Abstract
Breast cancer is a heterogeneous disease and one of the most common
cancers among women. Recently, microRNAs (miRNAs) have been used as
biomarkers due to their effective role in cancer diagnosis. This study
proposes a support vector machine (SVM)-based classifier SVM-BRC to
categorize patients with breast cancer into early and advanced stages.
SVM-BRC uses an optimal feature selection method, inheritable
bi-objective combinatorial genetic algorithm, to identify a miRNA
signature which is a small set of informative miRNAs while maximizing
prediction accuracy. MiRNA expression profiles of a 386-patient cohort
of breast cancer were retrieved from The Cancer Genome Atlas. SVM-BRC
identified 34 of 503 miRNAs as a signature and achieved a 10-fold
cross-validation mean accuracy, sensitivity, specificity, and Matthews
correlation coefficient of 80.38%, 0.79, 0.81, and 0.60, respectively.
Functional enrichment of the 10 highest ranked miRNAs was analysed in
terms of Kyoto Encyclopedia of Genes and Genomes and Gene Ontology
annotations. Kaplan-Meier survival analysis of the highest ranked
miRNAs revealed that four miRNAs, hsa-miR-503, hsa-miR-1307,
hsa-miR-212 and hsa-miR-592, were significantly associated with the
prognosis of patients with breast cancer.
Introduction
Breast cancer is one of the major leading causes of death among women,
and it accounts for 14% of cancer deaths worldwide^[26]1,[27]2. There
are different types of breast carcinomas depending on the specific
cells in the breast that are affected; most breast cancers are a type
of adenocarcinoma. According the American Joint Committee on Cancer,
the three features used to stage breast cancer are the size of the
primary breast tumour (T), the spread of cancer to lymph nodes (N) and
distant metastasis (M)^[28]3. In the TNM staging system, the T category
represents the primary breast tumour and the spread within the tumour.
The T category comprises stages T1 to T4 based on the tumour size. T1
tumours are subdivided into T1a, T1b and T1c, and the tumour size is
>10 mm and ≤2 cm in dimension. T2 tumours are >2 cm, T3 tumours are
>5 cm, and T4 tumours are any size and may spread to the breast skin or
chest wall^[29]3. The estimated numbers of invasive and in situ breast
cancer cases and breast cancer deaths in 2013 in the United States are
32,340, 64,640 and 39,620, respectively^[30]4. Approximately 252,710
new cases and 40,610 breast cancer deaths are estimated for US women in
2017 according to the surveillance, epidemiology, and end result
programme (SEER 2017) statistics. Breast cancer survival rates are
associated with the stage of the cancer. The 5-year survival rates for
stages I, II and III are 100%, 93%, and 72%, respectively;
unfortunately, the 5-year survival rate for stage IV breast cancer is
only 22%^[31]5. Despite the advances in the treatment of breast cancer,
metastatic breast cancer remains incurable, and mortality rate is still
high due to the emergence of therapy-resistant cancer cells^[32]6 and
limitations in the current treatment strategies. A better understanding
of the molecular markers that affect breast tumours at different stages
may lead to the development of new therapeutic strategies.
Recent evidence demonstrated that molecular marker-based targeted
therapies have potential for the prognosis and diagnosis of various
diseases. Molecular target-based studies focused on advances in
microRNA (miRNA) expression profiling because of their prominent role
in tumour development and metastasis. MiRNAs are small noncoding RNAs
that regulate gene expression and are involved in human
carcinogenesis^[33]7. Over the past few years, many studies reported
the significant role of miRNAs in the molecular pathogenesis of breast
tumours. MiRNA profiling studies have identified miRNAs that are
aberrantly expressed in breast tumours and their functions. For
instance, miRNAs such as miR-125b, miR-145, miR-155, and miR-21 are
significantly deregulated in breast tumour tissues compared to normal
tissue^[34]8. Potential association between miRNA and breast neoplasm
has been predicted in studies^[35]9,[36]10. Functionally, miRNAs are
act as tumour suppressor^[37]11 and oncogene^[38]12 in breast tumour
progression and metastasis. Gene expression and miRNA expression
profiling has been used to classify different tumour
types^[39]13,[40]14. However, it has been confirmed that miRNA
expression profiles can classify tumour types more accurately than gene
expression profiles^[41]14.
