (P values and fold

changes) from the two datasets are extracted and

integrated to identify genes that are significantly

altered in expression, based on overall evidence (Xia

et al., 2014). The significant genes of the datasets are

presented in PPI networks and visualization analysis.

In the PPI network, the number of nodes, edges, and

seed proteins are summarized for each network (Xia

et al., 2015). The clustering analysis of expression

levels of hub genes is performed using interactive

heatmaps or enrichment networks. The heatmap

visualization tool shows detailed gene expression

patterns underlying individual functions; while the

enrichment network tool provides an overview of all

enriched functions with similar ones connected by

edges (G. Zhou et al., 2019) that uses different

databases mentioned above.

3 RESULTS

3.1 Identification of DEGs

Following the preprocessing of the raw dataset, and

then thoroughly running it through the LIMMA

package, a total of 395 significant genes was

identified in the dataset of E-GEOD-21816. With cut-

off statistic criteria of p-value ≤ 0.05 and fold change

(FC) ≥ 2 or FC < -2, each set has generated their own

up and down regulated genes, for E-GEOD-21816

consists of 148 down-regulated genes and 247 up-

regulated genes. The visualization of the resulted

DEGs of BHDS dataset was done through volcano

plots heatmaps (Figure 1). Based on the DEGs,

heatmap analysis showed clear segregation of

patients with BHDS from the control sample (Figure

1A and Figure 1B). The top up and down regulated

DEGs ranked by fold change in BHDS is listed in

Table 2.

3.2 Pathway Enrichment Analysis

It was concluded that while PPIs are reliable in

discerning specific hub nodes that can describe the

gene’s centrality towards protein genes, it is only

enclosed within a specific subnetwork, hence the

usage of gene set enrichment analysis (GSEA). GSEA

is primarily used as a visual data analysis within the

NetworkAnalyst to produce gene count for the

enriched KEGG and GO pathways. P-value was added

to determine the probability of connection between the

pathway and BHDS genes seen in Tables 3 & 4.

Figure 1: Hierarchical clustering heatmap of (A) BHDS and

control sample. Volcano plot of DEGs between (B) BHDS

sample and control sample. The red circles found in the

volcano plots signify up-regulated genes while the blue

circles are the down-regulated genes, and the white circles

are non-significant genes. From the heatmap, the first six

columns from the left are the normal kidney tissue and the

last 6 columns are the tumor samples. The blue shade

signifies low expressed genes while the red shade defines

the high expressed genes.

3.3 Protein-Protein Interaction

Using both up and down regulated DEGs that were

produced by the statistical analyzation of the provided

sets, Hub nodes were identified through the string

interactome database and therefore established the

protein-protein interaction. As stated above, Table 5

Bioinformatics Analysis of Gene Targets for Birt-Hogg-Dube Syndrome Associated with Renal Cell Cancer using NetworkAnalyst

shows PPI can be used to determine the specific

subtype’s centrality towards BHDS genes. Figure 3

contains the visual representation of PPI network

from DEGs of BHDS.

4 DISCUSSION

The different types of renal cell carcinoma may pose

some difficulty in differentiating histologically as

many features of the subtypes overlap each other.

And because they all have designated treatment and

as well as contrasting prognosis, the utilization of

gene-expression microarray analysis is therefore

essential in the identification of molecular

pathogenesis that will aid in distinguishing

biomarkers that is important in clinical diagnosis,

especially in diseases where there is a limited amount

of information available due to the rarity of some

disorders (Caliskan, Andac, & Arga, 2020).

With that in mind, NetworkAnalyst was chosen as

the designated program that will generate gene

expression profiles as an innovative move to further

test the program if it is accurate enough to be used not

only to detect biomarkers but also construct pathways

specifically for BHDS.

As previously stated, renal cell carcinoma has a

high prevalence rate (Fisher et al., 2013) and within

the aforementioned subtype; chromophobe, though

not as much predominantly known as clear cell RCC

is shown more significant than its counterparts. And

from the previously gathered studies, BHDS has been

mentioned the most by papers by the papers amongst

the tumors that are enclosed within the chromophobe

subtype and was therefore selected to be analyzed

thoroughly.

4.1 Birt-Hogg-Dube Syndrome (BHDS)

Birt-Hogg-Sube syndrome (BHDS), a subtype of

chromophobe renal cell cancer (crRCC), is a

hereditary condition characterized by skin

fibrofolliculomas, pulmonary cysts, spontaneous

pneumothoraces, and multiple RCCs (Nickerson et

al., 2002). The germline mutation in the folliculin

(FLCN) gene affects this disorder but its function

remains unknown.

