Integration of Cancer Gene Co-expression Network and Metabolic

Article pubs.acs.org/jpr

Integration of Cancer Gene Co-expression Network and Metabolic Network To Uncover Potential Cancer Drug Targets Jingqi Chen,†,§ Ming Ma,‡,§ Ning Shen,† Jianzhong Jeff Xi,*,‡ and Weidong Tian*,† †

State Key Laboratory of Genetic Engineering, Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, China ‡ Biomedical Engineering Department, College of Engineering, Peking University, Beijing, China S Supporting Information *

ABSTRACT: Cell metabolism is critical for cancer cell transformation and progression. In this study, we have developed a novel method, named Metexpress, that integrates a cancer gene co-expression network with the metabolic network to predict key enzyme-coding genes and metabolites in cancer cell metabolism. Met-express successfully identified a group of key enzyme-coding genes and metabolites in lung, leukemia, and breast cancers. Literature reviews suggest that approximately 33−53% of the predicted genes are either known or suggested anti-cancer drug targets, while 22% of the predicted metabolites are known or high-potential drug compounds in therapeutic use. Furthermore, experimental validations prove that 90% of the selected genes and 70% of metabolites demonstrate the significant anti-cancer phenotypes in cancer cells, implying that they may play important roles in cancer metabolism. Therefore, Met-express is a powerful tool for uncovering novel therapeutic biomarkers. KEYWORDS: cancer, gene co-expression network, metabolic network, network integration, drug targets, metabolism

■

INTRODUCTION In response to major oncogenic events, cancer cells undergo significant metabolic alterations and adaptations,1−3 such as the “Warburg effect”, which refers to a significant increase in aerobic glycolysis.4 On the other hand, metabolic reprogramming in cancer also contributes to the invasion, metastasis, and immunosuppression of cancer cells.3 For example, enhanced reactive oxygen species (ROS) generation can activate the expression of hypoxia inducible factor-1 alpha subunit (HIF1α), which in turn promotes tissue invasion and metastasis.3,5 Such mixed cause−effect roles of cancer cell metabolism complicate our understanding of the molecular basis of carcinogenesis, thus making it challenging to discover both novel diagnostic and therapeutic biomarkers by exploiting cancer cell metabolism.6,7 One of the most important reasons for altered metabolism in cancer cells is the expression alterations of a large number of enzyme-coding genes involved in diverse metabolic pathways.1,2,6−8 For example, glucose-6-phosphate dehydrogenase (G6PD)9−12 and the transketolase-1 isoform (TKL1)10,13,14 are over-expressed in multiple cancer cells, contributing to the accumulation of ribose 5-phosphate in support of the rapid growth of cancer cells.10−13 Detection of genes with similar patterns of expressional alterations may therefore provide insight into the mechanistic organization of cancer-specific alterations. However, because of significant reprogramming of metabolic pathways,10 gene set analysis based on known pathways may be insufficient to detect abnormal patterns of expression changes. Gene co-expression network construction © 2013 American Chemical Society

followed by identification of gene co-expression modules does not rely on the presumption of known pathway division and is well suited to unravel novel regulation and association patterns of genes under pathological conditions.15−19 To further identify genes that may play critical roles in cancer metabolisms, however, information beyond gene expression should be used. Adding the metabolic network to a gene co-expression network can not only provide us a deeper understanding of the mechanistic aspects of cancer cell metabolism but also suggest therapeutic targets for cancer treatment. In this study, we aimed to integrate a cancer gene coexpression network with the metabolic network to predict key enzyme-coding genes in cancer metabolism. For this purpose, we have developed a novel method named “Met-express”. The key idea of Met-express is to identify the enzyme-coding genes that are co-expressed with significantly more metabolite-sharing enzyme-coding genes in a cancer-specific gene co-expression module. We hypothesize that these genes likely bear a higher potential to alter the metabolism of cancer cells and therefore may serve as anti-cancer drug targets. Using Met-express, we have predicted a number of key enzyme-coding genes and metabolites in lung cancer, leukemia, and breast cancer. Literature validation reveals that 33−53% of predicted genes in different types of cancer are either known or suggested anticancer drug targets, while 22% of predicted metabolites are either known or suggested high-potential anti-cancer drugs. Received: December 18, 2011 Published: April 16, 2013 2354

dx.doi.org/10.1021/pr400162t | J. Proteome Res. 2013, 12, 2354−2364

Journal of Proteome Research

Article

metabolites, with the direction of an edge coming from one enzyme producing the metabolite to another catalyzing the metabolite. Following previously described procedures,20,44 only those metabolites that (i) participate in fewer than 18 reactions (see Supplementary Figure 3) and (ii) are not involved in the “Xenobiotics Biodegradation and Metabolism” pathway are used to link enzymes. If an enzyme catalyzes a reversible reaction, then this reaction was split into two reactions with both directions. In this study, we only considered the outward edges of each enzyme, as we reasoned that altering such enzyme would have a large impact on cancer metabolism. Predicting Key Enzyme-Coding Genes by Metexpress. For a given enzyme-coding gene, Met-express computes an importance score by the following equation:

Our experiments proved that 90% of the selected genes and 70% of the metabolites demonstrate the significant anti-cancer phenotypes in cancer cells. Thus, our method has a broad application for not only identifying key enzymes and metabolites in cancer metabolism but also providing novel targets for the development of therapeutic candidate compounds.

