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Lipid metabolic pathways as lung cancer therapeutic targets: a computational study.
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PMID:  22211244     Owner:  NLM     Status:  MEDLINE    
Inhibitors of lipid metabolic pathways, particularly drugs targeting the mevalonate pathway, have been suggested to be valuable in enhancing the effectiveness of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) and these compounds may also be effective in patients with inherent or acquired resistance to EGFR-TKIs. The present study examined gene expression profiles in lung adenocarcinoma to characterize the interaction between growth factor signals and lipid metabolic pathways at the transcriptional level. Gene expression correlation analysis showed that genes involved in the mevalonate pathway and unsaturated fatty acid synthesis were negatively correlated with the expression of EGFR, MET and other growth factor receptor genes, as well as with the expression of genes involved in cell migration and adhesion. On the other hand, the expression of genes related to cell cycle progression, DNA repair and DNA replication were positively correlated with the metabolic pathway genes mentioned above, and a significant number of such genes had promoter domains for nuclear factor Y (NFY). Genes whose expression showed a positive correlation with NFY expression and mevalonate pathway genes were found to exhibit protein-protein interactions with several 'hub' genes, including BRCA1, that have been associated with both lung cancer and cell division. These results support the idea that inhibition of lipid metabolic pathways may be valuable as an alternative therapeutic option for the treatment of lung adenocarcinoma, and suggest that NFY is a possible molecular target for such efforts.
Kojiro Yano
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2011-12-30
Journal Detail:
Title:  International journal of molecular medicine     Volume:  29     ISSN:  1791-244X     ISO Abbreviation:  Int. J. Mol. Med.     Publication Date:  2012 Apr 
Date Detail:
Created Date:  2012-02-06     Completed Date:  2012-05-29     Revised Date:  2013-06-26    
Medline Journal Info:
Nlm Unique ID:  9810955     Medline TA:  Int J Mol Med     Country:  Greece    
Other Details:
Languages:  eng     Pagination:  519-29     Citation Subset:  IM    
Faculty of Information Science and Technology, Osaka Institute of Technology, Hirakata-City, Osaka, Japan.
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MeSH Terms
Adenocarcinoma / genetics,  pathology,  therapy*
BRCA1 Protein / genetics,  metabolism
CCAAT-Binding Factor / genetics,  metabolism
Computer Simulation
DNA Repair / genetics
DNA Replication / genetics
Gene Expression Regulation, Neoplastic
Lipid Metabolism / genetics*
Lung Neoplasms / genetics,  pathology,  therapy*
Mevalonic Acid / metabolism
Protein Kinase Inhibitors / therapeutic use
Protein-Tyrosine Kinases / antagonists & inhibitors,  genetics,  metabolism
Receptor, Epidermal Growth Factor / antagonists & inhibitors,  genetics,  metabolism*
Signal Transduction
Reg. No./Substance:
0/BRCA1 Protein; 0/BRCA1 protein, human; 0/CCAAT-Binding Factor; 0/Protein Kinase Inhibitors; 0/nuclear factor Y; 150-97-0/Mevalonic Acid; EC protein, human; EC Kinases; EC, Epidermal Growth Factor

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Journal Information
Journal ID (nlm-ta): Int J Mol Med
Journal ID (iso-abbrev): Int. J. Mol. Med
Journal ID (publisher-id): IJMM
ISSN: 1107-3756
ISSN: 1791-244X
Publisher: D.A. Spandidos
Article Information
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Copyright © 2012, Spandidos Publications
Received Day: 03 Month: 11 Year: 2011
Accepted Day: 13 Month: 12 Year: 2011
collection publication date: Month: 4 Year: 2012
Electronic publication date: Day: 30 Month: 12 Year: 2011
Print publication date: Month: 4 Year: 2012
pmc-release publication date: Day: 30 Month: 12 Year: 2011
Volume: 29 Issue: 4
First Page: 519 Last Page: 529
PubMed Id: 22211244
ID: 3573709
DOI: 10.3892/ijmm.2011.876
Publisher Id: ijmm-29-04-0519

Lipid metabolic pathways as lung cancer therapeutic targets: A computational study
Faculty of Information Science and Technology, Osaka Institute of Technology, 1-79-1 Kitayama, Hirakata-City, Osaka, Japan
Correspondence: Correspondence to: Dr Kojiro Yano, Faculty of Information Science and Technology, Osaka Institute of Technology, 1-79-1 Kitayama, Hirakata-City, Osaka, Japan, E-mail:


