Because of the intrinsic nature of biological systems and processes, the unequivocal identification of a gene's involvement in a specific process is nontrivial. Since genes participate in a varied spectrum of biomolecular phenomena with a varying degree of involvement, the experimental work involves inference that admits some false positives and negatives and hence does not always necessarily yield clear-cut answers. Nonetheless, within a cellular machinery such as AM it is possible to identify: (i) putative core nodes, ubiquitously expressed genes endowed with a central biomolecular role in the activation or inhibition of apoptosis (e.g., death receptors, members of the Bcl2 family, caspases); (ii) putative peripheral nodes, genes with tissue-, pathway-, or disease-specific functions: in many cases these perform a regulatory role. We first searched the GO database 24 by using as key words apoptosis and cell death and compiled a preliminary list of human genes associated with apoptosis; this was then manually filtered by using literature data from PubMed, in order to experimentally verify the involvement of these genes in apoptosis 25. The final list of AM genes was compiled by using interactome data for verifying the functional connections among these proteins 2627. This approach (i.e., combining GO, PubMed and Interactome data) allowed us to establish a dataset consisting of 342 genes involved in apoptosis, whose products were physically or functionally interacting. The experimentally supported data of interactions between AM transcripts and MIRs were extracted from Tarbase 28. Recent comparative studies among target prediction methods have shown that no single one of them is significantly better than others. Accordingly, it has become common to use multiple prediction tools, concentrating on the intersecting results, in order to overcome the well-known problems related to MIR targets identification 29. In this project, we used miRGen 30, which intersects the results from three different classic tools (miRanda, PicTar, and TargetScan) to predict the MIRs controlling AM genes. The data about MIRs genomic position were extracted from miRBase 31.

The cytogenetic and genomic position of each AM gene was obtained by searching the database Gene of NCBI 25. The genome map of AM genes was displayed by Caryoscope 32 to identify the position of AM genes in the genome. The distribution of AM genes in each chromosome was compared to that of all human genes 33. AM cytogenetic map was compared to that of tumour-associated chromosomal structural mutations. The latter was determined by using data from the Mitelman database and CGH informations from Progenetix (a database of cytogenetic abnormalities in cancer) for the most frequent anatomic cancer sites (breast, cervix, colon, kidney, liver, lung, neuroblastoma, ovary, pancreas, prostate, skin, stomach, and thyroid) 3435. The overlap of the two maps was examined through Caryoscope. A red-green matrix (AM genes × tumours, in which the positive values indicate the frequency of gains while the negative ones indicate the frequency of losses) led to identification of AM genes clusters: these were displayed with MeV4.2 36. The information on cancer-related gene mutations was obtained by screening the database Cosmic 37. Cancer-related methylation data were from PubMeth 38.

To characterize the phylogenetic distribution of AM proteins, we collected AM orthologs from a large number of taxa [Prokaryotes (Bacteria, Archaea), Unicellular Eukaryotes (Alveolata, Diplomonadida group, Entamoebidae, Euglenozoa, Mycetozoa), Viridiplantae, Fungi, Invertebrates (Acoelomata, Pseudocoelomata, Protostomia), Vertebrates (Actinopterygii, Amphibia, Sauropsida, Mammalia)] and Viruses (both dsDNA viruses and retro-transcribing viruses) from the databases Homologene 39, iProClass 40 and Metazome 41. The search for AM orthologs was performed by using BLASTp with human AM proteins as queries against all protein databases 42. The total number of organisms used for analyzing AM phylogenesis was ninety. The evolutionary rate of AM proteins was assessed by carrying out a multiple alignment (MA) of each human protein with its orthologs from a series of model organisms (Mus musculus, Gallus gallus, Xenopus laevis, Danio rerio, Takifugu rubripes, Drosophila melanogaster, Caenorhabditis elegans, Dictyostelium discoideum, Bacillus subtilis, Methanococcus jannaschii). MA was performed with ClustalW 43, while aligned sequences were processed with Mega 3.0 44 to obtain the Poisson Corrected distance (k): this provides an estimate of the difference between two sequences (meant as percentage of amino acid substitutions) for all pairs of orthologs. The evolutionary rate (ν) was calculated through Kimura's parameter, ν = k/2 t, where t represents the time of divergence between two species 45. Histone H4 (a highly conserved protein), SOD2 (a protein with an average degree of conservation) and Fibrinopeptide (a protein characterized by a low conservation level) were used as evolutionary markers 46. The evolutionary rate was calculated for those proteins expressed at least in all vertebrates.