Machine learning methods have been developed for cancer survival
calculation, risk classification and prognosis prediction in various
cancers, including breast cancer^[42]15–[43]17. Several researchers
have used different machine learning models and the Wisconsin breast
cancer dataset to categorize benign and malignant breast cancers. For
instance, M.F. Akay has used a support vector machine (SVM) combined
with feature selection for a medical decision making system to diagnose
breast cancer^[44]18. Abonyi and Szeifert have used a supervised
rule-based fuzzy classifier to categorize benign and malignant breast
cancers^[45]19. Pena-Reyes and Sipper have utilized a fuzzy-genetic
algorithm method to classify benign and malignant breast
tumours^[46]20. In addition, other well-known machine learning methods,
such as the feed forward neural network algorithm^[47]21, the C4.5
decision tree method^[48]22, the linear discreet analysis method^[49]23
and the neuron-fuzzy technique^[50]24, have been developed for breast
cancer diagnosis. Most machine learning methods developed for breast
cancer classification using breast tumor images^[51]25 and gene/miRNA
expression profiles^[52]26 to distinguish molecular subtypes^[53]27.
Shimomura et al. identified five miRNAs to distinguish the breast
cancer from other cancer types^[54]28. However, there are few studies
of identifying the miRNA signature associated with the breast cancer
stage for exploring the molecular level changes at various breast
cancer stages.
Although there are methodologies for breast cancer treatment,
challenges regarding early stage detection of breast tumours exist.
Early stage detection may help to obtain a better treatment diagnosis.
Therefore, we explored whether miRNA expression profiling could be used
to categorize early stage breast tumours accurately. In this study, we
collected the breast cancer data from the cancer genome atlas (TCGA)
database and proposed a SVM-based classifier called SVM-BRC to
categorize early stage and advanced stage patients with breast cancer
using their miRNA expression profiles. SVM-BRC is based on an SVM
incorporating an optimal feature selection method referred to as the
inheritable bi-objective combinatorial genetic algorithm
(IBCGA)^[55]29. We retrieved the miRNA expression profile data on 386
patients with breast cancer, with 193 patients in the early stage and
the remaining 193 patients at an advanced stage groups. To the best of
our knowledge, this is the first study to use miRNA expression profiles
to identify the miRNA signature for predicting the breast cancer stage.
SVM-BRC identified a signature consisting of 34 of 503 miRNAs that can
distinguish early stage breast cancer patients from advanced stage
breast cancer patients and achieved a 10-fold cross-validation (10-CV)
mean accuracy, sensitivity, specificity, and Matthews correlation
coefficient (MCC) of 80.38%, 0.79, 0.81, and 0.60, respectively.
Further, we ranked the identified miRNAs based on the MED scores. The
10 highest ranked miRNAs were analysed based on their involvement in
breast cancer and other cancer types. Functional enrichment of the 10
highest ranked miRNAs were analysed using Kyoto Encyclopedia of Genes
and Genomes (KEGG) and Gene Ontology (GO) annotations. Kaplan-Meier
survival analysis of the identified miRNAs revealed that four miRNAs
among the 10 highest ranked miRNAs, hsa-miR-503, hsa-miR-1307,
hsa-miR-212 and hsa-miR-592, were significantly associated with the
overall survival of patients with breast cancer.
Results and Discussion
Prediction performance of SVM-BRC
We used a dataset consisting of 386 patients with breast cancer and 503
miRNA expression profiles. The dataset was divided into early stage
(Stages I & II) and advanced stage (Stages III & IV) groups. Then, we
attempted to categorize the early stage and the advanced stage groups
using miRNA expression alone. The proposed SVM-BRC includes the feature
selection algorithm IBCGA to select a significant miRNA signature that
is associated with the tumour stage of breast cancer patients. SVM-BRC
identified a miRNA signature (34 miRNAs) that can classify early stage
and advanced stage groups and achieved a 10-CV mean accuracy,
sensitivity, specificity, and MCC of 80.38% ± 1.55%, 0.79 ± 2.7,
0.81 ± 2.26, and 0.60 ± 0.03, respectively. SVM-BRC achieved a 10-CV
accuracy, sensitivity, specificity, MCC and AUC of 83.16%, 0.84, 0.81,
0.66 and 0.87, respectively (shown in Table [56]1), and a jackknife
test accuracy of 63.89%. The prediction performance of SVM-BRC was
evaluated using a receiver operating curve (ROC), as shown in
Fig. [57]1.
Table 1.
Comparison of SVM-BRC with the some classifiers for the 386-patient
breast cancer cohort.
Method 10-CV accuracy (%) Sensitivity Specificity MCC
SVM-BRC-Mean 80.38 ± 1.55 0.79 ± 2.7 0.81 ± 2.26 0.60 ± 0.03
SVM-BRC-Best 83.16 0.84 0.81 0.66
Random forest 66.83 0.66 0.67 0.33
Multilayer perceptron 57.25 0.57 0.57 0.14
SMO 62.69 0.62 0.63 0.25
Naïve Bayes 64.50 0.63 0.65 0.29
Decision tree 50.25 0.50 0.50 0.01
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Figure 1.
