Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.
BackgroundGenome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment.MethodsIn this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions.FindingsWe identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line.InterpretationOur approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.
Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.
Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA).We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments.Source code is available at https://github.com/XinmengLiao/Open_MoA.
Liver pyruvate kinase (PKL) has recently emerged as a new target for non-alcoholic fatty liver disease (NAFLD), and inhibitors of this enzyme could represent a new therapeutic option. However, this breakthrough is complicated by selectivity issues since pyruvate kinase exists in four different isoforms. In this work, we report that ellagic acid (EA) and its derivatives, present in numerous fruits and vegetables, can inhibit PKL potently and selectively. Several polyphenolic analogues of EA were synthesized and tested to identify the chemical features responsible for the desired activity. Molecular modelling studies suggested that this inhibition is related to the stabilization of the PKL inactive state. This unique inhibition mechanism could potentially herald the development of new therapeutics for NAFLD.
Lung and liver cancer rank among the most prevalent malignancies affecting global health. While herbs are extensively utilized in Traditional Chinese Medicine (TCM) for cancer treatment, the mechanisms behind these herbs remain largely unknown.
Methods
We treated A549 and Huh7 cells with extracts from 14 different Chinese herbs. Herbs which showed significant efficacy in reducing cancer cell viability were selected for RNA sequencing. Bio-informatics analysis including differential gene expression, gene set functional, transcription factor prediction and similar compound recognition using LINCS database were performed to reveal the mechanisms underlying their therapeutic effects, followed by experimental validation (figure 1).
Results
Pulsatilla chinensis showed the best anticancer potency in A549 and Huh7 cells among 14 herbs tested, followed by Bupleurum chinense, and Polyporus umbellatus. We observed different mechanisms of anti-cancer action in A549 and Huh7 cells induced by P. chinensis treatment. Our integrative approach, combining transcriptomic data and experimental validation, has revealed that P. chinensis exhibits anti-cancer properties by inducing apoptosis through regulating the p53-MDM2 and TNF-α/NF-κB pathways in A549 and Huh7 cells, respectively. Using our own designed algorithm based on the LINCS L1000 database, We identified two drugs, AMG232 and Nutlin-3, that had treatment effects similar to P. chinensis in A549 cells. Both drugs are inhibitors of the MDM2-p53 interaction. Besides, western blot analysis revealed elevated levels of apoptotic proteins, including cleaved PARP, active caspase-3, active caspase-8, P53, and MDM2, in A549 cells treated with P. chinensis. Interestingly, an increase in MDM2 protein levels was not observed in Huh7 cells, indicating that P. chinensis induces MDM2-p53-related apoptotic cell death specifically in A549 cells. Besides, we found that although P. chinensis affected different pathways in A549 and Huh7 cells, the triggered transcriptional factors were similar and they were associated with the cell cycle regulation.
Conclusions
We conclude that P. chinensis has a significant anticancer effect on both A549 and Huh7 cells, signifying its potential for clinical translation in cancer treatment after additional preclinical and clinical studies.
Liver pyruvate kinase (PKL) is a major regulator of metabolic flux and ATP production during liver cell glycolysis and is considered a potential drug target for the treatment of non-alcoholic fatty liver disease (NAFLD). In this study, we report the first ADP-competitive PKL inhibitors and identify several starting points for the further optimization of these inhibitors. Modeling and structural biology guided the optimization of a PKL-specific anthraquinone-based compound. A structure–activity relationship study of 47 novel synthetic derivatives revealed PKL inhibitors with half-maximal inhibitory concentration (IC50) values in the 200 nM range. Despite the difficulty involved in studying a binding site as exposed as the ADP site, these derivatives feature expanded structural diversity and chemical spaces that may be used to improve their inhibitory activities against PKL. The obtained results expand the knowledge of the structural requirements for interactions with the ADP-binding site of PKL.
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