Abstract
Recent advances in high-throughput technologies have provided an
unprecedented opportunity to identify molecular markers of disease
processes. This plethora of complex-omics data has simultaneously
complicated the problem of extracting meaningful molecular signatures
and opened up new opportunities for more sophisticated integrative and
holistic approaches. In this era, effective integration of data-driven
and knowledge-based approaches for biomarker identification has been
recognised as key to improving the identification of high-performance
biomarkers, and necessary for translational applications. Here, we have
evaluated the role of circulating microRNA as a means of predicting the
prognosis of patients with colorectal cancer, which is the second
leading cause of cancer-related death worldwide. We have developed a
multi-objective optimisation method that effectively integrates a
data-driven approach with the knowledge obtained from the
microRNA-mediated regulatory network to identify robust plasma microRNA
signatures which are reliable in terms of predictive power as well as
functional relevance. The proposed multi-objective framework has the
capacity to adjust for conflicting biomarker objectives and to
incorporate heterogeneous information facilitating systems approaches
to biomarker discovery. We have found a prognostic signature of
colorectal cancer comprising 11 circulating microRNAs. The identified
signature predicts the patients’ survival outcome and targets pathways
underlying colorectal cancer progression. The altered expression of the
identified microRNAs was confirmed in an independent public data set of
plasma samples of patients in early stage vs advanced colorectal
cancer. Furthermore, the generality of the proposed method was
demonstrated across three publicly available miRNA data sets associated
with biomarker studies in other diseases.
MiRNA biomarker discovery: a network-based, knowledge-driven approach
The identification of robust and reproducible molecular markers is one
of the biggest challenges in personalised cancer medicine. The
increasing use of systems biology approaches has prompted researchers
to integrate heterogeneous data into existing knowledge bases whose
incorporation into the biomarker discovery workflow may adjust for data
heterogeneity and limitation, and offer more precise, robust and
consistent biomarkers. In this study, we have sought to determine
network-based miRNA biomarker signatures from the plasma of colorectal
cancer patients that hold prognostic utility. We performed miRNA
profiling and then constructed an miRNA-mediated gene regulatory
network and developed a multi-objective optimisation-based
computational framework to identify miRNA biomarkers using both the
miRNA expression profile and knowledge from this miRNA-mediated
regulatory network. We have demonstrated the ability of the proposed
approach in identifying robust, accurate and reproducible biomarkers.
Introduction
The identification of robust and reproducible molecular markers is one
of the biggest challenges in personalised cancer medicine. The
complexity and heterogeneity of cancer, noise and nonlinearities in
high-throughput data, and relatively small sample sizes can all
contribute to the observed inconsistencies across different biomarkers
reported for identical clinical conditions. However, the increasing use
of systems biology approaches has prompted researchers to integrate
heterogeneous data into existing knowledge bases in order to facilitate
the system-level understanding of disease. Now, incorporating such
knowledge bases into the biomarker discovery workflow may adjust for
data heterogeneity and limitation, and offer more precise, robust and
consistent biomarkers.^[42]1
Colorectal cancer (CRC) is the second leading cause of cancer-related
mortality both in Australia^[43]2 and worldwide,^[44]3 and in
Australia, is the second-most prevalent cancer in both men and
women.^[45]2 While survival rates have increased over the past 30 years
with the introduction of screening programmes and new systemic
treatment agents, the 5-year relative survival from CRC remains only
68%.^[46]2 Of those patients who undergo curative surgery for CRC, one
in three will experience disease recurrence.^[47]4 For patients with
metastatic disease, 5-year survival is only around 13%.^[48]5 An
important challenge, therefore, is identifying those patients who have
undergone curative resection who are at higher risk of recurrence and
selecting those likely to derive benefit from adjuvant chemotherapy.
Similarly, for patients with metastatic disease, early identification
of those who are likely to develop more severe toxicities or derive
little or no response from what can be expensive cytotoxic and targeted
agents would allow for the selection of alternate, better tolerated
therapies; tailored doses of the same agents; or the use of
prophylactic supportive therapies, such as antibiotics or growth
factors. These limitations highlight the need for novel biomarkers that
facilitate the early identification of patients with poor prognosis.
Currently, performance status and cancer stage are the main indicators
for treatment selection and survival prognostication. There is now a
crucial need for the personalisation of treatment using molecular
biomarkers, in conjunction with baseline clinical and laboratory
variables. Blood-based biomarkers are particularly attractive given
that blood is a readily available, minimally invasively obtained medium
that allows for simple, inexpensive and repeated sampling.
MicroRNAs (miRNAs) are small (19- to 25-nucleotide) noncoding RNA
molecules that regulate gene expression at the translational level.
They are involved in a number of biological processes, including human
cancers, where they are differentially expressed.^[49]6 MiRNAs have
been shown to have roles as tumour suppressor genes and oncogenes, and
their diagnostic, prognostic, predictive and therapeutic implications
are now being explored. Both plasma and serum are stable sources of
circulating miRNAs^[50]7,[51]8 and both are suitable for investigations
of miRNAs as blood-based biomarkers.^[52]7
Several studies on colorectal tumour tissue or cell lines have been
performed which have sought miRNAs for use as prognostic or predictive
biomarkers, and those involved in biological processes such as
tumorigenesis and metastasis.^[53]9,[54]10 Plasma-derived miRNAs have
been mostly used as diagnostic biomarkers in CRC
patients.^[55]11,[56]12 However, while a few studies to date have
examined the utility of circulating miRNAs as prognostic CRC
biomarkers,^[57]13–[58]16 the reported miRNAs do not overlap between
studies.
Predicting patient clinical outcomes via molecular expression
information has traditionally focused on the study of individual
molecules (i.e., differential expression analysis). This approach,
however, does not adequately take into account the informational
complexity underpinning many clinical states. The over-reliance on such
hypothesis-driven, reductionist approaches to biomarker discovery,
despite the valuable achievements so far, may limit the translation of
fundamental research into new clinical applications due to their
limited ability to unravel the multivariate and combinatorial
characteristics of cellular networks implicated in multi-factorial
diseases such as cancer.^[59]17
Instead, systems-based biomarker discovery approaches may more
accurately reflect the underlying biology than traditional reductionist
approaches. In this context, biomarkers, as indicators of a clinical
state, are computationally derived from networks of interacting
molecular entities and incorporate measurements from the expression of
molecules with the information on clinically meaningful biological
interactions.^[60]17
In recent years, network-based approaches of gene expression analysis
have grown in popularity for their capacity to explain emergent
properties such as biological heterogeneity, modularity or phenotypic
variability.^[61]18 It has been frequently shown that molecular
networks (e.g., protein−protein interaction, gene regulatory and
signalling networks) are sources for identifying powerful biomarkers;
network-based biomarkers can capture changes in downstream effectors
and in many cases are more useful for prediction compared to any
individual gene.^[62]19–[63]23 Several approaches exist involving the
utilisation of networks of molecular interactions in gene expression
signature modelling.^[64]20,[65]24–[66]26 Nonetheless, the advantage of
network-based approaches has rarely been applied to miRNA biomarkers,
possibly because miRNA networks are not prevalent and readily available
as opposed to gene or protein interaction networks.