Figure 1
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SVM-BRC performance evaluation using the ROC curve. The area under the
ROC curve is 0.87 using a 386-patient breast cancer cohort.
We compared SVM-BRC with some machine learning methods of Weka such as
Random forest (RF), Multilayer perceptron (MLP), Sequential minimal
optimization (SMO), Naïve Bayes, and Decision tree. We used information
gain for feature selection and Ranker attribute evaluator method, and
obtained 14 miRNAs to distinguish early stage and advanced stage
groups. The accuracies of RF, MLP, SMO, Naïve Bayes, and Decision tree
methods using the 14 miRNAs with 10-CV were 66.83%, 57.25%, 62.69%,
64.50%, and 50.25% respectively. The results of performance comparison
are shown in Table [60]1. The performance of SVM-BRC is much better
than the other machine learning methods in distinguish the early stage
and advanced stage groups.
Prioritizing the miRNA signature
We ranked the miRNAs identified by SVM-BRC using main effect difference
(MED) analysis^[61]30. The 10 highest ranked miRNAs based on their
contribution to the prediction accuracy are hsa-miR-200c, hsa-miR-503,
hsa-miR-1307, hsa-miR-361, hsa-miR-212, hsa-miR-592, hsa-miR-1185-1,
hsa-miR-146b, hsa-miR-1468, and hsa-miR-769. The 10 highest ranked
miRNAs and their MED scores are listed in Table [62]2. The 34 miRNA
signature and their rankings are shown in Supplementary Table [63]1.
Further, the significance of the 10 highest ranked miRNAs in breast
cancer is discussed.
Table 2.
Ten highest ranked miRNAs and feature knockout analysis of individual
miRNAs.
Rank miRNA MED scores Accuracy difference (%)
1 hsa-miR-200c 69.68 20.99
2 hsa-miR-503 65.02 20.73
3 hsa-miR-1307 48.44 21.25
4 hsa-miR-361 47.92 21.25
5 hsa-miR-212 46.89 20.99
6 hsa-miR-592 46.89 19.95
7 hsa-miR-1185-1 43.26 20.73
8 hsa-miR-146b 43.26 19.69
9 hsa-miR-1468 34.45 21.25
10 hsa-miR-769 30.82 20.47
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Hsa-miR-200c
Hsa-miR-200c scored 69.68 and ranked one according to the MED ranking
index, which means that the contribution of this miRNA is higher than
that of the others. The miR-200 family of miRNAs possesses a unique
role in cancer stem cells^[65]31, neurogenesis^[66]32, and
chemosensitivity^[67]33. Hsa-miR-200c is aberrantly expressed in
several cancers, including breast cancer. A retrospective analysis of
210 breast tumour samples revealed that hsa-miR-200c expression was
associated with poor distant relapse-free survival^[68]34. A luciferase
reporter assay study reported that hsa-miR-200c regulates cancer stem
cell functions such as proliferation and self-renewal; miR-200c
modulates the expression of the BM1 protein, which is an essential stem
cell self-renewal regulator in breast cancer stem cells^[69]35. It is
also observed that hsa-miR-200c suppresses the tumourigenicity of
breast cancer stem cells^[70]35. This miRNA targets class III beta
tubulin and increases the chemosensitivity in breast tumours^[71]33.
Hsa-miR-200c is also significantly expressed in several other tumours,
such as bladder cancer^[72]36, colorectal cancer^[73]37 and ovarian
cancer^[74]38.
Hsa-miR-503
Hsa-miR-503 expression was found to be downregulated in breast cancer
cells, and overexpression of this miRNA reduced cell proliferation by
targeting CCND1^[75]39. A quantitative RT-PCR study involving screening
a series of 12 inflammatory breast cancer cells showed that hsa-miR-503
was differently expressed and was used as a predictor for an
inflammatory breast cancer phenotype^[76]40. Recently, overexpression
of hsa-miR-503 was found in breast cancer tissue and plasma compared to
that in healthy tissue; upregulation of this miRNA in breast cancer
cells suppresses the expression of the epithelial-mesenchymal
transition-related protein SMAD2 and the epithelial marker protein
E-cadherin^[77]41. Experimental evidence showed that hsa-miR-503
regulates the oncogene ZNF217 and that higher expression of this miRNA
is associated with improved survival in breast cancer^[78]42.
Hsa-miR-503 acts as a tumour suppressor by targeting DDHD2 in breast
cancer cells^[79]43.
