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Neural approach to characterize salivary gland neoplasms (SGN)
Applied Soft Computing Journal xxx (xxxx) xxx
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Immune/Neural approach to characterize salivary gland neoplasms (SGN) Carlos Rafael Lima Monção a , Eloa Mangabeira Santos a , Thiago Silva Prates a , Alfredo Maurício Batista de Paula b , Claudio Marcelo Cardoso a,d , Lucyana Conceição Farias b , ∗ Sérgio Henrique Sousa Santos e , Marcos Flávio Silveira Vasconcelos D’Angelo c , , André ∗ Luiz Sena Guimarães b , a
Graduate Program in Health Sciences, State University of Montes Claros, Montes Claros, Brazil Department of Dentistry, State University of Montes Claros, Montes Claros, Brazil c Department of Computer Science, State University of Montes Claros, Montes Claros, Brazil d Dilson Godinho Hospital, Montes Claros, Brazil e Institute of Agricultural Sciences, Federal University of Minas Gerais, Montes Claros, Brazil b
article
info
Article history: Received 7 February 2019 Received in revised form 13 August 2019 Accepted 17 October 2019 Available online xxxx Keywords: Bioinformatics Salivary gland neoplasm Immune/Neural approach
a b s t r a c t The purpose of the current study was to use immune-inspired algorithm ClonALG whose performance is increased by using the Kohonen neural network training algorithm (Immune/Neural approach), to characterize the nature of salivary gland neoplasms (SGNs). The leader gene approach in order to identify biomarkers for SGNs. Extensive data were obtained for each of the 35 types of neoplasms. The gene leaders for each type of SGN were identified in a table and then divided according to the two different methods: K-means clustering and Immune/Neural approach. Genes related to SGNs were identified using PubMed, OMIM and Genecards databases. A bioinformatics algorithm was then applied, and the STRING database was employed to build networks of protein–protein interactions for each nature of an SGNs. The weighted number of links (WNL) and total interactions score (TIS) values were then obtained. Finally, the genes were clustered, and the gene leaders were identified using the K-means clustering method and the Immune/Neural approach. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Salivary gland neoplasms (SGNs) are a group of heterogeneous and rare neoplasms [1]. They vary considerably in their anatomic site of origin, histology and biological behavior. The World Health Organization (WHO) classifies SGNs according to their histopathological features [2,3]. The great variety of named neoplasms is due to the heterogeneity between different types of neoplasms [2,3]. It is possible that the presence of hybrid neoplasms and progression to malignancy are associated with this morphological variability [2,3]. These characteristics can make histopathological diagnosis difficult [2,4]. There is no one predominant factor known to be implicated in the development of cancer of the salivary gland, although there are many factors that may play a role [5], such as radiation, cosmetics, nickel compounds and tobacco [6–8]. A biopsy of an SGN is required to make the histopathological diagnosis and to conclude whether it is benign or malignant [1]. ∗ Corresponding authors. E-mail addresses: [email protected] (M.F.S.V. D’Angelo), [email protected] (A.L.S. Guimarães).
However, diagnosis can be challenging, even for an expert pathologist [1]. Moreover, complementary molecular tests are often required to diagnose SGNs [9]. Surgical resection is the ideal treatment for SGNs. However, radiation therapy is also used in high-grade neoplasms, those with a positive surgical margin, and for the inoperable disease. Currently, chemotherapy and molecular-targeted therapies are reserved for the treatment of recurrent and metastatic disease [10]. Some studies have focussed on the expression of biological targets in salivary gland neoplasms, including c-kit, EGFR, HER2, and AR, among others, all of which could be potential biomarkers, prognostic markers or molecular targets [11–21]. However, only a few studies have explored large-scale screening techniques. Bioinformatics approaches have provided meaningful results in the analysis of a wide variety of diseases [22–26]. The use of bioinformatics is helpful for genomic and proteomic analyses. Moreover, bioinformatics could contribute to the discovery of new cancer biomarkers. The leader gene approach has been used and improved for specific conditions [22–26]. In the current study, the main contribution was to incorporate an immune/neural classification [27] with the leader gene
https://doi.org/10.1016/j.asoc.2019.105877 1568-4946/© 2019 Elsevier B.V. All rights reserved.
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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approach in order to characterize the nature of SGNs. SGN present different phenotypic characteristics and various clinical outcomes, which proved to be a diagnostic challenge [28,29]. Recently, the screening process to identify molecular targets related to SGN were suggested [28,29]. However, previous studies were limited to investigate patients’ samples [28,29]. The current study was the first to develop an algorithm to differentiate malignant from benignant SGN. Moreover, the present study demonstrated that immune/neural classification method improve the gene leader approach sensitivity to K-means. This immune/neural classification method is based on the association of ClonALG immune systems [30] with the Kohonen neural network [31]. The stopping criteria and the antibody selection of the proposed new approach had to be modified in order to accommodate a non-fixed number of antibodies. One of the contributions of this paper is to propose this new classification scheme in which there is no requirement of a mathematical or statistical models for the plant or signals, neither any threshold specification. Other important contribution is to propose an approach that has low implementation complexity with high performance in fault detection and isolation, which is a crucial point for on-line real-world applications. 2. Bioinformatics and systems biology analysis A bioinformatics approach was performed as described previously [22–26]. Briefly, we firstly performed searches using Medical Subject Headings (MeSH) terms, comprising all types of SGNs. The second step was to identify genes involved in SGNs in the on the subject was held search in the MEDLINE/PubMed (http: //www.ncbi.Nlm.nih.gov/), Online Mendelian Inheritance in Man R (OMIM) and Genecards⃝ (www.genecards.org) databases, only considering human genes [32,33]. We then checked the names and abbreviations of each gene with the Human Genome Organization (HUGO) to ensure consensus with gene names used by the scientific community. After this, the list of potential genes involved in SGNs was expanded using the web-available Search Tool for the Retrieval of Interacting Genes (STRING) software (version 10.0), which is a database that lists protein–protein interactions [34]. Only interactions with the highest degrees of confidence (above 0.900, range 0–0.99) were considered. Bioinformatics is an approach based on a systematic search to identify genes involved in the specific pathological process, which are then classified in order of importance according to the number of interactions determined experimentally. This rank was created based on a calculation of the interactions between genes, called the ‘‘weighted number of links’’ (WNL), which represents the gene interactions in a closed network. These interactions were identified using the STRING database, which allows scores to be obtained for each interaction. Maps are subsequently built among the identified genes to create networks [34]. Every gene identified was adjusted by multiplying to 1000 [22,25,35]. We also calculated the total interaction score (TIS), which comprises all of the gene interactions in the STRING database. A topological analysis was performed with Cytoscape, which is a software platform commonly used to visualize molecular interaction networks [36,37]. The Ontological analysis, performed using the Biological Networks Gene Ontology (BiNGO) tool according to a previously described method [38,39]. Using the values of WNL and TIS for each gene related to the various types of SGN, the following clustering procedure was carried out using SPSS (version 18.0; IBM, New York, NY, USA). We firstly used K-means clustering, then validated the results with ANOVA and Tukey–Kramer post hoc test, with a P-value of <0.001 considered statistically significant [22,30]. The genes with the highest rank of associations were identified as leader
Fig. 1. Algorithm flow chart, where a part of the mutation mechanism is carried out by the Kohonen neural network.