Gene expression data on AM genes were obtained from three datasets: Human Transcriptome Map (HTM), NCI60 Cancer Microarray Project, and Oncogenomics for cancer and normal tissues 474849. Because of the small sample size, fdr (false discovery rate) analysis with an empirical null model (as proposed by Efron) was deemed infeasible. Instead, the t-test was applied to identify those genes with expression values that differ significantly between normal and tumour samples (for HTM and Oncogenomics datasets) in each cancer model. We performed a test between subjects: Group A (normal tissues), Group B (cancer tissues); Welch approximation; Alpha (overall threshold p-value) = 0.001; the p-value is based on permutation (100 permutation per gene); the significance was determined by the Adjusted Bonferroni Correction. The resulting differentially expressed genes were further analyzed: for each differentially expressed gene, in each cancer model, we calculated the natural logarithm of the ratio between the mean of expression values of group B and the mean of expression values of group A. We chose the stringent cut-off of +1 and -1 (about three fold up- or down-regulated) to determine the up regulation and down regulation, respectively: the genes with ln ratio values between +1 and -1 were considered not significantly changed 5051. To further strengthen our analysis, we compared and completed these data with expression values from NCI60 datasets (analyzed as described above). Data analysis was performed and displayed with MeV4.2. The overlapping of AM transcriptome map with the AM mutation map was performed by Caryoscope. Expression data on MIRs, that target AM genes in different normal and tumour tissues, were derived from the database VITA 52, which contains MIR profiling obtained from bead-based flow cytometry of mature forms of MIRs 53.

AM protein expression data were obtained from The Human Protein Atlas and NCI60 protein datasets 5455. Due to the incomplete structure of these databases, some important cancer-related proteins were not included in our analysis. The data concerning protein features such as PTM, the presence of metal ions or an intrinsically unfolded structure, were obtained from HPRD and DisProt databases 2756. The structural characterization of protein motifs and domains was performed by screening the database ExPASy 57.

Data on protein-protein and protein-DNA interactions were obtained from BIND 26 and HPRD 27 databases, while pathway data are from Kegg and Biocarta 5859. The AM interactome was analyzed and visualized by Cytoscape 60, CentiBin 61, and iVici 62. By using Cytoscape plug-ins, interactome data were interpolated with structural information on AM proteins, to derive the relationships between structural motifs and interactions. Phenome data from knock-out experiments in the mouse were obtained from Mouse Genome Informatics 63. Drug targeting information was extracted from DrugBank 64.

The K562 cell line was derived from a patient with CML in blast crisis and expressing the chimaeric non-receptor tyrosine kinase p210^Bcr-Abl 65. Cell were cultured in RPMI 1640-glutamax, supplemented with 10% heat-inactivated fetal bovine serum (FBS; Gibco BRL), 2% L-glutamine, 1% penicillin/streptomycin. Cells were grown under 5% CO₂/95% O₂ in a 37°C humified incubator. Imatinib (STI571, Gleevec) was provided by Novartis Pharma (Basel, Switzerland). A 10 mM stock solution was freshly prepared in sterile phosphate-buffered saline and diluted in RPMI 1640 medium before use. K562 cells, exposed to 1 μM imatinib and relative controls, were collected and analyzed at different time points (0 h, 2 h, 4 h, 7 h, 12 h, 24 h, 48 h, 72 h). Human SH-SY5Y neuroblastoma cells, derived from a patient with a bone marrow metastasis 66, were grown in a 1:1 mixture of DMEM and Ham's F-12 medium (Cambrex Bio Science, Verviers Belgium), supplemented with 10% fetal bovine serum, 1% L-glutamine and 1% penicillin/streptomycin (Gibco, Invitrogen, Carlsbad, CA), in a humidified atmosphere of 5% CO₂ in air. For apoptosis induction, 3 × 10⁶ cells were seeded into 75 cm² flasks in 15 ml of culture medium. Fenretinide (dissolved in absolute ethanol) was added to cultures to a final concentration of 3 μM; an equal volume of ethanol (~0.3% of culture volume) was used to treat control cells. SH-SY5Y cells exposed to 3 μM fenretinide and relative controls were collected and analyzed at different time points (0 h, 12 h, 24 h, 48 h, 72 h, 96 h).

