MILANO – custom annotation of microarray results using automatic literature searches

BACKGROUND: High-throughput genomic research tools are becoming standard in the biologist's toolbox. After processing the genomic data with one of the many available statistical algorithms to identify statistically significant genes, these genes need to be further analyzed for biological significance in light of all the existing knowledge. Literature mining – the process of representing literature data in a fashion that is easy to relate to genomic data – is one solution to this problem. RESULTS: We present a web-based tool, MILANO (Microarray Literature-based Annotation), that allows annotation of lists of genes derived from microarray results by user defined terms. Our annotation strategy is based on counting the number of literature co-occurrences of each gene on the list with a user defined term. This strategy allows the customization of the annotation procedure and thus overcomes one of the major limitations of the functional annotations usually provided with microarray results. MILANO expands the gene names to include all their informative synonyms while filtering out gene symbols that are likely to be less informative as literature searching terms. MILANO supports searching two literature databases: GeneRIF and Medline (through PubMed), allowing retrieval of both quick and comprehensive results. We demonstrate MILANO's ability to improve microarray analysis by analyzing a list of 150 genes that were affected by p53 overproduction. This analysis reveals that MILANO enables immediate identification of known p53 target genes on this list and assists in sorting the list into genes known to be involved in p53 related pathways, apoptosis and cell cycle arrest. CONCLUSIONS: MILANO provides a useful tool for the automatic custom annotation of microarray results which is based on all the available literature. MILANO has two major advances over similar tools: the ability to expand gene names to include all their informative synonyms while removing synonyms that are not informative and access to the GeneRIF database which provides short summaries of curated articles relevant to known genes. MILANO is available at

Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model

<p>Abstract</p> <p>Background</p> <p>Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO). However, the annotation of genes is a labor-intensive process; and the vocabularies are generally incomplete, leaving some important biological domains inadequately covered.</p> <p>Results</p> <p>We propose a statistical method that uses the primary literature, i.e. free-text, as the source to perform overrepresentation analysis. The method is based on a statistical framework of mixture model and addresses the methodological flaws in several existing programs. We implemented this method within a literature mining system, BeeSpace, taking advantage of its analysis environment and added features that facilitate the interactive analysis of gene sets. Through experimentation with several datasets, we showed that our program can effectively summarize the important conceptual themes of large gene sets, even when traditional GO-based analysis does not yield informative results.</p> <p>Conclusions</p> <p>We conclude that the current work will provide biologists with a tool that effectively complements the existing ones for overrepresentation analysis from genomic experiments. Our program, Genelist Analyzer, is freely available at: <url>http://workerbee.igb.uiuc.edu:8080/BeeSpace/Search.jsp</url></p

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download

Literature-aided interpretation of gene expression data with the weighted global test

Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.UB – Publicatie

Comparison of automated literature based gene-disease association using gene set enrichment analysis

Cancer is a leading cause of death in Australia: more than 43,000 people have been estimated to have died from cancer in 2010. However, the genetic causes of cancer remain elusive despite voluminous genetic data in the public domain. Our goal is to identify genes in order to understand the molecular mechanisms of cancer so that diagnosis, prognosis and treatment can be optimized. Microarrays measure gene expression levels in disease tissue relative to normal tissue. However, microarray data are noisy and computational methods are required to associate aberrant gene expression with disease. Subramanian et al. (2005) developed an approach called Gene Set Enrichment Analysis (GSEA) that annotates microarray data with functional terms from a background ontology. The enriched gene sets have shown to improve the quality of microarray annotation compared to single gene annotation. Nevertheless, GSEA falls short when used to predict disease-gene associations. We hypothesized that GSEA’s shortfall is caused by limited knowledge embedded in its ontology. Thus we have proposed a novel method, which automatically constructs ontologies for use in GSEA directly from the biomedical literature and then associates genes with diseases. This thesis tests this hypothesis. My results show that using knowledge derived automatically from biomedical literature outperforms GSEA’s default catalogues and achieves high area under the receiver operating characteristic curve (AUC) scores when tested on breast and colorectal cancer samples. The results indicate that the automated literature-based approach is a promising method for discovering novel gene-disease associations. In conclusion, I have shown that literature-based generated catalogues are accurate and viable for prediction of gene-disease associations