As DEGs from the dataset were used to produce

the pathway enrichment network analysis using

GSEA (Figure 2), therefore it conveys a much larger

visualization in terms of connection of gene towards

the disease. Referring to Tables 3 & 4 that include the

KEGG and GO pathways enriched from the DEGs of

BHDS.

Figure 2: Visual representation of GO: MF pathway

enriched in BHDS using GSEA network.

4.2 Pathway for BHDS

From reviewing the result in Table 4, it was

interpreted that while the research of Moch et al. may

say that the chromophobe form of RCC has a lowered

risk for metastasis (Moch & Ohashi, 2021), but its

subtype; specifically BHDS showed that cytoskeletal

protein binding has the highest genome count with

Table 2: The top up and down regulated DEGs of BHDS ranked by log2FC.

-re

ulated

enes Down-re

ulated

enes

Gene Symbol Log2Fc P-value Gene Symbol Log2FC P-value

Birt-Hogg-Dube Syndrome (BHDS)

CXCL14 6.689 7.1641E-13 TMEM255A -7.9156 3.856E-18

ALDOB 6.558 2.346E-9 HHATL -7.3352 3.413E-14

CALB1 6.177 2.567E-9 DAPL1 -5.6567 1.465E-6

UMOD 6.106 6.362E-10 PVALB -4.8824 5.800E-7

NAT8 6.048 2.388E-7 CKMT2 -4.6471 1.508E-12

PDZK1IP1 6.0416 1.1997E-9 PDZK1IP1 6.0416 1.1997E-9

ASS1 5.8721 3.8722E-9 ASS1 5.8721 3.8722E-9

PAH 5.3302 6.6718E-8 PAH 5.3302 6.6718E-8

BBOX1 5.2498 1.6913E-7 BBOX1 5.2498 1.6913E-7

PROM1 5.1054 2.7515E-7 PROM1 5.1054 2.7515E-7

BIOINFORMATICS 2022 - 13th International Conference on Bioinformatics Models, Methods and Algorithms

Table 3: Top KEGG pathways in the enrichment analysis

of significant DEGs associated with BHDS.

Birt-Hogg-Dube Syndrome (BHDS)

Pathway Gene

Count

P. value

Focal adhesion 168/199 1.57E-4

Fluid shear stress and

atherosclerosis

119/139 1.64E-4

Cell adhesion molecules 105/146 1.64E-4

Leukocyte transendothelial

migration

89/112 1.68E-4

TGF-

eta si

nalin

athwa

71/92 1.71E-4

Protein digestion and

absorption

70/90 1.72E-4

Bios

nthesis of amino acids 61/75 1.75E-4

Complement and

coa

ulation cascades

59/79 1.75E-4

Glycolysis/Gluconeogenesis 51/68 1.77E-4

Oxidative

hos

hor

lation 106/133 2.55E-4

Table 4: Top GO (BP, MF, CC) terms in the enrichment

analysis of significant DEGs associated with BHDS.

PATHWAY GENE

COUNT

P-VALUE

Biological Process (BP)

Wound Healing 483/610 1.4E-4

Positive regulation of cell

proliferation

478/668 1.4E-4

Regulation of anatomical

structure morphogenesis

478/605 1.4E-4

Regulation of body fluid

levels

469/595 1.4E-4

Vasculature development

429/523 1.43E-4

Response to biotic

stimulus

432/614 1.43E-4

Response to other

mechanism

413/586 1.44E-4

Negative regulation of

development process

404/563 1.44E-4

Regulation of growth

408/518 1.44E-4

Negative regulation of

cell proliferation

409/526 1.44E-4

Molecular Function (MF)

Cytoskeletal protein

binding

497/635 1.39E-4

Calcium ion binding

442/662 1.41E-4

Structural molecule

activity

428/624 1.43E-4

Actin binding

278/356 1.51E-4

Metal ion transmembrane

transporter activity

267/373 1.52E-4

Enzyme inhibitor activity

229/322 1.54E-4

Substrate specific channel

activity

228/376 1.54E-4

Anion transmembrane

transporter activity

165/229 1.60E-4

Secondary active

transmembrane activity

146/192 1.61E-4

Glycosaminoglycan

binding

136/172 1.63E-4

Cellular Component (CC)