To detect apoptosis or necrosis, one aliquot (2 × 10⁵ cells) was used for flow cytometric analysis. Cell fluorescence was assessed with a Facscalibur flow cytometer (Becton Dickinson, San Jose, CA) by using the CellQuest software (Becton Dickinson). Standard protocols were followed to determine annexin-positivity with an Annexin V-FITC Apoptosis Detection Kit (Sigma, Saint Louis, Missouri).

Total RNA was extracted with Trizol (Invitrogen) from treated cells and untreated controls after trypsinization (for the neuroblastoma cell line) and centrifugation (for both lines) at 1200 rpm for 10 min. Total RNA (3 μg) was reverse-transcribed using SuperScript II and random hexamers (Roche Diagnostics GmbH, Mannheim, Germany) 19. cDNA (30 ng) was added to each well of a PCR array for quantitative PCR (Apoptosis PCR arrays and RT² Profiler PCR Array, SuperArray Bioscience Corporation, MD, USA). The array consisted of 96 primers for 84 genes of AM core protein-encoding genes, plus five housekeeping genes and three RNA and PCR quality controls. PCR cycles were performed according to the manufacturer instructions. The relative level of mRNA expression for each gene in each sample was first normalized to the expression of housekeeping genes (also provided in the array) in treated samples and then normalized with respect to the level of cDNA expression in control samples according to the 2^-ΔΔCT method 67. Quantitative real-time PCR was performed on Mx3005P™ QPCR system (Stratagene, La Jolla, CA, USA). The results were considered significant when the expression of a specific cDNA was at least either three times higher or lower than that of controls.

Understanding the molecular bases of complex and biomedically important phenotypes, as cancer and degenerative diseases, is one of the critical challenges for contemporary BioMedicine 1478. Different approaches have been proposed to explain cancer onset and heterogeneity, ranging from the study of single genes to large-scale system analysis 7980. The molecular oncology data obtained in the last decades have shown that the neoplastic phenotype is mainly due to genetic or epigenetic defects of two complex molecular machineries: the Cell Cycle and DNA Replication Apparatus (CCDRA) and the Apoptotic Machinery (AM) 228283. Typically, the biopathology of tumour cells is double faced: these cells are prone to uncontrolled proliferation with a scarce propensity to differentiate 8485, but they also possess an ever evolving tendency to escape cell death: the consequent immortality provides them with a strong selective advantage over the wild-type ones, also endowing them with resistance to chemotherapy. Accordingly, many mutations and dysregulations of CCDRA and AM genes are found in all cancer types 228283. However, the involvement of apoptosis dysfunctions in cancer has remained elusive at the system level. Based on these premises, we decided to characterize the Omics of the Apoptotic Machinery for using them: (i) as a tool to identify new cancer genes; (ii) to profile cancers for diagnostic purposes; and finally, (iii) to pinpoint new targets for pharmacological therapy. Our data suggest that the potential involvement of AM genes is heterogeneous in different cancer models, both for number and type of chromosomal breakpoints as for frequency of genomic mutations, suggesting heterogeneity of pathogenesis (Figures 5, 6). The highest levels of chromosomal mutations involving AM loci were observed in neuroblastoma, lung and pancreatic cancers (Figure 6). Their paucity in other cancers suggests either different pathomolecular mechanisms (e.g., still uncharacterized point mutations outside coding regions), or the involvement of other cellular machineries and pathways, such as CCDRA or the DRA 8687.

