scholarly journals Identifying Personalized Metabolic Signatures in Breast Cancer

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 20
Author(s):  
Priyanka Baloni ◽  
Wikum Dinalankara ◽  
John C. Earls ◽  
Theo A. Knijnenburg ◽  
Donald Geman ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Estrogen Metabolism ◽  
Acid Oxidation ◽  
A Genome ◽  
Context Specific

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

scholarly journals Identifying personalized metabolic signatures in breast cancer

2020 ◽  
Author(s):  
Priyanka Baloni ◽  
Wikum Dinalankara ◽  
John C. Earls ◽  
Theo A. Knijnenburg ◽  
Donald Geman ◽  
...  
Keyword(s):  
Drug Targets ◽  
Metabolic Networks ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Estrogen Metabolism ◽  
Acid Oxidation ◽  
A Genome ◽  
Context Specific

Cancer cells are adept at reprogramming energy metabolism and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism and ROS detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Knockdown of PGM1 Synergistically Enhances Anticancer Effects of Orlistat in Gastric Cancer Under Glucose Deprivation

2021 ◽  
Author(s):  
Bo Cao ◽  
Huan Deng ◽  
Hao Cui ◽  
Ruiyang Zhao ◽  
Hanghang Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cell Proliferation ◽  
Lipid Metabolism ◽  
Fatty Acid Synthase ◽  
Enrichment Analysis ◽  
Glucose Deprivation ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas

Abstract Background Phosphoglucomutase 1 (PGM1) acts as an important regulator in glucose metabolism. However, the role of PGM1 in gastric cancer (GC) remains unclear. This study aims to investigate the role of PGM1 and develop novel regimens based on metabolic reprogramming in GC. MethodsCorrelation and enrichment analysis of PGM1 was conducted based on The Cancer Genome Atlas database. Data derived from the Kaplan-Meier Plotter database were analyzed for correlations between PGM1 expression and survival time of GC patients. CCK-8, EdU, flow cytometry assays, generation of subcutaneous tumor and lung metastasis mouse models were used to determine growth and metastasis in vitro and in vivo. Cell glycolysis was detected by a battery of glycolytic indicators, including lactate, pyruvic acid, ATP production and glucose uptake. Fatty Acid Synthase (FASN) activity and detection of lipid regulators levels by western blot were used to reflect on the cell lipid metabolism. ResultsCorrelation and enrichment analysis suggested that PGM1 was closely associated with cell proliferation and metabolism. PGM1 was overexpressed in GC tissues and cell lines. High PGM1 expression served as an indicator of shorter survival for specific subpopulation of GC patients, which was also correlated with some clinicopathological features, including T stage and TNM stage. Under low glucose conditions, knockdown of PGM1 significantly suppressed cell proliferation and glycolysis levels, whereas lipid metabolism was enhanced. Orlistat, as a drug that was designed to inhibit FASN activity for obesity treatment, effectively induced apoptosis, suppressed FASN activity. However, orlistat conversely increased glycolytic levels in GC cells. Orlistat exhibited more significant inhibitive effects on GC progression after knockdown of PGM1 under glucose deprivation due to combination of glycolysis and lipid metabolism. ConclusionsDownregulation of PGM1 expression under glucose deprivation synergistically enhanced anti-cancer effects of orlistat. This combination application may serve as a novel strategy for GC treatment.

scholarly journals Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guangyu Gao ◽  
Zhen Yao ◽  
Jiaofeng Shen ◽  
Yulong Liu
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

scholarly journals Predicting context specific enhancer-promoter interactions from ChIP-Seq time course data

2017 ◽  
Author(s):  
Tomasz Dzida ◽  
Mudassar Iqbal ◽  
Iryna Charapitsa ◽  
George Reid ◽  
Henk Stunnenberg ◽  
...  
Keyword(s):  
Estrogen Receptor Alpha ◽  
Time Course ◽  
Target Genes ◽  
Mcf7 Cells ◽  
Genome Wide ◽  
A Genome ◽  
Machine Learning Approach ◽  
Protein Occupancy ◽  
Context Specific ◽  
Over Time

We have developed a machine learning approach to predict context specific enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

scholarly journals Combined genetic analysis of juvenile idiopathic arthritis clinical subtypes identifies novel risk loci, target genes and key regulatory mechanisms

2020 ◽  
pp. annrheumdis-2020-218481 ◽  
Author(s):  
Elena López-Isac ◽  
Samantha L Smith ◽  
Miranda C Marion ◽  
Abigail Wood ◽  
Marc Sudman ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Fine Mapping ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Enrichment Analysis ◽  
Joint Analysis ◽  
Integrative Approach ◽  
Nucleotide Polymorphisms ◽  
A Genome