genes. The other clustering procedure is based on the immuneinspired algorithm ClonALG [30], aided by the Kohonen neural network [31] training algorithm in order to increase its performance, as presented in [27]. The Immune/Neural Approach to Clustering Procedures is given in Section 3. 3. Immune/Neural Approach to Clustering Procedures The Clustering Procedures is based on the immune-inspired algorithm ClonALG [30], aided by the Kohonen neural network [31] training algorithm in order to increase its performance. Inspired in the biological immune system, the artificial system must be able to react to any foreign substance that affects its integrity. These substances are called antigens and the system elements that react to them are called antibodies [40]. The ClonALG approach explicitly takes into account the antibodies affinity where only the fittest are selected to proliferate through a process called maturation (or mutation). In this paper, the affinity criterion is determined by the Euclidian distance. 3.1. Training process Every antigen used in the training step was tagged with its type so that the algorithm could train each antibody to recognize a specific type of antigen. The metric used to determine if an antibody recognized an antigen was the Euclidian distance, in which the antigen will be recognized by the nearest antibody. The training process begins with one antibody created in a central position (related to the antigens’ positions in space). Then, all antibodies are submitted to three steps that iterate until the stopping criteria are satisfied: cloning, mutation and selection (Fig. 1). It is important to know that this approach is based on a non-fixed size population of antibodies. 3.1.1. Selection mechanism As shown in Fig. 2, the antibody selection happens when all combinations of two elements out of the antibodies set are analyzed. When the set of antigens recognized by two of them are of the same type and they are close with each other, a new antibody is created between them and those two must be deleted. Also, the antibodies which did not recognize any type of antigen are deleted. The threshold used as proximity criterion between two antibodies is calculated at every iteration; it is set to 25% of the average distance between every two antibodies.
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 3. Cloning mechanism flow chart.
Fig. 4. Mutation mechanism flow chart.
(Ab )
pop Fig. 2. Selection mechanism flow chart, where represents every two 2 element combination out of the antibody population set.
3.1.2. Cloning mechanism In the cloning mechanism (Fig. 3), each antibody of the system is cloned twice and each one of these clones suffers a random mutation using a uniform distribution in its space position. This guarantees diversity in the antibody population.
3.1.3. Mutation mechanism The mutation began at the cloning mechanism and will be improved by a Kohonen neural network (Fig. 4). Each antigen linearly reduces the distance between him and the closest antibody. This algorithm changes the antibody’s position as if it was weights of a neuron.
3.1.4. Stopping criteria Two stopping criteria were defined to limit the amount of repetitions of the processes of cloning, mutation and selection. The first criterion checks the algorithm’s convergence; the proposed methodology has two steps, cloning and selection, that creates and deletes antibodies, respectively. It was a good approximation to confirm its convergence by checking if it is creating and deleting antibodies by the same amount. Nevertheless, there is some situations where these numbers (created and deleted) are never equal, so the use of another criterion is necessary. The second criterion limits the maximum number of iterations by an integer K. To show the advantage of the proposed methodology over some types of problems, an experiment with two different scenarios was carried out. The first scenario (Fig. 5) describes a problem with three patterns spread in space but not too mixed with each other. The proposed methodology was put against the ClonALG and Kohonen algorithms individually. It is important to know that ClonALG and Kohonen algorithms use a fixed number of cluster prototypes (antibodies for ClonALG
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 5. Scenario 1: spread patterns without mixing.
Fig. 6. Scenario 2: spread and heavily mixed patterns.
Table 1 Average performance statistics using the PDBAC’s mean in scenario 1. Algorithm
Average (%)
Best case (%)
Worst case (%)
Hybrid
86.86%
88.93%
77.47%
ClonALG (3 cluster prototypes)
58.22%
73.03%
45.94%
ClonALG (7 cluster prototypes)
67.75%
80.03%
55.15%
Kohonen (3 cluster prototypes)
60.33%
80.25%
50.88%
Kohonen (7 cluster prototypes)
78.68%
88.55%
66.99%
Table 2 Hybrid methodology behavior. #
Average
Best case
Worst case
Iterations to convergence Antibodies
16.65 6.9
3 6
50 8
and neurons for Kohonen). In this example, they were configured to work with 3 and 7 cluster prototypes; the number 7 was chosen to offer a fair competition given the average number of antibodies used by the proposed methodology (Table 2). The maximum number of iterations was set to 50 and 100 simulations were performed for each algorithm. For multiclass problems, using the sample accuracy (correct predictions divided by total of examples) can lead to some erroneous interpretations, because this method does not account for class imbalances in the data set [41]. The alternative was to use a Bayesian approach to estimate the posterior distribution of the balanced accuracy (PDBAC) for a multiclass problem, given in [42]. The results using this metric are presented in Table 1. It is possible to argue that a simple increase in the cluster prototypes’ number would boost these algorithms’ performances, but it is important to remember that this is an illustrative problem with low dimensionality and only a few data types. As for the proposed algorithm, some numbers are important to show how it behaves. These numbers are shown in Table 2. For a simple algorithm convergence analysis of Hybrid methodology, the maximum number of iterations was set to 50, the number of performed simulations was 100 and the results are presented in Table 2, illustrating perfectly the second stopping criteria. In some simulations, the algorithm could not stop before the maximum (K = 50) number of iterations, but the majority
Table 3 Average performance statistics using the PDBAC’s mean in scenario 2. Algorithm
Average (%)
Best Case (%)
Worst Case (%)
Hybrid 3-NN 5-NN Binary tree Naive Bayes
86.67% 75.56% 74.02% 69.48% 38.01%
92.28% 87.62% 85.04% 81.56% 72.89%
80.39% 62.99% 59.24% 54.51% 23.16%
stopped before that by checking the amount of created and deleted antibodies. Clearly, more tests are needed in order to show the advantage of the proposed methodology. Let us consider another scenario, this time with the patterns strongly mixed. This experiment consists in testing over a new data set (Fig. 6) other known classification algorithms against the proposed hybrid. To validate the proposed methodology, a training set containing 70% randomly picked (using a uniform distribution) observations was extracted from the original data set; the other 30% were used to validate the performance of each classifier using PDBAC as the metric, once again. For this experiment, the chosen algorithms were: K nearest neighbors (with K = 3, 5), binary tree and naive Bayes. Table 3 shows that the proposed methodology could beat the simplest forms of some well known classification algorithms.
3.1.5. Algorithm summary The steps of the methodology are given by Algorithm 1. Algorithm 1 Immune/neural algorithm Data: Dataset Result: Dataset rate begin while Does not reach the stopping criteria do Selection mechanism Cloning mechanism Mutation mechanism end return Dataset rate end
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 7. Initialization with one antibody.
Fig. 9. Kohonen neural network position adjustment.
Fig. 8. Cloning and first mutation of that antibody.
Fig. 10. Cloning and mutating each antibody.
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3.2. Generic example
In this example is considered five types of antigens which has been randomly created. Fig. 7 shows the algorithm initialization, in which the first antibody is created in the space’s central position. Initially, the selection mechanism would take place but, when the number of antibodies is smaller than the number of types of antigens, no antibody will be deleted. Therefore, only the mechanisms of cloning and mutation were depicted. Fig. 8 shows that the first antibody is cloned twice and each clone receives a random mutation using a uniform distribution. The Kohonen neural network is responsible for the second mutation process. In Fig. 9, the positions of the previously created antibodies were adjusted to a new set of antigens. Again, the selection mechanism was not depicted because of the lower number of antibodies created so far. Fig. 10 shows the previously explained cloning mechanism over three antibodies. Then, the Kohonen network algorithm was applied in order to increase the affinity of an antibody with a set of antigens (Fig. 11). Finally, every antibody that did not recognize at least one antigen must be deleted. The final situation of the training is depicted in Fig. 12.
