Abstract
Autophagy is a complex cellular digestion process involving multiple
regulators. Compared to post-translational autophagy regulators,
limited information is now available about transcriptional and
post-transcriptional regulators such as transcription factors (TFs) and
non-coding RNAs (ncRNAs). In this study, we proposed a computational
method to infer novel autophagy-associated TFs, micro RNAs (miRNAs) and
long non-coding RNAs (lncRNAs) based on TFs and ncRNAs coordinated
regulatory (TNCR) network. First, we constructed a comprehensive TNCR
network, including 155 TFs, 681 miRNAs and 1332 lncRNAs. Next, we
gathered the known autophagy-associated factors, including TFs, miRNAs
and lncRNAs, from public data resources. Then, the random walk with
restart (RWR) algorithm was conducted on the TNCR network by using the
known autophagy-associated factors as seeds and novel autophagy
regulators were finally prioritized. Leave-one-out cross-validation
(LOOCV) produced an area under the curve (AUC) of 0.889. In addition,
functional analysis of the top 100 ranked regulators, including 55 TFs,
26 miRNAs and 19 lncRNAs, demonstrated that these regulators were
significantly enriched in cell death related functions and had
significant semantic similarity with autophagy-related Gene Ontology
(GO) terms. Finally, extensive literature surveys demonstrated the
credibility of the predicted autophagy regulators. In total, we
presented a computational method to infer credible autophagy regulators
of transcriptional factors and non-coding RNAs, which would improve the
understanding of processes of autophagy and cell death and provide
potential pharmacological targets to autophagy-related diseases.
Keywords: autophagy regulator, transcriptional factor, non-coding RNA,
regulatory network, RWR algorithm
1. Introduction
Autophagy is a process of cytoplasmic degradation that is essential in
homeostasis and stress-response, as well as in protein degradation and
organelles turnover [[42]1]. The regulation of autophagy is critical in
human health and disease. Both its insufficient and overdriven activity
can disturb the body functions, including causing cancers. For example,
autophagy deficiency causes oxidative stress and genome instability
which is a known cause of cancer initiation and progression [[43]2] and
up-regulation of autophagy in RAS-transformed cancer cells promotes
their growth, survival, tumorigenesis invasion, and metastasis [[44]3].
The process of autophagy involves multiple kinds of regulators,
including autophagy-related (ATG) genes, ATG proteins and non-coding
RNAs (ncRNAs). For instance, the autophagy database archived a list of
582 experimentally demonstrated ATG proteins [[45]4] and Wu et al.
provided a comprehensive bioinformatics resource to dissect
ncRNA-mediated autophagy interactions [[46]5]. In addition, regulation
of autophagy by targeting autophagy regulators is a promising strategy
for cancer therapy [[47]6]. For example, temsirolimus could
significantly prolong progression-free survival of mantle cell lymphoma
(MCL) patients by inhibiting the mechanistic target of rapamycin (mTOR)
protein, a post-translational autophagy regulator [[48]7].
Currently, post-translational autophagy regulators, such as ATG
proteins, are well known while limited information is available about
transcriptional and post-transcriptional regulators, such as
transcription factors (TFs) and ncRNAs [[49]8]. Inferring that novel
transcriptional and post-transcriptional autophagy regulators will help
to dissect the autophagy regulation mechanisms and provide possible
pharmacological targets to regulate autophagy. The TFs and ncRNAs
coordinated regulatory (TNCR) network has demonstrated its power as a
tool to study biological issues such as regulatory pathways in human
diseases, classifiers for drug resistance and so on
[[50]9,[51]10,[52]11]. For example, Liang et al. performed
deconvolution on the transcriptional network and demonstrated that
BACH1 was the master regulator of breast cancer bone metastasis
[[53]12]. Wang et al. identified disease-related regulatory cascades by
dissecting the TF and miRNA regulatory network, which helped understand
the pathogenesis [[54]13]. Recently, lncRNAs were found to be targeted
by miRNAs and functioned as miRNA sponges to attenuate the inhibition
ability of miRNAs to mRNAs. Furthermore, lncRNAs were also shown to
play crucial roles in the regulation of gene expression at
transcriptional and post-transcriptional levels [[55]14,[56]15]. Thus,
lncRNAs introduce an extra layer of complexity to the TNCR network,
enhancing the analytical ability of the regulatory network.