The Functions of Autophagy Genes in Lymphocytes and Osteoclasts

Macroautophagy: herein autophagy) is a process by which cells degrade long-lived proteins and organelles. The autophagy pathway and autophagy genes have been implicated in many functions in the cell such as protecting against metabolic stress, degrading damaged organelles, and regulating vesicular trafficking. To study the role of autophagy in primary cells with important physiologic functions, we generated mice lacking essential autophagy genes in B lymphocytes, T lymphocytes, and osteoclasts. We found that the essential autophagy gene Atg5 was important for B cell development and for the maintenance of B-1a B cell numbers but not peripheral B-2 B cell numbers. In T cells, deletion of the essential autophagy genes Atg5 or Atg7 resulted in decreased thymocyte and peripheral T cell numbers in vivo and a decrease in cell proliferation in vitro. Autophagy genes play a critical role in T cell homeostasis, but do not appear important for peripheral B-2 B cell homeostasis in vivo. Whole-genome transcriptional profiling of Atg5-deficient and wild-type thymocytes suggested abnormalities in mitochondria in the absence of Atg5. We confirmed this observation by demonstrating that peripheral Atg5-deficient T cells had an increase in mitochondrial mass that correlated with increased Annexin-V staining in these cells. We speculate that autophagy is required in T cells for the removal of damaged or aged mitochondria and that excess mitochondria contribute to increased cell death in autophagy-deficient T cells. In contrast to lymphocytes, deletion of autophagy genes in osteoclasts did not result in dramatic abnormalities in cell development. However, the biochemical pathway necessary for autophagy was critical for directional secretion in osteoclasts. We found that the autophagosome marker LC3 localized to the resorptive microenvironment in osteoclasts. Deleting Atg5 or Atg7 or overexpressing a dominant negative mutant of ATG4B to inhibit LC3 conjugation reduced localization of lysosomal markers at the resorptive surface and decreased bone resorption in vitro. Furthermore, mice lacking Atg5 in osteoclasts and other myeloid-lineage cells were protected from ovarectomy-induced bone loss, a mouse model of osteoporosis. Together, these studies demonstrate that autophagy genes are important in cell development, survival, mitochondrial maintenance, and directional secretion in physiologically important, primary mammalian cells

MILANO – custom annotation of microarray results using automatic literature searches-2

<p><b>Copyright information:</b></p><p>Taken from "MILANO – custom annotation of microarray results using automatic literature searches"</p><p>BMC Bioinformatics 2005;6():12-12.</p><p>Published online 20 Jan 2005</p><p>PMCID:PMC547913.</p><p>Copyright © 2005 Rubinstein and Simon; licensee BioMed Central Ltd.</p>ed to reveal known p53 targets. B. Average number of articles per gene in the different queries. C. Venn diagram depicting the different functions of p53 affected genes as reflected by a GeneRIF search. Search term is "p53 AND (target OR transcriptional OR activation OR repression)