It is well understood that miRNAs cooperate to achieve gene regulation
and that each miRNA has the potential to target a large number of
genes.^[67]27 Our increasing knowledge of the miRNA-mediated regulatory
network has underlined the importance of miRNA control over tumour cell
biology. miRNAs associated with patient outcome have been found to be
oncogenic or tumour suppressive, affecting multiple cancer-associated
pathways by targeting oncogenes or tumour suppressor genes.^[68]6
Overall, the miRNA-mediated gene regulatory network carries key
information on the functional role of miRNAs in cancer whose
utilisation in miRNA expression signature modelling may lead to the
identification of biologically relevant markers when miRNAs are
released from cancer cells, or linked to systemic processes.
In this study, we have sought to determine network-based miRNA
biomarker signatures from the plasma of CRC patients that hold
prognostic utility. To this end, we performed miRNA profiling and then
constructed an miRNA-mediated gene regulatory network, and developed an
innovative multi-objective optimisation-based computational framework
to identify miRNA biomarkers using both the miRNA expression profile
and information from this miRNA-mediated regulatory network.
Methods
Patient selection, blood collection and preparation of plasma
Patients with a histologically confirmed diagnosis of locally advanced
or metastatic CRC receiving adjuvant or palliative chemotherapy
respectively attending the medical oncology outpatients’ clinics at
Concord and Royal Prince Alfred Hospitals in Sydney, Australia, were
eligible for inclusion. Patients were required to have good performance
status (ECOG 0−2), and adequate organ function. Patients were excluded
if they had prior chemotherapy for metastatic CRC or completed adjuvant
chemotherapy within the past 6 months. This study was performed in
accordance with relevant guidelines and regulations and with the
approval of the individual ethics committees of the institutions where
the patients were being treated.
Plasma samples were taken prior to commencing chemotherapy. Blood was
collected by routine venepuncture in 10 ml Vacutainer Plus K[3]EDTA
tubes (BD Biosciences). Tubes were inverted ten times immediately after
collection, and were centrifuged at 2500 × g for 20 min at room
temperature within 30 min of collection. Plasma was stored at −80°C
until further processing.
RNA isolation, quality control and OpenArray analysis
Total RNA was isolated from plasma using the MirVana PARIS miRNA
isolation kit (Ambion/Applied Biosystems, Foster City, CA) according to
a modified protocol.^[69]28 Isolated plasma samples were assessed for
haemolysis by examination of free haemoglobin and miR-16 levels, the
latter being an miRNA found in red blood cells. Quantification of free
haemoglobin was performed as described previously^[70]28 on an Implen
Nanophotometer (Implen GmbH, Munich, Germany), and miR-16 levels were
quantified by real-time RT-qPCR. Quantification details are provided in
the Supplementary file [71]1, Section 1.1. Samples deemed haemolysed
were excluded from further analysis.
Global profiling of miRNAs in the plasma samples was carried out using
the OpenArray platform (Applied Biosystems), according to the
manufacturer’s instructions. The entire RT reaction was used for
pre-amplification carried out on a ViiA 7 instrument (Applied
Biosystems). The resultant cDNA was combined with the OpenArray
real-time PCR Master Mix and loaded onto the OpenArray miRNA panel
plates (Applied Biosystems) using the AccuFill autoloader. The loaded
plates were run on the BioTrove OpenArray real-time PCR instrument
(Flinders Medical Centre, SA) and run according to the default protocol
for reaction conditions. See Supplementary file [72]1, Section 1.2 for
details.
Statistical data preprocessing
The pre-processing of miRNA cycle quantification (Cq) values from
quantitative RT-qPCR assays were performed using MATLAB 2014b,
Bioinformatics Toolbox and Statistics Toolbox. The preprocessing
workflow includes quality assessment, normalisation and filtering. The
chosen parameters are justified in Supplementary file [73]1, Section
1.3. QC plots for non-detects and Cq distributions were used to examine
the quality of the data and deviated trends. Quantile normalisation was
used to adjust for technical variability across multiple samples.
MiRNAs that are missing in >50% of samples were excluded to acquire
acceptable distribution of non-detects for down-stream analysis.
Missing data was imputed using the nearest-neighbour method
(KNNimpute), shown to be one of the most sensitive and robust methods
for missing value estimation in expression data.^[74]29 Patients were
dichotomised to long vs short survival using a 2-year cut-off point. To
adjust for unbalanced class distribution, the under-represented class
(i.e., short survival) was doubled using SMOTE: Synthetic Minority
Oversampling Technique^[75]30 as implemented by the R ‘DMwR’ package.
Oversampling was only used in the model selection phase to highlight
performance differences across compared classifiers. Original data was
used for the identification of the final miRNA signature reported in
this study. Given that the data was not normally distributed,
differential expression analyses were conducted using non-parametric
approaches, namely two-sample Kolmogorov−Smirnov (KS) and Wilcoxon
tests for the null hypothesis that the miRNA Cq values in short vs long
survival patients are from the same continuous distribution.
Biomarker discovery
Biomarker identification as an optimisation problem
Identification of a prognostic molecular expression signature can be
thought of as a problem of finding a set of molecules (e.g., miRNAs)
whose expression profile best stratifies patients into the groups of
interest—i.e., shorter vs longer survival. This can be modelled as an
optimisation problem that is defined as finding a solution, out of all
possible solutions, that minimise/maximise an objective function. An
optimisation problem is typically formulated as
[MATH:
minxf(
x) :MATH]
, s.t.