Fig. 11. Kohonen neural network position adjustment.
3.3. Comparison of the proposed methodology with Principal Component Analysis (PCA) and Support vector Machine (SVM) applied Tennessee Eastman Benchmark
The proposed approach was applied in the Tennessee Eastman (TE) Benchmark [43]. The TE is a Benchmark for Fault Detection
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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of SGN according to WHO classification of head and neck tumors [46]. Thirteen patients with benign and malign SGNs were included in the current study from surgery services from 2014 to 2016. The samples were divided into two groups according to the WHO classification and SGNs nature. Group 1 (Benign, n = 6) was comprised of patients with pleomorphic adenoma. For group 1 the exclusion criteria were the presence of malignant neoplasia in medical history. Group 2 (Malignant, n = 7) was comprised of patients with mucoepidermoid carcinoma (n = 4) and myoepithelial carcinoma (n = 3). 4.3. RNA isolation and real-time PCR
Fig. 12. Removing antibodies who did not recognize any pattern. Table 4 Results of fault isolation. Description
Correctness (%) Proposed methodology
Correctness (%) PCA
Correctness (%) SVM
Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault
100% 100% 61% 68% 100% 98% 97% 95% 36% 98% 88% 100% 100% 38% 100% 98% 97% 85% 100% 100% 100%
87,19% 87,5% 18,33% 72,71% 4,06% 90,21% 89,69% 85% 20,21% 76,15% 65,42% 85,83% 69,06% 86,56% 23,02% 69,48% 74,48% 59,9% 84,06% 77,5% 85%
87,19% 85,83% 15,21% 49,48% 50,60% 78,85% 88,85% 32,19% 12,81% 22,60% 11,88% 50,21% 21,46% 54,90% 18,85% 12,81% 48,02% 32,19% 46,25% 38,96% 8,23%
1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21:
The mRNA from 13 samples was used for qRT-PCR. Isolation of RNA was performed using Trizol (Thermo Fisher Scientific, Waltham, MA, USA), according to a previously published protocol [22]. Total RNA was treated with amplification grade DNase I (catalog number 18068015, Invitrogen, Carlsbad, CA, USA), and 1.5 µg of RNA was reverse-transcribed using the R SuperScript⃝ First-Strand Synthesis System for qRT-PCR (catalog number 11904018, Invitrogen, Carlsbad, CA, USA). Each reaction for SYBR green-based qRT-PCR (total volume 20 µl) contained 10 µl of SYBR Green master mix, 0.25 µM of both forward and reverse primers, 1 µl of cDNA (66 ng/reaction) and 8.5 µl H2 O. A non-template control (NTC) was included for each assay. The thermal cycling conditions were 95 ◦ C for 10 min, followed by 40 cycles of 95 ◦ C for 15 s and 60 ◦ C for 1 min. The specific primers/probes (Life Technologies, Carlsbad, CA, USA) have been described previously [22]. Specifically, the primers for RPS15 were forward 5’ - TGGAGGCTCGAATTATTGCCTTG 3’ and reverse 5’ - TAGCGTTTGTGGGCTTTGTC - 3’. Beta-actin was used to normalize RPS15 expression and was amplified using the following primers: 5’-TGCCGACAGGATGCAGAAG-3’ and 5’-CTCAGGAGGAGCAATGATCTTGA-3’. Quantitative PCR was performed on a StepOne Real-Time PCR System (Life Technologies, Carlsbad, CA, USA). The Cq values of endogenous controls were subtracted from the Ct values of the respective targets to calculate the ∆Cq. The ∆Cq values from each experimental group were averaged and converted to log base 2 using the equation 2ˆ − ∆∆Cq to compare expression among different samples. 5. Results
and Isolation (FDI). For each fault, the proposed FDI system obtained the results presented in Table 4, with a good correctness performance to deal with fault isolation, when compared with PCA and SVM techniques presented by [44]. The results of the proposed approach were worse than the PCA results in faults 4 and 14. In comparison with SVM, the proposed approach showed worse results in fault 14. Comparing the proposed method with the results showed in [45], we note that the proposed method performs better. 4. Targets identification and validation 4.1. Identification of targets After clustering genes related to benign and malignant neoplasms, these genes were placed in a string to identify the central gene interactions. After expansion of the targets, genes were designated as described above in the bioinformatics and systems biology analysis section. 4.2. Validation Ethical approval for this study was obtained from the Institutional Review Board. The inclusion criteria were the diagnose
5.1. Network characteristics Our database was derived from bioinformatics analysis of 35 different histological classes of SGNs. There is extensive data about each type of neoplasia (Table in Supplementary Material). All networks presented significant representation based on the values for correlation and R2. The ontological analysis identified the common biological paths among specifics groups of genes, and certain diseases showed mechanisms related to the three primary neoplasms of salivary glands. The network of malignant and benign neoplasms showed completely different patterns (Fig. 13A and B). Interestingly, the leader genes of malignant and benign lesions also showed different patterns of interaction (Fig. 13C and D). 5.2. Targets To identify genes related to benign SGNs, we combined Kmeans with ClonALG approaches. Genes exclusively related to malignant or benign SGNs were selected to obtain two networks for comparison. As suggested previously [47], in order to determine therapeutic targets, genes with a stronger relationship
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 13. The network of malignant (A) and benign (B) salivary gland tumors, including all identified genes. Network with only the leader genes for malignant (C) and benign (D) tumors.
between WNL and TIS were considered as target genes. A network was built for malignant and benign neoplasms (Fig. 14). The leader genes of each of the 35 SGNs are presented according to which of the two methods (K-means and ClonALG) they were obtained (Table in Supplementary Material). In 62.85% (22/35) of lesions, ClonALG identified fewer genes when compared to the K-means method. The genes with a higher WNL and a lower TIS, associated with malignant neoplasms, included INS, HRAS, SRC, and JUN (Table 5). On the other hand, benign neoplasms were related to UBE2K, CUL1, HGS, VCP, UBE2D1, SDHB, UBQLN1, UBE2D2, RPS3, and RAD23A. Two new networks were produced based on the STRING expansion. In this new expansion, RPS15 was the primary target for benign neoplasms, while PIK3CA was the target for malignant neoplasms (Fig. 14). Ontological analyses demonstrated that malignant SGNs were related to positive regulation of DNA replication (Fig. 15A). On the
Table 5 Genes associated with malingnancies. Genes leaders of genes associated exclusively with malignant tumors
Genes leaders of genes associated exclusively with benign tumors
INS HRAS, SRC, JUN
UBE2K, CUL1, HGS, VCP, UBE2D1, SDHB, UBQLN1, UBE2D2, RPS3, RAD23A
other hand, benign SGNs were found to be related to translational elongation (Fig. 15B). The strength of the benign ontology was higher than that for malignant neoplasms (Fig. 15). To validate the results of the theoretical study, mRNA of RSP15 was evaluated in malignant and benign SGNs. Malignant SGNs presented a reduction in RSP15 expression, confirming the results (Fig. 16).
