Molecular interactions between metformin and D-limonene inhibit proliferation and promote apoptosis in breast and liver cancer cells.

BACKGROUND: Cancer is a fatal disease that severely affects humans. Designing new anticancer strategies and understanding the mechanism of action of anticancer agents is imperative. HYPOTHESIS/PURPOSE: In this study, we evaluated the utility of metformin and D-limonene, alone or in combination, as potential anticancer therapeutics using the human liver and breast cancer cell lines HepG2 and MCF-7. STUDY DESIGN: An integrated systems pharmacology approach is presented for illustrating the molecular interactions between metformin and D-limonene. METHODS: We applied a systems-based analysis to introduce a drug-target-pathway network that clarifies different mechanisms of treatment. The combination treatment of metformin and D-limonene induced apoptosis in both cell lines compared with single drug treatments, as indicated by flow cytometric and gene expression analysis. RESULTS: The mRNA expression of Bax and P53 genes were significantly upregulated while Bcl-2, iNOS, and Cox-2 were significantly downregulated in all treatment groups compared with normal cells. The percentages of late apoptotic HepG2 and MCF-7 cells were higher in all treatment groups, particularly in the combination treatment group. Calculations for the combination index (CI) revealed a synergistic effect between both drugs for HepG2 cells (CI = 0.14) and MCF-7 cells (CI = 0.22). CONCLUSION: Our data show that metformin, D-limonene, and their combinations exerted significant antitumor effects on the cancer cell lines by inducing apoptosis and modulating the expression of apoptotic genes.

High dose isoleucine stabilizes nuclear PTEN to suppress the proliferation of lung cancer.

PURPOSE: Cancer cells require a supply of amino acids, particularly essential amino acids such as branched-chain amino acids (BCAAs, i.e., valine, leucine, and isoleucine), to meet the increased nutrient demands of malignant tumors. The cell-autonomous and non-autonomous roles of altered BCAA supply have been implicated in cancer progression. The critical proteins involved in BCAA uptake, transport, metabolism, etc. serve as potential therapeutic biomarkers in human cancers. Here, we summarize the potential anti-tumor mechanism of BCAA by exploring the chain reaction triggered by increased BCAA supply in the tumor. METHOD: A system-wide strategy was employed to provide a generic solution to establish the links between BCAA and cancer based on comprehensive omics, molecular experimentation, and data analysis. RESULTS: BCAA over-supplementation (900 mg/kg) significantly inhibited tumor growth and reduced tumor burden, with isoleucine having the most pronounced effect. Surprisingly, isoleucine inhibited tumor growth independently of mTORC1 activation, a classical amino acid sensor. Exploratory transcriptome analysis revealed that Phosphatase and tensin homolog (PTEN) is the critical factor in the anti-tumor effect of isoleucine. By inhibiting PTEN ubiquitination, isoleucine can promote PTEN nuclear import and maintain PTEN nuclear stability. Interestingly, this process was regulated by isoleucine-tRNA ligase, cytoplasmic (IARS), a direct target of isoleucine. We demonstrated the enhanced interaction between IARS and PTEN in the presence of excess isoleucine. At the same time, IARS knockout leads to loss of isoleucine tumor suppressor ability. CONCLUSION: Overall, our results provide insights into the regulation of the IARS-PTEN anti-tumor axis by isoleucine and reveal a unique therapeutic approach based on enhancing cellular isoleucine supply.

Unraveling the mechanism of alkaloids from Sophora alopecuroides Linn combined with immune checkpoint blockade in the treatment of non-small cell lung cancer based on systems pharmacology.