Lung adenocarcinoma accounts for about half of all non-small cell lung cancer (NSCLC) cases and is one of the major causes of death in developed countries (1). Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have been intensively assessed over the past several years as targeted agents for advanced NSCLC. Whereas EGFR-TKIs are highly effective in the treatment of adenocarcinoma associated with specific EGFR mutations that cause sustained receptor activity, drug effectiveness is significantly lower in patients without the activating mutations, and even patients with the mutations frequently develop resistance to EGFR-TKI (2). Therefore, new therapeutic targets that can overcome inherent or acquired resistance to EGFR-TKIs are highly desirable. Recently, it has been suggested that acquired resistance to EGFR-TKIs may be related to amplification of a hepatocyte growth factor (HGF) receptor, termed MET (3). HGF expression can induce EGFR-TKI resistance to lung adenocarcinoma cells with EGFR-activating mutations (4), and MET inhibition can reduce proliferation of lung adenocarcinoma cell lines that show resistance to EGFR-TKIs (3). MET amplification occurs in about 20% of NSCLC patients and is associated with poor survival.

The lipid metabolism pathway may also modulate the effectiveness of EGFR-TKIs in lung adenocarcinoma patients. It has been suggested that lipid-lowering drug statins may reduce cancer risk (5), and a large case-control study of US veterans found that this may be true for lung cancer (6), although some reports claim otherwise (7,8). In vitro studies have shown that inhibition of the mevalonate pathway by statins reduces EGFR autophosphorylation (9), downstream AKT signaling (10), and EGF-induced RhoA translocation to the plasma membrane (11). Enhancement of EGFR-TKI effectiveness by statins seems to occur not only in cells with EGFR-activating mutations but also in EGFR-TKI-resistant NSCLC cell lines (12). The mechanism of EGFR signaling inhibition is not fully characterized, but reduced prenylation of small GTP-binding proteins may be of importance (13). However, depletion of cholesterol in the plasma membrane is known to increase EGFR signaling activity, perhaps by releasing EGFR from lipid rafts and inhibiting receptor internalization (14,15). This suggests that the lipid metabolism pathway can influence EGFR signaling in both a positive and negative manner.

This study sought to characterize the lipid metabolism pathway in lung adenocarcinoma using gene expression correlation analysis of microarray data. More specifically, pathway genes that show associations with EGFR or MET were examined in detail, because EGFR and MET are among the best-studied growth signals in lung cancer patients. Gene expression profiles have been used to classify lung cancer (16), to discover gene sets which are predictive of disease prognosis (17), and to investigate molecular mechanisms of disease progression (18). However, large-scale analysis of the association between metabolic and growth factor signaling pathways has not been conducted in lung cancer tissue. In the present study, a set of lipid metabolism pathway genes, the expression of which are highly correlated with EGFR or MET, were first selected. Next, genes in the microarray dataset showing significant correlation with selected genes were examined in terms of functional properties. Finally, possible regulatory mechanisms of correlated expression were inferred using known transcription factor target sequences. This type of analysis predicts how the lipid metabolic pathway may functionally interact with EGFR, MET, and other biological processes in lung cancer cells, and offers an insight into the roles of EGFR and MET inhibition in lung cancer therapeutics.

Materials and methods
Microarray data

The microarray dataset GSE10072 (19) from the Gene Expression Omnibus (20) was used for analysis. The dataset contains expression profiles of 58 tumor and 49 non-tumor tissues. The information was originally obtained using the Affymetrix Human Genome U133A Array. The data from 22,215 probes in the array were normalized using the quantile normalization function (quantilenorm) of the Matlab Bioinformatics Toolbox (MathWorks, Natick, MA).

Classification of genes by Gene Ontology

The DAVID functional annotation tool [version 6.7b (21,22)] was used to classify gene sets by Gene Ontology identifiers or using UCSC transcription factor binding sites (23). Functional categories with a Benjamini-Hochberg statistic (24) of <0.025 were considered statistically significant.

Statistical analysis

Pearson correlation coefficients were calculated using the ‘corr’ function from Matlab. The 2.5th and 97.5th percentiles of coefficients for 100,000 pairwise combinations between randomly selected genes in the dataset were −0.379 and 0.428, respectively, and these were used as threshold values for significantly negative and positive correlations. Two-sample t-testing was achieved using the ‘ttest2’ function from Matlab.