■

MATERIALS AND METHODS

Met-express

Microarray Data Processing. For each of lung cancer, leukemia, and breast cancer microarray data sets, we selected two GDS files from the GEO database (www.ncbi.nlm.nih.gov/ geo) and required them to (i) have more than 10 samples, (ii) include samples from both normal and cancer tissues, and (iii) have not been treated by any stimulus nor derived from cancer cell lines or patient PBMC (except for leukemia samples) (see Supplementary Table 1 for selected data sets). When processing the microarray data, we removed those genes whose corresponding probe sets had a median ratio of null values greater than or equal to 80% and analyzed only those genes presented in both data sets of the same cancer type. Gene expression values were normalized and imputed using R package “limma” and “impute” and transformed into log2-based values. Co-expression Network Construction and Module Identification. We followed a previously described rankbased method42 to construct the gene co-expression networks. Briefly, the Pearson correlation coefficients (PCC) of the expression values of all pairs of genes were computed. Then, the gene co-expression network was constructed by keeping only the top three genes with the highest PCC to a given gene (see Supplementary Figure 1 for network degree distributions). After network construction, Qcut,43 a modularity-based method that combined spectral graph partitioning with local search for network partition, was used to divide the network into gene coexpression modules. After filtering out gene modules with fewer than 10 genes, for each gene module we determined the median gene expression value of genes inside the module in every sample (cancer or normal). By sorting the median gene expression values from the largest to the smallest, for every median gene expression value, we counted the number of true positives (cancer samples) and the number of false positives (normal samples) above the value and then calculated the false positive rate (FPR) (false positives/all normal samples) and the true positive rate (TPR) (true positives/all cancer samples). Then, we plotted the ROC curve by plotting the corresponding TPR against FPR at every threshold. If the median expression values of the gene module in cancer samples are generally greater than those in normal samples, then the ROC curve will be above the diagonal and vice versa. The area under the ROC curve (AUCROC) indicates the specificity of the module to cancer (see Supplementary Figure 2 as an example). Modules with no specificity to cancer will have an AUC around 0.5, whereas those strongly up-regulated or down-regulated will have an AUC close to 1 or 0. Metabolic Network Reconstruction. We downloaded the previous free version of the human KEGG Markup Language (KGML) files that contain enzymatic reactions, associated metabolites, and gene enzyme information from the KEGG database (http://www.genome.jp/kegg/). We constructed the metabolic network by linking enzymes through their shared

⎛ C /C ⎞ Scorei = |AUCROC − 0.5| log 2⎜ in all ⎟ ⎝ Nin /Nall ⎠

where the first term |AUCROC − 0.5| represented the cancer specificity of a gene co-expression module where the gene locates, and the second term indicates the enrichment degree of metabolic links of this gene within the module. Here, Cin and Call are the total numbers of enzyme-coding genes connected from the test gene inside the module and inside the whole network according to the metabolic network, respectively, and Nin and Nall refer to the total numbers of enzyme-coding genes in the module and in the metabolic network for each background, respectively. We used median of none-NA scores as cutoffs for each cancer type, as illustrated in Supplementary Figure 4. Single Network-Based Methods

Methods using either the co-expression network or the metabolic network alone to make predictions were named as Express-only and Met-only, respectively. For Express-only, cancer-specific gene modules from the co-expression network (AUC ≥ 0.8 or AUC ≤ 0.2) are used, and those enzyme-coding genes with a degree ranked among the top 40% inside the module are then selected. Then, those genes presented in both data sets of the same type of cancer are predicted as key enzyme-coding genes, and the metabolites associated with the genes are predicted to have potential anti-cancer therapeutic use. Met-only computes the degree of metabolic links of each enzyme-coding gene in the metabolic network and predicts the top 109 (degrees larger than or equal to 25) enzyme-coding genes as key genes and their associated metabolites as key metabolites. CoMet

CoMet method was carried out for the same GDS data sets. Differentially expressed genes for each data set were determined by t test (P-value ≤ 0.05). Due to the nature of this method, the reversible reactions of the metabolic network were discarded. The method to select metabolites with up- and down-concentrations in cancer cells was the same as the original paper described.20 Afterward, for each type of cancer, intersections of metabolites generated by the corresponding two data sets were used as the prediction results. Functional Analysis of Predicted Enzyme-Coding Genes

GO enrichment analysis was performed by the web-based tool FuncAssociate.45 Only GO terms in the category of “Biological Process” that are annotated with fewer than 1000 genes were included in the analysis. KEGG pathway enrichment analysis 2355



■

was performed by Fisher’s Exact test (corrected by FDR), using pathways with fewer than 500 enzymes.

Article

RESULTS

Development of Met-express for Predicting Key Enzyme-Coding Genes and Metabolites in Cancer Metabolism

Experimental Assay

Preparation of siRNA Duplex Mimics and Overexpression Plasmid. We obtained all siRNA duplexes from GenePharma Company and tested their silence efficiency using quantitative real-time PCR at 24−48 h after transfection (Supplementary Figure 5). All reactions were run in triplicates. The over-expression sequences for selected genes were PCRamplified from cDNA and cloned into the pcDNA3.1 vector backbone. For siRNA experiments, siRNA with random sequence was used as the negative control (untreated cells), and let-7a,46,47 mir-24,48 and mir-23b49 were used as positive controls for the apoptosis and necrosis assay, the proliferation assay, and the migration assay, respectively. For plasmid experiments, blank vector was used as negative control (untreated cells), and no positive control was used due to the complexity of experiment. Cell Culture. We cultured both human breast cancer cell line MCF7 and human lung adenocarcinoma cell line A549 in Dulbecco’s Modified Eagle Medium containing 10% fetal bovine serum, 100 U mL−1 penicillin and 0.1 mg mL−1 streptomycin under humidified conditions in 95% air and 5% CO2 at 37 °C. Experiments were carried out using MD highcontent machine with 96-well plates. Apoptosis and Necrosis Assay. We used Annexin VFITC Kit (Biosea Biotechnology Co. Ltd.) for apoptosis assay. Necrotic cells were identified by double labeling with Annexin V and PI stain, while apoptotic cells were identified by Annexin V stain only. Cell numbers were counted 48 h after the application of siRNA or over-expression plasmids, and larger numbers of apoptotic or necrotic cells meant better anti-cancer effect. Migration Assays. Cells were cultured in the upper chamber of a Transwell insert (pore size, 8 μm; Costar) in 100 μL of serum-free medium per well. The lower chamber was filled with medium (600 μL) containing 10% serum as chemoattractant. After 48 h, nonmigratory cells were removed from the upper chamber by scraping with a cotton bud, and migratory cells penetrating to the lower surface of the insert were fixed with 2% formaldehyde (Sigma) and stained by DAPI (Roche). Then, the total numbers of migratory cells were obtained by counting stained cells, and smaller numbers of migratory cells meant better anti-cancer effect. Proliferation assays. Cells were cultured in 96-well plates. At 24, 48, and 72 h, cells were stained with Hoechest 33342 (Sigma) solutions for 10 min and then counted. Smaller numbers of cells indicated better anti-cancer potential. Metabolites Experiments. Selected predicted metabolites were added into two cell lines at the concentration of 100 nM. For the apoptosis and necrosis assays, cells were treated with 95% alcohol for 15 s of induction as positive control.50,51 For the migration and the proliferation assays, dhMoc52/Circumin 53 and Paclitaxol 54 were used as positive controls, respectively. For all assays, DMSO or water was used as the negative control (untreated cells), depending on what the metabolites were dissolved in.

