Luminal B sub-networks were related to these of luminal A cells this similarity is not astonishing because luminal A and B cells had been also grouped in only one particular class, luminal, as shown in S1 Table. Comparison of luminal A and B mobile traces with triple unfavorable showed up-regulation of (i) the evading progress elements ERBB2/three in luminal A, and (ii) YWHAB and PA2G4 in sub-network of differentially expressed genes amongst MDA-MB-231 (Triple-Adverse) and MCF10A, represented in a round layout. Nodes represent genes whilst back links depict interaction between genes. Size nodes reveal connectivity and color represents an expression sample between tumoral versus non-tumoral breast mobile line. (A) p<0.05. (B) p<0.01. purchase 465-99-6Gephi was used to present and visualize the networks.Venn diagram including comparative analysis of the subtype-specific networks to predict subtype-specific therapy. Up-regulated genes (A) and down- regulated genes (B). Luminal A classification includes MCF-7, T47D e ZR751 cell lines Luminal B, includes BT-474 and triple negative, BT20, MDA-MB-231 and MDA-MB-468 luminal B. YWHAB and PA2G4 have been implicated in cell resistance to death, the sustainment of proliferative signaling and invasion and metastasis activation (see S1 Table). To gain insight into the significance of the network circuit related to breast tumor progression, we also classified down-regulated genes with respect to functional biology and hallmarks of cancer (see S4 Table) [17,22,25,426]. The down-regulated genes covered a wide range of processes implicated in cancer biology. As expected, down-regulated genes are preferentially related to the maintenance of equilibrium conditions we found functions such as: (i) cell death signaling (GABARAPL2, GABARAP, MAP1LC3A, TP53), (ii) cytoskeleton stability (ACTB, ACTG1, TUBA1A), and (iii) proliferation (TP53, GRP78, NFKBIA, GSK3B, BHLHE40). To summarize, these results indicate the complexity of signaling through these networks and the massive consequences induced by protein hub deregulation on cross- talk between regulators of cellular events.Our results have three major implications for cancer therapy (i) in helping to define a strategy to identify potential oncotargets for breast cancer treatment (ii) in unveiling key regulatory circuits between down- and up-regulated genes responsible for the cell physiology of breast tumor progression and (iii) in providing fast protein target identification in the context of personalized medicine that could match individual tumors types and histological subtypes. The methodology described should help to establish a quantitative relationship between putative oncotargets and a relevant therapeutic strategy. Our study also provides a framework for the identification of key players involved in breast malignancy, and may lead to new insights useful in the development of therapeutic interventions for breast cancer treatment and prevention. Further work is required to functionally validate these oncotargets starting with a pre- clinical testing at an in vitro level. Although several reports have demonstrated the importance of protein networks in breast cancer [57,58], only a few studies have identified the expression profile of their corresponding genes [59,60]. While other reports have addressed protein networks subtype- specific cell lines in breast cancer, none of them has normalized expression patterns of these malignant cells to a non-tumoral breast cell line. Our report focuses on the profiling of gene expression of hub proteins, which emerge as suitable for drug development with a lower rate of negative collateral effects for patient health. Actually, as for type I and type II errors, there is a compromise concerning the choice of protein targets with a p-value of 0.1% or 5%. The number of targets available under a p-value of 0.1% is of course lower than under 5%, but, in contrast, their inhibition is expected to cause fewer side effects to the patient because of a larger difference of gene expression between malignant and normal cells. By contrast, when considering a p-value of 5%, the number of potential target increases, but the price to be pay is a higher level of adverse side effects. The full set of interacting human proteins that we used is based on *10,000 genes, which is about one third of the whole human genome set that has been evaluated to be *30,000 based on data of expressed sequence tags [61] a sample of one third of the whole human gene set is considered here as highly statistically significant. Breitkreutz et al. [10] showed that signaling network modeling is suitable for cancer hallmarks identification as it provides important insights into how gene mutations may affect cell physiology and lead to cancer as well as to identify putative cancer biomarkers. The existence of an interacting sub-network between down- and up-regulated genes indicates that the differentially expressed genes, in addition to being induced by specific cancer pathways, are interacting with each other apparently in a compensatory way, which further indicates that tumorigenesis and tumoral progression require multiple