Hsa-miR-1307
Hsa-miR-1307 was found to be upregulated in breast cancer. Hsa-miR-1307
was differentially expressed with a fold-change of 0.36 between breast
cancer and the adjacent normal control tissue^[80]44. Hsa-miR-1307
expression was upregulated in BRCA1-associated breast carcinoma
compared to that in the normal counterparts^[81]45.
Hsa-miR-361
A miRNA expression profiling study of 376 human miRNAs reported that
hsa-miR-361 expression was downregulated in MCF-7 docetaxel-resistant
breast cancer cells^[82]46. A screening study of miRNAs related to
different subtypes of breast cancers showed that hsa-miR-361 was
upregulated in metastatic breast tumours^[83]47. A microarray-based
study of 375 breast tumour cases revealed that overexpression of
hsa-miR-361 is correlated with the better disease-free survival in
patients with breast cancer^[84]48. Downregulation of hsa-miR-361 was
observed in 60 breast cancer tissues; hsa-miR-361 targets FGFR1 and
MMP-1, resulting in inhibition of glycolysis and invasion in breast
cancer cells^[85]49.
Hsa-miR-212
A case study of patients diagnosed with breast invasive ductal
carcinoma reported that hsa-miR-212 was significantly downregulated in
breast tumours by 0.328-fold and that this reduced expression was
prominent in high grade breast tumours^[86]50. Hsa-miR-212 expression
was downregulated in 30 paired triple-negative breast cancer samples,
and its expression inhibited cell migration and invasion during cancer
progression by targeting Prrx2^[87]51.
Hsa-miR-592
A real-time PCR study of a nonmetastatic breast cancer cell line
reported the overexpression of hsa-miR-592^[88]52. Antonio Colaprico et
al. identified differentially expressed miRNA-regulating pathway
crosstalk between breast cancer and healthy samples; hsa-miR-592
expression was approximately twenty-three times higher in breast cancer
samples than in healthy samples and regulated the extrinsic prothrombin
activation pathway^[89]53. Recently, marked downregulation of
hsa-miR-592 was observed in a breast cancer cell line compared to that
in a normal breast cell line and further, hsa-miR-592 acted as tumour
suppressor by targeting the transforming growth factor β-2 in breast
cancer^[90]54.
Hsa-miR-146b
Hsa-miR-146b was downregulated and negatively regulated nuclear
factor-kappaB, resulting in a reduction of the metastatic potential in
breast cancer cells^[91]55. Higher expression of hsa-miR-146b induced
interleukin-6 expression and signal transducer and activator
transcription 3 phosphorylation, and this expression was positively
correlated with survival in some breast cancer subtypes^[92]56.
Hsa-miR-146a and hsa-miR-146b were found to be the most expressed in
breast cancer metastasis suppressor 1-expressing cells, and
upregulation of hsa-miR-146b was observed in the MDA-MB-435 breast
cancer cell line^[93]57. A reporter assay study of triple negative
breast tumours reported that hsa-miR-146b negatively regulates BRCA1 in
triple negative sporadic breast cancer^[94]58. An RT-PCR study of 120
young women with primary breast tumours and 130 patients with breast
fibroadenoma reported that downregulation of hsa-miR-146b expression in
breast cancer cells was associated with the development and
deterioration of breast cancer^[95]59.
Hsa-miR-769
Examination using the Nanostring nCounter assay on 43 miRNAs reported
that hsa-miR-769 can inhibit the expression of N-myc
downstream-regulated gene 1 upon reoxygenation in the breast
adenocarcinoma cell line MCF-7 and that overexpression of hsa-miR-769
significantly enhanced apoptosis^[96]60. A study of triple negative
breast cancer comparing African-American and non-Hispanic white women
reported that 26 miRNAs, including hsa-miR-769, were differentially
expressed between these groups^[97]61. Hsa-miR-769 found to be
upregulated with a log2-fold change of 1.355 between triple negative
breast cancer in African-American and non-Hispanic white women^[98]61.
Differential expression of hsa-miR-769 was also found in male breast
cancers^[99]62,[100]63.
Our analysis of the 10 highest ranked miRNAs acknowledged that two
miRNAs, hsa-miR-1185-1 and hsa-miR-1468, among the 10 highest ranked
miRNAs are not directly involved in breast cancer but are implicated in
other cancers. For instance, hsa-miR-1185-1 expression was abnormally
low in Alzheimer’s disease^[101]64 and atherosclerosis^[102]65. The
expression of hsa-miR-1468 was upregulated in hepatocellular carcinoma
tissue^[103]66. Dysregulation of hsa-miR-1468 was observed in
epithelial ovarian cancer^[104]67. Hsa-miR-1468 was significantly
associated with the recurrence-free survival in lung
adenocarcinoma^[105]68. Therefore, these two miRNAs are important
molecules to validate further in breast cancer. Eight miRNAs among the
10 highest ranked miRNAs are involved not only in breast cancer but
also in several major cancer types.