Cell surface

351/488 1.47E-4

Extracellular matrix

313/424 1.48E-4

Actin cytoskeleton

292/366 1.5E-4

Proteinaceous

extracellular matrix

262/362 1.51E-4

Cell-cell junction

232/292 1.54E-4

Apical plasma membrane

169/212 1.62E-4

Extracellular matrix part

139/178 1.64E-4

External side of plasma

membrane

142/202 1.64E-4

Apical junction complex

96/117 1.7E-4

Anchored to membrane

97/146 1.7E-4

Table 5: The top 10 significant hub genes of BHDS

according to their betweenness and their designated p-

values.

Hub Nodes P-value Betweenness

BHDS

KIT 7.00E-9 102791.3

RHOB 2.74E-9 68625.94

UBC 0.062274 63768.85

PLG 0.003165 40525.61

AGT 4.70E-5 40265.16

THBS1 4.09E-7 38286.66

SRC 0.36754 33653.63

KNG1 2.34E-5 30817.65

FHL2 7.28E-6 29003.64

PRKAR2B 8.04E-7 27625.94

497/635 and has significantly lowered p-value of

1.39E-4 which means that a lot of genes that is

involved in cytoskeletal protein binding pathway is

included with the progression of BHDS. This in turn,

may potentially point out that unlike the previously

constructed views of BHDS, it may possibly have a

higher risk of metastasis. Figure 4 shows a schematic

representation of the mechanism of cytoskeletal

protein binding pathway enriched in BHDS.

4.2.1 Cytoskeletal Protein Binding

Cytoskeletal proteins contain different sub-families

of proteins mainly which are Microtubules, Actin,

and Intermediate Filaments (Pacheco & Gallo, 2016).

The mechanism of these proteins is altered in cancer

cells as they promote tumor growth by increasing the

cells’ migratory and invasive function alongside its

ability to proliferate and the resistance to cellular

Bioinformatics Analysis of Gene Targets for Birt-Hogg-Dube Syndrome Associated with Renal Cell Cancer using NetworkAnalyst

environmental stress such as: mitochondrial and

oxidative stress (Allen et al., 2020). Mutations from

these genes may result in metastasis and therefore and

because of the high genome count and the

significance of its p-value, this may very well allude

to the possible metastatic characteristic of BHDS.

4.3 Hub Nodes for BHDS

In alignment with the statement above, three hub

nodes were selected in terms of increasing

betweenness. As this describes the gene’s centrality

towards other genes that are involved in the diseases’

progression. These are: KIT (betweenness:

102791.3), RHOB (betweenness: 68625.94), and

UBC (betweenness: 63768.85) in descending order.

4.3.1 c-KIT Gene Expression

C-KIT proto-oncogene is located on chromosome 4q

and is considered to be part of class III of tyrosine

kinase receptor (TKR) family. It is known to regulate

several physiological functions such as:

hematopoiesis, erythropoiesis, lymphopoiesis,

megakaryopoiesis, gametopoiesis, and

melanogenesis (Martinez-Anton, Gras, Bourdin,

Dubreuil, & Chanez, 2019). All of these are essential

to the biological process of human beings. Numerous

research have suspected that this particular gene

could be a potential biomarker to the chromophobe

type of RCC as evidences show that it is found 77%

to 100% in cases of this type of variety, and therefore

is also a potentially targeted for therapeutic

modalities (Yamazaki et al., 2003).

Using NetworkAnalyst, Figure 5 shown that C-

KIT is connected to a gene called RAC1. RAC1 is

considered as one of the key regulators for cellular

motility and structure as the members of RAC family

is considered to hold regulatory functions over

cytoskeletal structures, mainly Actin (Tejada-Simon,

2015). As it primarily controls the mechanism behind

the moderation of other signaling pathways that are

involved in cell cycle regulation, cellular growth,

formation of cell-cell adhesion, and contact

inhibition, and these mediated activities are

considered to be highly involved in progression of

malignancy as it is included in angiogenesis,

invasion, and metastasis which are dependent from

the mutations from the genes assigned in it (Olson &

Sahai, 2009).

Figure 3: (A) The top 10 extracted hub nodes (Left to Right,

Top to Bottom: PRKAR2B, KIT, FHL2, UBC, RHOB,

KNG1, AGT, SRC, THBS1, PLG) The red-colored gene

are seed genes, while purple-colored gene are: protein gene.