Transcriptome analysis in cervical cancer

Cervical cancer is the second most frequent cancer in women worldwide. Approximately 80% of cases occur in developing countries and the majority of cases are diagnosed in advanced stages. The goals of this study were: (i) to identify a gene expression profile to predict the treatment response, since about 35% of patients with locally advanced cervical cancer will not respond to treatment; (ii) to identify genes and gene groups, pathways and gene networks, with different expression between cervical tumor and normal adjacent tissue, in order to identify genes related to cervical cancer pathogenesis and to better understand the molecular mechanisms involved in this cancer. Methods: To perform the genome-wide gene expression analysis we used microarrays containing oligonucleotides corresponding to ~14,000 genes. We used 23 slides hybridized with tumor samples for the treatment response analysis and for the comparison analysis between tumor and normal tissues we used 34 slides with tumor samples and 20 with normal samples. For data analysis we used the following programs: BRB Array Tools to search the molecular predictor and to obtain gene list; DAVID, Babelomics e Ingenuity Pathway Analysis to identify the over-represented pathways in the gene list; MILANO to verify how our findings relate to the published literature; DAVID, CFinder e Cytoscape for gene networks analysis. Results: (i) we did not identify a gene expression profile that could predict the treatment response; (ii) we found 810 differentially expressed genes between tumor and normal tissues, 341 were up-regulated and 469 were down-regulated in tumor samples. We identify 13 overrepresented pathways and among them, we found previously known (e.g. ‘Cell Cycle’ and ‘p53 signaling’) and unknown molecular pathways (‘Oxidative Phosphorylation’ and ‘Ribosome’) in cervical cancer. Several pathway genes, even in the previously known pathways related to cervical cancer, have not been studied in this cancer. In the gene network analysis, we found 23 subnetworks. Among them we highlighted the subnetwork that contains genes from the kallikrein family. Our results also suggest that our gene profile can also be applied for cervical cancer cases from the other studies and for esophagus cancer. Conclusions: Using genome-wide transcriptome analysis we identified genes and gene groups, pathways and gene networks, involved in cervical cancer. These results might help to understand some molecular mechanisms involved in the cervical cancer pathogenesis and the genes might be interesting candidates to further susceptibility and biomarker studies in cervical cancer.O câncer de colo do útero é o segundo tipo de câncer mais comum entre mulheres. Cerca de 80% dos casos ocorrem em países em desenvolvimento onde a maior parte são diagnosticados em estadios relativamente avançados. Os objetivos desse trabalho foram: (i) identificar um perfil de expressão gênica capaz de predizer a resposta ao tratamento, visto que, cerca de 35% das pacientes com câncer localmente avançado não respondem ao tratamento; (ii) identificar genes e grupos funcionais de genes (vias e redes gênicas) com expressão alterada em amostras de tecido tumoral em relação ao normal adjacente, para identificar genes e mecanismos envolvidos na patogênese do câncer de colo uterino. Método. Realizamos análise de expressão gênica em larga escala, utilizando microarrays contendo oligonucleotídeos impressos correspondentes a aproximadamente 14.000 genes. No estudo de resposta ao tratamento, utilizamos 23 lâminas hibridizadas com amostras de tumor e, no estudo comparativo entre tecido tumoral e normal, utilizamos 34 lâminas de tumor e 20 de tecido normal. Para a análise dos dados foram utilizados os seguintes programas: BRB Array Tools para busca de um preditor molecular e geração da lista de genes; DAVID, Babelomics e Ingenuity Pathway Analysis para verificar as vias representadas na lista; MILANO para verificar a literatura em relação aos nossos achados; DAVID, CFinder e Cytoscape para análises de redes gênicas. Resultados: (i) Não identificamos nenhum perfil de expressão gênica capaz de predizer de forma consistente a resposta ao tratamento; (ii) identificamos 810 genes diferencialmente expressos entre tecido tumoral e normal, sendo que 341 tinham expressão aumentada no tumor e 469 expressão diminuída. Na análise de vias, identificamos 13 vias, entre as quais havia vias já descritas em câncer de colo uterino, como a via do ciclo celular e da p53 e vias ainda não estudadas nesse câncer, como a da fosforilação oxidativa e a do ribossomo. Além disso, verificamos que vários genes pertencentes as vias, mesmo as já descritas como relacionadas com o câncer de colo uterino, ainda não haviam sido estudados nesse câncer. Na análise de redes gênicas encontramos 23 sub-redes. Dentre essas, destacamos a rede formada pelos genes da família das calicreínas. Nossos resultados também sugerem que o perfil de genes identificados também se aplica a casos de câncer de colo de útero estudados em outros trabalhos assim como a câncer de esôfago. Conclusão: O uso da análise de expressão gênica em larga escala possibilitou a identificação de genes e grupos funcionais de genes, vias e redes gênicas, alterados no câncer de colo uterino. Esses resultados podem ajudar no entendimento dos mecanismos moleculares envolvidos na patogenia deste câncer e os genes encontrados podem ser candidatos promissores para estudos de marcadores a serem utilizados no diagnóstico precoce e estudos de susceptibilidade genética em câncer de colo do útero.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP: 05/51329-6TEDEBV UNIFESP: Teses e dissertaçõe