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 14. The relationship between a weighted number of links (WNL) and total interaction score (TIS) of leader genes associated with malignant (A) and benign (B) neoplasms.
6. Discussion There are many data in the literature about certain histological types of SGNs; however, some subtypes are very rare [1,5]. As a consequence, it is difficult to obtain tissue samples for these SGNs, making it challenging to identify therapeutical targets. As an alternative, bioinformatics approaches may help to identify genes with a significant association with this neoplasia, which could represent prognostic markers or potential therapeutic targets [25]. The objective of the current study was to identify specific therapeutic targets using two different bioinformatics methods. Another critical aspect, which should be considered, is the fact that most neoplasms of the salivary glands are rare, and because of this, there were not enough tissue samples to proceed with experimental studies. Classic malignant epithelial neoplasia biomarkers, such as epidermal growth factor receptor (EGFR), epidermal growth factor
receptor 2 (HER2) and androgen receptor (AR), have been observed in malignant SGNs [12,20,48]. EGFR has been reported to be present in the majority of SGNs, mainly in malignant histotypes [48]. HER2 is usually expressed in neoplasia derived from the excretory duct [20]. C-kit is present mainly in neoplasms originating from the intercalated duct in both benign and malignant histologies [12]. The AR, as well as estrogen and progesterone receptors, are rarely present in SGNs [12,15]. Another gene that is also cited in several studies is tumor protein 53 (TP53) [49]. The present study identified a significant number of genes related to each a neoplasia nature, which made it possible to make comparisons between groups according to whether they were benign or malignant neoplasia, their histological origin, and invasiveness, among other factors. The K-means approach is a more traditional and well-studied procedure for clustering genes. It has some limitations which should be considered, for example, the definition of the number
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 15. Ontological analyses for malignant (A) and benign (B) neoplasms, showing that the translational elongation process is more robust for differentiating between malignant and benign salivary gland neoplasms.
of clusters [50]. Because of this, the results should be carefully interpreted. There is evidence that clustering, precisely the number of clusters that are defined, is one of the most critical processes that need improvement [26,50]. Because of this, our study used an alternative approach in addition to K-means. The main limitation of the current study is the theoretical basis of the methodology, which is considered a disadvantage of this data mining technique. On the other hand, there is a significant cost benefit for this type of analysis [24,25]. To obtain data for ClonALG, which is inspired by the biological immune system, all genes are known to be involved in SGNs were collected with their respective values for WNL and TIS. This is an alternative method to find possible relationships and associations of the particular gene with different histological types.
Other interesting findings were obtained from our database. R After reviewing the NCBI, Genecards⃝ and OMIM databases, followed by expansion using STRING software, we identified a total of 764 genes related to all 35 histological types of SGN. Of these, 24 are malignant, and 11 are benign. The distribution of these genes between malignant and benign neoplasms is remarkable. Some 92 genes are related only to benign lesions, while 189 were associated exclusively with malignant lesions. The remaining genes were linked to both types of neoplasms. This could be relevant information for future studies to identify genes and proteins that are more common in benign or malignant neoplasms. All of this data is available in the Supplementary Material. Additionally, using the K-means method of clustering genes, 258 of the 764 genes were considered to be gene leaders. It is
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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is widely accepted that the TIS is associated with a broad range of interactions in the STRING database. On the other hand, the WNL is associated with a distinct network interaction [25]. In conclusion, there is the difference in targets of malignant and benign SGNs. The ontological analyses demonstrated that malignant SGNs are related to positive regulation of DNA replication. On the other hand, benign SGNs are related to translational elongation. The investigation suggested by immune-based algorithm ClonALG and confirmed in patients’ sample demonstrated that RPS15 levels were reduced in malignant SGNs. 7. Conclusions
Fig. 16. Gene expression analysis for RPS15. Levels of RPS15 mRNA differed depending on the nature of salivary gland neoplasms. Expression of RPS15 was reduced in malignant neoplasms.
important to note that the distribution of gene leaders showed considerable variation, and also varied among the 35 types of SNGs. Therefore, each histological type should be analyzed with caution, looking for peculiarities. For example, of all of the leader genes of malignant neoplasms, four were found to be the most common. These include the genes CCND1, ERBB2, PGR, and TP63. On the other hand, five central leader genes were detected in benign neoplasms, which were KRT14, KRT19, KRT7, KRT8, and S100B. Cyclin D1 (CCND1) is a regulatory constituent of the cyclin D1-CDK4 (DC) complex, which phosphorylates and inhibits members of the retinoblastoma (RB) protein family including RB1, as well as regulating the cell-cycle during the G(1)/S transition. Amplification, overexpression, and mutations in this gene usually cause alterations in cell cycle progression, and consequently, may be involved in tumorigenesis. CCND1 is present in seven neoplasms, including not otherwise specified adenocarcinomas, ductal papillomas, malignant sebaceous neoplasia, mammary analog secretory carcinomas, metastasizing pleomorphic adenomas, mucoepidermoid carcinomas, and myoepithelial carcinomas. Interestingly, the findings of this study showed that CCND1 had the highest WNL and lowest TIS in 16 of the 35 neoplasms (see Table in Supplementary Material). Erb-B2 receptor tyrosine kinase 2 (ERBB2), which controls peripheral microtubule stabilization and outgrowth, was identified as a gene leader in seven different types of neoplasia. Interestingly, however, this gene showed the highest WNL and lowest TIS in 12 different SGNs. The progesterone receptor (PGR) is involved in the activation of c-SRC/MAPK signaling in hormone stimulation. It was found to be a gene leader in six malignant neoplasms. It is fascinating that none of the 35 neoplasms showed PGR to have the highest WNL and lowest TIS. Tumor protein P63 (TP63) is responsible for initiating apoptosis in response to cellular insults and activated oncogenes. Seven malignant and two benign neoplasia histotypes showed the TP63 as a gene leader. TP63 had the highest WNL and lowest TIS in only three neoplasia (epithelial–myoepithelial carcinoma, mammary analog secretory carcinoma, and oncocytoma), but it was not a gene leader. The genes KRT14, KRT19, KRT7 and KRT8, all members of the keratin family, and S100B, a calcium-binding protein, were present most of the time in benign neoplasms. Genes with higher WNL and relatively lower TIS are probably more acceptable as therapeutic targets, as genes with a higher TIS act in a wide variety of biological process [25,26] (Table 5). It
In this paper an approach to characterize salivary gland neoplasms is presented. The proposed approach to characterize salivary gland neoplasms consists in two main modules: (i) a bioinformatics algorithm was then applied, and the STRING database was employed to build networks of protein–protein interactions for each histotype of an SGNs and, (ii) a immune/neural approach which indicates leader genes. This method for data classification is based on the association of ClonALG immune systems with the Kohonen neural network. To show the advantages of this approach, experiments over K-means was carried out. The advantage of the proposed approach over K-means in controlled scenarios makes it a good candidate for real-world testing. Declaration of competing interest No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.asoc.2019.105877. Acknowledgments This study was supported by grants from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and the Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG). Dr. Guimarães, Dr. D’Angelo, Dr. Santos and Dr. de Paula are research fellows of the CNPq. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval for this study was obtained from the Institutional Review Board (52767316600005146). Informed consent Informed consent was obtained from all individual participants included in the study. Appendix A. Supplementary data Supplementary material related to this article can be found online at https://doi.org/10.1016/j.asoc.2019.105877.