[MATH: x∈X :MATH]
, where X is the set of all possible solutions and f:x→ℝ is an
objective function that maps any feasible solution onto a real number
evaluating the ‘goodness’ of the solution instance. By convention, the
standard form defines a minimisation problem. A maximisation problem
can be treated by negating/inversing the objective function. In this
study, we used a popular and powerful class of optimisation algorithms
known as evolutionary algorithms (EAs).^[76]31 EAs are generic
population-based metaheuristic optimisation algorithms whose mechanisms
are inspired by biological evolution. An EA procedure begins with a
population of solutions usually generated at random. It then
iteratively updates the current population to generate a new population
by the use of four main operators, namely selection, crossover,
mutation and elite-preservation. The operation stops when one or more
pre-specified termination criteria are met (e.g., the optimum is found,
the population is converged, or a pre-specified number of generations
is passed).
An EA relies on the specification of (1) solution instance, and (2)
objective function (usually referred to as the fitness function). Here,
a solution instance encodes a set of miRNAs selected out of all N
miRNAs under study and is represented by a binary string of length
[MATH: l=N, :MATH]
where each bit in the string corresponds to a particular miRNA, m[i]
whose value ‘1’ or ‘0’ encodes the inclusion or exclusion of m[i],
respectively. Each solution can be thought as a potential biomarker and
the optimisation algorithm searches for a set of miRNAs whose
expression profile best classifies patients into groups with shorter vs
longer survival. Therefore, to evaluate each solution, the expression
values of the corresponding miRNAs are fed into a classifier which is
an algorithm or a function that maps these expression values (known as
features) to the binary space of long or short survival. The
classification error rate is then considered as the fitness function
and the EA is set to find a solution with minimal misclassification
rate.
Construction of miRNA-mediated gene regulatory network
We have developed an algorithm that constructs a network of
miRNA-mediated regulatory cascades and used this network to discover
miRNA signatures. In a mathematical formulation, a network or a graph
consists of a set of nodes V and a set of edges E between nodes. Here,
a node is an miRNA or a gene and an edge is a directed association
representing the regulation of a target gene (TG) by the source nodes
that is either an miRNA or a transcription factor (TF). Human miRNA
targets were retrieved from publicly available data sets of
experimentally validated and predicted data sets using
multiMiR^[77]32—updated on 12/22/2016. MultiMiR is an miRNA-target
interaction R package and database that compiles nearly 50 million
records in human and mouse from 11 different databases: validated
targets were collected from miRecords,^[78]33 miRTarBase,^[79]34 and
TarBase^[80]35 and predictions from DIANA-microT-CDS,^[81]36
ElMMo,^[82]37 MicroCosm, miRanda,^[83]38 miRDB,^[84]39 PicTar,
PITA,^[85]40 and TargetScan.^[86]41 Targets of miRNAs under study were
included in the network if experimentally validated or predicted by at
least two databases. Additionally, a gene regulatory network—i.e., a
collection of validated TF−TG interactions—was obtained from the ORTI
database,^[87]42 an open-access comprehensive repository of regulatory
interactions that compiles mammalian TFs and their associated TGs from
publicly available databases of TF−TG interactions, namely HTRI,^[88]43
TFactS,^[89]44 TRED,^[90]45 TRRD,^[91]46 PAZAR,^[92]47 and
NFI-Regulome,^[93]48 and the literature. The miRNA-mediated regulatory
network was then constructed using an iterative process as outlined
below:
Starting from an empty network, the set of miRNA-target interactions
for each miRNA under study were first added to the network. The miRNA
targets may comprise TFs that can in turn target other genes and pass
on the regulation to the second level. Those TF−TG interactions were
then added to the network. Similarly, the newly added TGs (i.e., the
targets of the targets of the miRNAs) may contain TFs that extend the
regulation cascade to deeper levels. This process continues until
‘convergence’, i.e., when no new TF−TG interaction can be added to the
network, meaning that all TFs and TGs reachable from the initial miRNAs
have already been traversed and added to the network. The pseudocode of
an efficient recursive implementation of the proposed algorithm is
shown in Supplementary file [94]1, Section 1.4.
Annotation of the CRC-related genes on the network
The miRNA-mediated regulatory network can be used to identify miRNAs
which target, either directly or indirectly, genes functionally
associated with CRC, and thus have the potential to play a role in the
cellular mechanisms underlying CRC pathogenesis. This requires the
annotation of the network genes according to their association with
CRC. We used the MalaCards human disease database,^[95]49 which is an
integrated compendium of annotated diseases mined from multiple data
sources. MalaCards provides the list of genes affiliated with a queried
disease accompanied with a prioritising algorithm to rank the gene
list. It distinguishes ‘elite’ genes as those likely to be associated
with causing the disease, since their gene–disease associations are
supported by manually curated and trustworthy sources. The relevance of
the MalaCards retrieved genes to CRC were ranked into two levels—rank
‘1’ for elite genes and rank ‘2’ for the rest of CRC associated genes.
These genes were then annotated with their ranks on the miRNA-mediated
regulatory network.
Network-based CRC functional relevance score
An miRNA can target multiple CRC-related genes either directly or
indirectly. The probability of the mechanistic involvement of an miRNA
in CRC increases if the miRNA targets more CRC genes in a shorter
distance within the network. We aggregated these measurements into a
scoring function to quantify the functional relevance (FR) of each
miRNA to CRC pathogenesis for the subsequent biomarker modelling.
Equation ([96]1) shows the FR formulation, where m[i] is an miRNA in
the miRNA-mediated regulatory network, TG = {g[k]} is the set of all
CRC TGs reachable from m[i] on the network, d(m[i], g[k]) is the
shortest distance from m[i] to g[k] on the network computed using the
Bellman–Ford algorithm,^[97]50 and r[gk] is the CRC rank assigned to TG
g[k]. ε is a small constant (i.e., 10E-3) to avoid FR = 0 and ‘division
by zero’ in subsequent analyses.
[MATH: FRmi=ε+
∑gk
∈TG
exp-dmi,gk<
/msub>+rgk<
/mrow>. :MATH]
1
According to this formulation, the farther the distance (or the higher
the rank), the higher the magnitude of the exponent, and thus, the
smaller the increment of the aggregated FR score. Figure [98]1b
exemplifies FR calculation on a schematic miRNA network.
Fig. 1.
[99]Fig. 1
[100]Open in a new tab
Outline of the method. a The construction steps of the miRNA-mediated
regulatory network: (1) miRNA target genes (TGs) that are either
validated experimentally or predicted by two different data sets were
retrieved using multiMiR which is an R package providing access to 11
publicly available data sets. Transcription factor (TF) targets were
retrieved from ORTI database which compiles validated mammalian TF-TG
interactions from six public data sets as well as the literature. The
miRNA-mediated regulatory network was constructed using a recursive
algorithm described in Supplementary Figure [101]S3. (2) The network
was then annotated using 339 CRC-associated genes identified by
MalaCards; 35 ‘elite’ genes with strong causal associations with CRC
progression were ranked ‘1’ and the rest of CRC genes were ranked ‘2’.
