Hu et al BMC Systems Biology 2018 12(Suppl 4)54
httpsdoiorg101186s129180180566x
RESEARCH Open Access
KFfinder identification of key factors
from hostmicrobial networks in cervical
cancer
Jialu Hu12 Yiqun Gao1 Yan Zheng1 and Xuequn Shang1*
From The 11th International Conference on Systems Biology (ISB 2017)
Shenzhen China 1821 August 2017
Abstract
Background The human body is colonized by a vast number of microbes Microbiota can benefit many normal life
processes but can also cause many diseases by interfering the regular metabolism and immune system Recent
studies have demonstrated that the microbial community is closely associated with various types of cell carcinoma
The search for key factors which also refer to cancer causing agents can provide an important clue in understanding
the regulatory mechanism of microbiota in uterine cervix cancer
Results In this paper we investigated microbiota composition and gene expression data for 58 squamous and
adenosquamous cell carcinoma A hostmicrobial covariance network was constructed based on the 16s rRNA and
gene expression data of the samples which consists of 259 abundant microbes and 738 differentially expressed genes
(DEGs) To search for risk factors from hostmicrobial networks the method of bipartite betweenness centrality (BpBC)
was used to measure the risk of a given node to a certain biological process in hosts A webbased tool KFfinder was
developed which can efficiently query and visualize the knowledge of microbiota and differentially expressed genes
(DEGs) in the network
Conclusions Our results suggest that prevotellaceade tissierellaceae and fusobacteriaceae are the most abundant
microbes in cervical carcinoma and the microbial community in cervical cancer is less diverse than that of any other
boy sites in health A set of key risk factors anaerococcus hydrogenophilaceae eubacterium PSMB10 KCNIP1 and KRT13
have been identified which are thought to be involved in the regulation of viral response cell cycle and epithelial cell
differentiation in cervical cancer It can be concluded that permanent changes of microbiota composition could be a
major force for chromosomal instability which subsequently enables the effect of key risk factors in cancer All our
results described in this paper can be freely accessed from our website at httpwwwnwpubioinformaticscomKF
finder
Keywords 16s rRNA Hostmicrobial network Cervical carcinoma
Background
Cervical cancer is the second most common cancer in
women [1] Over 500000 women worldwide die of cer
vical cancer each year [2] It is known that a persistent
human papillomavirus (HPV) infection appears to be one
of major causes of cervical carcinoma HPV16 or HPV18
*Correspondence shang@nwpueducn
1School of Computer Science Northwestern Polytechnical University West
Youyi Road 127 710072 Xi’an China
Full list of author information is available at the end of the article
has been found in more than 70 of cases [3–5] These
oncogenic HPVs are also common risk factors in some
other cancers such as head and neck cancers [6] How
ever there are still gaps in the knowledge of cervical
cancer to answer the question of why HPV is necessary
to cause cell carcinoma although it is not a sufficient
requirement [1 7]
Thanks to the advent of highthroughput technolo
gies researchers are able to analyze the cervical car
cinogenesis at the genomic level using sequencing data
© The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 40
International License (httpcreativecommonsorglicensesby40) which permits unrestricted use distribution and
reproduction in any medium provided you give appropriate credit to the original author(s) and the source provide a link to the
Creative Commons license and indicate if changes were made The Creative Commons Public Domain Dedication waiver
(httpcreativecommonsorgpublicdomainzero10) applies to the data made available in this article unless otherwise statedHu et al BMC Systems Biology 2018 12(Suppl 4)54 Page 42 of 166
[8] Genomewide association studies and subsequent
metaanalyses showed that differentially expressed genes
(DEGs) in cervical cancer are more likely to locate in
the region of frequent chromosomal aberration [9–12]
It indicates that cancer may strongly associate with the
chromosomal instability [13] A recent study suggests that
microbiota might play important roles in the develop
ment of cervical cancer [14] There exists a significant
difference in microbiota’s diversity between noncervical
lesion (NCL) HPV negative women and these with cer
vical cancer Further compared to the microbial commu
nity in NCLHPV negative ones these in cervical cancer
samples have higher variation within groups All these
findings implicate that cervical microbiota is an impor
tant clue in the research of the cervical cancer pathology
In order to understand how the microbial community