Correlation of lipid metabolism genes with EGFR expression

A total of 301 genes classified as ‘lipid metabolic process’ (GO:0006629) by gene ontology were selected and Pearson correlation coefficients were calculated between the expression of such genes and EGFR and MET. Although no gene showed a positive correlation with EGFR or MET expression, eight and nine such genes displayed a negative correlation with EGFR and MET expression, respectively, in cancer samples (Table I). The negative correlations were not evident in normal lung samples, except for MVK, which showed a significant negative correlation with MET in both cancerous and normal cells. Among the negatively correlated genes, HMG-coenzyme A synthase 1 (HMGCS1), farnesyl-diphosphate farnesyltransferase 1 (FDFT1), farnesyl diphosphate synthase (FDPS), isopentenyl-diphosphate δ isomerase 1 (IDI1), lanosterol synthase (LSS), emopamil binding protein (EBP) and mevalonate kinase (MVK) are known to be involved in the first steps of steroid biosynthesis (Fig. 1). FAS and stearoyl-CoA desaturase (SCD) mediate the synthesis of monounsaturated fatty acids from acetyl-CoA, and fatty acid desaturase 1 (FADS1), fatty acid desaturase 2 (FADS2), and elongation of very long chain fatty acids (fen1/elo2, sur4/elo3, yeast)-like 2 (ELOVL2) catalyze the production of polyunsaturated fatty acids, including arachidonic acid (Fig. 2). Fatty acid 2-hydroxylase (FA2H) is involved in sphingolipid metabolism and mutations in this gene are known to cause leukodystrophy, whereas phosphatidylglycerophosphate synthase 1 (PGS1) is involved in glycerophospholipid metabolism, synthesizing phosphatidyl-glycerophosphate from CDP-diacylglycerol. These results suggest that EGFR and MET are closely, but negatively, associated with the expression of a variety of fatty acid biosynthesis genes in lung adenocarcinoma tissue.

Functional gene categories associated with lipid metabolism genes anti-correlated to EGFR

Next, associations of the ‘anti-EGFR/MET’ lipid metabolism genes with other genes were evaluated by calculation of the intergene Pearson correlation coefficients in lung cancer samples. Table II shows the number of genes demonstrating significant positive or negative associations with mevalonate pathway genes (FDFT1, FDPS, HMGCS1, IDI1, LSS, EBP and MVK).

Among these seven genes, FDPS, HMGCS1, IDI1 and MVK, all of which mediate farnesyl pyrophosphate synthesis from mevalonate, showed particularly large numbers of correlated genes. In addition, 166 genes in the microarray dataset displayed significant positive associations with three or more of the mevalonate pathway genes. According to DAVID, gene functional categories were dominated by GO Biological Processes related to the cell cycle, DNA replication, response to DNA damage, and lipid metabolism, suggesting close links between the regulation of cell division and cholesterol biosynthesis (Table III). On the other hand, 235 genes had significant negative associations with three or more of the mevalonate pathway genes. The functional categories were principally related to cell adhesion, cell migration, blood vessel development, extracellular matrix synthesis, and defense responses (Table III). This gene set also included regulators of cell proliferation, including endothelin receptor type A (EDNRA), platelet-derived growth factor receptor, α polypeptide (PDGFRA), protein kinase Cα (PRKCA), ras-related C3 botulinum toxin substrate 2 (RAC2), transforming growth factor β, receptor II (TGFBR2), and vitamin D receptor (VDR). These data may suggest that mevalonate pathway genes were negatively associated with processes mediating signal transduction from the extracellular space, but positively associated with pathways involving the nucleus. Similarly, anti-EGFR/MET lipid metabolism genes involved in fatty acid synthesis (FADS1, FADS2, FASN, SCD, ELOVL2, PGS1 and FA2H) were evaluated (Table II). Most of these genes showed smaller numbers of correlations than genes of the mevalonate pathway. Only 18 and 35 genes displayed significant positive and negative correlations, respectively, with three or more of the fatty acid synthesis genes. The positively correlated genes belonged to sets of functional categories similar to those positively correlated with mevalonate pathway genes (Table III); these were genes of the cell cycle, cell division and lipid metabolism. No functional category was significantly enriched in negatively correlated genes.