Additionally, we employed miRNA knockout analysis to observe the
difference in the prediction performance by removing one miRNA from the
signature. Each miRNA of the 10 highest ranked miRNAs can affect the
prediction performance with a mean accuracy difference of 20.73 ± 0.54.
We report the results of knockout of the 10 highest ranked miRNAs in
Table [106]2. The accuracy difference after removing each miRNA is
depicted in Fig. [107]2. The accuracy differences obtained from feature
knockout analysis for 34 miRNA signature are shown in Supplementary
Table [108]1.
Figure 2.
Figure 2
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Feature knockout analysis. Prediction performance difference for
individual miRNAs using feature knockout analysis.
Difference of expression profiles between early stage and advanced stage
groups
We measured expression levels of the 10 highest ranked miRNAs in early
stage and advanced stage groups. We observed a slight expression
difference between early and advanced stage groups for 10 highest
ranked miRNAs. Of the 10 highest ranked miRNAs, the mean expression
values of hsa-miR-200c, hsa-miR-503, hsa-miR-1307, hsa-miR-361,
hsa-miR-212, hsa-miR-592, hsa-miR-1185-1, hsa-miR-146b, hsa-miR-1468,
and hsa-miR-769 are 13.34 ± 0.94, 3.44 ± 1.28, 10.16 ± 1.04,
8.35 ± 0.57, 2.20 ± 0.83, 1.93 ± 1.11, 0.24 ± 0.39, 9.03 ± 0.94,
2.50 ± 1.10, and 4.88 ± 0.70, respectively, in the early stage group,
and 13.28 ± 0.77, 3.80 ± 1.39, 9.93 ± 1.12, 8.30 ± 0.55, 2.20 ± 0.80,
1.80 ± 1.11, 0.35 ± 0.39, 9.20 ± 0.96, 2.45 ± 1.06, and 4.75 ± 0.77,
respectively, in the advanced stage group. Box-plot representation of
expression difference in the early stage and advanced stage groups is
given for the signature of 34 miRNAs in Supplementary Fig. [110]1.
KEGG pathway enrichment analysis
To investigate the functional mechanism of the 10 highest ranked
miRNAs, we employed KEGG pathway analysis using the DIANA-mirPath v.3
web server^[111]69. The 10 highest ranked miRNAs are significantly
enriched in pathways involving fatty acid biosynthesis, fatty acid
metabolism, adherens junction, protein processing in endoplasmic
reticulum, cytokine-cytokine interaction, bacterial invasion of
epithelial cells, spliceosome, and proteoglycans in cancer. The
significantly enriched in KEGG pathways for the 10 highest ranked
miRNAs and the target genes involved in each pathway are listed in
Table [112]3. The 10 highest ranked miRNAs and the number of targeted
genes are shown in Fig. [113]3. A detailed summary of the 10 highest
ranked miRNAs, the enriched KEGG pathways and the number of targeted
genes is provided in Supplementary Table [114]2.
Table 3.
Enriched KEGG pathways and the corresponding target genes for the 10
highest ranked miRNAs.
KEGG pathway p-value Target genes
Fatty acid biosynthesis (hsa00061) <1e-325 FASN
Fatty acid metabolism (hsa01212) <1e-325 FASN
TECR
ACOX1
Adherens junction (hsa04520) 4.47E-06 TGFBR1, MET, WASL, SMAD2, ACTG1,
IQGAP1, IGF1R, VCL, RHOA, TJP1, MLLT4, CDH1, CTNNB1, CTNNA1, WASF2,
ACTN4, CREBBP
Protein processing in endoplasmic reticulum (hsa04141) 0.00083483
HSPA1A, EIF2AK1, SSR1, RAD23B, AMFR, UGGT1, YOD1, SEL1L, HSP90AA1,
DNAJC10, UBE2E2, STT3B, HSPH1, PDIA6, RAD23A, PRKCSH, VCP, HSPA8,
LMAN1, RPN2, DERL1, HSPA1B
Cytokine-cytokine receptor interaction (hsa04060) 0.002767508 IL6ST
Bacterial invasion of epithelial cells (hsa05100) 0.01255968 ARPC5L,
MET, ITGB1, WASL, SEPT11, ACTG1, VCL, RHOA, CD2AP, CDH1, CLTA, WASF2,
FN1, ARPC2
Spliceosome (hsa03040) 0.02884541 RBM25, HSPA1A, HNRNPA1, DDX23, PPIL1,
U2SURP, PRPF8, SRSF1, HNRNPM, DHX15, HSPA8, DHX16, SRSF3, HSPA1B,
SNRPC, SNRNP200, SRSF8
Proteoglycans in cancer (hsa05205) 0.03666157 PDCD4, MET, ITGB1, EZR,
ARHGEF12, ACTG1, FRS2, IQGAP1, RHOA, ERBB3, ITGAV, LUM, HOXD10, FN1,
MAP2K1, SDC4, TWIST1, VEGFA, MDM2, SMAD2, WNT5A, PPP1CC, ACTG1, TIAM1,
IGF1R, AKT2, PTK2, CTNNB1, ITGA2, DDX5, GAB1
[115]Open in a new tab
Figure 3.