(3) Using the annotated network, a functional relevance (FR) score was
calculated for each miRNA (using Eq. ([102]1)) and a look up table was
returned to be used in the subsequent biomarker discovery. b FR
calculation on an example network. c Schematic view of the proposed
multi-objective optimisation-based biomarker discovery workflow: The
pre-processed samples were partitioned to validation and discovery sets
using fivefold cross-validation. The multi-objective optimiser was run
on discovery set where objectives are prediction errors and averaged FR
scores of the population of putative signatures. Optimal miRNA
signatures (i.e., Pareto front solutions) and their corresponding
predictive models were then used to classify test samples and the
performance measures were reported. The whole process repeated for 50
times to account for random partitioning of samples and the average
performance measures were reported (Fig. [103]3)
Multi-objective optimisation: essentials
A multi-objective optimisation is an optimisation problem that involves
multiple objective functions, formulated as:
[MATH:
minf1(x),…,fk<
mrow>(x) :MATH]
, where integer
[MATH: k≥2 :MATH]
is the number of objectives x is a solution instance in the solution
space X and
[MATH: f:x→Rk :MATH]
is an objective function that maps each solution instance into a vector
of real-valued vector of objectives.
In non-trivial multi-objective optimisation problems where the
objective functions are conflicting, no feasible solution that
simultaneously minimises all objective functions typically exists.
Therefore, attention is paid to Pareto optimal solutions, i.e.,
solutions that cannot be at least one of the other objectives. A
feasible solution
[MATH:
x1∈X :MATH]
is said to (Pareto) dominate another solution
[MATH:
x2∈X :MATH]
, if:
[MATH: fi
x1≤f<
mi>ix2∀i∈1,2…,<
mi>k,fj
x1<f<
mi>jx2∃j∈1,2…,<
mi>k. :MATH]
A solution
[MATH:
x1∈X :MATH]
is called Pareto optimal if it is not dominated by any other solution
in the solution space.^[104]51 The set of all feasible non-dominated
solutions in X is referred to as the Pareto optimal set, and the
corresponding objective vectors are called the Pareto front. For many
problems, the number of Pareto optimal solutions is enormous and a
multi-objective optimiser is usually aimed at identifying a
representative set of solutions which (1) lie on the Pareto front, and
(2) are diverse enough to represent the entire range of the Pareto
front.^[105]52
A popular approach to generate Pareto optimal solutions is to use EAs.
The use of a population of solutions allows an EA to find multiple
optimal solutions, thereby facilitating the solution of multi-objective
optimisation problems. Furthermore, EAs have essential operators to
converge towards a set of non-dominated points which are as close as
possible to the Pareto-optimal front, and yet diverse among the
objectives.^[106]53
Currently most evolutionary multi-objective optimisation algorithms
apply Pareto-based ranking schemes. A standard example is the
Non-dominated Sorting Genetic Algorithm-II (NSGA-II).^[107]54 NSGA-II
sorts the population into various fronts such that the first front is a
completely non-dominant set in the current population (rank 1
individuals), and the second front is only dominated by the individuals
in the first front (rank-2 individuals) and this process continues
until the entire population is ranked. In addition to the individuals’
ranks, another parameter called crowding distance is calculated for
each individual. Crowding distance is a measure of how close an
individual is to its neighbours. NSGA-II selects individuals based on
the rank and the crowding distance.
Multi-objective optimisation in network-based miRNA biomarker discovery
We developed a bi-objective optimisation workflow to identify multiple
miRNA biomarkers by simultaneously optimising for two objectives: (1)
the predictive power and (2) functional relevance. We used
NSGA-II^[108]54 to search for multiple sets of plasma miRNAs whose
expression profiles can precisely predict patients’ survival outcome
and, at the same time, target CRC pathways on the miRNA-mediated
regulatory network. The predictive power was estimated as the minimal
misclassification rate using a classifier, and the functional relevance
for each putative biomarker was estimated by aggregating over FR scores
of the corresponding biomarker miRNAs. In mathematical terms, let
[MATH: X=mi,i=1,<
/mo>…,n :MATH]
be the set of all
[MATH: n :MATH]
miRNAs under study, and
[MATH: Xi⊆X :MATH]
be a subset of miRNAs (i.e., a solution or a putative biomarker), the
optimisation problem is then formulated as
[MATH: minErrXi
,1∕FR¯Xi
s.t.Xi⊆X, :MATH]
where the biomarker functional relevance is computed by:
[MATH: FR¯Xi
=
∑mk
∈Xi
FRmk. :MATH]
The functional relevance shall be maximised and thus inverted to adhere
with the standard minimisation problem.
[MATH: ErrXi
:MATH]
is the average of error rates (i.e., number of incorrectly classified
samples divided by total number of samples) over multiple runs of
fivefold cross validation using X[i] expression profile as the
classification feature set. Figure [109]1c illustrates the proposed
biomarker discovery workflow. NSGA-II parameters were set as follows:
population size was set to 100, (scattered) crossover and (uniform)
mutation rates were set to 0.8 and 0.01, respectively. The maximum
number of generations was set to 50. The solver stops after iterating
for 50 generations or when the average change in the spread of the
Pareto front is less than 1E-4. Crowding distance was used as the
distance function and Pareto front population fraction was chosen to be
20%. The workflow was coded in MATLAB R2014b and R. MATLAB optimisation
toolbox was used to implement NSGA-II. Codes are available at
[110]https://github.com/VafaeeLab/multiobj_miR_marker_discovery.
Significance assessment of identified biomarkers
The statistical significance of each biomarker/Pareto solution was
assessed using permutation hypothesis testing. Accordingly, for each
Pareto solution an equivalent random individual was generated which has
an equal number of miRNAs, but randomly chosen from the pool of miRNAs
under study. The objective vector of the random solution was then
estimated and this process was repeated 1000 times to generate a null
distribution of objective vectors. For ease of assessment, we replaced
each objective vector with a scalar value by computing its Euclidean
distance with the ideal optimum that is origin 〈0; 0〉. The nominal p
value for each Pareto solution/biomarker was then calculated as the
proportion of random samples whose distance to origin is closer than or
equal to that of the Pareto solution.