Few AM peripheral proteins are expressed in Prokaryotes and unicellular Eukaryotes, possibly with biological roles unrelated to metazoan apoptosis 10. At the time of pluricellularity onset, the molecular core of apoptosis inductors and executors (caspases, death receptors, and adapters) was already assembled (Figure 1). With the appearance of Vertebrates, the evolution of the Apoptotic Machinery proceeded by accretion 8889 (that is, through the recruitment of new molecules capable of interacting with the existing ones), possibly accelerated by the presence of molecular motifs favouring specific interactions. Its complexity further increased with the arrival of mammals, very likely due to the expansion of gene families that characterized their evolution (Figure 1) 909192. This is a common trend in the evolution of Eukaryotes, previously observed for other molecular machineries such as those controlling intracellular molecular trafficking or RNA interference 9394. The analysis of a more complex molecular apparatus (such as CCDRA) showed that Homo sapiens shares only 60% of its CCDRA genes with other eukaryotes (e.g., Saccharomyces cerevisiae and Arabidopsis thaliana). Interestingly, a sizeable fraction of the genes expressed during the cell cycle do not have orthologs in all the organisms analyzed: this suggests that each species may also synthesize a different and specific set of cell cycle proteins 9596. By analogy, the presence of different AM transcriptome profiles in different organisms could be expected. Phylogenetic analysis of unicellular eukaryotes identified orthologs of several AM genes encoding transcription factors and kinases, that in Metazoa are involved in the control of cell cycle, proliferation, differentiation and apoptosis (Figure 1). This could suggest that a molecular apparatus, responsible for the execution of a genetically guided form of cell death, was present in the first Eukaryotes: however, many of the genes performing it seem to be exclusive of unicellular eukaryotes 97. As expected, higher Eukaryotes share only few AM genes with Bacteria. It seems logical to hypothesize that the biomolecular function of these proteins could be different in the two Domains and not necessarily bear a functional relationship with cell death in Bacteria (original sin hypothesis) 1098. AM genes shared with viral genomes (both DNA and RNA viruses) are transcription factors involved in the control of cell cycle, proliferation, differentiation and in the MAP kinase pathway. These viral genes possess an oncogenic potential and presumably are the result of a horizontal gene transfer from vertebrates 99. Most AM proteins show a medium-to-low conservation degree and have an evolutionary rate similar to that of fibrinopeptide (Figure 1). This high rate of evolution suggests a relaxed structure/function relationship, that would allow them to stand many types of mutations without impairing their biological role. Many AM genes were found to be mutated in different types of neoplasia. Indeed, the loss of the self-destruction ability of a cell gives it an immediate selective advantage over the others, even though it may be detrimental for the whole organism 100. Furthermore, almost 50% of AM genes are members of a gene family: these genes tend to evolve more quickly because of the functional redundancy that characterizes the family after their duplication 101102. AM proteins characterized by a medium-to-high conservation degree (< 10%) are peripheral AM nodes, frequently at the boundaries with other cell machineries: they also are involved in other important functions, as cell cycle regulation (e.g., AKT3, the members of the cullin family, GSK3B, and SRC), signal transduction (e.g., HRAS, MAPK, PDCD6, YWHAE, YWHAG), metabolism, post-translational modifications (e.g., BECN1, MASK, RPL5, STK25) (Figure 1). Their biological role mainly consists in the regulation of the activity of receptors, transductors, and executors of the apoptotic program. Since they perform a critical role, these molecules were subjected to a strong selective pressure and have largely preserved structure and functions. In many cases, these proteins function within some of the most ancient biological processes: about 50% of these molecules are found in unicellular Eukaryotes and in Prokaryotes (e.g., the MAP kinases) 103. Our data confirm results previously published on the evolution of the human genome: proteins operating in regulatory processes (e.g., transcription factors and receptors) have remained relatively well conserved during eukaryotic evolutions 104. However, it remains true that the most conserved genes are those involved in such critical biological processes as metabolism, protein synthesis, and molecular transport 104. Our data seem also to suggest that in most of cancer models the genome regions most frequently affected by loss-type mutations contain the most conserved AM genes, suggesting a strong phenotype – genotype correlation (see Results, Oncogenomics).