interplay with host genes and cause cell carcinoma in the
molecular level more and more research groups make
efforts of identify key factors also known as cancer
causing agents which can drive the progress of cervical
carcinogenesis
Microbiota is a possible suspect causing the frequent
gains and losses in chromosome It is abundantly dis
tributed in women cervices They are involved in many
of the host’s normal life processes but also can destroy
the host’s normal gene regulatory network by gene trans
fer which may activate oncogene expression and lead to
cancer [15] Therefore many researchers take efforts to
study how the human microbiota cause structural varia
tion of human genomes and alter the immune system and
metabolic system to support the development of cervi
cal pathogenesis [16] Permanent changes of microbiota
may be a major cause of chromosomal instability subse
quently discharge the tumor suppressor gene retinoblas
toma (RB) and tumor protein TP53 Some association
measures can be used to build a covariance network for
microbes and host genes [17] Hostmicrobial networks
provide a systematic way to study the regulation system
between microbiota and host genes [18] However the
role of host response to the change of microbiome in
cervical cancer is still unknown And there are only a
few public tools specifically designed for analyzing host
microbial networks [19–21] Therefore there is a pressing
demand to develop fast and efficient computational tools
to examine how microbiota regulate the gene expression
chromosomal instability and cell carcinoma
As a remedy for these limitations we proposed a new
computational framework to identify the key risk factors
using 16s rRNA and gene expression data of 58 squamous
and adenosquamous cell carcinoma in uterine cervix A
series of metaanalyses was performed which include
error correction spearman rank correlation differential
expression analysis and bipartite betweenness central
ity A webbased tool KFfinder was developed which can
provide users a fastandeasy way to query and visualize
the knowledge of microbiota and genes in cervical cancer
Further a set of novel risk factors were identified that may
give helpful suggestions for these researchers focusing on
drug design and pharmacology
Methods
In order to investigate gene expression and microbiome
composition in cervical cancer we collected 133 squa
mous and adenosquamous cell carcinoma samples 58 out
of which were used for microbial DNA library prepa
ration The 16s rRNA sequencing was performed using
Illumina MiSeq Human gene expression was quantified
using WG6 BeadArray
OTU assignment
Each 16s sequence was assigned to an operational taxo
nomic unit (OTU) To count the reads number for each
OTU (microbe) 16s sequences obtained from MiSeq
were aligned to the reference Greengene OTU builds
The Qiime script assigne_taxonomypy (see more at
httpqiimeorgscriptsassign_taxonomyhtml)wasper
formed in the data processing Reference sequences are
preassigned with OTU described in the id_to_taxonomy
file Any sequence alignment tools such as uclust Sort
MeRNA blast RDP Mothur etc can be called by
the assign_taxonomy script for the sequence alignment
between the 16s sequences and reference sequences For
example the script will assign taxonomy with the uclust
consensus taxonomy assigner by default using the follow
ing command assign_taxonomypy i repr_set_seqsfasta r
ref_seq_setfna t id_to_taxonomytxtOTUredundancy
matrix was normalized from the sequence number of each
sample Since these less abundant microbes are unlikely to
be a destroying force for host immune system we selected
the top259 most abundant OTUs for further studying
Comparison with the controls
To study the remarkable difference of microbiota between
cancer cases and the controls we compared our 16s
raw data to those data from 300 healthy human sub
jects released by Human Microbiome Project (HMP)
[22](httpwwwhmpdaccorg) To find a map between
OTUs from our data and OTUs from healthy data a
commonly used alignment tool blastn was performed to
compare their representative sequences These pairs with
evalue<1e5 and pident>80 were used for establishing
the map These OTUs matched with a same OTU in HMP
were collapsed into one OTU The Qiime scripts were
performed to analyze the 16s raw data [23]
Calculation of correlation
Abundant microbes and DEGs were selected for recon
structing hostmicrobial networks DEGs in cervicalHu et al BMC Systems Biology 2018 12(Suppl 4)54 Page 43 of 166
cancer were collected from published data [9] which
were verified in five cohorts of tumor and normal sam
ples Hence the DEGs are more reliable than these
obtained from only one cohort The spearman rank corre
lation method was employed to calculate the correlation
between each pair of nodes Note that the gene expres
