Cancer Metabolic Networks Research Articles

A cancer ubiquitome landscape identifies metabolic reprogramming as target of Parkin tumor suppression.

Changes in metabolism that affect mitochondrial and glycolytic networks are hallmarks of cancer, but their impact in disease is still elusive. Using global proteomics and ubiquitome screens, we now show that Parkin, an E3 ubiquitin ligase and key effector of mitophagy altered in Parkinson's disease, shuts off mitochondrial dynamics and inhibits the non-oxidative phase of the pentose phosphate pathway. This blocks tumor cell movements, creates metabolic and oxidative stress, and inhibits primary and metastatic tumor growth. Uniformly down-regulated in cancer patients, Parkin tumor suppression requires its E3 ligase function, is reversed by antioxidants, and is independent of mitophagy. These data demonstrate that cancer metabolic networks are potent oncogenes directly targeted by endogenous tumor suppression.

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling spatial heterogeneity in neoplastic metabolic aberrations is critical for tumor treatment. Genome-scale metabolic network models have been used successfully to simulate cancer metabolic networks. However, most models use bulk gene expression data of entire tumor biopsies, ignoring spatial heterogeneity in the TME. To account for spatial heterogeneity, we performed spatially-resolved metabolic network modeling of the prostate cancer microenvironment. We discovered novel malignant-cell-specific metabolic vulnerabilities targetable by small molecule compounds. We predicted that inhibiting the fatty acid desaturase SCD1 may selectively kill cancer cells based on our discovery of spatial separation of fatty acid synthesis and desaturation. We also uncovered higher prostaglandin metabolic gene expression in the tumor, relative to the surrounding tissue. Therefore, we predicted that inhibiting the prostaglandin transporter SLCO2A1 may selectively kill cancer cells. Importantly, SCD1 and SLCO2A1 have been previously shown to be potently and selectively inhibited by compounds such as CAY10566 and suramin, respectively. We also uncovered cancer-selective metabolic liabilities in central carbon, amino acid, and lipid metabolism. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer and other cancers where spatial transcriptomics datasets are available.

LncRNAs regulate metabolism in cancer.

Metabolic reprogramming is a hallmark of cancer. Mammalian genome is characterized by pervasive transcription, generating abundant non-coding RNAs (ncRNAs). Long non-coding RNAs (lncRNAs) are freshly discovered functional ncRNAs exerting extensive regulatory impact through diverse mechanisms. Emerging studies have revealed widespread roles of lncRNAs in the regulation of various cellular activities, including metabolic pathways. In this review, we summarize the latest advances regarding the regulatory roles of lncRNAs in cancer metabolism, particularly their roles in mitochondrial function, glucose, glutamine, and lipid metabolism. Moreover, we discuss the clinical application and challenges of targeting lncRNAs in cancer metabolism. Understanding the complex and special behavior of lncRNAs will allow a better depiction of cancer metabolic networks and permit the development of lncRNA-based clinical therapies by targeting cancer metabolism.

Identification of Drug Targets in Breast Cancer Metabolic Network.

Genome-scale metabolic models have been proven to be valuable for defining cancer or to indicate the severity of cancer. However, identifying effective metabolic drug target (DT) of the active small-molecule compound is difficult to unravel and needs to be investigated. In this study, we identify effective DT for breast cancer using proposed network analysis of enzyme-centric network in the metabolic model. Our network-based analysis revealed that high degree nodes (HDNs) of enzymes are key to progression/development of cancer. These HDNs show high interconnections inside the network. It has been found that these HDNs are crucial driver nodes for effectively targeting in breast cancer metabolic network. Furthermore, based on the correlation and principal component analysis, we have shown that certain proteins play a significant role in the network and can be used as an effective DT in cancer therapeutics. In addition, these proteins stimulate the active site of enzymes to activate the target metabolites. Overall, we have shown that a better understanding of the metabolic networks using statistical model could be valuable in DT identification for developing effective therapeutic approaches and personalized medicine.