In this study, we proposed a computational method to predict novel
autophagy-associated TFs, miRNAs and lncRNAs based on the TNCR network.
First, experimentally verified transcriptional and post-transcriptional
regulatory relationships among TFs, miRNAs and lncRNAs were collected
and a comprehensive regulatory network was constructed. Next, the known
autophagy-associated TFs, miRNAs and lncRNAs were gathered from public
data resources. The random walk with restart (RWR) algorithm was
implemented on the regulatory network to prioritize autophagy
regulators. Leave-one-out cross-validation (LOOCV) achieved an area
under the curve (AUC) of 0.889. Functional enrichment analyses and
extensive literature surveys demonstrated the credibility of predicted
regulators. Altogether, we presented a computational method of
inferring credible autophagy regulators and we believed that this would
help improve the understanding of the autophagy regulation mechanisms.
2. Materials and Methods
2.1. Construction of a Comprehensive TNCR Network
We integrated five types of experimentally verified transcriptional and
post-transcriptional regulatory relationships among TFs, miRNAs and
lncRNAs, including TF-miRNA, TF-lncRNA, miRNA-lncRNA, miRNA-TF,
lncRNA-TF. The TFs regulations of miRNAs were downloaded from the
database TransmiR, which manually surveyed literature and recorded
experimentally supported TF-miRNA regulation [[57]16]. The TFs
regulations of lncRNAs were obtained from the database ChIPBase, which
decoded the transcriptional regulation of lncRNAs from ChIP-seq data in
diverse tissues and cell lines [[58]17]. Here, only TF-lncRNA
regulations that were identified in more than 20 datasets were
retained. In order to improve the credibility of the regulations, we
also used the TRANSFAC Match program to assure transcription factor
binding sites (TFBS) in lncRNA sequences [[59]18] using minimum
false-positive profiles of vertebrate high quality matrices. The final
TF-lncRNA regulations were obtained by intersecting the ChIPBase data
source with the TRANSFAC results. The miRNAs regulations of TFs were
integrated from two databases, miRecords [[60]19] and miRTarBase
[[61]20]. Both of these two databases collected experimentally
validated miRNA-target interactions, and we retained the union set of
the relationships presented in these two databases. The miRNAs
regulations of lncRNAs were derived from LncBase v2 which provided
experimentally supported and in silico predicted miRNA recognition
elements (MREs) on lncRNAs [[62]21]. We retained the interactions
presented in the experimental module and the prediction scores should
have been equal to or greater than 0.95. The lncRNAs regulations of TFs
were downloaded from LncReg [[63]22] and LncRNA2Target [[64]23]. The
database LncReg collected validated lncRNA-associated regulatory
entries while LncRNA2Target curated differentially expressed genes
after the lncRNA knockdown or overexpression. We kept the union set of
the lncRNAs regulations of TFs which were provided by these two
databases. Integrating all of the above regulations, we constructed a
comprehensive TNCR network.
2.2. Collection of Known Autophagy Regulators
The known autophagy-associated TFs, miRNAs and lncRNAs were collected
from public data resources. We first obtained human genes in autophagy
related Gene Ontology (GO) terms from the AmiGO-2 database. Next, we
downloaded the human autophagy-associated genes from the autophagy
database [[65]4], a multifaceted online resource providing information
on genes and proteins related to autophagy across several eukaryotic
species. The union set of these two gene sets were regarded as known
autophagy-associated genes. As for autophagy-associated miRNAs and
lncRNAs, we resorted to the database ncRDeathDB, a comprehensive
bioinformatics resource archiving ncRNA-associated cell death
interactions and picked up the autophagy-associated miRNAs and lncRNAs
[[66]5]. All the autophagy-associated genes, miRNAs and lncRNAs we
obtained were mapped onto the TNCR network, and the intersections were
regarded as seeds for RWR algorithm.