and crosstalk signaling. The networks associated to different cell subtypes and their specific patterns observed here are in good agreement with the data from the literature. With the fast pace of modern technology development, we can make a safe prediction that at some point in a not-too-distant future, when a patient is diagnosed with cancer, it will still be possible to sequence both malignant and normal cells through biopsy in order to inform the treatment plan. When specific oncotargets are identified, it will become theoretically possible to define a personalized drug cocktail on the basis of existing knowledge or even, on the fly, by in silico simulations (docking and molecular dynamics) of inhibitors with these oncotargets. Theoretically, this strategy is compatible with individual medicine, in the sense, that whenever the strategy is designed, it can be, in principle, largely automated. As the response rates to a specific chemotherapeutic drug might be relatively low in an unselected pre-treated patient population, it is a pre- requisite, that the repurposing strategy includes pre-selection of those patients with a favorable molecular profile in their cancer cells, i.e., those patients with the highest likelihood to benefit from the treatment. Our strategy differs from the traditional view of drug repurposing in expecting to find new indications for cocktail therapies that should affect essential pathways/mechanisms resulting in cancer cell death with minimal side effects for normal cells. In other words, we simultaneously aim to maximize efficacy and minimize toxicity of a given treatment regimen. This strategy is expected to overcome intrinsic and acquired resistance, tumor heterogeneity, adaptation, and genetic instability of cancer cells. However, multiple alternative signaling routes exist in tumors that make them resistant to drug treatment. Thus, a number of issues should be addressed to ascertain that the strategy proposed is as powerful as predicted since biological complexity always brings unexpected situations. Importantly, the entire set of predicted drug targets has been experimentally validated by available drugs or siRNA (S5 Table) [36,38,40,626], which shows that the approach presented here is consistent with the state of the art and should behave similarly in new situations where knowledge is scarce. Thus, the approach described here can conceivably be implemented for a substantial number of currently used chemotherapeutic drugs, since their molecular mechanisms of action are well understood with thousands of studies available in the literature.It is hoped that our strategy will allow the elucidation of molecular networks of different tumors and histological subtypes. We believe that this strategy is valuable and can, potentially, add new tools to the armamentarium of drugs at the disposal of oncologists. Since malignant transformation has been described as involving a defined set of physiological changes, we classified these top 5-genes into the hallmarks of cancer, according to the criteria and examples proposed by Hanahan and Weinberg [3] and based on the current functional understanding of the genes from BLAST to gene ontology (Blast2GO). GO terms are used to identify a list of potential components, functions and processes that are significantly pinpointed in the selected genes. This classification revealed that top five up- and down-regulated genes for each cell line contribute to all six acquired capabilities required for tumor progression. These results suggest that our approach is useful in identifying oncotargets suitable for breast cancer treatment. It seems reasonable to find genes involved in cell cycle, considering that the dataset concerns breast cancer and proliferating malignant cells where expression of cell cycle regulators is crucial and reflecting the high mitotic index typically associated with breast tumors. Indeed, since highly proliferating cells require energy, glycolysis is a major pathway involved in energy production. According to this landscape, our results reveal GABARAPL1 and GAPDH as hubs in BT-20 cells (triple negative). Another class of targets is the group of genes involved in cell signaling and cell communication such as membrane proteins, HER2 and 3 or EGFR, signal transduction proteins such as MYC, TK1, NPM, YWHAB, MCM7, EIF4A3, HDGF, GRB2, CHD3, PAK2, PA2G4, and transport proteins such as KPNA2. It is not surprising that in this work, we pinpointed control genes of the cell cycle or apoptosis such as MAPK13, HSP90AB1, MAGOH, CSNK2B, EEF1G, PDIA3, ICT1, SRPK1, and also those involved in the EMT process such as VIM, which play a major role in tumor development. We identified HSP90AB1 as the only up-regulated protein hub common to all cell types in our study, which highlights the fact that the cancer subtypes addressed here all share a core of proliferative signaling pathways common in breast cancers, but with many specificities. The part of the expression pattern that is common to all cell lines included can further be influenced by the underlying genetic background of the tumor cells and the stage of tumor progression at which the cell