Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

BACKGROUND: The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT) and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. RESULTS: Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others) with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable rules for the diagnostic task. CONCLUSION: Although the proposed method is tested on a SRBCT data set, it is quite general and can be applied to other cancer data sets. Our scheme takes into account the interaction between genes as well as that between genes and the tool and thus is able find a very small set and can discover novel genes. Our findings suggest the possibility of developing specialized microarray chips or use of real-time qPCR assays or antibody based methods such as ELISA and western blot analysis for an easy and low cost diagnosis of the subgroups

Artificial neural network techniques to investigate potential interactions between biomarkers

High-throughput technologies in biomedical sciences, including gene microarrays, supposed to revolutionise the post-genomic era, have barely met the great expectations they inspired to the biomedical community at first. Current efforts are still focused toward improving the technology, its reproducibility and accuracy. In the meantime, computational techniques for the analysis of the data from these technologies have achieved great progresses and show encouraging results. New approaches have been developed to extract relevant information out from these results. However, important work needs to be further conducted in order to extract even more meaningful and relevant information. These techniques offer great possibilities to explore the overall dynamic held within a living organism. The potential information contained in their output can reveal important leads at deciphering the interconnection, interaction or regulation influences that can exist between several molecules. In front of an increasing interest of the scientific community toward the exploration of these dynamics, some groups have started to develop solutions based on different technologies to extract these information related to interactions. Here we present an Artificial Neural Network-based methodology for the study of interactions in gene transcriptomic data. This will be applied and validated in a breast cancer context

ENDOMET database – A means to identify novel diagnostic and prognostic tools for endometriosis