sion data and 16s rRNA were tested on the same sam
ple Therefore the spearman correlation in the network
makes sense In contrast to pearson correlation spearman
correlation coefficient can efficiently avoid the environ
mental noise and experimental errors caused from the
nonuniform samples
Error correction
To improve the confidence of the hostmicrobial network
calculated by spearman correlation we removed these
edges that are less likely to be a true one (false positive
errors) and added some new edges that are very likely to
correlate with each other (false negative errors) The false
positive edges include two scenes 1) these negatively cor
related edges that connected two interactors with a same
type of regulation (ie both of them are up regulated or
down regulated) 2) these positive correlated edges that
connected two interactors with different types of regula
tion (ie one is up regulated the other down regulated)
3) selfloops 4) multiple loops All these false positive
edges are removed in our network These false negative
edges are these pairs of nodes between OTUs and DEGs
which satisfying two conditions 1) the OTU was collapsed
from a set of subnodes 2) all these subnodes strongly
correlated with the DEG All these false negative edges
were added in the hostmicrobial network False positive
and false negative edges were detected and corrected
according to the coherence of regulation and correla
tion relationships A workflow of the reconstruction of
hostmicrobial network was illustrated in Fig 1
Bipartite betweenness centrality
To search for risk factors from hostmicrobial network bi
partite betweenness centrality (BpBC) [24] adapted from
betweenness centrality was used to quantify the risk of
a given node written as g(v) The definition can be for
matted as g(v) 
st δst(v)δst Here s and t are two
nodes from two separate subnetworks And δst represents
the number of shortest paths from s to t δst(v) the num
ber of shortest paths going through node v from s to t
Given a node v g(v) reflects the probability of how likely a
shortest path could go through v from one subnetwork to
another
Results and discussion
Composition of the microbiota
To study the microbial community in cervical cancer we
examined the 16s raw data of cancer cases and assigned
taxonomy to each sequence The definition of opera
tional taxonomic unit (OTU) was used to classify groups
of closely related microbiome based on sequence simi
larity Reference data sets and idtoOTU maps for 16s
rRNA sequence was downloaded from the Greengenes
reference OTU builds [25] All these sequences were
grouped into different categories based on their family
level OTU labels As shown in Fig 2 prevotellaceade
followed by tissierellaceae appears to be the most abun
dant microbes accounting for 137 of the microbiota
Fig 1 A workflow of the reconstruction of hostmicrobial network Through the comparison between 16s rRNA and HMP data each sequence was
mapping to an operational taxonomic unit (OTU) Error correction was performed for these false positive and false negative nodes which were
detected according to the coherence of regulation and correlationHu et al BMC Systems Biology 2018 12(Suppl 4)54 Page 44 of 166
Fig 2 The microbial community in cervical carcinoma Each 16s rRNA sequence was assigned to an operational taxonomic unit (OTU) and all these
sequences were grouped into different categories based on their familylevel OTU labels
community There are four other groups accounting for
more than 5 of the microbiota which are fusobacte
riaceae porphyromonadaceae planococcaceae and bac
teroidaceae Totally twentysix familylevel OTU groups
make up more than 87 of the whole community To
examine the diversity of cervical microbiota the PCoA
analysis was performed to analyze the microbial commu
nity in cervical carcinoma skin mouth and vagina As
shown in Fig 3 microbiota in cervical carcinoma (red
dots) is less diverse than microbiota in any other body
sites Hence we indeed found remarkable changes of
microbial composition in the cancer cases
Reconstruction of hostmicrobial network
A hostmicrobial network was reconstructed from the 16s
raw data and gene expression data Nodes in the net
work refer to microbes or DEGs edges the regulation
relationships between each pair of microbes Two nodes
were connected if and only if they are strongly corre
lated (ie |γ | > 04 and pvalue < 005) As show in
Fig 4 a network with 997 nodes was connected by 4262
edges Nodes in the network consist of 259 microbes
and 738 DEGs We grouped all the DEGs into four cat
egories named as cell cycle antiviral response epithelial
cell differential and the other DEGs according to their
function in the development of cervical cancer The three
functional DEGs groups (excluding the other DEGS) are
three major densely connected subnetworks in the host