2.3. Prioritization of Novel Autophagy Regulators with the RWR Method
We performed the RWR method on the constructed TNCR network to
prioritize novel autophagy regulators. The RWR method simulates a
random walker that starts on given seed nodes and transits randomly
from the current node to neighboring nodes in the network with the
restart probability to teleport to the start nodes. Here, the known
autophagy regulators were used as seed nodes. We denoted P[0] as the
initial probability vector and P[t] as a vector in which the i-th
element held the probability of finding the random walker at node i in
step t. Let α be the restart probability of the random walk in each
step at the source nodes. W denotes the probability transition matrix
and is derived from the adjacency matrix of the TNCR network. The
formula is defined as:
[MATH:
w(i<
mo>,j)={A(i<
mo>,j)/∑j
A(i,j)<
/mo>,if ∑j
A(i,j)<
/mo>≠00
,otherwise
:MATH]
(1)
where w (i, j) represents the element in the probability transition
matrix, and A (i, j) represents the element in the adjacency matrix.
The probability vector in step t + 1 can be described as follows:
[MATH:
pt+<
mn>1=(1−<
/mo>α)wp<
mi>t+1+αp0 :MATH]
(2)
Based upon the previous work, the restart probability (α) was set as
0.5, and the initial probability (P[0]) of each seed node was set as
1/n (where n is the number of seed autophagy regulators) while the
initial probability of all non-seed nodes was set as zero
[[67]24,[68]25]. With the iteration steps going on, the probability of
the RWR algorithm will become stable. We defined the stable probability
as
[MATH: P∞
:MATH]
when the difference between P[t] and P[t+1] was less than 10^−10. The
stable probability of
[MATH: P∞
:MATH]
can be used as a measure of proximity to the seed regulators. If
[MATH:
P∞(
nodei)
>P∞(
nodej) :MATH]
, then node[i] will be in closer proximity to the seed regulators in
the regulatory network than node[j]. As a result, all candidate nodes
in the regulatory network can be ranked according to
[MATH: P∞
:MATH]
and the top ranked elements can be expected to have a high probability
of being associated with autophagy.
2.4. Functional Analysis for Predicted Autophagy Regulators
To demonstrate the credibility of the proposed prediction method, we
performed functional analysis for the predicted autophagy regulators.
We first retrieved the top 100 ranked regulator candidates (excluding
seeds), including TFs, miRNAs and lncRNAs, and performed separately the
functional enrichment analyses. For the obtained TFs, we used DAVID to
perform GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
enrichment analysis [[69]26]. For the obtained miRNAs, we collected the
experimentally verified miRNA targets from the miRecords [[70]19] and
miRTarBase [[71]20]; we then used the union set of the miRNA targets to
perform GO and KEGG pathway enrichment analysis with DAVID. For the
obtained lncRNAs, we utilized the recently developed function
annotation tool of non-coding RNA (FARNA), a knowledgebase of inferred
functions of human ncRNA transcripts, to implement function annotation
analysis. We searched the FARNA database by using each obtained lncRNA,
and retrieved promoter-associated transcription factors and
transcription co-factors for the lncRNA. Then, all the obtained
transcription factors and transcription co-factors were inputted into
DAVID to perform GO and KEGG pathway enrichment analysis. In addition,
we also performed GO enrichment analysis for the known
autophagy-associated TFs, miRNAs and lncRNAs separately, as described
above. The union set of the significant GO categories were considered
as the autophagy related GO terms. All these DAVID analyses adopted the
same criteria that the biological process (BP) category was used for GO
analysis, and the significance of enrichment was set at p-value < 0.05.
Finally, we calculated the functional similarity scores between the GO
terms enriched in the predicted autophagy regulators and the autophagy
related GO terms. The computational procedure was implemented using R
package GOSemSim [[72]27] and the rcmax method was chosen as a combined
method for aggregating multiple GO terms. We also performed 1000 random
tests to evaluate the significance of obtained functional similarity
scores. In each random test, we randomly chose the same number of GO
terms as in the real situation and calculated the functional similarity
scores as above. The statistical p-value was calculated as the ratio of
random functional similarity scores higher than the real functional
similarity score.
3. Results
3.1. Characteristics of the TNCR Network
In this study, we integrated five types of experimentally verified
transcriptional and post-transcriptional regulatory relationships from
public data resources and constructed a comprehensive TNCR network (see
Materials and Methods for details). The TNCR network comprised of 4529
edges, including 155 TFs, 681 miRNAs and 1332 lncRNAs ([73]Figure 1A,
[74]Supplementary Table S1). To get an overview of the TNCR network, we
examined the degree distribution of the network. As shown in [75]Figure
1B, most nodes (50.4%) had degree one and few nodes had a high degree.