Χρωμοσωμικές συσχετίσεις στη γονιδιακή έκφραση νεοπλασμάτων μεσοδερμικής προέλευσης: ραβδομυοσάρκωμα

Στη σημερινή εποχή, ο συνεχής αγώνας στη μάχη κατά του καρκίνου καθιστά απαραίτητη την κατανόηση των μηχανισμών που διέπουν την ογκογένεση, ώστε να ανακαλυφθούν περισσότερο εξειδικευμένα και πιο αποτελεσματικά φάρμακα, με όσο το δυνατόν μικρότερο κόστος. Στην παρούσα έρευνα θα χρησιμοποιήσουμε τα μαθηματικά και τη φυσική για την ανάλυση των βιολογικών φαινομένων. Θα παρουσιάσουμε μία υπολογιστική και συστημική προσέγγιση μελετώντας τους κοινούς μηχανισμούς μεταξύ δύο νεοπλασιών της παιδικής ηλικίας μεσοδερμικής προέλευσης, της οξείας λεμφοβλαστικής λευχαιμίας και του ραβδομυοσαρκώματος. Με τη χρήση των μεθόδων υψηλής απόδοσης, όπως οι μικροσυστοιχίες, θα πάρουμε πληροφορίες σε επίπεδο γονιδιακής έκφρασης και θα προσπαθήσουμε να διερευνήσουμε την κοινή γονιδιακή ρύθμιση με βάση τις χρωμοσωμικές συσχετίσεις. Με την διερεύνηση των πιθανών κοινών ρυθμιστικών μηχανισμών, ίσως μπορέσουμε να κατανοήσουμε τα αίτια της ογκογένεσης και το ρόλο των καρκινικών βλαστικών κυττάρων. Τα πειράματα, που αναφέρονται στην παρούσα εργασία, διεξήχθησαν στο Χωρέμειο Ερευνητικό Εργαστήριο. Χρησιμοποιήθηκαν οι κυτταρικές σειρές CCRF-CEM (Οξεία Λεμφοβλαστική Λευχαιμία (ΟΛΛ), Acute Lymphoblastic Leukemia (ALL)) και TE-671 (Ραβδομυοσάρκωμα (ΡΜΣ), Rhabdomyosarcoma (RMS)) ως πειραματικό μοντέλο. Τα κοινά πρότυπα γονιδιακής έκφρασης μεταξύ των δύο κυτταρικών τύπων διερευνήθηκαν με τη χρήση μικροσυστοιχιών. Ο απώτερος στόχος ήταν να βρεθούν αιτιακές σχέσεις μεταξύ των επιπέδων γονιδιακής έκφρασης σε σχέση με τη χρωμοσωμική θέση. Χρησιμοποιώντας ανάλυση παλινδρόμησης διαπιστώσαμε ότι αρκετά γονίδια εκφράζουν σημαντικές σχέσεις. Αυτός ο τύπος ανάλυσης μπορεί να συμβάλλει στην κατανόηση των κοινών μηχανισμών που μετατρέπουν τα φυσιολογικά κύτταρα σε κακοήθη. Επίσης αποκαλύπτεται ένας νέος ολιστικός τρόπος κατανόησης της δυναμικής εμφάνισης του όγκου καθώς και το πώς λειτουργούν οι ογκογόνοι μηχανισμοί. Τα αποτελέσματα της παρούσας έρευνας είναι κλινικά χρήσιμα για την πρόβλεψη γονιδιωματικών στόχων που θα μπορούσαν να μελετηθούν περαιτέρω προκειμένου να ανακαλυφθούν οι μηχανισμοί δημιουργίας και εξέλιξης του όγκου, καθώς συμβάλλουν και στην ανακάλυψη νέων θεραπευτικών προσεγγίσεων.At the present, the constant struggle in the fight against cancer makes it necessary to understand the mechanisms that dominate on tumorigenesis in order to discover more specialized and more effective drugs at as low a cost as possible. In this research we will use mathematical and physical approaches, in order to analyze complex biological phenomena. We will present a computational and systemic approach by studying the common mechanisms between two childhood neoplasms of mesodermal origin i.e. Acute Lymphoblastic Leukemia (ALL) and Rhabdomyosarcoma (RMS). Using high-efficiency methods, such as microarrays, we will get information at the level of gene expression and try to investigate common gene regulation based on chromosomal correlations. By exploring possible common regulatory mechanisms, we may be able to understand the causes of oncogenesis and the role of cancer stem cells. The experiments were conducted at Choremeio Research Laboratory. The CCRF-CEM (T cell ALL) and TE-671 (RMS of medulloblastic origin) were used as an experimental model. Using microarrays we have discovered the common patterns of gene expression between the two cell types. Our target was to attempt to find causal relations between gene expression levels with respect to chromosomal location. Using regression analysis, we found that several genes manifested significant relations. This type of analysis can help to understand the common mechanisms that transform normal cells into malignant. It also reveals a new holistic way of understanding the tumor dynamics as well as the mechanics of oncogenic drivers. The results of this research are clinically useful for predicting genomic targets that could be further studied in order to discover the mechanics of tumor creation and progression and will contribute to the discovery of new therapeutic approaches