microbial networks They are functionally enriched by
GO terms cell cycle response to virus epithelial cell
differentiation respectively They don’t have any over
lap between each pair of groups In the whole network
403 edges are negatively correlated 3859 positively cor
related Negative correlation indicates inhibition between
two biological subjects In a negative correlation one vari
able increases as the other decreases Positive correlation
Fig 3 Principal Coordinates Analysis (PCoA) plot of microbial community for samples from cervical carcinoma skin mouth and vagina The red
green orange and blue dots represent samples from cervical carcinoma skin mouth and vagina respectivelyHu et al BMC Systems Biology 2018 12(Suppl 4)54 Page 45 of 166
Fig 4 An illustration of the hostmicrobial network Nodes refer to
differentially expressed genes (DEGs) or abundant microbes edges
the regulation relationship between DEGs and microbes Nodes in
pink are up regulated and these in cyan are down regulated Edges in
grey are positively correlated and these in green are negatively
correlated
indicates activation or coexistence between two subjects
of interest In a positive correlation one variable increases
as the other increase or one variable decreases while the
other decreases This network integrates all the regulation
relationships between host genes and microbiota
Risk factors in cervical cancer
The risk factors in cancer may activate oncogene expres
sion and cause a series of functional disorder in metabolic
and immune systems In the development of cancer the
most remarkable differences between tumor and normal
samples are 1) the upregulation of viral responses 2) the
speedup in the progression of cell cycle 3) the inhibition
of epithelial cell differentiation To study how microbiota
regulates the viral response cell cycle and epithelial cell
differentiation we searched for key risk factors using
BpBC These key factors are thought to be cancercausing
agents that can drive the progress of cervical carcinogen
esis Nodes that organizing communication between two
cancerrelated groups are more likely to be key factors
Since BpBC is such a measure to evaluate the impor
tance of a node in the network topology we choose these
nodes in the top list of BpBC as candidates of key fac
tors These key factors with high BpBC value may play
crucial roles in the communication between two different
subnetworks
The results show that Anaerococcus (labeled as
OTU_9718428) and proteasome subunit beta 10
(PSMB10) are significantly higher than the others (see
in Fig 5 left) between the subnetworks of microbe and
antiviral response genes PSMB10 was an upregulated
gene in cervical cancer Between the subnetworks of
microbe and cell cycle KCNIP1 and Hydrogenophi
laceae (labeled as OTU_972777) are the most important
regulators (see in Fig 5 middle) Eubacterium (labeled
as OTU_9710051) and KRT13 are the most important
regulators between the subnetworks of microbes and
epithelial cell differentiation (see in Fig 5 right) It proves
that the interplay between microbiota and differentially
expressed genes might be the driving force that regulates
the progress of cell cycle epithelial cell differentiation
and viral response
Query and visualization
In order to fast and easily query and visualize the host
microbial networks we developed a webbased tool KF
finder Multiple web programming languages were used
in the development which includes PHP mysql and
javascripts Each node and its neighborhood in the net
work can be searched by a query term in the panel of
Search And the induced subnetwork will be visualized in
the panel of View For example one can input a gene sym
bol CYP2A7 as a query term in the Search panel A list
of nodes associated with CYP2A7 will show out in a user
friendly panel as well as a graphic view of the induced sub
network (see in Fig 6) Except for visualization and query
KFfinder can also sort microbes and DEGs in a decreas
ing order by the value of BpBC in microbeantivirus
microbecell cycle or microbeepithelial cell differentia
tion Download and advanced search have been enabled
on the web server All our test datasets and results of users’
Fig 5 Risk factors in hostmicrobial network in cervical cancer The BpBC value of each node was calculated for three pairs of different
subnetworks including microbeantivirus microbecell cycle and microbeepithelial cell differentiationHu et al BMC Systems Biology 2018 12(Suppl 4)54 Page 46 of 166
Fig 6 A graphic view of the induced subnetwork of CYP2A7 The subnetwork includes interactions between CYP2A7 and its neighbors
interactions between its neighbors
personal jobs can be downloaded Advanced search allows
us search for genes and microbes based on string patterns
or value constriction KFfinder enables us to query and
visualize the knowledge of hostmicrobial network in a
