Proliferation inhibition of cisplatin-resistant ovarian cancer cells using drugs screened by integrating a metabolic model and transcriptomic data

Terbinafine prevents colorectal cancer growth by inducing dNTP starvation and reducing immune suppression

Existing evidence indicates that gut fungal dysbiosis might play a key role in the pathogenesis of colorectal cancer (CRC). We sought to explore whether reversing the fungal dysbiosis by terbinafine, an approved antifungal drug, might inhibit the development of CRC. A population-based study from Sweden identified a total of 185 patients who received terbinafine after their CRC diagnosis and found that they had a decreased risk of death (hazard ratio = 0.50) and metastasis (hazard ratio = 0.44) compared with patients without terbinafine administration. In multiple mouse models of CRC, administration of terbinafine decreased the fungal load, the fungus-induced myeloid-derived suppressor cell (MDSC) expansion, and the tumor burden. Fecal microbiota transplantation from mice without terbinafine treatment reversed MDSC infiltration and partially restored tumor proliferation. Mechanistically, terbinafine directly impaired tumor cell proliferation by reducing the ratio of nicotinamide adenine dinucleotide phosphate (NADP+) to reduced form of nicotinamide adenine dinucleotide phosphate (NADPH), suppressing the activity of glucose-6-phosphate dehydrogenase (G6PD), resulting in nucleotide synthesis disruption, deoxyribonucleotide (dNTP) starvation, and cell-cycle arrest. Collectively, terbinafine can inhibit CRC by reversing fungal dysbiosis, suppressing tumor cell proliferation, inhibiting fungus-induced MDSC infiltration, and restoring antitumor immune response.

Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction

MOTIVATION

Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organization across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms.

RESULTS

We propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature.

AVAILABILITY AND IMPLEMENTATION

The method, the datasets and all the results obtained in this study are available at: https://doi.org/10.6084/m9.figshare.c.5237687.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hydralazine and Enzalutamide: Synergistic Partners against Prostate Cancer

Advanced prostate cancers frequently develop resistance to androgen-deprivation therapy with serious implications for patient survival. Considering their importance in this type of neoplasia, epigenetic modifications have drawn attention as alternative treatment strategies. The aim of this study was to assess the antitumoral effects of the combination of hydralazine, a DNA methylation inhibitor, with enzalutamide, an antagonist of the androgen receptor, in prostate cancer cell lines. Several biological parameters, such as cell viability, proliferation, DNA damage, and apoptosis, as well as clonogenic and invasive potential, were evaluated. The individual treatments with hydralazine and enzalutamide exerted growth-inhibitory effects in prostate cancer cells and their combined treatment displayed synergistic effects. The combination of these two drugs was very effective in decreasing malignant features of prostate cancer and may become an alternative therapeutic option for prostate cancer patient management.

Targeting epigenetic modulation of cholesterol synthesis as a therapeutic strategy for head and neck squamous cell carcinoma

The histone methyltransferase EZH2 silences gene expression via H3 lysine 27 trimethylation and has been recognized as an important antitumour therapeutic target. However, the clinical application of existing EZH2 inhibitors is not satisfactory for the treatment of solid tumours. To discover novel strategies against head and neck squamous cell carcinoma (HNSCC), we performed genomics, metabolomics and RNA omics studies in HNSCC cells treated with EZH2 inhibitors. It was found that EZH2 inhibitors strongly induced the expression of genes in cholesterol synthesis. Through extensive drug screening we found that inhibition of squalene epoxidase (a key enzyme of endogenous cholesterol synthesis) synergistically increased the squalene content and enhanced the sensitivity of HNSCC cells to EZH2 inhibitors. Our findings provide an experimental and theoretical basis for the development of new combinations of EZH2 inhibitors to treat HNSCC.

SLC6A8 Knockdown Suppresses the Invasion and Migration of Human Hepatocellular Carcinoma Huh-7 and Hep3B Cells

Liver cancer is considered the sixth most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths worldwide. Currently, there is no specific and effective therapy for hepatocellular carcinoma. Therefore, developing novel diagnostic and therapeutic strategies against hepatocellular carcinoma is of paramount importance. Solute carrier family 6 member 8 (SLC6A8) encodes the solute carrier family 6-8 to transport creatine into cells in a Na⁺ and Cl^-- dependent manner. SLC6A8 deficiency is characterized by intellectual disabilities, loss of speech, and behavioral abnormalities. Of concern, the association of SLC6A8 with hepatocellular carcinoma remains elusive. In this study, we revealed that SLC6A8 knockdown significantly induced apoptosis and suppressed the migration and invasion of Hep3B and Huh-7 cells. These findings depicted the vital role of SLC6A8 in the initiation and progression of human hepatocellular carcinoma.

Bipolar Disorder Treatments and Ovarian Cancer: A Systematic Review

OBJECTIVE

We reviewed literature on drugs for bipolar disorders (BD), utilized in ovarian cancer (OC).

METHOD

We adhered to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines in completion of this systematic review.

RESULTS

We identified 73 papers. Thirty-two studies were finally included. BD is rarely diagnosed in OC patients. Limited finding from case reports is available. Drugs used to treat BD (mainly lithium and valproic acid) have been extensively studied in add-on to chemotherapy for treatment-resistant OC cells or in animal models, with promising results in vitro but not in vivo.