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Immune/Neural approach to characterize salivary gland neoplasms (SGN) Carlos Rafael Lima Monção a , Eloa Mangabeira Santos a , Thiago Silva Prates a , Alfredo Maurício Batista de Paula b , Claudio Marcelo Cardoso a,d , Lucyana Conceição Farias b , ∗ Sérgio Henrique Sousa Santos e , Marcos Flávio Silveira Vasconcelos D’Angelo c , , André ∗ Luiz Sena Guimarães b , a
Graduate Program in Health Sciences, State University of Montes Claros, Montes Claros, Brazil Department of Dentistry, State University of Montes Claros, Montes Claros, Brazil c Department of Computer Science, State University of Montes Claros, Montes Claros, Brazil d Dilson Godinho Hospital, Montes Claros, Brazil e Institute of Agricultural Sciences, Federal University of Minas Gerais, Montes Claros, Brazil b
article
info
Article history: Received 7 February 2019 Received in revised form 13 August 2019 Accepted 17 October 2019 Available online xxxx Keywords: Bioinformatics Salivary gland neoplasm Immune/Neural approach
a b s t r a c t The purpose of the current study was to use immune-inspired algorithm ClonALG whose performance is increased by using the Kohonen neural network training algorithm (Immune/Neural approach), to characterize the nature of salivary gland neoplasms (SGNs). The leader gene approach in order to identify biomarkers for SGNs. Extensive data were obtained for each of the 35 types of neoplasms. The gene leaders for each type of SGN were identified in a table and then divided according to the two different methods: K-means clustering and Immune/Neural approach. Genes related to SGNs were identified using PubMed, OMIM and Genecards databases. A bioinformatics algorithm was then applied, and the STRING database was employed to build networks of protein–protein interactions for each nature of an SGNs. The weighted number of links (WNL) and total interactions score (TIS) values were then obtained. Finally, the genes were clustered, and the gene leaders were identified using the K-means clustering method and the Immune/Neural approach. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Salivary gland neoplasms (SGNs) are a group of heterogeneous and rare neoplasms [1]. They vary considerably in their anatomic site of origin, histology and biological behavior. The World Health Organization (WHO) classifies SGNs according to their histopathological features [2,3]. The great variety of named neoplasms is due to the heterogeneity between different types of neoplasms [2,3]. It is possible that the presence of hybrid neoplasms and progression to malignancy are associated with this morphological variability [2,3]. These characteristics can make histopathological diagnosis difficult [2,4]. There is no one predominant factor known to be implicated in the development of cancer of the salivary gland, although there are many factors that may play a role [5], such as radiation, cosmetics, nickel compounds and tobacco [6–8]. A biopsy of an SGN is required to make the histopathological diagnosis and to conclude whether it is benign or malignant [1]. ∗ Corresponding authors. E-mail addresses: [email protected] (M.F.S.V. D’Angelo), [email protected] (A.L.S. Guimarães).
However, diagnosis can be challenging, even for an expert pathologist [1]. Moreover, complementary molecular tests are often required to diagnose SGNs [9]. Surgical resection is the ideal treatment for SGNs. However, radiation therapy is also used in high-grade neoplasms, those with a positive surgical margin, and for the inoperable disease. Currently, chemotherapy and molecular-targeted therapies are reserved for the treatment of recurrent and metastatic disease [10]. Some studies have focussed on the expression of biological targets in salivary gland neoplasms, including c-kit, EGFR, HER2, and AR, among others, all of which could be potential biomarkers, prognostic markers or molecular targets [11–21]. However, only a few studies have explored large-scale screening techniques. Bioinformatics approaches have provided meaningful results in the analysis of a wide variety of diseases [22–26]. The use of bioinformatics is helpful for genomic and proteomic analyses. Moreover, bioinformatics could contribute to the discovery of new cancer biomarkers. The leader gene approach has been used and improved for specific conditions [22–26]. In the current study, the main contribution was to incorporate an immune/neural classification [27] with the leader gene
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Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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approach in order to characterize the nature of SGNs. SGN present different phenotypic characteristics and various clinical outcomes, which proved to be a diagnostic challenge [28,29]. Recently, the screening process to identify molecular targets related to SGN were suggested [28,29]. However, previous studies were limited to investigate patients’ samples [28,29]. The current study was the first to develop an algorithm to differentiate malignant from benignant SGN. Moreover, the present study demonstrated that immune/neural classification method improve the gene leader approach sensitivity to K-means. This immune/neural classification method is based on the association of ClonALG immune systems [30] with the Kohonen neural network [31]. The stopping criteria and the antibody selection of the proposed new approach had to be modified in order to accommodate a non-fixed number of antibodies. One of the contributions of this paper is to propose this new classification scheme in which there is no requirement of a mathematical or statistical models for the plant or signals, neither any threshold specification. Other important contribution is to propose an approach that has low implementation complexity with high performance in fault detection and isolation, which is a crucial point for on-line real-world applications. 2. Bioinformatics and systems biology analysis A bioinformatics approach was performed as described previously [22–26]. Briefly, we firstly performed searches using Medical Subject Headings (MeSH) terms, comprising all types of SGNs. The second step was to identify genes involved in SGNs in the on the subject was held search in the MEDLINE/PubMed (http: //www.ncbi.Nlm.nih.gov/), Online Mendelian Inheritance in Man R (OMIM) and Genecards⃝ (www.genecards.org) databases, only considering human genes [32,33]. We then checked the names and abbreviations of each gene with the Human Genome Organization (HUGO) to ensure consensus with gene names used by the scientific community. After this, the list of potential genes involved in SGNs was expanded using the web-available Search Tool for the Retrieval of Interacting Genes (STRING) software (version 10.0), which is a database that lists protein–protein interactions [34]. Only interactions with the highest degrees of confidence (above 0.900, range 0–0.99) were considered. Bioinformatics is an approach based on a systematic search to identify genes involved in the specific pathological process, which are then classified in order of importance according to the number of interactions determined experimentally. This rank was created based on a calculation of the interactions between genes, called the ‘‘weighted number of links’’ (WNL), which represents the gene interactions in a closed network. These interactions were identified using the STRING database, which allows scores to be obtained for each interaction. Maps are subsequently built among the identified genes to create networks [34]. Every gene identified was adjusted by multiplying to 1000 [22,25,35]. We also calculated the total interaction score (TIS), which comprises all of the gene interactions in the STRING database. A topological analysis was performed with Cytoscape, which is a software platform commonly used to visualize molecular interaction networks [36,37]. The Ontological analysis, performed using the Biological Networks Gene Ontology (BiNGO) tool according to a previously described method [38,39]. Using the values of WNL and TIS for each gene related to the various types of SGN, the following clustering procedure was carried out using SPSS (version 18.0; IBM, New York, NY, USA). We firstly used K-means clustering, then validated the results with ANOVA and Tukey–Kramer post hoc test, with a P-value of <0.001 considered statistically significant [22,30]. The genes with the highest rank of associations were identified as leader
Fig. 1. Algorithm flow chart, where a part of the mutation mechanism is carried out by the Kohonen neural network.