Figure 3
[116]Open in a new tab
KEGG pathway analysis of the 10 highest ranked miRNAs.
Most of the 34 miRNAs are prevalently involved in the biological
pathways. For example, 30 miRNAs of the signature are significantly
involved in the RAS signalling pathway, cGMP-signaling pathway, and
cancer pathways by targeting 123, 90 and 229 genes, respectively. There
are 29 miRNAs significantly involved in focal adhesion, PI3K-Akt
signaling pathway, MAPK signaling pathway, and viral carcinogenesis.
There are 28 miRNAs in proteoglycans in cancer pathway, ErbB signaling
pathway, cAMP signaling pathway, and estrogen signaling pathway to name
a few. Details of the miRNA signature involved in biological pathways
and their targeted genes are listed in Supplementary Table [117]3.
Gene ontology analysis
The biological significance of the 10 highest ranked miRNAs was
analysed using GO annotations at three levels, includes biological
process, molecular functions and cellular component. The 10 highest
ranked miRNAs were highly enriched in five biological processes:
mitotic cell cycle, cellular protein modification process, viral
process, small molecule metabolic process, and symbiosis, encompassing
mutualism through parasitism. The 10 highest ranked miRNAs were highly
enriched in the molecular functions enzyme binding, RNA binding, and
poly(A) RNA binding; the significantly enriched cellular components
include protein complex, nucleoplasm, cytosol, organelle and focal
adhesion. The enriched biological processes, molecular function and
cellular components of the 10 highest ranked miRNAs are shown in
Fig. [118]4(a–c). GO analysis of the 10 highest ranked miRNAs and the
targeted genes for biological process, molecular function and cellular
component are listed in Supplementary Tables [119]4, [120]5 and [121]6
respectively.
Figure 4.
[122]Figure 4
[123]Open in a new tab
Gene ontology (GO) annotations for the 10 highest ranked miRNAs. GO
enrichment analysis was performed for the 10 highest ranked miRNAs at
three levels: biological process (a), molecular functions (b), and
cellular component (c).
Survival analysis of the top ranked miRNAs
Survival analysis was performed using Kaplan-Meier plotter^[124]70 to
validate the prognostic value of the top ranked miRNAs. We selected the
TCGA dataset and systematically evaluated the patient data using the
Kaplan-Meier survival analysis. Four of the 10 highest miRNAs,
hsa-miR-503, hsa-miR-1307, hsa-miR-212 and hsa-miR-592, were
significantly associated with the prognosis of patients with breast
cancer. These four miRNAs, hsa-miR-503, hsa-miR-1307, hsa-miR-212 and
hsa-miR-592, obtained P-values of 0.0028, 0.0011, 0.005, and 0.045,
respectively, and hazard ratios of 2.14, 2.33, 0.42 and 0.51,
respectively, between the high and low expression groups. The
Kaplan-Meier survival curves for the four miRNAs are shown in
Fig. [125]5.
Figure 5.
[126]Figure 5
[127]Open in a new tab
Kaplan-Meier plots of hsa-miR-503, hsa-miR-1307, hsa-miR-212, and
hsa-miR-592 for the systemically treated breast cancer cohort.
To confirm the association between the four miRNAs with overall
survival, we utilized the METABRIC dataset. Two of the four miRNAs show
significant association with prognosis in patients with breast cancer.
Two miRNAs, hsa-miR-503 and hsa-miR-1307, obtained P-values of 0.046
and 0.0031, respectively, and hazard ratios of 0.82 and 1.37,
respectively, between the high and low expression groups. Whereas
another two miRNAs, hsa-miR-212 and hsa-miR-592, obtained P-values of
0.16 and 0.35, respectively, and hazard ratios of 0.87 and 0.9,
respectively, between the high and low expression groups.
Another four of the 10 highest miRNAs, hsa-miR-200c, hsa-miR-1185,
hsa-miR-146b and hsa-miR-769, were significantly associated with the
prognosis of patients with breast cancer. These four miRNAs,
hsa-miR-200c, hsa-miR-1185, hsa-miR-146b, and hsa-miR-769, obtained
P-values of 0.00017, 1.4e-05, 0.0018, and 0.0078, respectively, and
hazard ratios of 1.49, 0.6, 0.73, and 0.76, respectively, between the
high and low expression groups. The Kaplan-Meier survival curves for
the four miRNAs are shown in Supplementary Fig. [128]2.