Transcriptional regulatory mechanisms associated with anti-EGFR lipid metabolism genes

Gene expression correlation analysis showed that lipid metabolism genes were associated with specific biological processes, particularly the cell cycle. To determine a possible mechanism of correlated expression, enrichment of predicted transcription factor binding sites was examined by DAVID. It was found that genes positively associated with mevalonate pathway genes were enriched in the NFY binding site, with a Benjamini score of 3.4E-8. To examine the relationship between NFY and genes positively correlated with mevalonate pathway genes, a search was instituted for genes showing significant positive correlations with NFY. As NFY is composed of subunits encoded by three genes, NFYA, NFYB and NFYC, genes with positive correlations with at least one subunit were selected. Respectively 202, 889 and 133 genes were found to display a correlation with NFYA, NFYB and NFYC, and, in total, 1,166 genes displayed significant positive correlations with one or more of the NYF subunit genes. For each gene identified, Pearson correlation coefficients were calculated with respect to genes positively correlated with mevalonate pathway genes, and the number of significant positive correlations was enumerated. This disclosed that 53 genes showed positive correlations with 81 or more of mevalonate pathway-associated genes. This threshold of 81 is the top 2.5th percentile of the number of mevalonate pathway genes positively correlated with each gene in the microarray dataset. These 53 genes will be simply termed ‘NFY-correlated genes’ below.

A literature search found no reported direct physical association between NFY and any of the 53 gene products. However, according to DAVID, many of these genes were related to DNA metabolic processes, DNA repair, or mRNA metabolism (Table IV). To account for the observed associations between NFY and NFY-correlated genes, known protein interactions were sought using Genes2Networks (25). Fig. 3 shows the overall network, formed by NFY genes, NFY-correlated genes, and intermediate genes which connect these two gene sets. Extracts from the network, subnets 1 and 2, are shown in Figs. 4 and 5, respectively. Subnet 1 has 15 NFY-correlated genes showing relatively close associations with NFY genes in the interaction network (Table V). Six such genes are involved in DNA repair and five are associated with either the cell cycle (ASPM, FBXO5), DNA metabolic processes (ORC2L, HAT1), or both (MCM3). In this subnetwork, several intermediate or ‘hub’ genes were closely connected to the NFY-correlated genes. Namely, PCNA and BRCA1 were connected to four of the NFY-correlated genes, and each of MCM10, PLK1, MCM2 and RPA2 to three. In addition to these hub genes, CHEK2, CDK2, MCM7, CDC6, EP300 and ORC4L were connected to two of the NFY-correlated genes as well as to two hub genes. Of these genes, PCNA, MCM2, CDK2 and MCM7 showed significantly negative correlations with EGFR (Pearson coefficients, −0.446, −0.399, −0.381 and −0.401, respectively), whereas PLK1, MCM2 and CDK2 displayed significantly negative correlations with MET (Pearson coefficients, −0.373, −0.486 and −0.495, respectively). Moreover, the mean Pearson coefficients of all hub genes were −0.252 for EGFR and −0.240 for MET, both of which were significantly lower than the means for all genes in the dataset (−0.0089 for EGFR and −0.0313 for MET; P=1.678E-4 and 0.0029 by t-tests, respectively), demonstrating negative associations between hub genes and growth signals. Subnet 2 includes nine of the NFY-associated genes that were only distantly connected with NFY genes in the protein-protein interaction network. Five of these genes were related to RNA metabolic processes (PAIP1, SNRPE, DEK, UPF and LSM2) and two genes encoded proteins with histone-binding properties (NASP and CBX1). In this subnetwork, LSM1 showed high connectivity, displaying two edges with the NFY-correlated genes, and three with other intermediate genes. LSM1 is highly expressed in lung cancer and mesothelioma, and LSM1 inhibition retards tumor growth (26). Four other LMS genes were present in the subnet but there was no evidence of association with lung cancer.


In the present study, gene expression correlation patterns predicted that mevalonate metabolism and fatty acid synthesis processes were negatively associated with expression of EGFR and MET, but positively associated with cell division. Promoter analysis suggested that the NFY transcription factor may be involved in the regulation of genes involved in mevalonate metabolism, and the processes positively associated with them. Finally, gene expression correlation patterns and protein-protein interaction data indicate that the transcriptional regulation by NFY may be mediated by its interactions with other regulators of DNA metabolic processes and cell cycle genes.

The negative correlations between growth factor signaling and lipid metabolic pathways reported here seem to indicate an inhibitory effect of cholesterol on EGFR pathways in lung adenocarcinoma. Polyunsaturated fatty acids, such as oleic acid, are also known to inhibit the EGFR pathway, although the effects depend both on particular combinations of fatty acids and the cell type (2729). In lung adenocarcinoma, the mevalonate pathway synthesizes more non-sterol and fewer sterol products than seen in fibroblasts (30). This can result in a higher degree of prenylation of small GTP-binding proteins, and reduced levels of plasma membrane cholesterol, possibly leading to enhanced EGFR activity. Mevalonate metabolites can also influence the expression of metabolic genes through the intermediacy of the liver X receptor (LXR). For example, LXR can activate FDPS synthesis (31), but LXR is inhibited by geranylgeraniol (32), which is produced from isopentenyl-PP and farnesyl-PP. Indeed, expression of NR1H3 (LXR-α) showed a significant correlation with FDPS and EBP synthesis in lung cancer samples but not in normal lung samples (data not shown), suggesting a cancer-specific regulation of mevalonate pathway genes by LXR-α.