ObjectivesJuvenile idiopathic arthritis (JIA) is the most prevalent form of juvenile rheumatic disease. Our understanding of the genetic risk factors for this disease is limited due to low disease prevalence and extensive clinical heterogeneity. The objective of this research is to identify novel JIA susceptibility variants and link these variants to target genes, which is essential to facilitate the translation of genetic discoveries to clinical benefit.MethodsWe performed a genome-wide association study (GWAS) in 3305 patients and 9196 healthy controls, and used a Bayesian model selection approach to systematically investigate specificity and sharing of associated loci across JIA clinical subtypes. Suggestive signals were followed-up for meta-analysis with a previous GWAS (2751 cases/15 886 controls). We tested for enrichment of association signals in a broad range of functional annotations, and integrated statistical fine-mapping and experimental data to identify target genes.ResultsOur analysis provides evidence to support joint analysis of all JIA subtypes with the identification of five novel significant loci. Fine-mapping nominated causal single nucleotide polymorphisms with posterior inclusion probabilities ≥50% in five JIA loci. Enrichment analysis identified RELA and EBF1 as key transcription factors contributing to disease risk. Our integrative approach provided compelling evidence to prioritise target genes at six loci, highlighting mechanistic insights for the disease biology and IL6ST as a potential drug target.ConclusionsIn a large JIA GWAS, we identify five novel risk loci and describe potential function of JIA association signals that will be informative for future experimental works and therapeutic strategies.

scholarly journals Genome-Wide Phylogenetic Analysis, Expression Pattern, and Transcriptional Regulatory Network of the Pig C/EBP Gene Family

2021 ◽  
Vol 17 ◽  
pp. 117693432110413
Author(s):  
Chaoxin Zhang ◽  
Tao Wang ◽  
Tongyan Cui ◽  
Shengwei Liu ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Phylogenetic Analysis ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Genome Wide ◽  
A Genome

The CCAAT/enhancer binding protein (C/EBP) transcription factors (TFs) regulate many important biological processes, such as energy metabolism, inflammation, cell proliferation etc. A genome-wide gene identification revealed the presence of a total of 99 C/EBP genes in pig and 19 eukaryote genomes. Phylogenetic analysis showed that all C/EBP TFs were classified into 6 subgroups named C/EBPα, C/EBPβ, C/EBPδ, C/EBPε, C/EBPγ, and C/EBPζ. Gene expression analysis showed that the C/EBPα, C/EBPβ, C/EBPδ, C/EBPγ, and C/EBPζ genes were expressed ubiquitously with inconsistent expression patterns in various pig tissues. Moreover, a pig C/EBP regulatory network was constructed, including C/EBP genes, TFs and miRNAs. A total of 27 feed-forward loop (FFL) motifs were detected in the pig C/EBP regulatory network. Based on the RNA-seq data, gene expression patterns related to FFL sub-network were analyzed in 27 adult pig tissues. Certain FFL motifs may be tissue specific. Functional enrichment analysis indicated that C/EBP and its target genes are involved in many important biological pathways. These results provide valuable information that clarifies the evolutionary relationships of the C/EBP family and contributes to the understanding of the biological function of C/EBP genes.

scholarly journals A Comprehensive Network Pharmacology-Based Strategy to Investigate Multiple Mechanisms of HeChan Tablet on Lung Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Zhenjie Zhuang ◽  
Qianying Chen ◽  
Cihui Huang ◽  
Junmao Wen ◽  
Haifu Huang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Therapeutic Target ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Lung Cancer Patients ◽  
Ppi Networks

Background. HeChan tablet (HCT) is a traditional Chinese medicine preparation extensively prescribed to treat lung cancer in China. However, the pharmacological mechanisms of HCT on lung cancer remain to be elucidated. Methods. A comprehensive network pharmacology-based strategy was conducted to explore underlying mechanisms of HCT on lung cancer. Putative targets and compounds of HCT were retrieved from TCMSP and BATMAN-TCM databases; related genes of lung cancer were retrieved from OMIM and DisGeNET databases; known therapeutic target genes of lung cancer were retrieved from TTD and DrugBank databases; PPI networks among target genes were constructed to filter hub genes by STRING. Furthermore, the pathway and GO enrichment analysis of hub genes was performed by clusterProfiler, and the clinical significance of hub genes was identified by The Cancer Genome Atlas. Result. A total of 206 compounds and 2,433 target genes of HCT were obtained. 5,317 related genes of lung cancer and 77 known therapeutic target genes of lung cancer were identified. 507 unique target genes were identified among HCT-related genes of lung cancer and 34 unique target genes were identified among HCT-known therapeutic target genes of lung cancer. By PPI networks, 11 target genes AKT1, TP53, MAPK8, JUN, EGFR, TNF, INS, IL-6, MYC, VEGFA, and MAPK1 were identified as major hub genes. IL-6, JUN, EGFR, and MYC were shown to associate with the survival of lung cancer patients. Five compounds of HCT， quercetin, luteolin, kaempferol, beta-sitosterol, and baicalein were recognized as key compounds of HCT on lung cancer. The gene enrichment analysis implied that HCT probably benefitted patients with lung cancer by modulating the MAPK and PI3K-Akt pathways. Conclusion. This study predicted pharmacological and molecular mechanisms of HCT against lung cancer and could pave the way for further experimental research and clinical application of HCT.