Additionally, we estimated overall survival of the breast cancer
patients using Multiple linear regression^[129]71, and observed that
correlation between these four miRNAs and overall survival is better in
the advanced stage group when compared to the early stage group. The
correlation coefficient between actual and overall survival in early
stage and advanced stage groups is 0.26 and 0.40, respectively. The
correlation plots are shown in Supplementary Fig. [130]3.
Conclusions
The challenges for early stage detection of breast cancer are that
breast cancer is a heterogeneous disease with the potential for
metastatic spreading at an early stage. Detecting cancer at a treatable
stage and removing the lesions can prevent the development of lethal
invasive cancers and would prevent death from breast cancer. Currently,
it is widely reported that miRNAs can be potential biomarkers for
various cancers. Identifying the disease-related miRNAs aids to improve
the understanding of pathogenesis and diagnosis. Hence, various
potential computational models have been developed to investigate the
miRNA disease-association^[131]9,[132]72–[133]74. However, only a few
studies focused on identifying a miRNA signature for the early stage
detection of breast cancer. Accordingly, in this study, we proposed a
novel miRNA-based classification method to categorize the early stage
and the advanced stages of breast cancer. Recent development of
personalized medicine and growing trend in applications of machine
learning techniques improved the prognosis and cancer prediction.
Various machine learning methods and feature selection algorithms have
been widely used to identify the important factors that influence
cancer progression, cancer recurrence, and cancer survival. Generally,
machine learning based cancer prediction studies used mRNA/miRNA
expression profiles, histological variables and clinical factors as
input to the cancer prediction procedure^[134]75–[135]77. Success in
developing computational models for cancer predictions depends on
understanding of biological knowledge and limitations of the training
data set such as a small set of high-dimensional samples called “curse
of dimensionality”^[136]78. However, the over-training problem can be
coped with proper feature selection and cross-validation methods.
Hence, we proposed an SVM-based classifier called SVM-BRC that
incorporated the feature selection method IBCGA to identify a miRNA
signature that can distinguish early stage from advanced stage breast
cancer. SVM-BRC identified a 34-miRNA signature and obtained a 10-CV
accuracy, sensitivity, specificity, MCC and AUC of 83.16% 0.84, 0.81,
0.66 and 0.87, respectively. SVM-BRC obtained an average training
accuracy of 80.38% ± 1.55%. Further, we ranked the identified miRNAs
using MED scores. The significance of the 10 highest ranked miRNAs was
validated using the literature. The importance of the top-10 miRNAs in
breast cancer progression and other cancers is discussed. The
prediction performance difference was measured for the 10 highest
ranked miRNAs using feature knockout analysis. The functional
mechanisms of the 10 highest ranked miRNAs were analysed using KEGG
pathway enrichment and GO enrichment at three levels, including
biological process, molecular functions, and cellular components.
Survival analysis of the highest ranked miRNAs in the breast cancer
cohort using the Kaplan-Meier curve revealed that four miRNAs,
hsa-miR-503, hsa-miR-1307, hsa-miR-212, and hsa-miR-592, among the
top-10 miRNAs were significantly (P ≤ 0.05) associated with the
prognosis of breast cancer. We hope that our findings will help to
improve the early stage detection methodologies by using the miRNA
signature as a biomarker of breast cancer.
Materials and Methods
Dataset
The miRNA expression profiles of breast cancer cohort obtained from the
Illumina HiSeq 2000 miRNA sequencing platform were obtained from TCGA
database. We considered only the patients who underwent radiotherapy or
targeted molecular therapy. Further, we divided the patients into early
stage and advanced stage based on their pathological condition. After
the filtering, the final balanced dataset contained 386 patients, with
193 patients in the early stage group and 193 in the advanced stage
group, along with 503 miRNA expression profiles.
SVM-BRC
Support vector machines (SVMs) are based on statistical learning
theory^[137]79. The main idea of an SVM is to find the optimal
hyperplane between the two classes. SVMs have been used to solve
biological problems due to their potential discriminating ability. SVMs
have been widely used to detect tumour markers^[138]80 and to perform
cancer predictions^[139]81. Thus, we proposed an SVM-based classifier
SVM-BRC including the feature selection method IBCGA to categorize
early stage and advanced stage groups with breast cancers. The general
formulation of the SVM is
[MATH:
Minimiz<
mi>e12<
mo stretchy="false">∥w∥2+C∑i=1n
Si :MATH]
1
where w is vector of the hyperplane, C is the classifier parameter,
S[i]are the variables and n = number of vectors in the training
dataset.