In this study, we developed a novel method, termed Metexpress, that integrates a cancer gene co-expression network and the enzyme metabolic network to predict key enzyme coding-genes in the cancer metabolic network (see Figure 1 for the flowchart of Met-express). This method is divided into two

Figure 1. Workflow of Met-express. I. Data processing of microarray data and KEGG data in preparation of network construction. II. Network construction and partition. The co-expression network (left) and the human enzyme network (right) are constructed from microarray data and KEGG data, respectively. Then, the co-expression network is partitioned into gene co-expression modules. For each module, an ROC curve is plotted by using the median gene expression value of genes inside the module to distinguish cancer from normal samples (see Materials and Methods for details). Genes inside a gene module are then colored either red or green if the area under the ROC curve (AUCROC) is greater (up-regulated) or smaller (down-regulated) than 0.5. FPR refers to the false positive rate, and TPR refers to the true positive rate. III. Network integration to calculate the importance scores for enzyme coding genes in the gene co-expression modules. For a given enzyme-coding gene A, the corresponding gene coexpression module is identified (gray circle), and the importance score (ScoreA) is calculated following the formula. Here, Cin and Call are the number of within-module enzyme-coding genes (colored blue) and the number of all enzyme-coding genes that are connected from A in the metabolic network, respectively. Nin and Nall are the number of within-module enzyme-coding genes and the number of all enzymecoding genes in the co-expression network, respectively. 2356



Article

Table 1. Comparison of the Robustness of the Predictions Made by Different Methods across Three Cancer Types enzyme all Met-express Express-only CoMet

metabolites

in 2 cancers a

118 55

b

11 (9.3% )*** 1 (1.8%)***

in 3 cancers

all

in 2 cancers

in 3 cancers

3 (2.5%)*** 0 (0%)

493 253 151

177 (35.9%)** 16 (6.3%)* 32 (21.2%)***

50 (10.1%)*** 0 (0%) 11 (7.3%)***

The percentages of genes overlapped in at least two (or three) cancer types. bSignificance of P-values: * P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001. Here, P-values were calculated on the basis of simulations. Taking Met-express predictions for an example, the same numbers of genes were randomly selected for each cancer type, and then the numbers of overlapped genes (or metabolites) in two (or three) cancer types were counted. These procedures were repeated 10,000 times. The P-value was the proportion of times that the number of overlapped genes (or metabolites) was equal or greater than that of the predictions. a

Table 2. Comparison of the Efficacy of the Predictions Made by Different Methods across Three Cancer Types enzyme Met-express Express-only Met-only CoMet

metabolite

predicted enzymes

known anti-cancer drug targets

predicted metabolites

known anti-cancer drugs

118 55 109

6 (5.1%a)*b 1 (1.8%) 4 (3.7%)

50 0 294 11

4 (8.0%)** 0 3 (1.0%) 1 (9.1%)

The percentages of predicted enzymes/metabolites that are known anti-cancer drug targets/drugs. bSignificance of P-values: * P ≤ 0.05; ** P ≤ 0.01. Here, P-values were calculated by Fisher’s exact test. a

main steps. The first step is to identify cancer-specific gene modules from a cancer gene co-expression network. The second step is to predict key enzyme-coding genes by combining the cancer-specificity information of co-expressed gene modules with the degree distribution information of enzyme-coding genes in the metabolic network. By design, the predicted genes not only locate in cancer-specific gene modules but also co-express with significantly more metabolite-sharing enzyme-coding genes. We applied Met-express to three types of cancers, lung cancer, leukemia, and breast cancer, and identified 41, 34, and 57 key enzyme-coding genes, respectively (see Materials and Methods for experimental details and Supplementary Table 5 for details of the predicted genes). Among the total number of predicted genes, 11 are predicted in at least two cancers, and three (GALC, HPRT1, and TK1) are predicted in all three cancers. The up/down-regulated expression patterns of these 11 genes were generally consistent across the different types of cancer. Furthermore, we predicted that the metabolites associated with the predicted genes might have potential therapeutic use. We found 210, 250, and 260 metabolites associated with the predicted genes in lung cancer, leukemia, and breast cancer, respectively. Among them, 50 are common in all three types of cancer and may represent key metabolites in the process.

predicted a total of 294 associated metabolites. CoMet predicted 118, 30, and 46 metabolites in lung cancer, leukemia, and breast cancer, respectively. We first inspected the robustness of these different methods by examining the overlap of their predictions across different types of cancer (Table 1). For enzyme-coding genes, Metexpress predicted 11 (9.3%) and 3 (2.5%) that are common in two and three types of cancer, respectively (P-values = 0 and 0, respectively), which was in contrast to 1 (1.8%, P-value = 0.001) and none (0%) by Express-only, respectively. For metabolites, Met-express predicted a total of 177 (35.9%) and 50 (10.1%) metabolites common in two and three types of cancer, respectively (P-values = 0.006 and 0.0006, respectively), which was in contrast to 16 (6.3%, P-value = 0.03) and none (0%) by Express-only, respectively, and 32 (21.2%, P-value = 0) and 11 (7.3%, P-value = 0) by CoMet, respectively. Thus, Metexpress produced more robust predictions across different types of cancer than Express-only or CoMet individually. To further demonstrate the efficacy of Met-express, we compared the predictions by different methods with known cancer drug targets and cancer drugs in DrugBank.21 Out of all enzyme-coding genes and metabolites investigated in this study, 13 genes represent targets with approved anti-cancer drugs, while 8 metabolites are known anti-cancer drugs according to DrugBank. For anti-cancer drug targets, Met-express successfully predicted six in the 118 key enzyme-coding genes (P-value = 0.042), which was in contrast to 1 and 4 by Express-only and Met-only, respectively (both P-values were not significant). For anti-cancer drugs, Met-express predicted 4 in the 50 key metabolites (P-value = 0.002). In comparison, Met-only and CoMet predicted 3 and 1 drugs, respectively (both P-values were not significant), while Express-only failed to predict any (see Table 2 for details). Therefore, compared to single network-based methods and CoMet, Met-express is able to identify known anti-cancer drug targets and cancer drugs more effectively.