The positive correlations seen between the lipid metabolic pathway and cell division-related processes appear to be consistent with previous experimental evidence. Pravastatin is known to inhibit DNA synthesis, whereas addition of geranylgeranylpyrophosphate restores such synthesis and promotes the G1/S transition (33). However, inhibition of farnesyl-protein transferase induces p21 expression and G1 blockade in a p53-dependent manner, suggesting that regulation of the cell cycle by mevalonate metabolites occurs at both the transcriptional and translational levels. In lung carcinoma cell lines, farnesyl transferase inhibitors block farnesylation of centromeric proteins and inhibit the association of such proteins with microtubules (34). In retinoblastoma gene-deficient thyroid tumors, FDPS is overexpressed, leading to increased isoprenylation and activation of N-Ras and induction of the DNA damage response (35). These experimental findings seem to suggest that mevalonate metabolites can directly regulate the expression of genes related to cell division as well. Unsaturated fatty acids are also known to increase cell proliferation (36) (37), although the mechanism of such action is not clear. One possibility is that increased activity of intracellular signaling cascades, such as those mediated by intracellular calcium (38) or AKT (39), may enhance the response of cells to mitogenic signals. However, unsaturated fatty acids are substrates for lipid peroxidation and may cause DNA damage in lung cancer cells (4042). This may lead, in turn, to apparent (thus not real) correlated expression of unsaturated fatty acid metabolism genes and DNA damage response genes.

Transcription factor binding sequence analysis suggested that NFY may have a considerable influence on associations of lipid metabolism genes. NFY is a ubiquitous transcriptional factor which recognizes promoter CCAAT boxes (43). NFY is known to be involved in transcriptional regulation of a wide range of genes, but the regulatory roles of NFY in lipogenesis, the cell cycle, DNA repair, and DNA synthesis are of particular interest in the present context. In lipogenic gene regulation, NFY often functions with SREBPs and SP1 (44), and recent genome-wide scanning of SREBP1, SP1 and NFY occupancy showed that NFY shares about 20 and 40% of target genes with SREBP1 and SP1, respectively, in HepG2 cells (45). In the lung adenocarcinoma dataset, some mevalonate pathway genes displayed significant correlation with SREBP1 and SREBP2, but not SP1 (data not shown), suggesting possible coordinated regulation of such genes by NFY and SREBPs in cancer cells.

The regulation of cell cycle and DNA metabolism genes by NFY is also well documented. Expression of a dominant-negative NFY subunit significantly decreased the number of cells entering the S-phase and delayed the progress of this phase, resulting in retarded cell growth (46). NFY seems be involved in induction of S-phase-specific transcription, such as that resulting in synthesis of ribonucleotide reductase R2 (47), histone H3 (48), and cyclin B1 (49). NFY also mediates genotoxic stress-induced gene expression in a p53-independent manner (50), and suppresses gene expression in the presence of active p53 (51), suggesting a functional dependency on co-regulators. Therefore, it was important to define proteins interacting with NFY in the lung cancer cells of the present study. Combined analysis of gene expression correlation and protein-protein interaction identified several ‘hub’ genes which displayed high connectivity with NFY-correlated genes and other hub genes. Importantly, many of the hub genes have been associated with lung cancer. These include BRCA1 (52,53), PCNA (54,55), PLK1 (56,57), MCM2 (58), CHEK2 (59,60), CDK2 (61) and MCM7 (62), suggesting that the network discovered here is likely to be involved in progression of lung cancer. As some such genes were also sensitive to inhibition of the mevalonate pathway [BRCA1 (63), PCNA (64), MCM2 (65), CDK2 (66) and MCM7 (67)], hub genes may also be involved in the antitumor effects of pathway inhibitors in lung cancer. These hub genes do not have direct links to NFY-correlated genes and, although functional association with NFY has been experimentally shown for BRCA1 (68), CDK2 (49,69) and EP300 (70), other hub genes likely interact with NFY through intermediate genes, the expression of which was found to be correlated with that of NFY.