Inheritable bi-objective combinatorial genetic algorithm (IBCGA)
To select a small set of miRNAs (signature) from a large number of
expression profiles (503 miRNAs) we used a genetic algorithm (GA) based
feature selection algorithm IBCGA^[140]29. The feature selection
algorithm IBCGA uses an intelligent evolutionary algorithm^[141]82 to
solve the large parameter optimization problem. IBCGA has been
successfully applied in several bioinformatics problems, including the
prediction of human ubiquitination sites^[142]83, the prediction of the
regulatory roles of cyclic AMP receptor proteins^[143]84 and the
estimation of survival time for cancer patients^[144]85,[145]86.
In this study, we used IBCGA and identified a miRNA signature (m = 34
miRNAs) from a large number of miRNA expression profiles (n = 503
miRNAs) to distinguish the early stage and advanced stage groups with
breast cancer. We used traditional terms of GA, GA-gene and
GA-chromosome. The GA-chromosome of IBCGA consists of n binary GA-genes
for feature selection and two 4-bit GA-genes for encoding parameters C
and γ of SVM. Normalized miRNA expressions of patients with n miRNAs
were used as input of IBCGA in designing the SVM-based classifier. The
parameter setting of IBCGA was as follows: r[start] = 10, r[end] = 50,
N[pop] = 50, G[max] = 60, and r = r[start]. We used the LibSVM
package^[146]87 to implement SVM-BRC. The steps of IBCGA are as
follows.
Step 1: (Initialization) Randomly generate a population of N[pop]
individuals.
Step 2: (Evaluation) Evaluate the fitness value of all individuals
using the fitness function that is the prediction accuracy in terms of
10-fold cross-validation (10-CV).
Step 3: (Selection) Use a tournament selection method that selects the
winner from two randomly selected individuals to generate a mating
pool.
Step 4: (Crossover) Select two parents from the mating pool to perform
orthogonal array crossover operation.
Step 5: (Mutation) Apply a conventional mutation operator to the
randomly selected individuals in the new population. Mutation is not
applied to the best individuals to prevent the best fitness value from
deterioration.
Step 6: (Termination test) If the stopping condition for obtaining the
solution is satisfied, output the best individual as the solution.
Otherwise, go to Step 3.
Step 7: (Inheritance) If r < r[end], randomly change one bit in the
binary GA-genes for each individual from 0 to 1; increase the number r
by one, and go to Step 3. Otherwise, stop the algorithm.
Step 8: (Output) Obtain a set of m miRNAs from the GA-chromosome of the
best individual.
Weka classifier
Weka has implementations of all major learning techniques for
classification and regression methods. Some methods of Weka data mining
software^[147]88 were used to compare SVM-BRC such as Random forest
(RF), Multilayer perceptron (MLP), Sequential minimal optimization
(SMO), Naïve Bayes, and Decision tree for classification to
discriminate early stage and advanced stage groups with breast cancer.
We evaluated the prediction performance of SVM-BRC using the prediction
accuracy (ACC), sensitivity (Sn), specificity (Sp), Matthews
correlation coefficient (MCC), and area under the ROC curve (AUC).
[MATH:
ACC=TP+TNTP+<
/mo>TN+FP+FN :MATH]
2
[MATH:
Sensiti<
mi>vity=TPTP+FN :MATH]
3
[MATH:
Specifi<
mi>city=TNTN+FP :MATH]
4
[MATH:
MCC=TP×TN−FP×FN(TP+FP)(TP+FN)(TN+FP)(TN+FN) :MATH]
5
where TP is true positive; TN is true negative; FP is false positive;
and FN is false negative.
KEGG and GO term enrichment analysis
We used DIANA-mirPath v3.0 for KEGG pathway analysis. Fisher’s exact
t-test was used for enrichment analysis^[148]89. GO term analysis was
employed to determine the involvement of the 10 highest ranked miRNAs
in biological process, molecular functions and cellular components
using mirPath v3.0. The DIANA-Tarbase algorithm in the mirPath web
server was used to predict the experimentally validated miRNA
targets^[149]89.
Kaplan-Meier survival analysis
To identify the miRNAs associated with the prognosis of breast cancer
patients, we employed Kaplan-Meier survival analysis using the
mirPower-Kaplan-Meier plotter web-tool^[150]70. We selected TCGA breast
cancer dataset, and the analysis was restricted to only patients
systemically treated with chemotherapy.
Electronic supplementary material
[151]Supplementary Information^ (697.8KB, pdf)
Acknowledgements