line was derived. To further demonstrate the predictive power of subtype-specific networks, we attempted to predict subtype-specific therapeutic interventions. If a hub gene specifically appears in either a luminal A, B or triple-negative subtype-specific network, we expect that this gene could be a drug target specific for this subtype. Based on this criterion, GRB2, ICT1, PDIA3, KPNA2, NPM, PAK2, EIF4A3 and MCM7 are predicted as potential drug targets specific to the luminal A cell type, PA2G4 would be specific to the luminal B cell type, and MAGOH, MYC, SRPK1, VIM, GABARAPL1, GAPDH and CHD3 are predicted as potential drug targets specific of the triple negative subtype. We also found associations between the down-regulation of genes and therapy, which in some instances provide insights into the interplay between tumor suppressors and the cellular machinery in mediating drug sensitivity. For example, Zheng et al. [87] showed that overexpression of HER-2/neu could decrease the amount of wild- type p53 protein via the activation of the PI3K pathway, and the induction of MDM2 nuclear translocation in MCF-7 human breast cancer cells. Blockage of the PI3K pathway with its specific inhibitor LY294002 caused G1-S phase arrest, decreased the cell growth rate and increased chemoand radio-therapeutic sensitivity in MCF-7 cells expressing wild-type p53. Additionally, Wang et al. [88] showed that abrogation of GRP78 induced sensitivity of breast cancer cells to taxol and vinblastine.By using an integrative network analysis of the data derived from transcriptome and interactome public resources, we have predicted selective combinations of druggable targets to control key pathways in breast cancer. The molecular alterations observed in breast cancer cell lines represent either driver events and/or driver pathways that are necessary for breast cancer development or progression. However, it is clear that signaling mechanisms of the luminal A, B and triple negative subtypes are different. Furthermore, the up- and down-regulated networks predicted subtype-specific drug targets and possible compensation circuits between up- and down-regulated genes. Together with the finding that more connected genes could act as cancer regulators, these results may have significant clinical implications in the personalized treatment of cancer patients since every breast cancer can be considered as unique and reflects distinct qualitative and quantitative molecular traits. Thus, the knowledge of the entire set of molecular traits carried by any given breast cancer and patient is required for actual personalized therapy to be realized.We selected the two columns of UniprotKB identifiers (UID) in the intact-micluster.zip file and eliminated the incomplete pairs (marked as “-“, i.e., when an intact access number has no UniprotKB equivalent known). The resulting file contained 308,314 protein pairs.Since some UID were obsolete, we substituted them by their current name retrieved by querying the field search at UniprotKB using the format `replaces:obsolete UID’. Homologous hits were consideredsignificant when their score was !120, E-value 10-4 and identity rate !80% over !50%of query size. After elimination of subject redundancy (keeping the hit matching the largest identity rate), the final size of human CDS dataset fully described by protein interactions was 17,301.The gene expression profile was evaluated through a homology search with the human CDS sample of the Fedorov’s laboratory. The fifty bp sequences from transcriptome tags were used as queries in homology searches (BLASTn) in human CDSs. The homology redundancy in the BLASTn output file gave us the tag count per gene, i.e., a profile of human gene expression for the considered sample. 22410083Homologous hits were considered significant when covering !25 bp (50% of size). Each gene expression profile (tag count per gene) was normalized according to CDS size and whole tag count using the formula, where 109 is a correction factor, C is the number of reads that match a gene, N is the total mappable tags in the experiment, and L is the CDS size [90]. When tags were counted for more than one gene isoform (alternative splicing forms), we cumulated counts and allocated them to just one form (the largest one) this strategy means that we looked for gene expression and not isoform expression. To allow the comparison between independent gene expression profiles, we further applied Quantil-normalization (Q-norm) considering the eight samples of this study [91]. Up- and down-regulated genes were obtained by subtracting expression values (pair-wise comparisons) of a file containing malignant cell data from a control file (data from a non-tumoral breast cell line) and sorting on differential expression values (negative and positive values for down- and up-regulated genes, respectively). Then, we searched the best fit (95%) performed with a Gaussian function using PRISM on data of log10(xi+1) of the difference of expression values, where xi represents the values obtained from the subtraction of the two transcriptome data under comparison.
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