CONCLUSIONS

The clinical underestimation of BD in OC has leaded to the almost complete absence of evidences for a soundly based clinical guidance in this field. There is a urgent need for a systematic multi-disciplinary approach to OC.

Development and validation of a metabolic gene signature for predicting overall survival in patients with colon cancer

The reprogramming of cellular metabolism is a hallmark of tumorigenesis. However, the prognostic value of metabolism-related genes in colon cancer remains unclear. This study aimed to identify a metabolic gene signature to categorize colon cancer patients into high- and low-risk groups and predict prognosis. Samples from the Gene Expression Omnibus database were used as the training cohort, while samples from The Cancer Genome Atlas database were used as the validation cohort. A metabolic gene signature was established to investigate a robust risk stratification for colon cancer. Subsequently, a prognostic nomogram was established combining the metabolism-related risk score and clinicopathological characteristics of patients. A total of 351 differentially expressed metabolism-related genes were identified in colon cancer. After univariate analysis and least absolute shrinkage and selection operator-penalized regression analysis, an eight-gene metabolic signature (MTR, NANS, HADH, IMPA2, AGPAT1, GGT5, CYP2J2, and ASL) was identified to classify patients into high- and low-risk groups. High-risk patients had significantly shorter overall survival than low-risk patients in both the training and validation cohorts. A high-risk score was positively correlated with proximal colon cancer (P = 0.012), BRAF mutation (P = 0.049), and advanced stage (P = 0.027). We established a prognostic nomogram based on metabolism-related gene risk score and clinicopathologic factors. The areas under the curve and calibration curves indicated that the established nomogram showed a good accuracy of prediction. We have established a novel metabolic gene signature that could predict overall survival in colon cancer patients and serve as a biomarker for colon cancer.

Combining multi-dimensional data to identify a key signature (gene and miRNA) of cisplatin-resistant gastric cancer

Gastric cancer (GC) is one of the most lethal malignant tumors; the resistance of this type of tumor is the main source of GC treatment failure. In this study, we used bioinformatics analysis to verify differences in resistant GCs and identify an effective method for reversing drug resistance in GC. Microarray data [gene and microRNA (miRNA)] were analyzed using GEO2R software, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied to further enrich the genetic data. miRNA-gene interactions were determined using Cytoscape (v.3.5.1). Online software was used to analyze protein interactions and predict network structure. The Cancer Genome Atlas (TCGA) database was used to verify the expression levels of genes in GC resistance. miR-604 expression levels were verified by real-time PCR in GC cell lines. We screened 3981 GC resistance-associated genes and 244 miRNAs using bioinformatics methods. Six hub genes were identified and verified in the TCGA database, including five up-regulated genes, POLR2L, POLR2C, POLR2F, APRT, and LMAN2, and a down-regulated gene, NFKB2. The up-regulated genes POLR2L, POLR2C, APRT, and LMAN2 interact with miR-604; therefore, we focused on miR-604, which has low expression in drug-resistant GC. The results of this study indicate that through bioinformatics technologies, we have determined the hub genes and hub miRNAs related to drug resistance in GC. Among them, miR-604 could become a new indicator in the diagnosis of drug-resistant GC and may be used to investigate the pathogenesis of resistance in GC.

Molecular characterization of breast cancer cell response to metabolic drugs

Metabolic reprogramming is a hallmark of cancer. It has been described that breast cancer subtypes present metabolism differences and this fact enables the possibility of using metabolic inhibitors as targeted drugs in specific scenarios. In this study, breast cancer cell lines were treated with metformin and rapamycin, showing a heterogeneous response to treatment and leading to cell cycle disruption. The genetic causes and molecular effects of this differential response were characterized by means of SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes such as transcription. Moreover, flux balance analysis using protein expression values showed that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these diverse approaches provide complementary information and allow us to suggest hypotheses about the response to drugs that target metabolism and their mechanisms of action.

Proliferation inhibition of cisplatin-resistant ovarian cancer cells using drugs screened by integrating a metabolic model and transcriptomic data

OBJECTIVES

If screening to find effective drugs is possible, the inhibition of proliferation using existing drugs can be a practical strategy to control the drug resistance of cancer. Development of a system-oriented strategy to find effective drugs was the main aim of this research.

MATERIALS AND METHODS

An algorithm (transcriptional regulated flux balance analysis [TRFBA]) integrating a generic human metabolic model with transcriptomic data was used to identify genes affecting the growth of drug-resistant cancer cells. Drugs that inhibit activation of the target genes were found and their effect on the proliferation was experimentally evaluated.

RESULTS

Experimental assessments demonstrated that TRFBA improves the prediction of cancer cell growth in comparison with previous algorithms. The algorithm was then used to propose the system-oriented strategy to search drugs effective in limiting the growth rate of the cisplatin-resistant A2780 epithelial ovarian cancer cell. Experimental evaluations resulted in the selection of azathioprine, terbinafine, hydralazine and sodium valproate that appropriately inhibit the proliferation of resistant cancer cells while minimally affecting normal cells. Furthermore, experimental data indicate that the selected drugs are synergistic and can be used in combination therapies.

CONCLUSIONS

The proposed strategy was successful to identify drugs effective on the viability of resistant cancer cells. This strategy can enhance the potency of treatments for drug-resistant cancer cells and provides the possibility of using existing drugs.