scholarly journals CBMT-15. MET INHIBITION DRIVES PGC1A DEPENDENT METABOLIC REPROGRAMMING AND ELICITS UNIQUE METABOLIC VULNERABILITIES IN GLIOBLASTOMA

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi36-vi36
Author(s):  
Yiru Zhang ◽  
Trang Nguyen ◽  
Junfei Zhao ◽  
Enyuan Shang ◽  
Consuelo Torrini ◽  
...  
Keyword(s):  
Oxidative Phosphorylation ◽  
Treatment Resistance ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Metabolic Reprogramming ◽  
Acid Oxidation ◽  
Beta Oxidation ◽  
Combination Treatments ◽  
Extracellular Flux ◽  
Genetic Interference

Abstract The receptor kinase, c-MET, has emerged as a target for glioblastoma therapy. However, treatment resistance evolves inevitably. By performing a global metabolite screen with metabolite set enrichment coupled with transcriptome and gene set enrichment analysis and proteomic screening, we have identified substantial reprogramming of tumor metabolism, involving oxidative phosphorylation and fatty acid oxidation (FAO) with a substantial accumulation of acyl-carnitines accompanied by an increase of PGC1a in response to genetic (shRNA and CRISPR/Cas9) and pharmacological (crizotinib) inhibition of c-MET. Extracellular flux and carbon tracing analyses (U-13C-Glucose and U-13C-Glutamine) demonstrated enhanced oxidative metabolism, which was driven by FAO and supported by increased anaplerosis of glucose carbons. These findings were observed in concert with increased number and fusion of mitochondria and production of reactive oxygen species (ROS). Genetic interference with PGC1a rescued this oxidative phenotype driven by c-MET inhibition. Silencing and chromatin immunoprecipitation experiments demonstrated that CREB regulates the expression of PGC1a in the context of c-MET inhibition. Interference with both oxidative phosphorylation (metformin, oligomycin) and beta-oxidation of fatty acids (etomoxir) enhanced the anti-tumor efficacy of c-MET inhibition. Moreover, based on a high-throughput drug screen, we show that gamitrinib along with c-MET inhibition results in synergistic cell death. Finally, utilizing patient-derived xenograft models, we provide evidence that the combination treatments (crizotinib+etomoxir and crizotinib+gamitrinib) were significantly more efficacious than single treatment without induction of toxicity. Collectively, we have unraveled the mechanistic underpinnings of c-MET inhibitor treatment and identified novel combination therapies that may enhance the therapeutic efficacy of c-MET inhibition.

Dysregulated microRNAs in laryngeal cancer: a comprehensive meta-analysis using a robust rank aggregation approach

2020 ◽  
Vol 16 (33) ◽  
pp. 2723-2734
Author(s):  
Zaizai Cao ◽  
Yu Guo ◽  
Yinjie Ao ◽  
Shuihong Zhou
Keyword(s):  
Target Genes ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Rank Aggregation ◽  
The Cancer Genome Atlas ◽  
Aggregation Method ◽  
Multiple Cancer ◽  
Cancer Genome Atlas ◽  
Genome Atlas

We need a reasonable method of compiling data from different studies regarding the expression of microRNA (miRNA) in laryngeal squamous cell carcinoma (LSCC). The robust rank aggregation method was used to integrate the rank lists of miRNAs from 11 studies. The enrichment analysis was performed on target genes of meta-signature miRNAs. The Cancer Genome Atlas database was used to confirm the results of meta-analysis. Three meta-signature miRNAs (miR-21-5p, miR-196a-5p and miR-145-5p) were obtained. All three miRNAs could be prognostic for LSCC. The enrichment analysis showed that these miRNAs were associated significantly with multiple cancer-related signaling pathways. The robust rank aggregation approach is an effective way to identify important miRNAs from different studies. All identified miRNAs could be candidates for LSCC diagnostic and prognostic biomarkers.

Multiomics analysis identifies key genes and pathways related to N6-methyladenosine RNA modification in ovarian cancer

Epigenomics ◽  
2021 ◽  
Author(s):  
Haoya Xu ◽  
Xianli Li ◽  
Shengtan Wang ◽  
Feifei Li ◽  
Jian Gao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Rna Modification ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Number Variation ◽  
Cancer Genome Atlas ◽  
Patient Prognosis

Aims: To explore the pathways and target genes related to N6-methyladenosine (m6A) methylation in ovarian cancer and their effect on patient prognosis. Methods & materials: The Cancer Genome Atlas was used to screen genes related to m6A regulators in terms of gene expression, mutation and copy number variation. These genes were subjected to pathway enrichment analysis. Prognosis-related genes were screened and involved in risk signature construction. Immunohistochemistry was used for verification. Results: We obtained 1408 genes dysregulated in parallel to m6A regulators, which were mainly involved in the platelet activation pathway. The m6A-related signature was constructed based on the expression of four prognosis-related genes ( RPS6KA2, JUNB, HNF4A and P2RX1). Conclusion: This work provides new insights into the mechanism of m6A methylation in ovarian cancer.

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