Quinolizidine alkaloids, as essential active ingredients extracted from Sophora alopecuroides Linn (SAL), have been proven to be pharmacologically active in a variety of cancers including non-small cell lung cancer (NSCLC). However, whether these alkaloids have substantial benefits in combination with immune checkpoint blockade (ICB) for the treatment of NSCLC is unknown. Here, we explore the potential of these alkaloids in combination with ICB therapy based on a systems pharmacology and bioinformatics approach. We found that 37 alkaloids in SAL have highly similar characteristics in the molecular skeleton, pharmacological properties, and targets. The expression of targets of these alkaloids are significantly correlated with the infiltration level of tumor infiltrating lymphocytes and the expression levels of multiple immune checkpoints in NSCLC. They share similar molecular mechanisms in antitumor immunity. Sophocarpine (Sop) is one of the most representative constituents of these alkaloids. We demonstrated that the Sop promotes PD-L1 expression to improve the effects of PD-L1 blockade treatment via the ADORA1-ATF3 axis. In conclusion, our study identified these alkaloids as promising candidates for the treatment of NSCLC, either alone or in combination with ICB, with potential value for drug development and may provide a promising strategy for improving the survival of NSCLC patients.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning.

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes in the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

Pan-Cancer Analyses Reveal Oncogenic Role and Prognostic Value of F-Box Only Protein 22.

The F-box protein 22 (FBXO22), an F-box E3 ligase, has been identified to be critically involved in carcinogenesis. However, a systematic assessment of the role of FBXO22 across human cancers is lacking. Here, we performed a pan-cancer analysis to explore the role of FBXO22 in 33 cancer types using multiomic data from The Cancer Genome Atlas (TCGA). First, we found that high FBXO22 expression in multiple cancers was closely associated with poor overall survival and relapse-free survival. Next, we identified ten proteins that interact with FBXO22 and 13 of its target substrates using the STRING database and a literature search to explore the regulatory role of FBXO22 in tumorigenesis. Genes encoding these proteins were found to be significantly enriched in cell cycle negative regulation and ubiquitination pathways. This was confirmed in nonsmall cell lung cancer A549 cells, where FBXO22 overexpression enhanced cyclin-dependent kinase 4 (CDK4) protein levels and promoted cell proliferation. Similarly, overexpression or interference of FBXO22 changed the protein level of one of its substrates, PTEN. Additionally, we found that FBXO22 mutations were accompanied by altered substrate expression, especially in uterine corpus endometrial carcinoma and lung adenocarcinoma; endometrial carcinoma patients with FBXO22 genetic alterations also had better overall and relapse-free survival. Notably, FBXO22 methylation levels were also decreased in most tumors, and hypomethylation of FBXO22 was associated with poor overall survival, relapse-free interval, and progression-free interval in pancreatic adenocarcinoma. Finally, we analyzed the correlation between the abundance of tumor infiltrating lymphocytes (TILs) and FBXO22 expression, copy number variation, and methylation. Multiple algorithms revealed that high FBXO22 expression was associated with lower TIL levels, especially in lung adenocarcinoma, lung squamous cell carcinoma, and sarcoma. Taken together, our findings demonstrate that FBXO22 degrades tumor suppressor genes by ubiquitination and inhibits the cell cycle to promote nonsmall cell lung cancer progression. Our study also provides a relatively comprehensive understanding of the oncogenic role of FBXO22 in different tumors.

Systematic Evaluation of the Diagnostic and Prognostic Significance of Competitive Endogenous RNA Networks in Prostate Cancer.

Long non-coding RNA (lncRNA)-mediated competitive endogenous RNA (ceRNA) networks act as essential mechanisms in tumor initiation and progression, but their diagnostic and prognostic significance in prostate cancer (PCa) remains poorly understood. Presently, using the RNA expression data derived from multiple independent PCa-related studies, we constructed a high confidence and PCa-specific core ceRNA network by employing three lncRNA-gene inference approaches and key node filter strategies and then established a logistic model and risk score formula to evaluate its diagnostic and prognostic values, respectively. The core ceRNA network consists of 10 nodes, all of which are significantly associated with clinical outcomes. Combination of expression of the 10 ceRNAs with a logistic model achieved AUC of ROC and PR curve up to ∼96 and 99% in excluding normal prostate samples, respectively. Additionally, a risk score formula constructed with the ceRNAs exhibited significant association with disease-free survival. More importantly, utilizing the expression of RNAs in the core ceRNA network as a molecular signature, the TCGA-PRAD cohort was divided into four novel clinically relevant subgroups with distinct expression patterns, highlighting a feasible way for improving patient stratification in the future. Overall, we constructed a PCa-specific core ceRNA network, which provides diagnostic and prognostic value.