Comparison of Met-express with Single Network-Based Methods and CoMet

Met-express integrates both a gene co-expression network and metabolic network to predict key enzyme-coding genes and metabolites. For comparison, we used either network alone to make predictions and named them “Express-only” and “Metonly”, respectively (see Materials and Methods for details about the two methods). In addition, we followed the CoMet method, developed by Arakaki et al.,20 to predict key metabolites. In short, Express-only predicted 34, 3, and 19 enzyme-coding genes and 191, 4, and 66 metabolites for lung cancer, leukemia, and breast cancer, respectively. Met-only predicted the top 100 enzyme-coding genes ranked by their degrees of metabolic links in the metabolic network and further 2357



Article

Figure 2. Functional overlaps of predicted enzyme-coding genes. (a) Enriched GO terms and (b) KEGG pathways among predicted enzyme-coding genes in lung cancer, leukemia, and breast cancer. Enriched GO terms and KEGG pathways common in all three types of cancer are shown. (c) Relationships between predicted genes and the seven known cancer hallmarks. Predicted genes presented in down- or up-regulated modules are colored in green and red, respectively. Those that are presented in both up- and down-regulated modules from different data sets are colored in gray. For each cancer hallmark, the enriched metabolic pathways among the associated predicted genes are shown in blue.

Enzyme-Coding Genes Predicted by Met-express Likely Play Important Roles in Cancer Metabolism

Among the 11, 30, and 38 enriched GO terms in lung cancer, leukemia, and breast cancer, respectively, there were 5 common GO terms, such as “alcohol metabolic process” and “lipid metabolic process”, etc. In addition, among the 18, 16, and 21 enriched KEGG pathways in lung cancer, leukemia, and breast cancer, 6 are commonly enriched, including “glycolysis/

We next performed function enrichment analysis for the predicted genes. Both Gene Ontology and KEGG pathway enrichment analysis reveal significant functional overlap among the predicted genes in the three cancer types (Figure 2a,b). 2358



Article

Figure 3. Literature validation of predicted enzyme-coding genes as potential anti-cancer drug targets and predicted metabolites as potential cancer drugs. (A) Proportion of predicted genes in different categories of literature validations. Here “T”, “sT”, and “M” refer to categories of “known anticancer drug targets”, “suggested anti-cancer drug targets”, and “mechanistically-related to cancer”, respectively. (B) Proportion of predicted metabolites in different categories of literature validations. Here “D”, “hpD”, and “mpD” refer to categories of “known anti-cancer drugs”, “high potential anti-cancer drugs”, and “medium potential anti-cancer drugs”, respectively.

gluconeogenesis”, “fatty acid metabolism”, and “pyrimidine metabolism”, among others. Most of these pathways have been reported to be reprogrammed in cancer cells,10 indicating their important roles in cancer metabolism. Besides function enrichment analysis for predicted genes, we also conducted function enrichment analysis for gene coexpression modules from which these genes were predicted. To determine the relationship between enriched GO terms and cancer, we manually compiled lists of GO terms related to cancer hallmarks based on their descriptions (Supplementary Table 2). If a module was enriched with GO terms related to a cancer hallmark, we postulate that the predicted genes in this module were likely related to the cancer hallmark as well. Most of the predicted enzyme-coding genes were linked to one or more cancer hallmarks (Figure 2c). For example, HK3, LIPA, and CEPT1 are related to one cancer hallmark: “avoidance of immuno-surveillance”, while ADH1A, ADH1B, and MAOB are related to four cancer hallmarks: “self-sufficiency in growth signals”, “insensitivity to antigrowth signal”, “sustained angiogenesis”, and “tissue invasive and metastasis”. Many links between the predicted genes and cancer hallmarks are supported by literatures. For example, in this study NAMPT was linked to the cancer hallmarks of “sustained angiogenesis” and “tissue invasive and metastasis”. It encodes nicotinamide phosphoribosyltransferase, which is an enzyme that catalyzes NAD+ synthesis and was reported to regulate angiogenesis and have involvement in tissue invasion.22 LIPA here was linked to the cancer hallmark of “avoidance of immuno-surveillance”. It encodes lipase A, which plays important roles in lipid metabolism and has been reported to interfere with tumorspecific immune processes.18 Taken together, function enrichment analysis of predicted key enzyme-coding genes strongly suggests that they likely play important roles in cancer metabolism.

Validation of Predicted Key Enzyme-Coding Genes and Key Metabolites As Potential Novel Cancer Drug Targets and Potential Anticancer Drugs

We’ve shown that the predictions made by Met-express significantly overlap with known anti-cancer drug targets and cancer drugs, and the predicted genes likely play important roles in cancer metabolism. To further investigate the possibility of the predicted genes as anti-cancer drug targets and predicted metabolites for cancer therapeutic use, we conducted a comprehensive literature validation. Based on evidence in the literature, we categorized predicted genes into four groups: “known anti-cancer drug targets”, which are annotated as targets for approved anti-neoplastic agents in DrugBank; “suggested anti-cancer drug targets”, which are specifically suggested in the literatures as potential anti-cancer drug targets or are being tested in vivo as anti-cancer drug targets; “mechanistically related to cancer”, which are not currently annotated or suggested as anti-cancer drug targets yet have been found to be closely related to cancer in the literatures; and “others”. The predicted metabolites were similarly categorized as “known anti-cancer drugs”, which are known anti-cancer drugs in DrugBank; “high potential anticancer drugs”, which have been suggested as potential anticancer drugs in PubChem (http://www.ncbi.nlm.nih.gov/ pccompound/) or other literatures; “medium potential anticancer drugs”, which are closely related to known anti-cancer drugs or targets; and “others” (see Supplementary Table 5 for supporting data). We found that 33−53% of predicted genes in the three types of cancer are either “known anti-cancer drug targets” or “suggested anti-cancer drug targets” in the literatures (Figure 3A). If genes belonging to the category of “mechanistically related to cancer” are taken into consideration, over 70% of predicted genes in all three cancer types are potential anticancer drug targets. For the predicted metabolites, 11 (22%) are either “known anti-cancer drugs” or “high potential anticancer drugs” in the literature (Figure 3B). There were also 25 metabolites categorized as “medium potential anti-cancer 2359



Article

Figure 4. Experiment validation of selected genes in two cancer cell lines. Four phenotype assays (apoptosis, necrosis, proliferation, and migration) have been performed for selected genes in A549 (A−D) and MCF7 (E−H) cell lines. Each bar represents the ratio of mean cell counts of cancer cells when the expression of a tested gene is altered by either knock-down or over-expression experiment to that of cancer cells without the treatment. Error bars refer to the standard deviations of replicated experiments. The dashed lines correspond to the ratio of 1, i.e., the treatments have no effect. The anti-cancer phenotypes include promoted apoptosis and necrosis (ratios greater than 1) and inhibited proliferation and migration (ratios smaller than 1), for as a general understanding cancer cell lines have inhibited apoptosis/necrosis, and/or promoted proliferation/migration compared to normal cells. PC refers to positive controls (see Materials and Methods for details). Ratios with statistical significance are marked: * P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001.