The analysis of the AM transcriptome profile showed that the transcriptional involvement of this apparatus is heterogeneous in different cancer models for number and type of AM genes involved. These data suggest that AM could be involved in different tumours through tumour-specific mechanisms of transcriptional activation or repression 105106. According to our data, the main sources of AM transcriptional failure are represented by apoptosis regulators (i.e., transcription factors, kinases, and death receptors) rather than by its executors (e.g., members of the apoptosome, death transductors, and caspases) (Figure 7). This could suggest that frequently exploited mechanisms for cancer transformation are mutations of the genes at the top of the death signalling network: they are generally at the boundaries with other molecular machineries involved in cancer, such as the CCDRA or the DRM, and could have a pleiotropic effect on the activities of several other genes (Figure 7). The identification of specific AM molecular signatures (cancer profiling) could be useful for precise tumour diagnosis and specific therapy design, distinguishing the different molecular alterations associated to different tissues. As the number of AM genes transcriptionally altered in cancers is reasonably limited (between 25 and 200), specific low-cost platforms for AM transcriptome analysis could be designed for routine molecular screening of patients to understand the origin of the cancer and its metastases 107108. Furthermore, the comparison of molecular profiles would allow: (i) the identification of the common alterations among tumours; (ii) to understand the common basis of AM involvement in cancer transformation 107108. It is notable that a fraction of AM transcriptome dysregulations could be the result of an altered control of expression of these genes, triggered by the metabolism and cell cycle imbalance, and characteristic of neoplastic cells; in some cases, they could be due to aberrant gene methylation 83. Various transcriptome alterations were mirrored in the proteome and in some cases we also detected the corresponding genomic mutations (Figure 12). This approach allowed us to obtain a list of very reliable candidates for the malfunctioning apoptosis in cancer cells (Tables 7, 8). It is interesting to note that our functional analysis on AM dysregulated genes and proteins, based on GO, showed a possible different regulation of anti-apoptotic and pro-apoptotic genes. This could have different explanations: (i) its molecular basis could be a qualitative and quantitative mismatch between transcriptome and proteome, possibly due to post-transcriptional modifications, the latter being the real cause of cell phenotype 109110; (ii) the apoptotic cascade is mainly a protein signalling network based on protein/protein interaction and PTM (post-translational modification), frequently proteolysis and phosphorylation: usually, cells have most of the apoptotic actors constitutively present but in the inactive state 7; (iii) blocking of apoptosis does not necessarily require a global down-regulation of all pro-apoptotic genes or an overall up-regulation of all anti-apoptotic genes: in principle, it is sufficient that the expression of only a few of these genes (logically the hubs of the AM network, acting as masters) were altered to functionally modify cell behaviour 111. We should expect that the relationship between AM qualitative alterations and the neoplastic phenotype is neither consistent nor simple to read 112.