Προσομοίωση των μεταβολικών οδών στα νεοπλάσματα μεσοδερμικής προέλευσης με μεθοδολογίες συστημικής βιολογίας: η περίπτωση του ραβδομυοσαρκώματος

Η Οξεία Λεμφοβλαστική Λευχαιμία (ΟΛΛ), αποτελεί τη συχνότερη κακοήθεια της παιδικής ηλικίας. Η πρόοδος που έχει επιτευχθεί τα τελευταία χρόνια είναι σημαντική ως προς την ίαση, με τα ποσοστά να ανέρχονται από 80% μέχρι 90% σε διάφορες μελέτες. Ωστόσο, παρά την καλύτερη ταξινόμηση των ασθενών και τα σύγχρονα χημειοθεραπευτικά πρωτόκολλα, ένα ποσοστό 10-20% υποτροπιάζει. Στον αντίποδα, το ραβδομυοσάρκωμα, αποτελεί μια σπάνια κακοήθεια, η οποία έχει κακή πρόγνωση. Οι δύο αυτές νεοπλασίες έχουν το κοινό χαρακτηριστικό ότι είναι μεσοδερμικής προελεύσεως. Οι προσπάθειες για τη βελτίωση των ποσοστών επιβίωσης των ασθενών, έχουν επικεντρωθεί από τους ερευνητές σε ειδικούς προγνωστικούς παράγοντες που θα μπορούσαν με μεγαλύτερη σαφήνεια από τους ήδη υπάρχοντες, να ταξινομήσουν τους ασθενείς πριν την έναρξη της χημειοθεραπείας, ώστε η θεραπεία να τροποποιηθεί έγκαιρα. Με τις νέες εξελίξεις στην τεχνολογία της έρευνας, η ανάλυση του προφίλ του ασθενούς σε επίπεδο γονιδιακό, μετάφρασης και πρωτεϊνοσύνθεσης, δίνει ελπίδες ότι θα μπορεί στο μέλλον να διαμορφώνεται μία μοναδική ταυτότητα για κάθε ασθενή, που θα μπορεί να δίνει πληροφορίες για το γενετικό του υλικό και την ευαισθησία του ή όχι σε φαρμακευτικούς ή άλλους θεραπευτικούς παράγοντες, να καθορίζει το εξατομικευμένο θεραπευτικό σχήμα και να δίνει, με μεγάλη ευαισθησία, την πρόγνωση για την έκβαση της νόσου. Στην παρούσα εργασία, έγινε προσπάθεια να μελετηθεί η γονιδιακή έκφραση και οι γονιδιακές οδοί σε λευχαιμικά κύτταρα και ραβδομυοκύτταρα in vitro, με τη βοήθεια της τεχνολογίας των complementary Deoxyribonucleic Acid (cDNA) μικροσυστοιχιών για την ανίχνευση κοινών μεταβολικών οδών. Η μεταφορά των ευρημάτων στην κλινική πράξη με έλεγχο της έκφρασης του γονιδιακού προφίλ κατά τη διάγνωση και κατά τη θεραπεία, θα μπορούσε να μορφοποιήσει και να εξατομικεύσει το σχήμα θεραπείας του ασθενούς, μεγιστοποιώντας τις πιθανότητες επιτυχίας. Η εφαρμογή των μικροσυστοιχιών θα μπορούσε να ταξινομήσει τους ασθενείς για την καλύτερη εφαρμογή φαρμάκων που υπάρχουν και χρησιμοποιούνται ήδη, αλλά και να οδηγήσει στην ανακάλυψη νέων φαρμάκων που στοχεύουν σε συγκεκριμένους μεταβολικούς στόχους. Η τάση τόσο στην βασική όσο και στην κλινική έρευνα είναι η εξατομίκευση της θεραπείας. Σ’ αυτή τη μελέτη χρησιμοποιήθηκαν υπολογιστικά εργαλεία που δίνουν πρόσβαση σε μαζικό αριθμό δεδομένων και νέες μεθοδολογίες που επιτρέπουν την ανάλυση τέτοιων δεδομένων ώστε να μπορέσει κανείς να εξάγει συμπεράσματα από τη μαζικότητα και την πληθώρα των αποτελεσμάτων. Η συστημική θεώρηση των δεδομένων που παράγονται από την έρευνα και πιο συγκεκριμένα από τέτοιου είδους έρευνα βρίσκει την απαρχή της ήδη στις αρχές του 19ου αιώνα μέσα