A miRNA-mediated approach to dissect the complexity of tumor-initiating cell function and identify miRNA-targeting drugs
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE123121 | GEO | 2019/01/21
REPOSITORIES: GEO
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Project description:Tumor-initiating cells (TICs) contribute to drug resistance and tumor recurrence in cancers, thus experimental approaches to dissect the complexity of TICs are required to design successful TIC therapeutic strategies. Here, we show that miRNA-3' UTR sensor vectors can be used as a pathway-based method to identify, enrich, and analyze TICs from primary solid tumor patient samples. We have found that an miR-181ahigh subpopulation of cells sorted from primary ovarian tumor cells exhibited TIC properties in vivo, were enriched in response to continuous cisplatin treatment, and showed activation of numerous major stem cell regulatory pathways. This miRNA-sensor-based platform enabled high-throughput drug screening leading to identification of BET inhibitors as transcriptional inhibitors of miR-181a. Taken together, we provide a valuable miRNA-sensor-based approach to broaden the understanding of complex TIC regulatory mechanisms in cancers and to identify miRNA-targeting drugs.
| S-EPMC6335585 | biostudies-literature
Project description:A miRNA-mediated approach to dissect the complexity of tumor-initiating cell function and identify miRNA-targeting drugs
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Project description:Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT). We aimed to study the underlying mechanism by which ARF1 has been predicted playing crucial role during carcinogenesis and metastasis. To attest the key function of ARF1 during epithelial mesenchymal transition (EMT), loss of function in vitro study by means of siRNA knockdown of ARF1 in MDA-MB-231 breast cancer cell line was designed. Genome-wide gene expression of nine ARF1 knockdown samples and three control samples were profiled by using the Illumina HumanHT-12 V4.0 expression beadchip platform.
| S-ECPF-GEOD-49005 | biostudies-other
Project description:BACKGROUND: Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive. RESULTS: In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes. CONCLUSIONS: The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.
| S-EPMC2982694 | biostudies-literature
Project description:Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT).
2013-07-18 | GSE49005 | GEO
Project description:Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT). We aimed to study the underlying mechanism by which ARF1 has been predicted playing crucial role during carcinogenesis and metastasis. To attest the key function of ARF1 during epithelial mesenchymal transition (EMT), loss of function in vitro study by means of siRNA knockdown of ARF1 in MDA-MB-231 breast cancer cell line was designed. Genome-wide gene expression of nine ARF1 knockdown samples and three control samples were profiled by using the Illumina HumanHT-12 V4.0 expression beadchip platform.
2013-07-18 | E-GEOD-49005 | biostudies-arrayexpress
Project description:The study of human microRNAs is seriously hampered by the lack of proper tools allowing genome-wide identification of miRNA targets. We performed Ribonucleoprotein ImmunoPrecipitation-gene Chip (RIP-Chip) using antibodies against wild-type human Ago2 in untreated Hodgkin lymphoma (HL) cell lines. Ten to thirty percent of the gene transcripts from the genome were enriched in the Ago2-IP fraction of untreated cells, representing the HL miRNA-targetome. In silico analysis indicated that approximately 40% of these gene transcripts represent targets of the abundantly co-expressed miRNAs. To identify targets of miR-17/20/93/106, RIP-Chip with anti-miR-17/20/93/106 treated cells was performed and 1189 gene transcripts were identified. These genes were analyzed for miR-17/20/93/106 target sites in the 5'-UTRs, coding regions and 3'-UTRs. Fifty-one percent of them had miR-17/20/93/106 target sites in the 3'-UTR while 19% of them were predicted miR-17/20/93/106 targets by TargetScan. Luciferase reporter assay confirmed targeting of miR-17/20/93/106 to the 3'-UTRs of 8 out of 10 genes. In conclusion, we report a method which can establish the miRNA-targetome in untreated human cells and identify miRNA specific targets in a high throughput manner. This approach is applicable to identify miRNA targets in any human tissue sample or purified cell population in an unbiased and physiologically relevant manner.
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Project description:Since their discovery 20 years ago, miRNAs have attracted much attention from all areas of biology. These short (∼22 nt) non-coding RNA molecules are highly conserved in evolution and are present in nearly all eukaryotes. They have critical roles in virtually every cellular process, particularly determination of cell fate in development and regulation of the cell cycle. Although it has long been known that miRNAs bind to mRNAs to trigger translational repression and degradation, there had been much debate regarding their precise mode of action. It is now believed that translational control is the primary event, only later followed by mRNA destabilisation. This review will discuss the most recent advances in our understanding of the molecular underpinnings of miRNA-mediated repression. Moreover, we highlight the multitude of regulatory mechanisms that modulate miRNA function.
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Project description:A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.
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Project description:As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensin-converting enzyme 2 (ACE2), but whether or not renin-angiotensin-aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19.
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