Finally, the results presented in this article have several important clinical implications for the treatment of lung adenocarcinoma. First, the data support the importance of lipid metabolic pathway inhibition in adenocarcinoma patients, particularly in those insensitive to anti-EGFR therapy or patients who have developed resistance to such therapy. The effects of chemotherapy may be enhanced by downregulating genes related to cell division. Some of the hub genes identified in this article are already known as lung cancer markers, but exploration of the activity of combinations of such genes should better indicate the parts of the network that are active or inactive in cancer cells, thus possibly increasing therapeutic predictive power. Finally, drugs targeting NFY may be useful to improve the efficacy of other chemotherapeutic agents, by blocking multiple pathways related to lung carcinogenesis. The roles played by NFY in a variety of cancers have been highlighted in recent reports (71,72), and I believe that a new therapeutic strategy based on inhibition of NFY warrants further research and development.


This study was funded by AstraZeneca, UK.

1. Stinchcombe TE,Socinski MA. Current treatments for advanced stage non-small cell lung cancerProc Am Thorac Soc6233241Year: 200919349493
2. Mitsudomi T,Yatabe Y. Mutations of the epidermal growth factor receptor gene and related genes as determinants of epidermal growth factor receptor tyrosine kinase inhibitors sensitivity in lung cancerCancer Sci9818171824Year: 200717888036
3. Bean J,Brennan C,Shih JY,Riely G,Viale A,Wang L,et al. MET amplification occurs with or without T790M mutations in EGFR mutant lung tumors with acquired resistance to gefitinib or erlotinibProc Natl Acad Sci USA1042093220937Year: 200718093943
4. Yano S,Wang W,Li Q,Matsumoto K,Sakurama H,Nakamura T,et al. Hepatocyte growth factor induces gefitinib resistance of lung adenocarcinoma with epidermal growth factor receptor-activating mutationsCancer Res6894799487Year: 200819010923
5. Farwell WR,Scranton RE,Lawler EV,Lew RA,Brophy MT,Fiore LD,et al. The association between statins and cancer incidence in a veterans populationJ Natl Cancer Inst100134139Year: 200818182618
6. Khurana V,Bejjanki HR,Caldito G,Owens MW. Statins reduce the risk of lung cancer in humans: a large case-control study of us veteransChest13112821288Year: 200717494779
7. Taylor ML,Wells BJ,Smolak MJ. Statins and cancer: a meta-analysis of case-control studiesEur J Cancer Prev17259268Year: 200818414198
8. Haukka J,Sankila R,Klaukka T,Lonnqvist J,Niskanen L,Tanskanen A,et al. Incidence of cancer and statin usage-record linkage studyInt J Cancer126279284Year: 201019739258
9. Mantha AJ,McFee KE,Niknejad N,Goss G,Lorimer IA,Dimitroulakos J. Epidermal growth factor receptor-targeted therapy potentiates lovastatin-induced apoptosis in head and neck squamous cell carcinoma cellsJ Cancer Res Clin Oncol129631641Year: 200312942316
10. Mantha AJ,Hanson JE,Goss G,Lagarde AE,Lorimer IA,Dimitroulakos J. Targeting the mevalonate pathway inhibits the function of the epidermal growth factor receptorClin Cancer Res1123982407Year: 200515788691
11. Kusama T,Mukai M,Iwasaki T,Tatsuta M,Matsumoto Y,Akedo H,et al. Inhibition of epidermal growth factor-induced RhoA translocation and invasion of human pancreatic cancer cells by 3-hydroxy-3-methylglutaryl-coenzyme a reductase inhibitorsCancer Res6148854891Year: 200111406567
12. Park IH,Kim JY,Jung JI,Han JY. Lovastatin overcomes gefitinib resistance in human non-small cell lung cancer cells with K-Ras mutationsInvest New Drugs28791799Year: 201019760159
13. Buhaescu I,Izzedine H. Mevalonate pathway: a review of clinical and therapeutical implicationsClin Biochem40575584Year: 200717467679
14. Pike LJ,Casey L. Cholesterol levels modulate EGF receptor-mediated signaling by altering receptor function and traffickingBiochemistry411031510322Year: 200212162747
15. Ringerike T,Blystad FD,Levy FO,Madshus IH,Stang E. Cholesterol is important in control of EGF receptor kinase activity but EGF receptors are not concentrated in caveolaeJ Cell Sci11513311340Year: 200211884532