is involved in the propanoate metabolism pathway and has been reported to be a potential inhibitor of aldehyde dehydrogenase, which may be a critical factor in cell deaths.31

drugs”, which increases the percentage of predicted metabolites having direct or indirect supporting evidence up to 72%. Thus, data from the literature strongly confirm the usefulness of Metexpress predictions for potential cancer therapeutic development. Below are several examples. HPRT1 was predicted from the up-regulated gene modules in all three cancer types and is the target of a known anti-cancer drug, Mercaptopurine.23 The gene encodes hypoxanthine phosphoribosyltransferase 1 and is essential for nucleic acid biosynthesis. TK1 was predicted from up-regulated gene modules in all three cancer types and was a “suggested anti-cancer drug target”. It encodes thymidine kinase 1, was reported to be involved in cell division, and was proposed as a possible target for brain cancer24 and a marker for lymphoproliferative diseases.25 MTHFD2 was predicted in up-regulated gene modules in both lung and breast cancer and was “mechanistically related to cancer”. It functions in folatemediated metabolism and was reported to be over-expressed in breast cancer cell lines as a possible novel biomarker for breast cancer.26 Floxuridine is a known anti-neoplastic drug, with 5fluorouracil as its active form. It is the substrate of TK1 and acts as a pyrimidine analog to inhibit the S phase of cell division.27,28 Ceramide was a “high potential anti-cancer drug”. It is the product of galactocerebrosidase and is the central metabolite in sphingolipid metabolism. It was reported to mediate antiproliferative responses29 and be involved in the apoptotic response induced by another metabolite in the HL60 cell line.30 Propiolaldehyde was a “medium potential anti-cancer drug”. It

Experimental Validation of Predicted Key Enzyme-Coding Genes and Metabolites

In addition to literature validation, we conducted experiments on selected genes and metabolites based on the predictions. We select 11 genes based on the following considerations: (1) they either were predicted in at least two cancer types or were among the 20 top-scored genes predicted in one cancer type and are likely reliable predictions; (2) most of them belong to either the “mechanistically related to cancer” or the “others” categories, meaning that they have not been extensively studied; (3) they are involved in diverse pathways; and (4) the operability of the experiments was reasonable. The 11 selected genes were PAICS, AGPAT2, and TK1, which belonged to the category of “suggested anti-cancer drug targets”; AK2, MTHFD2, STT3A, MAOB, COASY, and PLOD1, which belonged to “mechanistically related to cancer”; and GANAB and OCRL, which belonged to “Others” (see Supplementary Table 6 for more information about these genes). Of the 11 genes, 8 were up-regulated and 3 were downregulated in cancer cells. Accordingly, siRNA duplex and overexpression plasmids were prepared for expression in cancer cell lines to assess the effects of knocking down or over-expressing these genes. We used two cancer lines for these experiments: A549 lung cancer cell line and MCF7 breast cancer cell line. Leukemia cell lines such as AML cell line were not used 2360



Article

Figure 5. Experiment validation of selected metabolites in two cancer cell lines. Four phenotype assays (apoptosis, necrosis, proliferation, and migration) are performed for selected metabolites in A549 (A−D) and MCF7 (E−H) cell lines. For apoptosis and necrosis essays, when the ratio is larger than 1, it means that the metabolite has potential anti-cancer effect. For proliferation and migration essays, when the ratio is smaller than 1, it means that the metabolite has potential anti-cancer effect. Other descriptions of the figures are the same as in Figure 4.

associated with any of the four cancer phenotypes in A549 or MCF7 cell lines (Figure 5; see Supplementary Table 5 for detailed information about these metabolites). Of these seven metabolites, xanthine and guanine belonged to the category of “high potential anti-cancer drugs” and the category of “others”, respectively, while the other five all belonged to “medium potential anti-cancer drugs”. Five metabolites (except deoxyuridine and dTMP) showed clear anti-neoplastic effects at the concentration of 100 nM, especially for apoptosis and cell proliferation. Indoleacetaldehyde, xanthine, and thymidine were especially effective. Here, we used indoleacetaldehyde and xanthine as two examples. Indoleacetaldehyde is the substrate of aldehyde dehydrogenase (ALDH2), which was up-regulated in lung cancer and related to high-risk nasal epithelium and lung cancers.36 A derivatives of a downstream product of indoleacetaldehyde, indole-3-acetic acid (IAA), has been considered as a promising prodrug in cancer therapies though it is very cytotoxic.37 In our study, we found that compared to the negative control, indoleacetaldehyde induced a 17.3-fold greater apoptosis and inhibited cell proliferation to only 4.2% in the A549 cell line. Xanthine is the substrate of HPRT1, which is the target of 3-iso-butyl-1-methylxanthine, a cancer drug that can inhibit lung cancer cell proliferation and reduce cell malignancy when used with other drugs.38 Lafleur et al. found that xanthine derivatives were inhibitors of human hepatocellular carcinoma receptor B4 (EphB4), which played a role in cancer angiogenesis.39 A methylxanthine derivative, pentoxifylline (PTX), can induce apoptosis and decrease proliferation in breast cancer cells.40 We found that xanthine itself can promote cell apoptosis and necrosis and inhibit cell proliferation in both cancer cell lines. Together, these experimental results strongly demonstrate that predictions made by Met-express are of great value for designing new cancer therapies.

because they do not attach to the plate bottom and are difficult to culture. Four cancer phenotypes were subsequently examined, including apoptosis, necrosis, proliferation, and migration. Of the 11 tested genes, 10 (except OCRL) showed at least one significant anti-cancer phenotype when their expression levels were altered. Eight tested genes showed at least two significant anti-cancer phenotypes in one of the two cell lines. Five (AK2, COASY, MTHFD2, STT3A and TK1) and two (MTHFD2 and STT3A) genes show three or four significant anti-cancer phenotypes in the A549 cell line and MCF7 cell lines, respectively (Figure 4). Disruption of MTHFD2 and STT3A had the strongest anti-cancer effects, with seven anticancer phenotypes together in two cell lines. Many of the anticancer phenotypes in these two cell lines were the first to be reported for these genes. For example, previous studies showed that COASY was up-regulated in HEK293 cells and could promote HEK293 cells proliferation.32,33 In our study, we found that down-regulating COASY in both A549 and MCF7 cell lines inhibited cell proliferation. In addition, we found that down-regulating COASY can significantly inhibit migration and promote apoptosis in the A549 cell line. PLOD1 was previously reported to be down-regulated in Rhabdoid tumors34 and involved in drug-resistant breast cancers.35 Here we found that over-expressing PLOD1 significantly induced both apoptosis and necrosis and markedly inhibited migration in the A549 cell line. In addition, over-expression of the gene promoted necrosis in the MCF7 cell line. Since depleting metabolites from cells can be difficult, we only selected metabolites that were the substrates of upregulated genes or products of down-regulated genes. These metabolites presumably have reduced concentration in cancer cells, and therefore increasing their concentration may have an impact on cancer cell growth. We selected seven metabolites (indoleacetaldehyde, deoxyuridine, thymidine, dTMP, xanthine, hypoxanthine, and guanine) that have not been reported to be 2361