genes. The other clustering procedure is based on the immuneinspired algorithm ClonALG [30], aided by the Kohonen neural network [31] training algorithm in order to increase its performance, as presented in [27]. The Immune/Neural Approach to Clustering Procedures is given in Section 3. 3. Immune/Neural Approach to Clustering Procedures The Clustering Procedures is based on the immune-inspired algorithm ClonALG [30], aided by the Kohonen neural network [31] training algorithm in order to increase its performance. Inspired in the biological immune system, the artificial system must be able to react to any foreign substance that affects its integrity. These substances are called antigens and the system elements that react to them are called antibodies [40]. The ClonALG approach explicitly takes into account the antibodies affinity where only the fittest are selected to proliferate through a process called maturation (or mutation). In this paper, the affinity criterion is determined by the Euclidian distance. 3.1. Training process Every antigen used in the training step was tagged with its type so that the algorithm could train each antibody to recognize a specific type of antigen. The metric used to determine if an antibody recognized an antigen was the Euclidian distance, in which the antigen will be recognized by the nearest antibody. The training process begins with one antibody created in a central position (related to the antigens’ positions in space). Then, all antibodies are submitted to three steps that iterate until the stopping criteria are satisfied: cloning, mutation and selection (Fig. 1). It is important to know that this approach is based on a non-fixed size population of antibodies. 3.1.1. Selection mechanism As shown in Fig. 2, the antibody selection happens when all combinations of two elements out of the antibodies set are analyzed. When the set of antigens recognized by two of them are of the same type and they are close with each other, a new antibody is created between them and those two must be deleted. Also, the antibodies which did not recognize any type of antigen are deleted. The threshold used as proximity criterion between two antibodies is calculated at every iteration; it is set to 25% of the average distance between every two antibodies.
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 3. Cloning mechanism flow chart.
Fig. 4. Mutation mechanism flow chart.
(Ab )
pop Fig. 2. Selection mechanism flow chart, where represents every two 2 element combination out of the antibody population set.
3.1.2. Cloning mechanism In the cloning mechanism (Fig. 3), each antibody of the system is cloned twice and each one of these clones suffers a random mutation using a uniform distribution in its space position. This guarantees diversity in the antibody population.
3.1.3. Mutation mechanism The mutation began at the cloning mechanism and will be improved by a Kohonen neural network (Fig. 4). Each antigen linearly reduces the distance between him and the closest antibody. This algorithm changes the antibody’s position as if it was weights of a neuron.
3.1.4. Stopping criteria Two stopping criteria were defined to limit the amount of repetitions of the processes of cloning, mutation and selection. The first criterion checks the algorithm’s convergence; the proposed methodology has two steps, cloning and selection, that creates and deletes antibodies, respectively. It was a good approximation to confirm its convergence by checking if it is creating and deleting antibodies by the same amount. Nevertheless, there is some situations where these numbers (created and deleted) are never equal, so the use of another criterion is necessary. The second criterion limits the maximum number of iterations by an integer K. To show the advantage of the proposed methodology over some types of problems, an experiment with two different scenarios was carried out. The first scenario (Fig. 5) describes a problem with three patterns spread in space but not too mixed with each other. The proposed methodology was put against the ClonALG and Kohonen algorithms individually. It is important to know that ClonALG and Kohonen algorithms use a fixed number of cluster prototypes (antibodies for ClonALG
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 5. Scenario 1: spread patterns without mixing.
Fig. 6. Scenario 2: spread and heavily mixed patterns.
Table 1 Average performance statistics using the PDBAC’s mean in scenario 1. Algorithm
Average (%)
Best case (%)
Worst case (%)
Hybrid
86.86%
88.93%
77.47%
ClonALG (3 cluster prototypes)
58.22%
73.03%
45.94%
ClonALG (7 cluster prototypes)
67.75%
80.03%
55.15%
Kohonen (3 cluster prototypes)
60.33%
80.25%
50.88%
Kohonen (7 cluster prototypes)
78.68%
88.55%
66.99%
Table 2 Hybrid methodology behavior. #
Average
Best case
Worst case
Iterations to convergence Antibodies
16.65 6.9
3 6
50 8
and neurons for Kohonen). In this example, they were configured to work with 3 and 7 cluster prototypes; the number 7 was chosen to offer a fair competition given the average number of antibodies used by the proposed methodology (Table 2). The maximum number of iterations was set to 50 and 100 simulations were performed for each algorithm. For multiclass problems, using the sample accuracy (correct predictions divided by total of examples) can lead to some erroneous interpretations, because this method does not account for class imbalances in the data set [41]. The alternative was to use a Bayesian approach to estimate the posterior distribution of the balanced accuracy (PDBAC) for a multiclass problem, given in [42]. The results using this metric are presented in Table 1. It is possible to argue that a simple increase in the cluster prototypes’ number would boost these algorithms’ performances, but it is important to remember that this is an illustrative problem with low dimensionality and only a few data types. As for the proposed algorithm, some numbers are important to show how it behaves. These numbers are shown in Table 2. For a simple algorithm convergence analysis of Hybrid methodology, the maximum number of iterations was set to 50, the number of performed simulations was 100 and the results are presented in Table 2, illustrating perfectly the second stopping criteria. In some simulations, the algorithm could not stop before the maximum (K = 50) number of iterations, but the majority
Table 3 Average performance statistics using the PDBAC’s mean in scenario 2. Algorithm
Average (%)
Best Case (%)
Worst Case (%)
Hybrid 3-NN 5-NN Binary tree Naive Bayes
86.67% 75.56% 74.02% 69.48% 38.01%
92.28% 87.62% 85.04% 81.56% 72.89%
80.39% 62.99% 59.24% 54.51% 23.16%
stopped before that by checking the amount of created and deleted antibodies. Clearly, more tests are needed in order to show the advantage of the proposed methodology. Let us consider another scenario, this time with the patterns strongly mixed. This experiment consists in testing over a new data set (Fig. 6) other known classification algorithms against the proposed hybrid. To validate the proposed methodology, a training set containing 70% randomly picked (using a uniform distribution) observations was extracted from the original data set; the other 30% were used to validate the performance of each classifier using PDBAC as the metric, once again. For this experiment, the chosen algorithms were: K nearest neighbors (with K = 3, 5), binary tree and naive Bayes. Table 3 shows that the proposed methodology could beat the simplest forms of some well known classification algorithms.
3.1.5. Algorithm summary The steps of the methodology are given by Algorithm 1. Algorithm 1 Immune/neural algorithm Data: Dataset Result: Dataset rate begin while Does not reach the stopping criteria do Selection mechanism Cloning mechanism Mutation mechanism end return Dataset rate end
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Fig. 7. Initialization with one antibody.
Fig. 9. Kohonen neural network position adjustment.
Fig. 8. Cloning and first mutation of that antibody.
Fig. 10. Cloning and mutating each antibody.
5
3.2. Generic example
In this example is considered five types of antigens which has been randomly created. Fig. 7 shows the algorithm initialization, in which the first antibody is created in the space’s central position. Initially, the selection mechanism would take place but, when the number of antibodies is smaller than the number of types of antigens, no antibody will be deleted. Therefore, only the mechanisms of cloning and mutation were depicted. Fig. 8 shows that the first antibody is cloned twice and each clone receives a random mutation using a uniform distribution. The Kohonen neural network is responsible for the second mutation process. In Fig. 9, the positions of the previously created antibodies were adjusted to a new set of antigens. Again, the selection mechanism was not depicted because of the lower number of antibodies created so far. Fig. 10 shows the previously explained cloning mechanism over three antibodies. Then, the Kohonen network algorithm was applied in order to increase the affinity of an antibody with a set of antigens (Fig. 11). Finally, every antibody that did not recognize at least one antigen must be deleted. The final situation of the training is depicted in Fig. 12.