fastandeasy way It can be accessed at httpwwwnwpu
bioinformaticscomKFfinder
A case study of PSMB10 in cervical cancer
Most vertebrates express immunoproteasomes (IPs) that
possess three IFNγ inducible homologues PSMB8
PSMB9 and PSMB10 Many studies show that expres
sion of IP genes including PSMB10 is upregulated in
most cancer types [26] IP genes can be expressed
by nonimmune cell and that differential cleavage of
transcription factors by IPs has pleiotropic effects on
cell function Indeed IPs modulate the abundance of
transcription factors that regulate signaling pathways
with prominent roles in cell differentiation inflamma
tion and neoplastic transformation (eg NFkB IFNs
STATs and Wnt) [27] Therefore PSMB10 is indeed a
risk factor involved in the antiviral response of cervical
caner
A case study of KRT13 in cervical cancer
KRT13’s full name is keratin 13 in human also known
as K13 and CK13 located in a region of chromosome
17q212 It is a downregulated gene in cervical carci
noma and a risk factor that involves in the progress of
uncontrolled epithelial cell differentiation Previous work
suggests that the loss of K13 or low K13 mRNA expression
is associated with invasive oral squamous cell carcinoma
(OSCC) [28 29] Epigenetic alteration of K13 is one major
reason resulting the inhibition of K13 in OSCC Besides
K13 was also reported that it played a directive role in
prostate cancer bone brain and soft tissue metastases
[30] More than 1000 single nucleotide polymorphisms
of K13 were found in the dbSNP database Totally 51
variations mentioned K13 in ClinVar seven out of which
are pathogenic All these evidences suggest KRT13 is
very likely to be a key risk factor involved in cervical
cancer
Conclusions
In this paper we examined the microbiota composition
and gene expression in 58 squamous and adenosquamousHu et al BMC Systems Biology 2018 12(Suppl 4)54 Page 47 of 166
cell carcinoma A hostmicrobial network was recon
structed from the 16s rRNA and gene expression data
The main contributions of this paper can be concluded
in three aspects (1) microbial community distributed in
cervical carcinoma cells is less diverse than that of other
body sites (2) a webbased tool MiteFinder was developed
which enables users to query and visualize hostmicrobial
networks microbes and differentially expressed genes in
a fastandeasy way (3) a set of key risk factors have been
identified which have proven to have association with
cancers in several previous publications Our results show
that six groups of OTU abundantly distributed in cervical
cancer samples including prevotellaceade tissierellaceae
fusobacteriaceae porphyromonadaceae planococcaceae
and bacteroidaceae Besides these six groups of OTU we
found that three differentially expressed genes and three
microbes may be key risk factors and play crucial roles in
the pathology of cervical carcinoma All of these results
suggest that permanent changes of microbiota compo
sition might be the key driving force in the pathology
of cervical carcinoma which result in the abnormal
ity of epithelial cell differentiation cell cycle and viral
response
Acknowledgements
Not applicable
Funding
This project has been funded by the National Natural Science Foundation of
China (Grant No 61332014 and 61702420) the China Postdoctoral Science
Foundation (Grant No 2017M613203) the Natural Science Foundation of
Shaanxi Province (Grant No 2017JQ6037) the Fundamental Research Funds
for the Central Universities (Grant No 3102015QD013) The publication
charges come from the National Natural Science Foundation of China (Grant
No 61702420)
Availability of data and materials
All the results can be found at httpwwwnwpubioinformaticscomKF
finder
About this supplement
This article has been published as part of BMC Systems Biology Volume 12
Supplement 4 2018 Selected papers from the 11th International Conference
on Systems Biology (ISB 2017) The full contents of the supplement are
available online at httpsbmcsystbiolbiomedcentralcomarticles
supplementsvolume12supplement4
Authors’ contributions
JH designed the computational framework performed all the analyses of the
data and wrote the manuscript YG and YZ developed the webbased tool
KFfinder to query and visualize the hostmicrobial network XS is the major
coordinator who contributed a lot of time and efforts in the discussion of this
project
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
Not applicable
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations
Author details
1School of Computer Science Northwestern Polytechnical University West
Youyi Road 127 710072 Xi’an China 2Centre of Multidisciplinary Convergence
Computing School of Computer Science Northwestern Polytechnical
University Dong Xiang Road 1 710129 Xi’an China
Published 24 April 2018
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