In addition, the power-law distribution of the forms
[MATH:
y=327.4×10−1.31 (R2=0.823) :MATH]
,
[MATH:
y=157.4×10−1.19 (R2=0.773
) :MATH]
and
[MATH:
y=224.4×10−1.36 (R2=0.774
) :MATH]
were fitted for degree, out-degree and in-degree respectively. These
results indicated that the TNCR network satisfied approximate
scale-free topology which is the common feature of most biological
networks [[76]28]. Next, we further investigated the in-degree and
out-degree distributions for TFs, miRNAs and lncRNAs, respectively
([77]Figure 1C). In general, few nodes had very high degrees and many
had low degrees, regardless of TFs, miRNAs or lncRNAs in-degree and
out-degree. Furthermore, TFs had a higher median in-degree and
out-degree than miRNAs and lncRNAs, which meant that TFs more likely
acted as hubs in the TNCR network.
Figure 1.
[78]Figure 1
[79]Open in a new tab
Characteristics of the TFs and ncRNAs coordinated regulatory (TNCR)
network. (A) Proportion of transcription factor (TF), microRNA (miRNA)
and long non-coding RNA (lncRNA) in the TNCR network. (B) Degree
distribution of all nodes in the TNCR network and the log-log plots for
the degree, out-degree and in-degree distributions of all nodes. (C)
In-degree and out-degree distributions of TFs, miRNAs and lncRNAs in
the TNCR network.
3.2. Performance Evaluation of the Proposed Method
By integrating data from AmiGO-2, the autophagy database and the
ncRDeathDB, we obtained 1222 known autophagy regulators in total
([80]Supplementary Table S2). After mapping these regulators onto the
TNCR network, we finally got 178 autophagy regulators as seeds,
including 25 TFs, 152 miRNAs and 1 lncRNAs ([81]Supplementary Table
S3). By performing the RWR method on the TNCR network with the seeds,
we finally prioritized novel autophagy regulators.
In order to evaluate the performance of our method for inferring
autophagy regulators, we performed LOOCV analysis. Each known autophagy
regulator was left out in turn as the test case and the other known
autophagy regulators were taken as seeds. All the other nodes in the
TNCR network were regarded as candidate autophagy regulators.
Sensitivity and specificity were calculated for each threshold.
Finally, a receiver operating characteristic (ROC) curve was plotted by
varying the threshold and then the value of the AUC was calculated. Our
method, tested on already known autophagy regulators, achieved an AUC
of 0.889 ([82]Figure 2), exhibiting excellent performance. Here, the
TNCR network incorporated three kinds of regulators (TFs, miRNAs and
lncRNAs) and five kinds of regulations (TF-miRNA, TF-lncRNA,
miRNA-lncRNA, miRNA-TF and lncRNA-TF). To demonstrate the effectivity
and reliability of the TNCR network, we compared the performance of
partial TNCR networks. The AUCs were calculated for a TNCR-ML network
(miRNAs and lncRNAs only) and a TNCR-TM network (TFs and miRNAs only)
separately by performing LOOCV (the TNCR-TL network (TFs and lncRNAs
only) was not analyzed because of missing seed regulators). The AUCs
were 0.697 and 0.544 respectively, which were lower than those using
the TNCR network ([83]Figure 2). To further determine whether the
results of the cross validation might have been generated by chance, we
performed randomization tests. The seeds were generated randomly from
candidate nodes in all three networks and the AUC values were
calculated by performing LOOCV, as above. The AUC values under
randomized tests were much lower than those in real situations (0.530,
0.549 and 0.519, respectively, for these three conditions), confirming
the valid and reliable performance of autophagy regulator seeds in our
method ([84]Figure 2). We also performed RWR on 1000 degree-preserving
randomized TNCR networks and the average value of the AUCs was
calculated. As shown in [85]Figure 2, the result based on the real TNCR
network and the real seed nodes performed best.
Figure 2.
[86]Figure 2
[87]Open in a new tab
Receiver operating characteristic (ROC) curves and area under the curve
(AUC) values for the random walk with restart (RWR) method on the
whole, partial and random TNCR networks with real seeds and random
seeds. The ROC curves were plotted and AUC values were calculated
separately by leave-one-out cross-validation (LOOCV) for the TNCR
network, TNCR-ML (miRNAs and lncRNAs only) network, TNCR-TM (TFs and
miRNAs only) network and the random TNCR network with real and random
seeds.