■

■

DISCUSSION

Article

ASSOCIATED CONTENT

S Supporting Information *

Cancer cell metabolism plays a critical role in cancer cell growth. In this study we have developed a novel method, Metexpress, that integrates cancer gene co-expression network with metabolic network for predicting the key enzyme-coding genes and metabolites involved in cancer cell metabolism. Compared with using either network alone or a previously developed approach named CoMet for predicting metabolites of cancer therapeutic use, Met-express produced more robust predictions across different types of cancer and was able to predict results that have more significant overlap with known anti-cancer drug targets and anti-cancer drugs. Thus, Met-express has demonstrated the power of network integration for uncovering key genes and metabolites that play important roles in cancer metabolism. We consider the predicted genes and metabolites to be important in cancer metabolism on the basis of the following evidence. First, both GO and KEGG pathway enrichment analysis of the predicted genes from different types of cancer reveal significant overlap in pathways that have been reported to be reprogrammed in cancer cells. Second, the predicted genes are co-expressed with other genes in gene co-expression modules that are significantly enriched with functions related to hallmarks of cancer. Third, literature validation showed that 33−53% of the predicted genes from the different types of cancers tested are either known or suggested to be anti-cancer drug targets, while a significant fraction of the predicted metabolites have been either confirmed or suggested to have cancer therapeutic use. Finally, experimental validation of selected genes and metabolites provides direct evidence that alterations of the expression of the genes and the concentration of the metabolites have a substantial impact on cancer cell growth, suggesting that they may play important roles in cancer metabolism. The predictions made by Met-express provide a valuable resource for exploring potential new cancer treatments. Traditional cancer treatments targeting a single gene often fail to suppress tumor growth and can lead to drug resistance. A combined therapy targeting on multiple genes may provide a solution.41 With Met-express predictions, we can conduct experiments to test different combinations of predicted genes or explore a combination of predicted metabolites in order to determine the ones with the best efficacy in suppressing cancer cell growth. In addition to the anti-cancer utility, the predictions by Met-express can help design new experiments to better understand the underlining mechanisms of cancer metabolism in cancer cell development. For example, the pyrimidine metabolic pathway was reported to be altered in cancer cells to facilitate cancer cell proliferation.10 In this study, we predicted a high portion of genes involved in this pathway, which provided a foundation for investigating how this pathway impacts cancer cell development and progression. Finally, Metexpress provides a general platform that is not restricted to the integration of the gene co-expression network and the metabolic networks, or application to cancer metabolism. Other types of data, such as the protein−protein interaction network and the gene regulatory network, as well as other types of diseases, such as neurodegenerative diseases, can be explored using Met-express.

This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]; [email protected]. Author Contributions §

These authors contribute equally to this work. W.T. conceived the idea and supervised the study. J.C. developed the Metexpress method and performed the analysis. N.S. participated in the analysis. M.M. conducted all the validation experiments. J.X. supervised the validation experiments. W.T., J.C., M.M., and N.S. drafted the manuscript. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We thank Zhaoyuan Fang for the inspiring discussion during the development of the method. We thank Wei Ning, Qingtian Gong & Yulan Lu for their help with preparing figures. This work was supported by the National Basic Research Program of China (Grant No. 2012CB316505, 2010CB529505) to WT, the National Natural Science Foundation of China (Grant No. 91231116, 31071113, 30971643 to WT, 81030040, 30600142 to JX), and MOST (Grant No. 2012AA020103).

■

REFERENCES

(1) Cairns, R. A.; Harris, I. S.; Mak, T. W. Regulation of cancer cell metabolism. Nat. Rev. Cancer 2011, 11 (2), 85−95. (2) Hsu, P. P.; Sabatini, D. M. Cancer cell metabolism: Warburg and beyond. Cell 2008, 134 (5), 703−707. (3) Kroemer, G.; Pouyssegur, J. Tumor cell metabolism: cancer’s Achilles’ heel. Cancer Cell 2008, 13 (6), 472−482. (4) Warburg, O. On the origin of cancer cells. Science 1956, 123 (3191), 309−314. (5) Ishikawa, K.; Takenaga, K.; Akimoto, M.; Koshikawa, N.; Yamaguchi, A.; Imanishi, H.; Nakada, K.; Honma, Y.; Hayashi, J. ROSgenerating mitochondrial DNA mutations can regulate tumor cell metastasis. Science 2008, 320 (5876), 661−664. (6) Weckwerth, W.; Morgenthal, K. Metabolomics: from pattern recognition to biological interpretation. Drug Discovery Today 2005, 10 (22), 1551−1558. (7) Kim, Y. S.; Maruvada, P.; Milner, J. A. Metabolomics in biomarker discovery: future uses for cancer prevention. Future Oncol. 2008, 4 (1), 93−102. (8) Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discovery 2002, 1 (2), 153−161. (9) Baba, M.; Yamamoto, R.; Iishi, H.; Tatsuta, M.; Wada, A. Role of glucose-6-phosphate dehydrogenase on enhanced proliferation of preneoplastic and neoplastic cells in rat liver induced by N-nitrosomorpholine. Int. J. Cancer 1989, 43 (5), 892−895. (10) Furuta, E.; Okuda, H.; Kobayashi, A.; Watabe, K. Metabolic genes in cancer: their roles in tumor progression and clinical implications. Biochim. Biophys. Acta 2010, 1805 (2), 141−152. (11) Koudstaal, J.; Makkink, B.; Overdiep, S. H. Enzyme histochemical pattern in human tumours. II. Oxidoreductases in carcinoma of the colon and the breast. Eur. J. Cancer 1975, 11 (2), 111−115. (12) Kuo, W. Y.; Tang, T. K. Effects of G6PD over-expression in NIH3T3 cells treated with tert-butyl hydroperoxide or paraquat. Free Radical Biol. Med. 1998, 24 (7−8), 1130−1138. 2362