Validation of altered expression of identified miRNAs in an independent data
set
The altered expression of the identified miRNAs was examined in an
independent public data set of qPCR miRNA CRC patient plasma
samples^[111]55 which employed TaqMan Array Human MicroRNA Cards Set
v2.0A/B and profiled the expression of 667 miRNAs in 48 plasma samples
that included patients with normal, polyps, adenoma, early-stage (stage
I/II) and advanced (stage III/IV) cancer. We downloaded the raw data
from NCBI GEO archive (accession no: [112]GSE67075). For consistency,
we followed the same statistical pre-processing pipeline that we used
to analyse our own data set. We performed differential expression
analysis using the two-sample Wilcoxon test as implemented by R
‘HTqPCR’ package to compare the early-stage vs advanced groups (8
samples per groups) and reported the p values of miRNAs of interest.
The statistical significance of the proportion of identified miRNAs
differentially expressed in the validation data set was assessed using
the right-sided Fisher’s exact test (‘stat’ R package, ‘phyper’
function).
Pathway overrepresentation analysis
We were interested to examine whether the identified miRNAs enrich
pathways relevant to CRC progression noting that pathway information
was not used to obtain miRNA FR scores. We used KEGG pathways retrieved
from the Molecular Signatures Database (MSigDB)-V 6.0.^[113]56 Targets
of the identified miRNAs were extracted from the miRNA regulatory
network and underwent pathway enrichment analysis using the right-sided
Fisher’s exact test whose p value for the null hypothesis is computed
based on the hypergeometric distribution:
[MATH: p=1Nn∑i=ki=n
niN-Kn-i, :MATH]
where N is the total number of annotated genes, n is the number of
genes targeted by signature miRNAs, K is the total number of genes
annotated by a pathway, and k is the number of TGs in the pathway; p
values were adjusted for multiple hypothesis testing using FDR
correction. The analysis was implemented in R using ‘stats’ packages.
Results and discussion
Patient characteristics and data preprocessing
The characteristics of patients included in this study are shown in
Table [114]1 and detailed in Supplementary file [115]2. Plasma samples
were profiled against 557 miRNAs whose Cq values are shown as a heatmap
in Supplementary file [116]1, Figure [117]S3. Names of miRNAs were
standardised to miRBase-Version 21 using miRSystem;^[118]57 12 miRNAs
that were unavailable or dead were excluded. For reliable downstream
analysis, miRNAs missing in > 50% of samples were filtered out,
resulting in 150 miRNAs.
Table 1.
Baseline patient characteristics
Characteristics n = 75 Description
Gender (F/M) 30/45 F: Female, M: Male
Age 59 years Average age at enrolment
Survival (mean ∓ std) 20.98 ∓ 11.67 months Survival times for 53
patients have not been reported as they have been alive at the end of
the follow-up and their prognostic status was considered as ‘long
survival’.
Tumour site (C/R/RS) 45/24/6 C: Colon, R: Rectum, RS: Rectosigmoid
Chemotherapy regime FOLFOX All 75 patients received FOLFOX-based
chemotherapy
[119]Open in a new tab
MiRNA-mediated gene regulatory network
Figure [120]1a depicts the workflow of miRNA-mediated regulatory
network construction. The constructed network comprises 150 miRNAs
under study, 591 TFs and 22,635 TGs with a total number of 170,617
interactions including both miRNA-TG and TF-TG interactions. The
network flat file is provided in Supplementary file [121]3. Once the
network was constructed, CRC-related genes/nodes on the network were
marked and ranked. Overall, 339 genes including 35 elite genes were
annotated and ranked. CRC-associated genes, including elite ones and
data sources used by MalaCards to imply CRC associations, are listed in
Supplementary file [122]4. Lastly, the functional relevance of each
miRNA was scored based on the rank and distance of miRNA’s CRC-related
targets on the network. The proposed functional relevance (FR) scoring
function takes into account direct miRNA targets as well as distant
targets; yet, the farthest a target is, the lower its contribution to
the FR score. Figure [123]1b schematically illustrates the FR
calculation on a sample miRNA network; the histogram of FR score
distribution is shown in Supplementary file [124]1, Figure [125]S4.
Performance comparison of different classifiers
An optimisation-based approach to biomarker discovery requires a choice
of classifier to compute the fitness (e.g., misclassification rate) of
solutions (i.e., putative signatures). We compared the performance of
Support Vector Machine (SVM),^[126]58 Random Forest (RF)^[127]59 and
AdaBoost^[128]60 with decision trees as weak learners as choices of
classifiers. The expression profiles of differentially expressed miRNAs
(i.e., p value < 0.05 using the KS test) were set as classifier
features, which is a commonly used approach for feature selection in
biomarker identification.^[129]13–[130]16,[131]55 The list of
differentially expressed miRNAs is available in Supplementary file
[132]5. We also measured the performance of different classifiers on a
population of 500 randomly selected sets of features (i.e., miRNAs)
providing the null distributions (Fig. [133]2a). Samples were divided
into discovery and validation sets using fivefold cross-validation;
cross-validation was then repeated ten times to account for random data
splitting (total of 50 independent runs). In each run, classifiers were
trained on the discovery sets and used to predict the corresponding
validation samples. The predictive performance of the classifiers in
terms of accuracy, sensitivity and specificity was estimated by
averaging over 50 rounds of predictions; ‘long survival’ was considered
as the ‘positive’ class implying that sensitivity is the classifier’s
ability to correctly identify patients with long survival whereas the
specificity represents the ability to correctly classify short
survival. While all classifiers performed similarly in terms of total
misclassification rates or accuracy (Fig. [134]2b), SVM significantly
outperformed other classifiers on detecting the under-represented event
of ‘short survival’ (i.e., higher specificity) and was thus chosen as
the choice of classifier in the optimisation processes (Fig. [135]2b).
Fig. 2.
[136]Fig. 2
[137]Open in a new tab
Performance comparison of different classifiers. The predictive
performance of three different classifiers namely AdaBoost (with
decision trees as weak learners), Random Forest (RF) and Support Vector
Machine (SVM) were assessed. a Predictive features were selected
randomly; the null distributions were set using 500 sets of randomly
chosen miRNAs. The distributions of classifiers’ accuracy, specificity
and sensitivity (with ‘long survival’ as positive class) as well as
functional relevance scores were plotted. Mean values are marked on
density plots. b The predictive features were the set of differentially
expressed genes (KS test, p value < 0.05); error bars show standard
deviations
Performance comparison with relevant approaches
To investigate the advantage of network-based multi-objective
optimisation workflow proposed in this work, we compared the
performance of resultant signatures with those achieved by a
single-objective optimisation approach. In this latter approach, a
genetic algorithm (GA), with similar experimental setup, was used to
find sets of miRNAs with utility as a prognostic biomarker, by
minimising the error rate in predicting patients’ survival status.