The prioritization of all candidate autophagy regulators is provided in
[88]Supplementary Table S4. The top 100 ranked candidate regulators,
including 55 TFs, 19 miRNAs and 26 lncRNAs, were further validated by
literature mining, in which 52 regulators had been verified to be
associated with autophagy in published papers ([89]Supplementary Table
S5). For example, the fifth ranked regulator MYC was recently proved to
mitigate its oncogenic activity by chaperone-mediated autophagy (CMA)
regulation [[90]29] and the ninth ranked regulator XIST was determined
to increase autophagy activity in non-small-cell lung cancer by
regulation of ATG7 [[91]30]. The extensive literature surveys
demonstrated the feasibility of our method to predict autophagy
regulators.
3.3. Functional Characteristics of Predicted Autophagy Regulators
The top 100 ranked candidate autophagy regulators were retrieved,
including 55 TFs, 19 miRNAs and 26 lncRNAs ([92]Supplementary Table
S4), then the functional analyses were performed separately for these
predicted autophagy regulators (see Materials and Methods for details).
The top 20 significantly enriched GO terms and KEGG pathways for TFs
are shown in [93]Figure 3. We observed that some cell death related GO
terms, such as cell cycle arrest and negative regulation of cell
proliferation, were enriched by these top ranked TFs. Several
significantly enriched KEGG pathways were also related to cell death,
for instance, cell cycle and adherens junction. In addition, some
cancer related pathways, such as colorectal cancer, prostate cancer and
thyroid cancer, were also enriched, indicating that the autophagy
regulators played important roles in cancer. This was consistent with
previous studies [[94]11,[95]31,[96]32]. The top 20 significantly
enriched GO terms and KEGG pathways by the top ranked miRNAs and
lncRNAs are shown in [97]Figure S1 and [98]Figure S2. Similar to the
top ranked TFs, the cell death related GO terms and KEGG pathways, such
as apoptotic process and cell proliferation, were also enriched by top
ranked miRNAs and lncRNAs. Cancer related pathways, such as pancreatic
cancer and small cell lung cancer, were enriched by top ranked miRNAs
and lncRNAs. We observed that there were obvious overlaps among GO
terms and KEGG pathways enriched by top ranked TFs, miRNAs and lncRNAs
([99]Figure 3 and [100]Figure 4A). All of the significantly enriched GO
terms and KEGG pathways (p-value < 0.05) for top ranked TFs, miRNAs and
lncRNAs were shown in [101]Supplemental Table S6.
Figure 3.
[102]Figure 3
[103]Open in a new tab
The top 20 Gene Ontology (GO) enrichment and Kyoto Encyclopedia of
Genes and Genomes (KEGG) enrichment results for top ranked TFs. The
common enriched GO terms and KEGG pathways among top ranked TFs, miRNAs
and lncRNAs are marked.
Figure 4.
[104]Figure 4
[105]Open in a new tab
Evaluation of the top ranked regulators associated with autophagy. (A)
Venn plot for the GO functional annotation comparison among the top
ranked TFs, miRNAs, lncRNAs and the known autophagy-associated factors.
(B) Distribution of random functional similarity scores for the top
ranked TFs and the autophagy-associated factors. The triangle indicates
the true functional similarity score for top ranked TFs and the known
autophagy-associated factors.
To further evaluate the top ranked regulators associated with
autophagy, we compared the GO terms enriched by the top 100 ranked
regulators with those enriched by known autophagy-associated factors
(including protein-coding genes, miRNAs and lncRNAs). As shown in
[106]Figure 4A, the numbers of overlapping enriched GO terms among
top-ranked TFs, miRNAs, lncRNAs and known autophagy-associated factors
were high (the significantly enriched GO terms for known
autophagy-associated factors were shown in [107]Supplemental Table S7).
We calculated the functional similarity scores between the GO terms
enriched by the top 100 ranked regulators and the autophagy related GO
terms. The functional similarity scores between the autophagy related
GO terms and those enriched by top ranked TFs, miRNAs, lncRNAs were
0.970, 0.978 and 0.949, respectively. The random functional similarity
scores for each kind of regulators, which were calculated by randomly
choosing the same number of GO terms as in the real situation, were
significantly lower than the real scores ([108]Figure 4B, [109]Figure
S3). All these p-values were less than 2.2 × 10^−16 (see Materials and
Methods for details). This meant that the top ranked regulators were
significantly associated with autophagy. The functional characteristics
of the top ranked regulators indicated that our method was capable of
identifying novel autophagy regulators.