16. Parmigiani G,Garrett-Mayer E,Anbazhagan R,Gabrielson E. A cross-study comparison of gene expression studies for the molecular classification of lung cancerClin Cancer Res1029222927Year: 200415131026
17. Wigle DA,Jurisica I,Radulovich N,Pintilie M,Rossant J,Liu N,et al. Molecular profiling of non-small cell lung cancer and correlation with disease-free survivalCancer Res6230053008Year: 200212036904
18. Rhodes DR,Yu J,Shanker K,Deshpande N,Varambally R,Ghosh D,et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progressionProc Natl Acad Sci USA10193099314Year: 200415184677
19. Landi MT,Dracheva T,Rotunno M,Figueroa JD,Liu H,Dasgupta A,et al. Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survivalPLoS One3e1651Year: 200818297132
20. Barrett T,Troup DB,Wilhite SE,Ledoux P,Rudnev D,Evangelista C,et al. NCBI GEO: archive for high-throughput functional genomic dataNucleic Acids Res37D885D890Year: 200918940857
21. Dennis G,Sherman BT,Hosack DA,Yang J,Gao W,Lane HC,et al. DAVID: Database for annotation, visualization, and integrated discoveryGenome Biol4P3Year: 200312734009
22. Huang DW,Sherman BT,Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resourcesNat Protoc44457Year: 200919131956
23. Rhee SY,Wood V,Dolinski K,Draghici S. Use and misuse of the gene ontology annotationsNat Rev Genet9509515Year: 200818475267
24. Benjamini Y,Drai D,Elmer G,Kafkafi N,Golani I. Controlling the false discovery rate in behavior genetics researchBehav Brain Res125279284Year: 200111682119
25. Berger SI,Posner JM,Ma’ayan A. Genes2networks: connecting lists of gene symbols using mammalian protein interactions databasesBMC Bioinformatics8372Year: 200717916244
26. Watson PM,Miller SW,Fraig M,Cole DJ,Watson DK,Boylan AM. CaSm (LSm-1) overexpression in lung cancer and mesothelioma is required for transformed phenotypesAm J Respir Cell Mol Biol38671678Year: 200818218995
27. Casabiell X,Zugaza JL,Pombo CM,Pandiella A,Casanueva FF. Oleic acid blocks epidermal growth factor-activated early intracellular signals without altering the ensuing mitogenic responseExp Cell Res205365373Year: 19938482341
28. McKenzie KE,Bandyopadhyay GK,Imagawa W,Sun K,Nandi S. Omega-3 and omega-6 fatty acids and PGE2 stimulate the growth of normal but not tumor mouse mammary epithelial cells: evidence for alterations in the signaling pathways in tumor cellsProstaglandins Leukot Essent Fatty Acids51437443Year: 19947535935
29. Mollerup S,Haugen A. Differential effect of polyunsaturated fatty acids on cell proliferation during human epithelial in vitro carcinogenesis: involvement of epidermal growth factor receptor tyrosine kinaseBr J Cancer74613618Year: 19968761379
30. Bennis F,Favre G,Gaillard FL,Soula G. Importance of mevalonate-derived products in the control of HMG-CoA reductase activity and growth of human lung adenocarcinoma cell line A549Int J Cancer55640645Year: 19938406993
31. Fukuchi J,Song C,Ko AL,Liao S. Transcriptional regulation of farnesyl pyrophosphate synthase by liver X receptorsSteroids68685691Year: 200312957674
32. Forman BM,Ruan B,Chen J,Schroepfer GJ,Evans RM. The orphan nuclear receptor LXRalpha is positively and negatively regulated by distinct products of mevalonate metabolismProc Natl Acad Sci USA941058810593Year: 19979380679
33. Terano T,Shiina T,Noguchi Y,Tanaka T,Tatsuno I,Saito Y,et al. Geranylgeranylpyrophosphate plays a key role for the G1 to S transition in vascular smooth muscle cellsJ Atheroscler Thromb516Year: 199810077451
34. Ashar HR,James L,Gray K,Carr D,Black S,Armstrong L,et al. Farnesyl transferase inhibitors block the farnesylation of CENP-E and CENP-F and alter the association of CENP-E with the microtubulesJ Biol Chem2753045130457Year: 200010852915
35. Shamma A,Takegami Y,Miki T,Kitajima S,Noda M,Obara T,et al. Rb regulates DNA damage response and cellular senescence through E2F-dependent suppression of N-ras isoprenylationCancer Cell15255269Year: 200919345325
36. Kasayama S,Koga M,Kouhara H,Sumitani S,Wada K,Kishimoto T,et al. Unsaturated fatty acids are required for continuous proliferation of transformed androgen-dependent cells by fibroblast growth factor family proteinsCancer Res5464416445Year: 19947987840