Single-objective optimisation has previously been used for biomarker
discovery in other contexts and has shown superior prediction
performance as compared to conventional approaches. For instance, Liu
et al.^[138]61 used GA combined with SVM classifier to identify
biomarkers for tumour categorisation. As another example, Petricoin et
al.^[139]62 reported the use of self-organising map coupled with GA to
search through raw mass spectrometry data to identify a proteomic
pattern discriminating ovarian cancer from non-cancer.
Optimisation-based approaches to biomarker discovery inherently select
features through the search process. We also included into the
comparison more classical models with the built-in feature selection
capacity. Accordingly, we evaluated the least absolute shrinkage and
selection operator^[140]63 (Lasso), a commonly used regression method
that inherently performs variable selection by producing coefficients
that are exactly 0. We used the ‘glmnet’ R package to fit the
generalised linear regression model with Lasso; a lambda value that
gives minimum mean cross-validated error was used for prediction and
extraction of model coefficients. We also assessed RF and SVM with
automatic feature selection. We used the ‘RRF’ R package to implement
guided regularised random forest (guided RRF).^[141]64 The coefficients
of regularisation were set to the normalised importance score of the
variables as recommended in the RRF package.^[142]65 We also adopted
the ‘penalizedSVM’ R-package that implements penalty functions for
automatic feature selection in SVM classification.^[143]66 We chose the
penalty function to be Smoothly Clipped Absolute Deviation
(SCAD)^[144]67 due to its superior performance.^[145]66
A similar pre-processing pipeline was followed for all compared
algorithms. Again, samples were divided into discovery and validation
sets using fivefold cross-validation and repeated ten times (50
independent runs). In each run, compared methods (i.e., multi-objective
optimiser, single-objective optimiser, Lasso, guided RRF and penalised
SVM) were trained on the discovery sets. The end-of-run models with the
selected features were then used to predict validation samples and
average accuracy, sensitivity and specificity were reported. The
functional relevance scores of the identified signatures (i.e.,
selected features) were also averaged across 50 runs and reported to
compare the biological implication of the identified biomarkers in CRC
underlying mechanisms. As Fig. [146]3a shows, Lasso was unable to
predict samples with ‘short survival’ and usually assigned all test
samples to a single class of ‘long survival’. We therefore observed a
very low specificity and high, but false, sensitivity with Lasso.
Single- and multi-objective optimisers performed comparatively better
than the other compared methods. Yet, multi-objective optimisation
performed significantly better than single-objective optimisation on
accuracy, specificity and functional relevance (Wilcoxon test p
value < 0.001). We observed that single-objective optimisation
overfitted to training data while multi-objective optimisation produced
comparative performance on training and test sets and thus better
generalised to independent data sets (Supplementary file [147]1, Figure
[148]S5). This demonstrates the advantage of using a data-independent
knowledge-based approach in avoiding overfitting to data. Along the
same lines, the multi-objective optimiser also controls for signature
sizes (Fig. [149]3b). Large signatures usually produce excessively
complex models overreacting to minor fluctuations in the training data.
Moreover, large signatures are usually functionally redundant with less
clinical utility and validation feasibility. The single-objective
optimiser and penalised SVM produced very large signatures. On the
other hand, Lasso produces models with no coefficient (with intercept
only) in ~20% of runs.
Fig. 3.
[150]Fig. 3
[151]Open in a new tab
Performance comparison with relevant approaches with inherent feature
selection. The performance of the proposed multi-objective optimiser
was compared with relevant methods with inherent feature
selection—i.e., single-objective optimiser, Lasso, guided RRF and
penalised SVM. a The accuracy, specificity sensitivity and functional
relevance score were averaged over 50 runs of sample partitioning using
fivefold cross validation. b Sizes of the identified signatures or the
number of features selected by each method over 50 independent runs
were shown as box plots. c As a measure of signature stability, Jaccard
Index was computed for all pairs of signatures identified by each of
compared methods across 50 runs and the average values were reported.
In all bar charts, error bars show standard deviations and
multi-objective optimiser bars were marked by ‘*’ when the proposed
method significantly outperforms others (Wilcoxon test p
values < 0.001)
We also estimated the stability of selected features across different
runs using Jaccard Index that measures the intersection over union of
two sets. Accordingly, Jaccard Index was computed for all pairs of
signatures identified by each of compared methods across 50 runs and
the average values were reported (Fig. [152]3c). The multi-objective
optimiser exhibits signatures with significantly higher stability than
those identified by the compared methods. Overall, the results
demonstrate biomarker reproducibility using the proposed network-based
multi objective optimisation approach.
Identified plasma miRNA signature of CRC prognosis
Once we confirmed the predictive power and stability of the signatures
obtained by the proposed multi-objective approach, we restricted the
search space to miRNAs with clinically reasonable variations across
samples with short vs long survival. This will assure that miRNAs
contained in the final signature can be technically detected and
verified in future experimental validations. We chose relatively loose
yet clinically feasible fold-change > 1.5 in either directions (i.e.,
fold change computed as 2^∆∆Ct using ‘HTqPCR’ R package), which
resulted in 51 miRNAs used to identify plasma signatures by the
proposed multi-objective optimiser.
We identified a prognostic signature (accuracy = 0.907, FR = 4.697)
comprising 11 plasma miRNAs namely hsa-let-7a, hsa-miR-106a,
hsa-miR-185, hsa-miR-21, hsa-miR-217, hsa-miR-25, hsa-miR-30a-5p,
hsa-miR-431, hsa-miR-483-5p, hsa-miR-615-5p, hsa-miR-892a1. The
statistical significance of the identified signature was assessed using
a permutation test and a nominal p value of zero was achieved. Figure
[153]4a shows boxplots representing the distributions of miRNA
expressions across short and long survival samples. The expression
levels of the identified miRNAs were examined in an independent public
data set of qPCR miRNA profiles obtained from CRC plasma samples
including eight early-stage and eight advanced samples.^[154]55 Four
miRNAs in the identified signature were not profiled (or filtered out)
in the plasma data set—i.e., hsa-miR-217, hsa-miR-431, hsa-miR-615-5p
and hsa-miR-892a. Out of the seven remaining biomarker miRNAs, four
miRNAs show significant differential expression based on the p value
cut-off of 0.1 (Fig. [155]4b). This proportion is statistically
significant (p value = 0.0084, Fisher’s exact test with parameters:
N = 310, K = 72, n = 7, k = 4).