Article

(13) Foldi, M.; Stickeler, E.; Bau, L.; Kretz, O.; Watermann, D.; Gitsch, G.; Kayser, G.; Zur Hausen, A.; Coy, J. F. Transketolase protein TKTL1 over-expression: A potential biomarker and therapeutic target in breast cancer. Oncol. Rep. 2007, 17 (4), 841−845. (14) Langbein, S.; Zerilli, M.; Zur Hausen, A.; Staiger, W.; RenschBoschert, K.; Lukan, N.; Popa, J.; Ternullo, M. P.; Steidler, A.; Weiss, C.; Grobholz, R.; Willeke, F.; Alken, P.; Stassi, G.; Schubert, P.; Coy, J. F. Expression of transketolase TKTL1 predicts colon and urothelial cancer patient survival: Warburg effect reinterpreted. Br. J. Cancer 2006, 94 (4), 578−585. (15) Emilsson, V.; Thorleifsson, G.; Zhang, B.; Leonardson, A. S.; Zink, F.; Zhu, J.; Carlson, S.; Helgason, A.; Walters, G. B.; Gunnarsdottir, S.; Mouy, M.; Steinthorsdottir, V.; Eiriksdottir, G. H.; Bjornsdottir, G.; Reynisdottir, I.; Gudbjartsson, D.; Helgadottir, A.; Jonasdottir, A.; Styrkarsdottir, U.; Gretarsdottir, S.; Magnusson, K. P.; Stefansson, H.; Fossdal, R.; Kristjansson, K.; Gislason, H. G.; Stefansson, T.; Leifsson, B. G.; Thorsteinsdottir, U.; Lamb, J. R.; Gulcher, J. R.; Reitman, M. L.; Kong, A.; Schadt, E. E.; Stefansson, K. Genetics of gene expression and its effect on disease. Nature 2008, 452 (7186), 423−428. (16) Kostka, D.; Spang, R. Finding disease specific alterations in the co-expression of genes. Bioinformatics 2004, 20 (Suppl 1), i194−199. (17) Zhang, B.; Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 2005, 4 (1), 1128. (18) Presson, A. P.; Sobel, E. M.; Papp, J. C.; Suarez, C. J.; Whistler, T.; Rajeevan, M. S.; Vernon, S. D.; Horvath, S. Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome. BMC Syst. Biol. 2008, 2, 95. (19) Taylor, I. W.; Linding, R.; Warde-Farley, D.; Liu, Y.; Pesquita, C.; Faria, D.; Bull, S.; Pawson, T.; Morris, Q.; Wrana, J. L. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 2009, 27 (2), 199−204. (20) Arakaki, A. K.; Mezencev, R.; Bowen, N. J.; Huang, Y.; McDonald, J. F.; Skolnick, J. Identification of metabolites with anticancer properties by computational metabolomics. Mol. Cancer 2008, 7, 57. (21) Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K.; Mak, C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A. C.; Wishart, D. S. DrugBank 3.0: a comprehensive resource for ’omics’ research on drugs. Nucleic Acids Res. 2011, 39 (Database issue), D1035−1041. (22) Adya, R.; Tan, B. K.; Punn, A.; Chen, J.; Randeva, H. S. Visfatin induces human endothelial VEGF and MMP-2/9 production via MAPK and PI3K/Akt signalling pathways: novel insights into visfatininduced angiogenesis. Cardiovasc. Res. 2008, 78 (2), 356−365. (23) Dubinsky, M. C. Azathioprine, 6-mercaptopurine in inflammatory bowel disease: pharmacology, efficacy, and safety. Clin. Gastroenterol. Hepatol. 2004, 2 (9), 731−743. (24) Barth, R. F.; Yang, W.; Wu, G.; Swindall, M.; Byun, Y.; Narayanasamy, S.; Tjarks, W.; Tordoff, K.; Moeschberger, M. L.; Eriksson, S.; Binns, P. J.; Riley, K. J. Thymidine kinase 1 as a molecular target for boron neutron capture therapy of brain tumors. Proc. Natl. Acad. Sci. U.S.A. 2008, 105 (45), 17493−17497. (25) Hannigan, B. M.; Barnett, Y. A.; Armstrong, D. B.; McKelveyMartin, V. J.; McKenna, P. G. Thymidine kinases: the enzymes and their clinical usefulness. Cancer Biother. 1993, 8 (3), 189−197. (26) Xu, X.; Qiao, M.; Zhang, Y.; Jiang, Y.; Wei, P.; Yao, J.; Gu, B.; Wang, Y.; Lu, J.; Wang, Z.; Tang, Z.; Sun, Y.; Wu, W.; Shi, Q. Quantitative proteomics study of breast cancer cell lines isolated from a single patient: discovery of TIMM17A as a marker for breast cancer. Proteomics 2010, 10 (7), 1374−1390. (27) Murakami, Y.; Kazuno, H.; Emura, T.; Tsujimoto, H.; Suzuki, N.; Fukushima, M. Different mechanisms of acquired resistance to fluorinated pyrimidines in human colorectal cancer cells. Int. J. Oncol. 2000, 17 (2), 277−283. (28) Kubota, T. Theoretical basis for low-dose CDDP/5-FU therapy. Gan To Kagaku Ryoho 1999, 26 (11), 1536−1541.