Fig. 11. Kohonen neural network position adjustment.
3.3. Comparison of the proposed methodology with Principal Component Analysis (PCA) and Support vector Machine (SVM) applied Tennessee Eastman Benchmark
The proposed approach was applied in the Tennessee Eastman (TE) Benchmark [43]. The TE is a Benchmark for Fault Detection
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of SGN according to WHO classification of head and neck tumors [46]. Thirteen patients with benign and malign SGNs were included in the current study from surgery services from 2014 to 2016. The samples were divided into two groups according to the WHO classification and SGNs nature. Group 1 (Benign, n = 6) was comprised of patients with pleomorphic adenoma. For group 1 the exclusion criteria were the presence of malignant neoplasia in medical history. Group 2 (Malignant, n = 7) was comprised of patients with mucoepidermoid carcinoma (n = 4) and myoepithelial carcinoma (n = 3). 4.3. RNA isolation and real-time PCR
Fig. 12. Removing antibodies who did not recognize any pattern. Table 4 Results of fault isolation. Description
Correctness (%) Proposed methodology
Correctness (%) PCA
Correctness (%) SVM
Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault Fault
100% 100% 61% 68% 100% 98% 97% 95% 36% 98% 88% 100% 100% 38% 100% 98% 97% 85% 100% 100% 100%
87,19% 87,5% 18,33% 72,71% 4,06% 90,21% 89,69% 85% 20,21% 76,15% 65,42% 85,83% 69,06% 86,56% 23,02% 69,48% 74,48% 59,9% 84,06% 77,5% 85%
87,19% 85,83% 15,21% 49,48% 50,60% 78,85% 88,85% 32,19% 12,81% 22,60% 11,88% 50,21% 21,46% 54,90% 18,85% 12,81% 48,02% 32,19% 46,25% 38,96% 8,23%
1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21:
The mRNA from 13 samples was used for qRT-PCR. Isolation of RNA was performed using Trizol (Thermo Fisher Scientific, Waltham, MA, USA), according to a previously published protocol [22]. Total RNA was treated with amplification grade DNase I (catalog number 18068015, Invitrogen, Carlsbad, CA, USA), and 1.5 µg of RNA was reverse-transcribed using the R SuperScript⃝ First-Strand Synthesis System for qRT-PCR (catalog number 11904018, Invitrogen, Carlsbad, CA, USA). Each reaction for SYBR green-based qRT-PCR (total volume 20 µl) contained 10 µl of SYBR Green master mix, 0.25 µM of both forward and reverse primers, 1 µl of cDNA (66 ng/reaction) and 8.5 µl H2 O. A non-template control (NTC) was included for each assay. The thermal cycling conditions were 95 ◦ C for 10 min, followed by 40 cycles of 95 ◦ C for 15 s and 60 ◦ C for 1 min. The specific primers/probes (Life Technologies, Carlsbad, CA, USA) have been described previously [22]. Specifically, the primers for RPS15 were forward 5’ - TGGAGGCTCGAATTATTGCCTTG 3’ and reverse 5’ - TAGCGTTTGTGGGCTTTGTC - 3’. Beta-actin was used to normalize RPS15 expression and was amplified using the following primers: 5’-TGCCGACAGGATGCAGAAG-3’ and 5’-CTCAGGAGGAGCAATGATCTTGA-3’. Quantitative PCR was performed on a StepOne Real-Time PCR System (Life Technologies, Carlsbad, CA, USA). The Cq values of endogenous controls were subtracted from the Ct values of the respective targets to calculate the ∆Cq. The ∆Cq values from each experimental group were averaged and converted to log base 2 using the equation 2ˆ − ∆∆Cq to compare expression among different samples. 5. Results
and Isolation (FDI). For each fault, the proposed FDI system obtained the results presented in Table 4, with a good correctness performance to deal with fault isolation, when compared with PCA and SVM techniques presented by [44]. The results of the proposed approach were worse than the PCA results in faults 4 and 14. In comparison with SVM, the proposed approach showed worse results in fault 14. Comparing the proposed method with the results showed in [45], we note that the proposed method performs better. 4. Targets identification and validation 4.1. Identification of targets After clustering genes related to benign and malignant neoplasms, these genes were placed in a string to identify the central gene interactions. After expansion of the targets, genes were designated as described above in the bioinformatics and systems biology analysis section. 4.2. Validation Ethical approval for this study was obtained from the Institutional Review Board. The inclusion criteria were the diagnose
5.1. Network characteristics Our database was derived from bioinformatics analysis of 35 different histological classes of SGNs. There is extensive data about each type of neoplasia (Table in Supplementary Material). All networks presented significant representation based on the values for correlation and R2. The ontological analysis identified the common biological paths among specifics groups of genes, and certain diseases showed mechanisms related to the three primary neoplasms of salivary glands. The network of malignant and benign neoplasms showed completely different patterns (Fig. 13A and B). Interestingly, the leader genes of malignant and benign lesions also showed different patterns of interaction (Fig. 13C and D). 5.2. Targets To identify genes related to benign SGNs, we combined Kmeans with ClonALG approaches. Genes exclusively related to malignant or benign SGNs were selected to obtain two networks for comparison. As suggested previously [47], in order to determine therapeutic targets, genes with a stronger relationship
Please cite this article as: C.R.L. Monção, E.M. Santos, T.S. Prates et al., Immune/Neural approach to characterize salivary gland neoplasms (SGN), Applied Soft Computing Journal (2019) 105877, https://doi.org/10.1016/j.asoc.2019.105877.
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Fig. 13. The network of malignant (A) and benign (B) salivary gland tumors, including all identified genes. Network with only the leader genes for malignant (C) and benign (D) tumors.
between WNL and TIS were considered as target genes. A network was built for malignant and benign neoplasms (Fig. 14). The leader genes of each of the 35 SGNs are presented according to which of the two methods (K-means and ClonALG) they were obtained (Table in Supplementary Material). In 62.85% (22/35) of lesions, ClonALG identified fewer genes when compared to the K-means method. The genes with a higher WNL and a lower TIS, associated with malignant neoplasms, included INS, HRAS, SRC, and JUN (Table 5). On the other hand, benign neoplasms were related to UBE2K, CUL1, HGS, VCP, UBE2D1, SDHB, UBQLN1, UBE2D2, RPS3, and RAD23A. Two new networks were produced based on the STRING expansion. In this new expansion, RPS15 was the primary target for benign neoplasms, while PIK3CA was the target for malignant neoplasms (Fig. 14). Ontological analyses demonstrated that malignant SGNs were related to positive regulation of DNA replication (Fig. 15A). On the
Table 5 Genes associated with malingnancies. Genes leaders of genes associated exclusively with malignant tumors
Genes leaders of genes associated exclusively with benign tumors
INS HRAS, SRC, JUN
UBE2K, CUL1, HGS, VCP, UBE2D1, SDHB, UBQLN1, UBE2D2, RPS3, RAD23A
other hand, benign SGNs were found to be related to translational elongation (Fig. 15B). The strength of the benign ontology was higher than that for malignant neoplasms (Fig. 15). To validate the results of the theoretical study, mRNA of RSP15 was evaluated in malignant and benign SGNs. Malignant SGNs presented a reduction in RSP15 expression, confirming the results (Fig. 16).