Fig. 4.
[156]Fig. 4
[157]Open in a new tab
Identified plasma miRNA signature of CRC prognosis. A prognostic
signature of 11 plasma miRNAs was identified using the proposed
network-based multi-objective optimisation approach. a Boxplots
represent the distributions of miRNA expressions across short and long
survival samples. b The expression values of the identified miRNAs were
examined in an independent public data set of qPCR miRNA profiles
obtained from CRC plasma of patients at early or late cancer stages
(accession no: [158]GSE67075). Early-stage vs advanced cancer was
compared using non-parametric Kolmogorov−Smirnov hypothesis testing.
The bar in front of each miRNA shows the achieved p value scaled by
–log10 to improve visibility. ‘NA’ indicates that the corresponding
miRNA was not profiled (or filtered out) in the data set; ‘*’ specifies
differentially expressed miRNAs based on the p value cut-off of 0.1. c
List of important overrepresented KEGG pathways and their corresponding
–log10 scaled p values, related to CRC mechanisms and inflammation that
is an important risk factor for the development of colon cancer
Targets of the identified miRNA signature enrich several cancer-related
as well as inflammatory pathways. There is a well-established
connection between inflammation and tumorigenesis with numerous
supporting evidence from genetic, pharmacological and epidemiological
data.^[159]68 Inflammation is an important risk factor for the
development of colon cancer.^[160]69 Figure [161]4c shows some of the
important pathways related to CRC mechanisms and inflammation that
highlights the biological implications of the identified biomarkers in
CRC development and progression.
Among the identified biomarker miRNAs, the utility of serum miRNA
miR-21as a marker of CRC progression and diagnosis has previously been
investigated.^[162]15,[163]16,[164]55 Downregulation of miR-106a in
tumour was previously shown to predict shortened survival in patients
with colon cancer.^[165]70 Also, experimental evidence suggests that
the let-7 family contributes to immune evasion by the tumour and there
is an association of let-7a expression with T-cell densities and
mortality^[166]71 in CRC. STIM1, a direct target of miR-185, is
associated with CRC poor prognosis and promotes tumour
metastasis.^[167]72 MiR-217 and miR-25 in CRC tumours are associated
with patient prognosis,^[168]73,[169]74 and miR-30a has an inverse
correlation with the staging in patients with colon cancer.^[170]75
MiR-892a was frequently upregulated in human CRC tissues and cell lines
promoting cell proliferation and colony formation of CRC.^[171]76 Our
study, however, is the first to demonstrate the utility of these miRNAs
as circulating markers of CRC progression.
Generality and flexibility of the proposed miRNA biomarker discovery approach
Although identifying circulating miRNA signatures of CRC survival was a
major objective of the current study, the proposed network-based
multi-objective approach is sufficiently general to identify signatures
of disease phenotypes in other miRNA biomarker studies. To evaluate the
generality and flexibility of the proposed approach, we sought for
other miRNA biomarker discovery studies whose data sets are available
to download from NCBI GEO repository.
Recently, circulating serum exosomal miRNAs have been studied as
potential diagnostic markers of oesophageal adenocarcinoma.^[172]77
MiRNAs in serum exosomes were profiled from a cohort of 19 healthy
controls and 18 individuals with locally advanced oesophageal
adenocarcinoma using OpenArray real-time PCR platform. We downloaded
the raw data (GEO accession no: [173]GSE63108) and pre-processed using
a similar pipeline followed in our study which resulted in 130 miRNAs
for downstream analyses (see Supplementary file [174]1, Section 1.5 for
preprocessing details). The corresponding miRNA-mediated gene
regulatory network was then constructed and annotated by 33 genes
associated with oesophageal adenocarcinoma in MalaCards (see
Supplementary file [175]6 for the list of genes).
We adopted a similar GA experimental setup used in previous experiments
(i.e., population size of 100 and maximum number of 50 generations).
Similarly, samples were divided into discovery and validation sets
using fivefold cross-validation and repeated five times. In each run,
compared methods (i.e., multi-objective optimiser, single-objective
optimiser, Lasso, guided RRF and penalised SVM) were trained on the
discovery sets and used to predict the validation samples. Average
accuracy, sensitivity, specificity, functional relevance, signature
size and signature stability were then reported for each of the
compared algorithms. Results presented in Fig. [176]5a (and detailed in
Supplementary file [177]1, Table [178]S1) show that the bi-objective GA
produced signatures with superior predictive power and higher relevance
to the disease underlying mechanisms. Bi-objective GA feature selection
was more robust to data partitioning and produced reasonably sized
signatures with higher stability.
Fig. 5.
[179]Fig. 5
[180]Open in a new tab
Performance comparison over three other miRNA data sets. The proposed
multi-objective optimiser and four benchmark methods were used to
identify signatures of disease phenotypes in three publicly available
data sets. The performance measures (i.e., accuracy, sensitivity and
specificity over test samples, functional relevance (FR), signature
size and stability based on Jaccard index) of compared methods were
aggregated across 25 independent runs (five runs of fivefold CV). Bar
charts represent the average values and error bars show standard
deviations. a [181]GSE63108: circulating exosomal miRNA expression
profiles in oesophageal adenocarcinoma and normal samples. b
[182]GSE76260: miRNA expression profiling in prostate cancer tumours vs
non-neoplastic tissues. Bi-objective GA searches for signatures that
simultaneously minimise the error rates and the inverse of FR.
Tri-objective GA minimises error rate, 1/FR and signature size
simultaneously. Increasing the number of objectives increases the
number of Pareto front solutions. In tri-objective GA, a Pareto front
solution performing better with respect to the first objective has been
chosen in each run. c [183]GSE70754: miRNA expression profiles in
locally advanced breast cancer tumour vs normal tissues
The optimisation-based biomarker discovery method is open to further
enhancement by improving the adopted search mechanism. For instance,
the search coverage can be extended by simply increasing the population
size. We increased the population size to 200 and observed a better
performance of single-objective GA while the performance of
bi-objective GA was not significantly improved. We hypothesised that
the early convergence of the bi-objective GA performance can be
attributed to the miRNA network poor annotation due to the relatively
small number of genes known to be associated to the disease under
study. This limitation may direct the search algorithm towards the
selection of fewer number of miRNAs resulting in immature convergence
to local minima.