Endometriosis is a common benign hormone reliant inflammatory gynecological disease that affects fertile aged women and has a considerable economic impact on healthcare systems. Symptoms include intense menstrual pain, persistent pelvic pain, and infertility. It is defined by the existence of endometrium-like tissue developing in ectopic locations outside the uterine cavity and inflammation in the peritoneal cavity. Endometriosis presents with multifactorial etiology, and despite extensive research the etiology is still poorly understood. Diagnostic delay from the onset of the disease to when a conclusive diagnosis is reached is between 7–12 years. There is no known cure, although symptoms can be improved with hormonal medications (which often have multiple side effects and prevent pregnancy), or through surgery which carries its own risk. Current non-invasive tools for diagnosis are not sufficiently dependable, and a definite diagnosis is achieved through laparoscopy or laparotomy. This study was based on two prospective cohorts: The ENDOMET study, including 137 endometriosis patients scheduled for surgery and 62 healthy women, and PROENDO that included 138 endometriosis patients and 33 healthy women. Our long-term goal with the current study was to support the discovery of innovative new tools for efficient diagnosis of endometriosis as well as tools to further understand the etiology and pathogenesis of the disease. We set about achieving this goal by creating a database, EndometDB, based on a relational data model, implemented with PostgreSQL programming language. The database allows e.g., for the exploration of global genome-wide expression patterns in the peritoneum, endometrium, and in endometriosis lesions of endometriosis patients as well as in the peritoneum and endometrium of healthy control women of reproductive age. The data collected in the EndometDB was also used for the development and validation of a symptom and biomarker-based predictive model designed for risk evaluation and early prediction of endometriosis without invasive diagnostic methods. Using the data in the EndometDB we discovered that compared with the eutopic endometrium, the WNT- signaling pathway is one of the molecular pathways that undergo strong changes in endometriosis. We then evaluated the potential role for secreted frizzled-related protein 2 (SFRP-2, a WNT-signaling pathway modulator), in improving endometriosis lesion border detection. The SFRP-2 expression visualizes the lesion better than previously used markers and can be used to better define lesion size and that the surgical excision of the lesions is complete.ENDOMET tietokanta – Keino tunnistaa uusi diagnostinen ja ennustava työkalu endometrioosille Endometrioosi on yleinen hyvänlaatuinen, hormoneista riippuvainen tulehduksellinen lisääntymisikäisten naisten gynekologinen sairaus, joka kuormittaa terveydenhuoltojärjestelmää merkittävästi. Endometrioositaudin oireita ovat mm. voimakas kuukautiskipu, jatkuva lantion alueen kipu ja hedelmättömyys. Sairaus määritellään kohdun limakalvon kaltaisen kudoksen esiintymisenä kohdun ulkopuolella sekä siihen liittyvänä vatsakalvon tulehduksena. Endometrioosin etiologia on monitahoinen, ja laajasta tutkimuksesta huolimatta edelleen huonosti tunnettu. Kesto taudin puhkeamisesta lopullisen diagnoosin saamiseen on usein jopa 7–12 vuotta. Sairauteen ei tunneta parannuskeinoa, mutta oireita voidaan lievittää esimerkiksi hormonaalisilla lääkkeillä (joilla on usein monia sivuvaikutuksia ja jotka estävät raskauden) tai leikkauksella, johon liittyy omat tunnetut riskit. Nykyiset ei-invasiiviset diagnoosityökalut eivät ole riittävän luotettavia sairauden tunnistamiseen, ja varma endometrioosin diagnoosi saavutetaan laparoskopian tai laparotomian avulla. Tämä tutkimus perustui kahteen prospektiiviseen kohorttiin: ENDOMET-tutkimuk-seen, johon osallistui 137 endometrioosipotilasta ja 62 terveellistä naista, sekä PROENDO-tutkimukseen, johon osallistui 138 endometrioosipotilasta ja 33 terveellistä naista. Tässä tutkimuksessa pitkän aikavälin tavoitteemme oli löytää uusia työkalujen endometrioosin diagnosointiin, sekä ymmärtää endometrioosin etiologiaa ja patogeneesiä. Ensimmäisessä vaiheessa loimme EndometDB –tietokannan PostgreSQL-ohjelmointi-kielellä. Tämän osittain avoimeen käyttöön vapautetun tietokannan avulla voidaan tutkia genomin, esimerkiksi kaikkien tunnettujen geenien ilmentymistä peritoneumissa, endo-metriumissa ja endometrioosipotilaiden endometrioosileesioissa EndometDB-tietokantaan kerättyjä tietoja käytettiin oireiden ja biomarkkeripohjaisen ennustemallin kehittämiseen ja validointiin. Malli tuottaa riskinarvioinnin endometrioositaudin varhaiseen ennustamiseen ilman laparoskopiaa. Käyttäen EndometDB-tietokannan tietoja havaitsimme, että endo-metrioositautikudoksessa tapahtui voimakkaita geeni-ilmentymisen muutoksia erityisesti geeneissä, jotka liittyvät WNT-signalointireitin säätelyyn. Keskeisin löydös oli, että SFRP-2 proteiinin ilmentyminen oli huomattavasti koholla endometrioosikudoksessa ja SFRP-2 proteiinin immunohistokemiallinen värjäys erottaa endometrioosin tautikudoksen terveestä kudoksesta aiempia merkkiaineita paremmin. Löydetyllä menetelmällä voidaan siten selvittää tautikudoksen laajuus ja tarvittaessa osoittaa, että leikkauksella on kyetty poistamaan koko sairas kudos

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases

Identifying Network Biomarkers for Each Breast Cancer Subtypes Along with Their Effective Single and Paired Repurposed Drugs Using Network-Based Machine Learning Techniques

Breast cancer is a complex disease that can be classified into at least 10 different molecular subtypes. Appropriate diagnosis of specific subtypes is critical for ensuring the best possible patient treatment and response to therapy. Current computational methods for determining the subtypes are based on identifying differentially expressed genes (i.e., biomarkers) that can best discriminate the subtypes. Such approaches, however, are known to be unreliable since they yield different biomarker sets when applied to data sets from different studies. Gathering knowledge about the functional relationship among genes will identify “network biomarkers” that will enrich the criteria for biomarker selection. Cancer network biomarkers are subnetworks of functionally related genes that “work in concert” to perform functions associated with a tumorigenic. We propose a machine learning framework that can be used to identify network biomarkers and driver genes for each specific breast cancer subtype. Our results show that the resulting network biomarkers can separate onesubtype from the others with very high accuracy. We also propose an integrated approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes

Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

<p>Abstract</p> <p>Background</p> <p>The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.</p> <p>Results and discussion</p> <p>To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis.</p> <p>Conclusion</p> <p>Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.</p

Genetic algorithm-neural network: feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data