(B) Along with the overall presentation of protein-protein

interaction network for BHDS.

4.3.2 RHOB Gene Expression

RhoB is part of the Ras Homolog gene gamily or

better known as Rho subgroup of GTPase which is

included along with RhoA and RhoC. This family of

genes is critical for analyzing regulation of cellular

action and modulation of cytoskeleton-mediated

motion and adhesion, as well as protein trafficking

(Haga & Ridley, 2016).

Rho GTPases functions are directed by

conversion of GDP-bound inactive states to GTP-

bound active states. This activation is caused by three

factors: Guanine nucleotide exchange factors (GEFs),

GTPase activating proteins (GAPs), and guanine

nucleotide dissociation inhibitors (GDI). The

switches between active and inactive form are critical

in regulating intracellular signaling pathways

(Gampel & Mellor, 2002; Haga & Ridley, 2016).

Though the three subgroups of Rho GTPase share

similar homology, they have different functions.

Mainly RhoB is believed to have a putative tumor

suppressor role, compared to the other two, which is

claimed to have an oncogenic association (Ju &

Gilkes, 2018). This particular function of RhoB

serves in the signaling pathways including the EGFR,

RAS, PI3K/AKT/mTOR, and MYC pathways

(Gutierrez et al., 2019).

4.3.3 UBC Gene Expression

Ubiquitin C gene is described as a stress-inducible

gene, upregulated upon different cell treatments as

well as in other diseases (Radici, Bianchi, Crinelli, &

Magnani, 2013). As it is one of the main hub nodes

shown in the table above that was detected through

the use of NetworkAnalyst, upon further inspection,

WNK4 a subfamily of WNK protein is the one

associated gene connected to UBC (Figure 5).

BIOINFORMATICS 2022 - 13th International Conference on Bioinformatics Models, Methods and Algorithms

Interestingly, recent studies about the link

between WNK4 and Rho GTPases have emerged; it

shows that WNK4 can be isolated in a complex with

Rho-GDI (Zhang et al., 2009) and while it is observed

that the expression of WNK4 was found to be

correlated with invasiveness as with metastatic

tumors such as infiltrative gliomas or in other neural

tumor cells the exact relationship between the genes

is still largely undetermined (Hong et al., 2007). In

addition, WNK4 is required for the activation of

extracellular signal-regulated kinases and Mitogen-

activated Protein/ERK Kinases (MEKK2/3)

pathways. Alongside other reports, this suggests that

WNK4 is involved in many factors that attribute to

carcinogenesis and is an important role in tumor cell

growth and remodeling of extracellular matrix for

tumor invasion (Sie et al., 2020).

Figure 4: The mechanism of the pathway enriched in

BHDS: Cytoskeletal protein binding. As C- KIT forms a

heterodimer, SCF binds to it resulting to activation of RHO

GTPase from inactive state. Then RHO GTPase will

promote the activation of an effector such as RAC1 that

regulates the Actin cytoskeleton. RAC1 can also be affected

with the correlation of WNK4 with RHO GDI.

Figure 5: Protein-Protein Interaction of WNK 4 gene

connecting to seed gene UBC and seed gene KIT to RAC1

gene.

And in that note, the connection between these

genes and the given pathways may still up for further

studies, they may support the suspicions that there are

other characteristics traits of BHDS that have not

been explored fully, and the presence of genes in the

result above, bodes significance in terms of

determinants for other biomarkers that may comprise

this disease but also for alternative targeted

treatments that may help patients in the future

5 CONCLUSION

The resulted hub genes from the PPI networks, which

were ranked according to its betweenness, correspond

with the high scored gene count and p-value enriched

pathways. With the relation of the pathway and hub

genes in the BHDS disease, it showed different

pathophysiological features of this subtype of RCC.

In this study, RAC1 and WNK4 genes were found to

be connected to KIT, RHOB, and UBC respectively.

These genes are known to highly affect the cell

metastasis of patient with BHDS and play crucial role

in Cytoskeletal protein binding, as these hub nodes

control the regulation of cellular action and

modulation of cytoskeletal structures.

In conclusion, this study gave significantly fresh

insights for further examination on topics of diagnosis

and the widening berth of therapeutic modalities for

BHDS.