The mere qualitative and quantitative analysis of the alterations of a molecular machinery could be misleading, if not inserted in a tetradimensional scenario (the three dimensions of space plus time) that would allow one to detect the relevance and the functional influence of all the elements of the machinery within the organism. The AM network shows a very interesting structure. Namely, approximately 50% of its core proteins (effectors and executor of death) have at least one death domain (DEATH, DED, CARD, and DAPIN): this demonstrates a very efficient strategy of modular signal transduction (Figures 2, 4). Only a few modules are sufficient to establish the reciprocal interactions among at least 50 different proteins. The relatively small number of functionally related protein modules within AM suggests a possible evolutionary pathway. Most likely, when the molecular machinery responsible for apoptosis first appeared, it may have comprised a small group of proteins, characterized by few classes of motifs and domains, which allowed reciprocal protein interactions to induce, transduct and execute the apoptotic process. The subsequent gene duplication events and the phenomena of exon shuffling or meiotic recombination determined the evolution of the apparatus. This phase included its growth by accretion due to the recruitment of new interacting partners, spurred by newly acquired molecular sequences important for specific molecular interactions 8889. However, the structural and functional protein modules remained unchanged, together with the signals modulating and executing apoptosis 113114. Some AM proteins are NUPs: this suggests that for an appropriate control of apoptosis it may be useful that some proteins possess a malleable structure, endowing them with functional features not shown by other rigidly ordered proteins involved in death signalling 115116. The advantages of the great conformational freedom of NUPs are the ability to fold upon binding to their biological targets (coupled folding and binding) and an increased speed of interaction. Their extended structure and freedom in orientational search enable them to contact their partners over a large binding surface and to recognize distant determinants on the target 115116. This flexibility may be critical for the assembly of specific macromolecular complexes, which cannot be made of rigid components because of their sizes and topological constraints. Finally, the NUPs have an extreme proteolytic sensitivity that allows them to very rapidly turn over upon receiving appropriate signals. We found that NUPs typically represent the hubs of the AM network, suggesting that their structural plasticity makes them very important for the biology of the cell (Figure 6). Malfunctions of these dynamic nodes affect the stability of the network and seriously modify cell behaviour. It is not surprising that many cancer-related AM genes carrying point mutations are NUPs, in particular some of those frequently mutated (as HRAS, TP53, and CDKN2A) (Figure 6). The analysis of AM alterations related to cancer, based on a network view, has pinpointed the critical role of the hubs, the structurally and functionally most important nodes of the network, which could critically impair network's stability and accordingly the physiologic status of the cell 117. In fact, these nodes are lethal – embryonic perinatal or lethal postnatal genes in knock-out mice, and unsurprisingly they show genomic alterations, coupled to transcriptional dysregulations, in many cancer models 118119. Moreover, we found that highly connected nodes are frequently mutated (Table 3; Figures 3, 6). All these data seem to suggest that tumour-related defects, occurring in the hubs of AM, are preferentially selected. The functional impairment of a few nodes, which control directly or indirectly the activities of many others in the context of the co-occurrence of multiple genetic defects, could represent a selective advantage during neoplastic transformation (Additional file 12). The relationship between network connectivity and molecular alterations of AM is also stressed by our observation that the genes with more links to mutated genes are themselves highly susceptible to be dysregulated in cancer (Figure 14). We propose to exploit this approach, based on the search for nodes connected to AM mutated hubs, to discover new cancer biomarkers 22. Accordingly, the knowledge of the critical role of AM hubs in the neoplastic phenotype could provide us with a powerful strategy for designing targeted pharmacological treatment. Apoptosis induction as strategy for cancer treatment is one of the most exploited therapeutic approaches. As we have shown in this paper, activation of cell death is the common result of the administration of different drugs (e.g., Imatinib and Fenretinide) in different cancer types (e.g., CML and neuroblastoma). Imatinib and Fenretinide work by triggering different AM pathways of molecular signalling (by blocking the ATP binding site of BCR-ABL or by highly increasing the production of ROS, respectively). However, these biochemical cascades lead to the same final event: apoptotic death 120121. The real – time transcriptome analysis of K562 and SH-SY5Y cells (treated with Imatinib and Fenretinide, respectively) showed similar expression trends, although with a different correlation to the corresponding morphological events (Figure 16). This analysis agrees with the observation that their AM dysregulation profiles at steady-state are very similar (Figure 7, Panel C). Caspase activation, coupled to the triggering of mitochondrial death and partial suppression of the extrinsic death pathway, are the common changes observed in both cancer cell lines following pharmacological administration. The transcriptome changes observed during apoptosis induction involved both AM genes dysregulated in cancer as AM genes previously thought to be unaffected (Additional file 10). We note that the expression of some AM genes, previously identified as candidates for leukaemia and neuroblastoma, was apparently unchanged in our models: this behaviour may suggest they may have suffered functional impairment, due to genomic mutations or epigenetic modifications. The choice of the molecular cascade, leading to drug induced – apoptosis, is probably constrained by defects in some parts of the death machinery (e.g., mutations or epigenetic repression of death receptors). Therefore, the knowledge of the cancer-associated alterations of AM could be a critical tool to predict whether a pharmacological treatment, based on apoptosis induction, will be effective 20122. The proposal to identify all of the weak points along the AM network of specific cancers could be exploited for designing pathway-specific therapies to induce apoptosis in tumours. This strategy could be especially useful for those cancers that apparently are very resistant to death induction: the exact knowledge of their AM's Achilles' heels should allow the identification of alternative molecular targets, usable to induce apoptosis by new unaffected and unexplored pathways 123. As the hubs seem to be the major actors in apoptosis defects related to cancer, they could be considered very appropriate targets for drug design. In fact, we are not surprised that most of AM proteins, targeted by drugs, are characterized by a high degree of "betweenness" (Figure 15). These characteristics indicate their relevance as organizing regulators within a specific network and their potential influence on maintaining the functions of signalling mechanisms 124. Ideally, drugs should be selected so as to preferentially target proteins controlling complex signalling cascades, rather than functionally isolated nodes: consequently, they would affect the behaviour of the cell in a significant way to produce relevant clinical effects (Figure 15). Moreover, the pharmacological targeting of the critical nodes of the AM network should be extended also to small RNAs that participate in regulating apoptosis. Our data suggest that more than 100 MIRs potentially target AM genes. These MIRs are localized in frequently altered regions in cancer and have an altered expression in many tumours (Figures 5, 9; Tables 5, 6). As already reported 125126, MIRs have an important role in the pathogenesis of cancer and it may be expected that many of them could have a critical function in determining defects of AM functions 127128. Knowledge of dysregulated MIRs and their target specificity could be used for new therapeutic approaches based on blocking the up regulated MIRs (e.g., by using the antagomirs) or restoring their function (by MIR transfection with liposoluble or synthetic carriers, such as microspheres) 129130. The use of MIRs as therapeutic repressors of oncogenes would be an innovative medical strategy, which could improve the efficacy of traditional pharmacological therapies, or in some cases replace it 131.