4. Discussion
Autophagy is an intracellular catabolic process for maintaining
homeostasis and involved systematic regulation at post-translational,
transcriptional, and post-transcriptional levels [[110]33]. Both its
insufficient and overdriven functions can disturb intracellular
homeostasis [[111]34]. Thus, the regulation of autophagy is critical
for body cells normal function. Although the knowledge of autophagy
regulation is making certain progress, the landscape of autophagy
regulators is far from completeness. In addition, autophagy
demonstrates a promising therapeutic target in several pathologies
[[112]35]. Thus, identification of novel autophagy regulators is
beneficial to targeted therapy of complex human diseases. Regulatory
networks provide global views of the transmission of genetic
information, and are proved to be powerful tools for studying
biological issues. In this study, we conducted a computational method
to infer novel autophagy regulators based on the regulatory network. We
first constructed a comprehensive regulatory TNCR network that
incorporated transcriptional and post-transcriptional regulators,
including TFs, miRNAs and lncRNAs. Network topological analysis
revealed that the degree distribution of the TNCR network approximately
followed the power-law distribution. Then, the candidate autophagy
regulators were ranked by implementing the RWR method on the TNCR
network using the known autophagy regulators as seed nodes. The AUC
values determined by LOOCV achieved 0.889, demonstrating the high
credibility of our method for recovering known autophagy regulators.
Furthermore, functional enrichment analyses revealed that the predicted
autophagy regulators were associated with cell death related functional
categories such as negative regulation of cell proliferation, cell
death and cell cycle arrest. Significantly high functional semantic
similarity scores were obtained between the obtained GO terms and the
autophagy related GO terms. In addition, extensive literature surveys
demonstrated that the top ranked regulators were verified to have
associations with autophagy. All these results indicate that our
approach is effective in inferring transcriptional and
post-transcriptional autophagy regulators and that it would help to
improve the understanding of the autophagy regulation mechanisms.
In the past several years, the landscape of TNCR networks has been
described elaborately [[113]12,[114]13]. Several experimentally
verified transcriptional and post-transcriptional regulatory databases
have been developed, such as TransmiR [[115]16], ChIPBase [[116]17],
miRTarBase [[117]20] and so on. However, the exhaustive transcriptional
and post-transcriptional regulatory relationships still need further
elucidation. For example, the characterization of lncRNAs regulation of
TFs is still at a primary level [[118]36]. Furthermore, the competing
endogenous RNA (ceRNA) relationships involved in TFs, miRNAs and
lncRNAs provide further complex regulations among transcriptional and
post-transcriptional factors which should be considered in the future
analysis of TNCR network [[119]37]. Our approach in this study was
based on the general regulatory network TNCR; however, the autophagy
plays tissue-specific and double-edged roles in the cellular
homeostasis and survival. We believe that the performance of our
approach would be improved if we use the data of a specific cancer. In
addition, the comprehensiveness of seeds is critical for the
performance of the RWR algorithm [[120]38]. Currently, protein-coding
regulators of the autophagic machinery are relatively well known, while
few studies have been conducted on the non-coding RNA regulators,
especially lncRNAs. With the abundance of research of autophagy related
regulators, we will obtain comprehensive seed autophagy regulators, and
provide more credible, verifiable autophagy regulators.
Supplementary Materials
The following are available online at
[121]http://www.mdpi.com/2073-4409/7/11/194/s1.
[122]Click here for additional data file.^ (2MB, zip)
Author Contributions
Conceptualization, X.C. and W.J.; data curation, Q.M. and X.Z.; formal
analysis, S.W. and W.W.; funding acquisition, S.W. and W.J.;
investigation, S.W.; methodology, S.Z.; supervision, W.J.; validation,
Q.M., H.L. and H.L.; visualization, W.W. and X.M.; writing—original
draft, S.W.; writing—review and editing W.J.
Funding
This work was supported by the National Natural Science Foundation of
China [61571169]; the Fundamental Research Funds for the Central
Universities [NE2018101]; the Harbin medical university scientific
research innovation fund [2017JCZX52].
Conflicts of Interest
The authors declare no conflicts of interest.
References