REFERENCES

Allen, A., Gau, D., Francoeur, P., Sturm, J., Wang, Y.,

Martin, R., … Roy, P. (2020). Actin-binding protein

profilin1 promotes aggressiveness of clear-cell renal

cell carcinoma cells. Journal of Biological Chemistry.

https://doi.org/10.1074/jbc.RA120.013963

Caliskan, A., Andac, A. C., & Arga, K. Y. (2020). Novel

molecular signatures and potential therapeutics in renal

cell carcinomas: Insights from a comparative analysis

of subtypes. Genomics. https://doi.org/10.1016/

j.ygeno.2020.06.003

Carlo, M. I., Hakimi, A. A., Stewart, G. D., Bratslavsky, G.,

Brugarolas, J., Chen, Y. B., … Coleman, J. A. (2019).

Familial Kidney Cancer: Implications of New

Syndromes and Molecular Insights. European Urology.

https://doi.org/10.1016/j.eururo.2019.06.015

Chen, F., Zhang, Y., Şenbabaoğlu, Y., Ciriello, G., Yang,

L., Reznik, E., … Creighton, C. J. (2016). Multilevel

Genomics-Based Taxonomy of Renal Cell Carcinoma.

Cell Reports. https://doi.org/10.1016/j.celrep.2016.

02.024

Chen, Y. Y., Hu, H. H., Wang, Y. N., Liu, J. R., Liu, H. J.,

Liu, J. L., & Zhao, Y. Y. (2020). Metabolomics in renal

cell carcinoma: From biomarker identification to

pathomechanism insights. Archives of Biochemistry

and Biophysics. https://doi.org/10.1016/j.abb.

2020.108623

Bioinformatics Analysis of Gene Targets for Birt-Hogg-Dube Syndrome Associated with Renal Cell Cancer using NetworkAnalyst

Fisher, R., Gore, M., & Larkin, J. (2013). Current and future

systemic treatments for renal cell carcinoma. Seminars

in Cancer Biology. https://doi.org/10.1016/

j.semcancer.2012.06.004

Gampel, A., & Mellor, H. (2002). Small interfering RNAs

as a tool to assign Rho GTPase exchange-factor

function in vivo. Biochemical Journal. https://doi.

org/10.1042/BJ20020844

Gutierrez, E., Cahatol, I., Bailey, C. A. R., Lafargue, A.,

Zhang, N., Song, Y., … Zhang, C. (2019). Regulation

of RhoB gene expression during tumorigenesis and

aging process and its potential applications in these

processes. Cancers. https://doi.org/10.3390/cancer

s11060818

Haga, R. B., & Ridley, A. J. (2016). Rho GTPases:

Regulation and roles in cancer cell biology. Small

GTPases. https://doi.org/10.1080/21541248.2016.123

2583

Hong, C., Moorefield, K. S., Jun, P., Aldape, K. D.,

Kharbanda, S., Phillips, H. S., & Costello, J. F. (2007).

Epigenome scans and cancer genome sequencing

converge on WNK2, a kinase-independent suppressor of

cell growth. Proceedings of the National Academy of

Sciences of the United States of America.

https://doi.org/10.1073/pnas.0700683104

Ju, J. A., & Gilkes, D. M. (2018). Rhob: Team oncogene or

team tumor suppressor? Genes. https://doi.org/

10.3390/genes9020067

Li, F., Jin, Y., Pei, X., Guo, P., Dong, K., Wang, H., …

Wang, Z. (2021). Bioinformatics analysis and

verification of gene targets for renal clear cell

carcinoma. Computational Biology and Chemistry.

https://doi.org/10.1016/j.compbiolchem.2021.107453

Linehan, W. M., Srinivasan, R., & Schmidt, L. S. (2010).

The genetic basis of kidney cancer: A metabolic

disease. Nature Reviews Urology. https://doi.org/

10.1038/nrurol.2010.47

Martinez-Anton, A., Gras, D., Bourdin, A., Dubreuil, P., &

Chanez, P. (2019). KIT as a therapeutic target for non-

oncological diseases. Pharmacology and Therapeutics.

https://doi.org/10.1016/j.pharmthera.2018.12.008

Moch, H., Cubilla, A. L., Humphrey, P. A., Reuter, V. E.,

& Ulbright, T. M. (2016). The 2016 WHO

Classification of Tumours of the Urinary System and

Male Genital Organs—Part A: Renal, Penile, and

Testicular Tumours. European Urology. https://doi.

org/10.1016/j.eururo.2016.02.029

Moch, H., & Ohashi, R. (2021). Chromophobe renal cell

carcinoma: current and controversial issues. Pathology.

https://doi.org/10.1016/j.pathol.2020.09.015

Murphy, E., Burns, A., Murtagh, F. E. M., Rooshenas, L.,

& Caskey, F. J. (2021). The Prepare for Kidney Care

Study: prepare for renal dialysis versus responsive

management in advanced chronic kidney disease.