Using (1)H-nuclear magnetic resonance metabolomics and gene ontology to establish pathological staging model for esophageal cancer patients

By combining the metabolomics and computational biology, to explore the relationship between metabolic phenotype and pathological stage in esophageal cancer patients, to find the mechanism of metabolic network disturbance and develop a new method for fast preoperative clinical staging. A prospective cohort study (from April 2013 to January 2016) was conducted. The preoperative patients from Sichuan Provincial People's Hospital, who were diagnosed with esophageal cancer from May 2013 to April 2014 were included, and their serum samples were collected to detect (1)H-nuclear magnetic resonance (NMR) metabolomics for the purpose of drawing the metabolic fingerprinting in different stages of patients with esophageal cancer. The data were processed with these methods-principal components analysis: partial least squares regression and support vector machine, for the exploration of the enzyme-gene network regulatory mechanism in abnormal esophageal cancer metabolic network regulation and to build the quantitative prediction model of esophageal cancer staging in the end. All data were processed on high-performance computing platforms Matalab. The comparison of data had used Wilcoxon test, variance analysis, χ(2) test and Fisher exact test. Twenty patients with different stages of esophageal cancer were included; and their serum metabolic fingerprinting could differentiate different tumor stages. There were no difference among the five teams in the age (F=1.086, P>0.05), the body mass index (F=1.035, P>0.05), the distance from the incisors to tumor (F=1.078, P>0.05). Among the patients with different TNM stages, there was a significant difference in plasma metabolome. Compared to ⅡB, ⅢA, Ⅳstage patients, increased levels of butanone, ethanol amine, homocysteine, hydroxy acids and estriol, together with decreased levels of glycoprotein, creatine, choline, isobutyricacid, alanine, leucine, valine, were observed inⅠB, ⅡA stage patients. Four metabolic markers (ethanol amine, hydroxy-propionic acid, homocysteine and estriol) were eventually selected. gene ontology analysis showed that 54 enzymes and genes regulated the 4 key metabolic markers. The quantitative prediction model of esophageal cancer staging based on esophageal cancer NMR spectrum were established. Cross-validation results showed that the predicted effect was good (root mean square error=5.3, R(2)=0.47, P=0.036). The systems biology approaches based on metabolomics and enzyme-gene regulatory network analysis can be used to quantify the metabolic network disturbance of patients with advanced esophageal cancer, and to predict preoperative clinical staging of esophageal cancer patients by plasma NMR metabolomics.

A novel mechanism of mTORC1-mediated serine/glycine metabolism in osteosarcoma development

Osteosarcoma is the major malignant primary bone cancer in children and adolescents, which is highly aggressive with frequent acquisition of chemoresistance phenotypes. Although much progress has been made, mechanisms of osteosarcoma rapid growth and chemoresistance are still not well elucidated. Generally, alternated metabolic characterization has been proposed to be a hallmark of cancer, yet it is lack of a systematic characterization of cancer metabolic networks. In the present study, we aim to characterize osteosarcoma metabolism and key regulators to reveal mechanisms of how osteosarcoma grows and resists apoptosis under stress conditions. The results demonstrate that mTORC1 pathway is hyperactivated in clinical osteosarcoma samples. However, inhibition of mTORC1 may not be enough to induce significant death of osteosarcoma cells. Results of GC-TOFMS suggested that inhibition of mTORC1 reduce one-carbon amino acids, serine and glycine, in osteosarcoma cells. Moreover, mTORC1 regulates serine/glycine de novo synthesis via modulating glycolysis and serine/glycine synthesis gene expressions. Further, mTORC1/serine/glycine metabolic axis promotes osteosarcoma proliferation and antioxidant ability to environmental stress, which finally leads to cell survival. Our results identify a novel mechanism of mTORC1-mediated serine/glycine metabolism as a significant protective system in osteosarcoma cells.

Organization principles of biological networks: An explorative study

The definition of general topological principles allowing for graph characterization is an important pre-requisite for investigating structure–function relationships in biological networks. Here we approached the problem by means of an explorative, data-driven strategy, building upon a size-balanced data set made of around 200 distinct biological networks from seven functional classes and simulated networks coming from three mathematical graph models.A clear link between topological structure and biological function did emerge in terms of class membership prediction (average 67% of correct predictions, p<0.0001) with a varying degree of ‘peculiarity’ across classes going from a very low (25%) recognition efficiency for neural and brain networks to the extremely high (80%) peculiarity of amino acid–amino acid interaction (AAI) networks.We recognized four main dimensions (principal components) as main organization principles of biological networks. These components allowed for an efficient description of network architectures and for the identification of ‘not-physiological’ (in this case cancer metabolic networks acting as test set) wiring patterns.We highlighted as well the need of developing new theoretical generative models for biological networks overcoming the limitations of present mathematical graph idealizations.

Controllability in Cancer Metabolic Networks According to Drug Targets as Driver Nodes

Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.