από τα λόγια του γνωστού μαθηματικού Henri Poincaré : “…life is a relationship among molecules and not a property of any molecule…” “…Science is built up of facts, as a house is with stones. But a collection of facts is no more a science, than a heap of stones is a house…”Acute Lymphoblastic Leukemia (ALL) is the most common malignancy of childhood. There has been significant progress in recent years on healing, with survival rates ranging from 80% to 90% in various studies. However, despite better patient classification and modern chemotherapy protocols, there is a 10-20% relapse rate. In contrast, rhabdomyosarcoma is a rare malignancy that has a poor prognosis. These two tumors have the common characteristic that are both of mesodermal origin. Efforts to improve patient survival rates have focused on specific prognostic factors that could more clearly classify patients prior to initiating chemotherapy so that treatment can be modified in a timely manner. With the latest advances in research technology, analyzing the patient profile at the gene, translation and protein synthesis level offers the hope that a unique identity for each patient can be formed in the future that can provide information on their genetic material. In addition, whether or not it is susceptible to pharmaceutical or other therapeutic agents, to determine the individual treatment regimen and to give, with great sensitivity, the prognosis for the outcome of the disease. In the present work, an attempt was made to study gene expression and gene pathways in leukemic cells and rhabdomyocytes in vitro, using complementary Deoxyribonucleic Acid (cDNA) microarray technology to detect common metabolic pathways. Translating the findings into clinical practice by controlling the expression of the gene profile during diagnosis and treatment, could shape and personalize the patient&apos;s treatment regimen, maximizing the chances of success. Implementation of microarrays could classify patients for better use of existing and already used drugs, but also lead to the discovery of new drugs that target specific metabolic targets. The tendency in both basic and clinical research is to personalize treatment. This study used computational tools that give access to massive amounts of data and new methodologies that allow the analysis of such data so that one can draw conclusions from the mass and abundance of results. A systematic view of the data generated by research and more specifically of such research finds its beginning in the early 19th century through the words of well-known mathematician Henri Poincaré: “… Life is a relationship between molecules and not a property of any molecule…”, “… Science is built on facts, like a house with stones. But a collection of facts is no more a science than a heap of stones is a house…