Nephrology, Dialysis, Transplantation : Official

Publication of the European Dialysis and Transplant

Association - European Renal Association.

https://doi.org/10.1093/ndt/gfaa209

Nickerson, M. L., Warren, M. B., Toro, J. R., Matrosova,

V., Glenn, G., Turner, M. L., … Schmidt, L. S. (2002).

Mutations in a novel gene lead to kidney tumors, lung

wall defects, and benign tumors of the hair follicle in

patients with the Birt-Hogg-Dubé syndrome. Cancer

Cell. https://doi.org/10.1016/S1535-6108(02)00104-6

Olson, M. F., & Sahai, E. (2009). The actin cytoskeleton in

cancer cell motility. Clinical and Experimental

Metastasis. https://doi.org/10.1007/s10585-008-9174-2

Pacheco, A., & Gallo, G. (2016). Actin filament-microtubule

interactions in axon initiation and branching. Brain

Research Bulletin. https://doi.org/10.1016/j.brain

resbull.2016.07.013

Radici, L., Bianchi, M., Crinelli, R., & Magnani, M. (2013).

Ubiquitin C gene: Structure, function, and transcriptional

regulation. Advances in Bioscience and Biotechnology.

https://doi.org/10.4236/abb.2013.412141

Sie, Z. L., Li, R. Y., Sampurna, B. P., Hsu, P. J., Liu, S. C.,

Wang, H. D.,Yuh, C. H. (2020). WNK1 kinase stimulates

angiogenesis to promote tumor growth and metastasis.

Cancers. https://doi.org/10.3390/cancers12030575

Singh, D. (2021). Current updates and future perspectives

on the management of renal cell carcinoma. Life

Sciences. https://doi.org/10.1016/j.lfs.2020.118632

Tejada-Simon, M. V. (2015). Modulation of actin dynamics

by Rac1 to target cognitive function. Journal of

Neurochemistry. https://doi.org/10.1111/jnc.13100

Thompson, R. H., Ordonez, M. A., Iasonos, A., Secin, F. P.,

Guillonneau, B., Russo, P., & Touijer, K. (2008). Renal

Cell Carcinoma in Young and Old Patients-Is There a

Difference? Journal of Urology. https://doi.org/1

0.1016/j.juro.2008.06.037

Xia, J., Benner, M. J., & Hancock, R. E. W. (2014).

NetworkAnalyst - Integrative approaches for protein-

protein interaction network analysis and visual

exploration. Nucleic Acids Research. https://doi.

org/10.1093/nar/gku443

Xia, J., Gill, E. E., & Hancock, R. E. W. (2015).

NetworkAnalyst for statistical, visual and network-

based meta-analysis of gene expression data. Nature

Protocols. https://doi.org/10.1038/nprot.2015.052

Yamazaki, K., Sakamoto, M., Ohta, T., Kanai, Y., Ohki,

M., & Hirohashi, S. (2003). Overexpression of KIT in

chromophobe renal cell carcinoma. Oncogene.

https://doi.org/10.1038/sj.onc.1206153

Zhang, Z., Xu, X., Zhang, Y., Zhou, J., Yu, Z., & He, C.

(2009). LINGO-1 interacts with WNK1 to regulate

nogo-induced inhibition of neurite extension. Journal

of Biological Chemistry. https://doi.org/10.1074/

jbc.M808751200

Zhou, G., Soufan, O., Ewald, J., Hancock, R. E. W., Basu,

N., & Xia, J. (2019). NetworkAnalyst 3.0: A visual

analytics platform for comprehensive gene expression

profiling and meta-analysis. Nucleic Acids Research.

https://doi.org/10.1093/nar/gkz240

Zhou, L., Li, Y., Li, Z., & Huang, Q. (2020). Mining

therapeutic and prognostic significance of STATs in

renal cell carcinoma with bioinformatics analysis.

Genomics. https://doi.org/10.1016/j.ygeno.2020.06.032

BIOINFORMATICS 2022 - 13th International Conference on Bioinformatics Models, Methods and Algorithms