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Fig. 14. The relationship between a weighted number of links (WNL) and total interaction score (TIS) of leader genes associated with malignant (A) and benign (B) neoplasms.
6. Discussion There are many data in the literature about certain histological types of SGNs; however, some subtypes are very rare [1,5]. As a consequence, it is difficult to obtain tissue samples for these SGNs, making it challenging to identify therapeutical targets. As an alternative, bioinformatics approaches may help to identify genes with a significant association with this neoplasia, which could represent prognostic markers or potential therapeutic targets [25]. The objective of the current study was to identify specific therapeutic targets using two different bioinformatics methods. Another critical aspect, which should be considered, is the fact that most neoplasms of the salivary glands are rare, and because of this, there were not enough tissue samples to proceed with experimental studies. Classic malignant epithelial neoplasia biomarkers, such as epidermal growth factor receptor (EGFR), epidermal growth factor
receptor 2 (HER2) and androgen receptor (AR), have been observed in malignant SGNs [12,20,48]. EGFR has been reported to be present in the majority of SGNs, mainly in malignant histotypes [48]. HER2 is usually expressed in neoplasia derived from the excretory duct [20]. C-kit is present mainly in neoplasms originating from the intercalated duct in both benign and malignant histologies [12]. The AR, as well as estrogen and progesterone receptors, are rarely present in SGNs [12,15]. Another gene that is also cited in several studies is tumor protein 53 (TP53) [49]. The present study identified a significant number of genes related to each a neoplasia nature, which made it possible to make comparisons between groups according to whether they were benign or malignant neoplasia, their histological origin, and invasiveness, among other factors. The K-means approach is a more traditional and well-studied procedure for clustering genes. It has some limitations which should be considered, for example, the definition of the number
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Fig. 15. Ontological analyses for malignant (A) and benign (B) neoplasms, showing that the translational elongation process is more robust for differentiating between malignant and benign salivary gland neoplasms.
of clusters [50]. Because of this, the results should be carefully interpreted. There is evidence that clustering, precisely the number of clusters that are defined, is one of the most critical processes that need improvement [26,50]. Because of this, our study used an alternative approach in addition to K-means. The main limitation of the current study is the theoretical basis of the methodology, which is considered a disadvantage of this data mining technique. On the other hand, there is a significant cost benefit for this type of analysis [24,25]. To obtain data for ClonALG, which is inspired by the biological immune system, all genes are known to be involved in SGNs were collected with their respective values for WNL and TIS. This is an alternative method to find possible relationships and associations of the particular gene with different histological types.
Other interesting findings were obtained from our database. R After reviewing the NCBI, Genecards⃝ and OMIM databases, followed by expansion using STRING software, we identified a total of 764 genes related to all 35 histological types of SGN. Of these, 24 are malignant, and 11 are benign. The distribution of these genes between malignant and benign neoplasms is remarkable. Some 92 genes are related only to benign lesions, while 189 were associated exclusively with malignant lesions. The remaining genes were linked to both types of neoplasms. This could be relevant information for future studies to identify genes and proteins that are more common in benign or malignant neoplasms. All of this data is available in the Supplementary Material. Additionally, using the K-means method of clustering genes, 258 of the 764 genes were considered to be gene leaders. It is
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is widely accepted that the TIS is associated with a broad range of interactions in the STRING database. On the other hand, the WNL is associated with a distinct network interaction [25]. In conclusion, there is the difference in targets of malignant and benign SGNs. The ontological analyses demonstrated that malignant SGNs are related to positive regulation of DNA replication. On the other hand, benign SGNs are related to translational elongation. The investigation suggested by immune-based algorithm ClonALG and confirmed in patients’ sample demonstrated that RPS15 levels were reduced in malignant SGNs. 7. Conclusions
Fig. 16. Gene expression analysis for RPS15. Levels of RPS15 mRNA differed depending on the nature of salivary gland neoplasms. Expression of RPS15 was reduced in malignant neoplasms.
important to note that the distribution of gene leaders showed considerable variation, and also varied among the 35 types of SNGs. Therefore, each histological type should be analyzed with caution, looking for peculiarities. For example, of all of the leader genes of malignant neoplasms, four were found to be the most common. These include the genes CCND1, ERBB2, PGR, and TP63. On the other hand, five central leader genes were detected in benign neoplasms, which were KRT14, KRT19, KRT7, KRT8, and S100B. Cyclin D1 (CCND1) is a regulatory constituent of the cyclin D1-CDK4 (DC) complex, which phosphorylates and inhibits members of the retinoblastoma (RB) protein family including RB1, as well as regulating the cell-cycle during the G(1)/S transition. Amplification, overexpression, and mutations in this gene usually cause alterations in cell cycle progression, and consequently, may be involved in tumorigenesis. CCND1 is present in seven neoplasms, including not otherwise specified adenocarcinomas, ductal papillomas, malignant sebaceous neoplasia, mammary analog secretory carcinomas, metastasizing pleomorphic adenomas, mucoepidermoid carcinomas, and myoepithelial carcinomas. Interestingly, the findings of this study showed that CCND1 had the highest WNL and lowest TIS in 16 of the 35 neoplasms (see Table in Supplementary Material). Erb-B2 receptor tyrosine kinase 2 (ERBB2), which controls peripheral microtubule stabilization and outgrowth, was identified as a gene leader in seven different types of neoplasia. Interestingly, however, this gene showed the highest WNL and lowest TIS in 12 different SGNs. The progesterone receptor (PGR) is involved in the activation of c-SRC/MAPK signaling in hormone stimulation. It was found to be a gene leader in six malignant neoplasms. It is fascinating that none of the 35 neoplasms showed PGR to have the highest WNL and lowest TIS. Tumor protein P63 (TP63) is responsible for initiating apoptosis in response to cellular insults and activated oncogenes. Seven malignant and two benign neoplasia histotypes showed the TP63 as a gene leader. TP63 had the highest WNL and lowest TIS in only three neoplasia (epithelial–myoepithelial carcinoma, mammary analog secretory carcinoma, and oncocytoma), but it was not a gene leader. The genes KRT14, KRT19, KRT7 and KRT8, all members of the keratin family, and S100B, a calcium-binding protein, were present most of the time in benign neoplasms. Genes with higher WNL and relatively lower TIS are probably more acceptable as therapeutic targets, as genes with a higher TIS act in a wide variety of biological process [25,26] (Table 5). It
In this paper an approach to characterize salivary gland neoplasms is presented. The proposed approach to characterize salivary gland neoplasms consists in two main modules: (i) a bioinformatics algorithm was then applied, and the STRING database was employed to build networks of protein–protein interactions for each histotype of an SGNs and, (ii) a immune/neural approach which indicates leader genes. This method for data classification is based on the association of ClonALG immune systems with the Kohonen neural network. To show the advantages of this approach, experiments over K-means was carried out. The advantage of the proposed approach over K-means in controlled scenarios makes it a good candidate for real-world testing. Declaration of competing interest No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.asoc.2019.105877. Acknowledgments This study was supported by grants from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and the Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG). Dr. Guimarães, Dr. D’Angelo, Dr. Santos and Dr. de Paula are research fellows of the CNPq. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval for this study was obtained from the Institutional Review Board (52767316600005146). Informed consent Informed consent was obtained from all individual participants included in the study. Appendix A. Supplementary data Supplementary material related to this article can be found online at https://doi.org/10.1016/j.asoc.2019.105877.
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