Therefore, to better assess the performance of the proposed method, we
sought for relevant data sets on diseases whose associated genes are
well studied and annotated in MalaCards producing relatively rich
annotation on miRNA-mediated network. We retrieved miRNA expression
profiles acquired from prostate clinical specimens, including 32 cancer
and 32 non-neoplastic tissues^[184]78 (GEO accession no:
[185]GSE76260). Data were preprocessed as detailed in Supplementary
file [186]1, Section 1.5, resulting in 103 miRNAs for the subsequent
analyses. The corresponding miRNA-mediated regulatory network was
annotated with 261 genes (including 29 elite genes) associated with
prostate cancer in MalaCards (Supplementary file [187]6). We kept the
GA population size at 200 and ran similar experiments performed for the
previous data set; results are presented in Fig. [188]5b and
Supplementary Table [189]S1. Bi-objective GA identified signatures with
significantly higher functional relevance scores. In terms of the
predictive power, bi-objective GA exhibited performance comparable to
guided-RRF (Wilcoxon p value = 0.861 comparing accuracies) and better
performance compared to other methods. Guided-RRF however produced more
compact signatures composing of fewer numbers of miRNAs.
To produce more compact signatures using the proposed multi-objective
approach, we considered size to be the third objective resulting in a
tri-objective GA search for miRNA signatures that simultaneously
minimise the misclassification error rate, maximise the functional
relevance and minimise the signature size. We also increased the
population size and the maximum number of generations by 50% to achieve
a more extensive search across the space of possible signatures and
reran a fivefold cross validation. Average performance measures are
reported in Fig. [190]5b. Interestingly, the tri-objective GA not only
discovered small-sized signatures (average size = 7.2 ± 1.3), but also
improved the predictive power by producing models with fewer number of
variables which avoid overfitting to the training sets.
We acquired a third data set investigating miRNA diagnostic markers of
breast cancer^[191]79 (GEO accession no: [192]GSE70754). We retrieved
normalised miRNA expression profiling of 66 samples including 19 normal
specimens, from patients with locally advanced breast cancer during
chemotherapy treatment. We preprocessed data as detailed in
Supplementary file [193]1, Section 1.5 and ended up with 160 miRNAs
used for biomarker discovery. As before, we constructed the
miRNA-mediated regulatory network and annotated it with 317 genes
(including 26 elite genes) associated with breast cancer by MalaCards
(Supplementary file [194]6). We set the GA population size and maximum
number of generations to 200 and 50, respectively. Signature size was
retained as the third objective of the multi-objective optimisation
approach. The performance measures of the compared methods were
aggregated over five runs of fivefold cross validation (25 independent
runs) and summarised in Fig. [195]5c and Supplementary Table [196]S1.
Results show that tri-objective GA outperforms its competitors with a
higher functional relevance score and smaller signature size (average
size = 5.0 ± 2.3) as it explicitly optimises for these objectives. It
is the second best performing in terms of accuracy, sensitivity and
specificity. Penalised SVM and Lasso produced signatures with higher
predictive power but were larger in size.
Conclusion
Accumulating evidence in recent years has convincingly demonstrated
that the expression of various miRNAs is frequently dysregulated in CRC
tissue.^[197]80,[198]81 More importantly, recent studies have shown
that some of these can also be detected in the circulation, and their
expression pattern can be directly related to physiological and
pathological alterations in patients with CRC.^[199]8 However, few
circulating miRNAs so far have been reported as markers of CRC
prognosis with limited consistency across different
studies.^[200]13–[201]16 In this study, we performed miRNA profiling
using 75 plasma samples of locally advanced and/or metastatic
colorectal patients. We identified a signature comprising 11 miRNAs
with utility as biomarkers of CRC prognosis with significant
alterations in an independent validation data set. The identified
signature also corroborates previous findings on miRNA prognostic
markers detected from plasma or tumours of CRC patients.
We have developed a powerful new miRNA biomarker discovery workflow to
identify clinically and biologically relevant miRNA biomarkers by
integrating advanced data-driven methodologies with a knowledge-based
approach, utilising information from an miRNA-mediated network
annotated with relevant cellular mechanisms. The miRNA-mediated
regulatory network can exploit miRNA control in biological circuits,
and provide insight into the consequences of miRNA dysfunction in
disease. While miRNA direct targets have been increasingly studied in
recent years and compiled in multiple data repositories, our study is
the first to study a network of miRNA-mediated regulations representing
deep regulatory cascades triggered by miRNAs. Such a network draws a
more comprehensive picture of cellular regulations triggered by miRNAs
as compared to miRNA-target direct interactions, and thus provides
deeper insights into pathological phenomena associated with miRNA
dysfunction. By constructing a network of miRNA-mediated regulatory
cascades and incorporating measured data from this network into a
multi-objective optimisation workflow, we have demonstrated the
potential for data-driven, knowledge-based approaches to discovering
new miRNA signatures. We have quantitatively compared the performance
of our multi-objective approach to relevant approaches with inherent
feature selection (e.g. single-objective optimiser, Lasso, guided RRF
and penalised SVM) and demonstrated that our approach outperforms on
all relevant metrics: accuracy, specificity, sensitivity, functional
relevance and stability in this particular data set. We confirmed the
generality and flexibility of the proposed method across three other
publicly available data sets used to investigate miRNA diagnostic
markers of oesophageal adenocarcinoma, prostate cancer and breast
cancer. We demonstrated the advantage of using a data-independent
knowledge base incorporated into a data-driven model to control
overfitting to expression data and avoid producing excessively large
signatures with poor predictive performance in independent data sets.
Additionally, the multi-objective optimisation framework provides the
flexibility to adjust for different objectives of interest and to
incorporate heterogeneous yet relevant information facilitating systems
approaches to biomarker discovery.
Data and code availability
The circulating miRNA expression profile of CRC samples collected in
this study can be accessed at NCBI Gene Expression Omnibus (GEO) using
the accession number [202]GSE112955. The proposed network-based
multi-objective optimisation workflow for miRNA biomarker discovery was
coded in MATLAB R2014b and R and is available at
[203]https://github.com/VafaeeLab/multiobj_miR_marker_discovery.
Electronic supplementary material
[204]Supplementary file 6^ (17.2KB, xlsx)
[205]Supplementary file 1^ (186KB, docx)
[206]Supplementary file 2^ (1KB, txt)
[207]Supplementary file 3^ (3MB, txt)
[208]Supplementary file 4^ (62.9KB, xlsx)
[209]Supplementary file 5^ (1.3KB, txt)
Acknowledgements