(29) Ponnusamy, S.; Meyers-Needham, M.; Senkal, C. E.; Saddoughi, S. A.; Sentelle, D.; Selvam, S. P.; Salas, A.; Ogretmen, B. Sphingolipids and cancer: ceramide and sphingosine-1-phosphate in the regulation of cell death and drug resistance. Future Oncol. 2010, 6 (10), 1603−1624. (30) Jiang, L.; Pan, X.; Chen, Y.; Wang, K.; Du, Y.; Zhang, J. Preferential involvement of both ROS and ceramide in fenretinideinduced apoptosis of HL60 rather than NB4 and U937 cells. Biochem. Biophys. Res. Commun. 2011, 405 (2), 314−318. (31) Canuto, R. A.; Muzio, G.; Salvo, R. A.; Maggiora, M.; Trombetta, A.; Chantepie, J.; Fournet, G.; Reichert, U.; Quash, G. The effect of a novel irreversible inhibitor of aldehyde dehydrogenases 1 and 3 on tumour cell growth and death. Chem. Biol. Interact. 2001, 130−132 (1−3), 209−218. (32) Nemazanyy, I.; Panasyuk, G.; Zhyvoloup, A.; Panayotou, G.; Gout, I. T.; Filonenko, V. Specific interaction between S6K1 and CoA synthase: a potential link between the mTOR/S6K pathway, CoA biosynthesis and energy metabolism. FEBS Lett. 2004, 578 (3), 357− 362. (33) Breus, O.; Nemazanyy, I.; Gout, I.; Filonenko, V.; Panasyuk, G. CoA Synthase influences adherence-independent growth and survival of mammalian cells in vitro. Biopolym. Cell 2009, 25. (34) Gadd, S.; Sredni, S. T.; Huang, C. C.; Perlman, E. J. Rhabdoid tumor: gene expression clues to pathogenesis and potential therapeutic targets. Lab. Invest. 2010, 90 (5), 724−738. (35) Cleator, S.; Tsimelzon, A.; Ashworth, A.; Dowsett, M.; Dexter, T.; Powles, T.; Hilsenbeck, S.; Wong, H.; Osborne, C. K.; O’connell, P. Gene expression patterns for doxorubicin (Adriamycin) and cyclophosphamide (Cytoxan)(AC) response and resistance. Breast Cancer Res. Treat. 2006, 95 (3), 229−233. (36) Pham, P.; Tanaka, M.; Kagawa, N.; Kaneko, K.; Kawamoto, T. Effects of acetaldehyde inhalation in mitochondrial aldehyde dehydrogenase deficient mice (Aldh2−/−). Front. Biosci. 2010, 2, 1344−1354. (37) Rossiter, S.; Folkes, L. K.; Wardman, P. Halogenated indole-3acetic acids as oxidatively activated prodrugs with potential for targeted cancer therapy. Bioorg. Med. Chem. Lett. 2002, 12 (18), 2523−2526. (38) Lange, A.; Gustke, H.; Glassmeier, G.; Heine, M.; ZangemeisterWittke, U.; Schwarz, J. R.; Schumacher, U.; Lange, T. Neuronal differentiation by indomethacin and IBMX inhibits proliferation of small cell lung cancer cells in vitro. Lung Cancer 2011, 74 (2), 178− 187. (39) Lafleur, K.; Huang, D.; Zhou, T.; Caflisch, A.; Nevado, C. Structure-based optimization of potent and selective inhibitors of the tyrosine kinase erythropoietin producing human hepatocellular carcinoma receptor B4 (EphB4). J. Med. Chem. 2009, 52 (20), 6433−6446. (40) Goel, P. N.; Gude, R. P. Unravelling the antimetastatic potential of pentoxifylline, a methylxanthine derivative in human MDA-MB-231 breast cancer cells. Mol. Cell. Biochem. 2011, 358 (1−2), 141−151. (41) Rodon, J.; Perez, J.; Kurzrock, R. Combining targeted therapies: practical issues to consider at the bench and bedside. Oncologist 2010, 15 (1), 37−50. (42) Ruan, J.; Dean, A. K.; Zhang, W. A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst. Biol. 2010, 4 (1), 8. (43) Ruan, J.; Zhang, W. Identifying network communities with a high resolution. Phys. Rev. E: Stat., Nonlinear, Soft Matter Phys. 2008, 77 (1 Pt 2), 016104. (44) Horne, A. B.; Hodgman, T. C.; Spence, H. D.; Dalby, A. R. Constructing an enzyme-centric view of metabolism. Bioinformatics 2004, 20 (13), 2050−2055. (45) Berriz, G. F.; Beaver, J. E.; Cenik, C.; Tasan, M.; Roth, F. P. Next generation software for functional trend analysis. Bioinformatics 2009, 25 (22), 3043−3044. (46) Subramanian, S.; Steer, C. J. MicroRNAs as gatekeepers of apoptosis. J. Cell. Physiol. 2010, 223 (2), 289−298. (47) Trang, P.; Medina, P. P.; Wiggins, J. F.; Ruffino, L.; Kelnar, K.; Omotola, M.; Homer, R.; Brown, D.; Bader, A. G.; Weidhaas, J. B. 2363



Article

Regression of murine lung tumors by the let-7 microRNA. Oncogene 2009, 29 (11), 1580−1587. (48) Lal, A.; Navarro, F.; Maher, C. A.; Maliszewski, L. E.; Yan, N.; O’Day, E.; Chowdhury, D.; Dykxhoorn, D. M.; Tsai, P.; Hofmann, O. miR-24 Inhibits cell proliferation by targeting E2F2, MYC, and other cell-cycle genes via binding to ″seedless″ 3′UTR microRNA recognition elements. Mol. Cell 2009, 35 (5), 610−625. (49) Zhang, H.; Hao, Y.; Yang, J.; Zhou, Y.; Li, J.; Yin, S.; Sun, C.; Ma, M.; Huang, Y.; Xi, J. J. Genome-wide functional screening of miR23b as a pleiotropic modulator suppressing cancer metastasis. Nat. Commun. 2011, 2, 554. (50) Israel, Y.; Kalant, H.; Orrego, H.; Khanna, J.; Videla, L.; Phillips, J. Experimental alcohol-induced hepatic necrosis: suppression by propylthiouracil. Proc. Natl. Acad. Sci. 1975, 72 (3), 1137−1141. (51) Mi, L. J.; Mak, K. M.; Lieber, C. S. Attenuation of alcoholinduced apoptosis of hepatocytes in rat livers by polyenylphosphatidylcholine (PPC). Alcohol.: Clin. Exp. Res. 2000, 24 (2), 207−212. (52) Adachi, S.; Yasuda, I.; Nakashima, M.; Yamauchi, T.; Yoshioka, T.; Okano, Y.; Moriwaki, H.; Kozawa, O. Rho-kinase inhibitor upregulates migration by altering focal adhesion formation via the Akt pathway in colon cancer cells. Eur. J. Pharmacol. 2011, 650 (1), 145− 150. (53) Lin, S. S.; Lai, K. C.; Hsu, S. C.; Yang, J. S.; Kuo, C. L.; Lin, J. P.; Ma, Y. S.; Wu, C. C.; Chung, J. G. Curcumin inhibits the migration and invasion of human A549 lung cancer cells through the inhibition of matrix metalloproteinase-2 and-9 and Vascular Endothelial Growth Factor (VEGF). Cancer Lett. 2009, 285 (2), 127−133. (54) Terzis, A.; Thorsen, F.; Heese, O.; Visted, T.; Bjerkvig, R.; Dahl, O.; Arnold, H.; Gundersen, G. Proliferation, migration and invasion of human glioma cells exposed to paclitaxel (Taxol) in vitro. Br. J. Cancer 1997, 75 (12), 1744.

2364


Integration of Cancer Gene Co-expression Network and Metabolic

Recommend Documents