Systems-level analysis identifies key regulators driving epileptogenesis in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) is the most prevalent and often devastating form of epilepsy. The molecular mechanism underlying the development of TLE remains largely unclear, which hinders the discovery of effective antiepileptogenic drugs. Here we adopted a systems-level approach integrating transcriptomic profiles of three epileptogenesis stages to identify key regulators underlying epilepsy progression. Associating stage-specific gene meta-signatures with brain cell-specialized modules revealed positive regulation of glial migration and adhesion, cytokine production, and neuron death, and downregulation of synaptic transmission and ion transport during epileptogenesis. We identified 265 key regulators driving these processes and 72 of them were demonstrated associating with seizure frequency and/or hippocampal sclerosis in human TLE. Importantly, the upregulation of FAM107A, LAMB2, LTBP1 and TGIF1, which are mainly involved in nervous system development, were found contributing to both conditions. Our findings present the evolution landscape of epileptogenesis and provide candidate regulators that may serve as potential antiepileptogenic targets.

Weak-binding molecules are not drugs?-toward a systematic strategy for finding effective weak-binding drugs.

Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimer's disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them. Weak-binding drugs can be characterized by their high dissociation rates and transient interactions with their targets. In addition, network topologies and dynamics parameters involved in the targets of weak-binding drugs also influence the effects of the drugs. Here, we first performed a dynamics analysis for 33 elementary subgraphs to determine the desirable topology and dynamics parameters among targets. Then, by applying the elementary subgraphs to the mitogen-activated protein kinase (MAPK) pathway, several optimal target combinations were obtained. Combining drug-target interaction prediction with molecular dynamics simulation, we got two potential weak-binding drug candidates, luteolin and tanshinone IIA, acting on these targets. Further, the binding affinity of these two compounds to their targets and the anti-inflammatory effects of them were validated through in vitro experiments. In conclusion, weak-binding drugs have real opportunities for maximum efficiency and may show reduced adverse reactions, which can offer a bright and promising future for new drug discovery.

Multi-scale modeling of cell survival and death mediated by the p53 network: a systems pharmacology framework.

The determination of cell fate is a key regulatory process for the development of complex organisms that are controlled by distinct genes in mammalian cells. To interpret the decision process in a rigorous, analytical framework, we performed a multi-scale simulation of cell fate decision mediated by the p53 regulatory network in a systems pharmacology framework. The model treats fate determination as a gradual response to stress that delays the initiation of apoptosis to give the cell an opportunity to survive. The newly proposed two-factor model: DNA-p53 coupling explains the phenomenon of the existing biological responses to stress damage for the p53 regulatory network. In addition, the model also reveals that the cell survival rate can be improved by lowering the p53 level in a feedback network to increase its robustness for external stimuli. The present work not only deepens our understanding of cell fate determination, but also provides a theoretical basis for rational drug discovery and development.

CancerHSP: anticancer herbs database of systems pharmacology.

The numerous natural products and their bioactivity potentially afford an extraordinary resource for new drug discovery and have been employed in cancer treatment. However, the underlying pharmacological mechanisms of most natural anticancer compounds remain elusive, which has become one of the major obstacles in developing novel effective anticancer agents. Here, to address these unmet needs, we developed an anticancer herbs database of systems pharmacology (CancerHSP), which records anticancer herbs related information through manual curation. Currently, CancerHSP contains 2439 anticancer herbal medicines with 3575 anticancer ingredients. For each ingredient, the molecular structure and nine key ADME parameters are provided. Moreover, we also provide the anticancer activities of these compounds based on 492 different cancer cell lines. Further, the protein targets of the compounds are predicted by state-of-art methods or collected from literatures. CancerHSP will help reveal the molecular mechanisms of natural anticancer products and accelerate anticancer drug development, especially facilitate future investigations on drug repositioning and drug discovery. CancerHSP is freely available on the web at http://lsp.nwsuaf.edu.cn/CancerHSP.php.