37. Renard CB,Askari B,Suzuki LA,Kramer F,Bornfeldt KE. Oleate, not ligands of the receptor for advanced glycation end-products, promotes proliferation of human arterial smooth muscle cellsDiabetologia4616761687Year: 200314595542
38. Graber MN,Alfonso A,Gill DL. Recovery of Ca2+ pools and growth in Ca2+ pool-depleted cells is mediated by specific epoxyeicosatrienoic acids derived from arachidonic acidJ Biol Chem2722954629553Year: 19979368016
39. Yun MR,Lee JY,Park HS,Heo HJ,Park JY,Bae SS,et al. Oleic acid enhances vascular smooth muscle cell proliferation via phosphatidylinositol 3-kinase/AKT signaling pathwayPharmacol Res5497102Year: 200616621593
40. Niki E. Lipid peroxidation: physiological levels and dual biological effectsFree Radic Biol Med47469484Year: 200919500666
41. Trombetta A,Maggiora M,Martinasso G,Cotogni P,Canuto RA,Muzio G. Arachidonic and docosahexaenoic acids reduce the growth of A549 human lung-tumor cells increasing lipid peroxidation and PPARsChem Biol Interact165239250Year: 200717275799
42. Maehle L,Lystad E,Eilertsen E,Einarsdottr E,Hstmark AT,Haugen A. Growth of human lung adenocarcinoma in nude mice is influenced by various types of dietary fat and vitamin EAnticancer Res1916491655Year: 199910470096
43. Matuoka K,Chen KY. Transcriptional regulation of cellular ageing by the CCAAT box-binding factor CBF/NF-YAgeing Res Rev1639651Year: 200212362892
44. Clarke SD. Polyunsaturated fatty acid regulation of gene transcription: a molecular mechanism to improve the metabolic syndromeJ Nutr13111291132Year: 200111285313
45. Reed BD,Charos AE,Szekely AM,Weissman SM,Snyder M. Genome-wide occupancy of SREBP1 and its partners NFY and SP1 reveals novel functional roles and combinatorial regulation of distinct classes of genesPLoS Genet4e1000133Year: 200818654640
46. Hu Q,Maity SN. Stable expression of a dominant negative mutant of CCAAT binding factor/NF-Y in mouse fibroblast cells resulting in retardation of cell growth and inhibition of transcription of various cellular genesJ Biol Chem27544354444Year: 200010660616
47. Chabes AL,Bjrklund S,Thelander L. S phase-specific transcription of the mouse ribonucleotide reductase R2 gene requires both a proximal repressive E2F-binding site and an upstream promoter activating regionJ Biol Chem2791079610807Year: 200414688249
48. Koessler H,Kahle J,Bode C,Doenecke D,Albig W. Human replication-dependent histone H3 genes are activated by a tandemly arranged pair of two CCAAT boxesBiochem J384317326Year: 200415320874
49. Katula KS,Wright KL,Paul H,Surman DR,Nuckolls FJ,Smith JW,et al. Cyclin-dependent kinase activation and S-phase induction of the cyclin B1 gene are linked through the CCAAT elementsCell Growth Differ8811820Year: 19979218875
50. Jin S,Fan F,Fan W,Zhao H,Tong T,Blanck P,et al. Transcription factors OCT-1 and NF-YA regulate the p53-independent induction of the GADD45 following DNA damageOncogene2026832690Year: 200111420680
51. Manni I,Mazzaro G,Gurtner A,Mantovani R,Haugwitz U,Krause K,et al. NF-Y mediates the transcriptional inhibition of the cyclin B1, cyclin B2, and cdc25C promoters upon induced G2 arrestJ Biol Chem27655705576Year: 200111096075
52. Kim HT,Lee JE,Shin ES,Yoo YK,Cho JH,Yun MH,et al. Effect of BRCA1 haplotype on survival of non-small-cell lung cancer patients treated with platinum-based chemotherapyJ Clin Oncol2659725979Year: 200819018088
53. Boukovinas I,Papadaki C,Mendez P,Taron M,Mavroudis D,Koutsopoulos A,et al. Tumor BRCA1, RRM1 and RRM2 mRNA expression levels and clinical response to first-line gemcitabine plus docetaxel in non-small-cell lung cancer patientsPLoS One3e3695Year: 200819002265
54. Volm M,Koomgi R. Relevance of proliferative and proapoptotic factors in non-small-cell lung cancer for patient survivalBr J Cancer8217471754Year: 200010817513
55. Oyama T,Osaki T,Nose N,Ichiki Y,Inoue M,Imoto H,et al. Evaluations of p53 immunoreactivity, nucleolar organizer regions, and proliferating cell nuclear antigen in non-small cell lung carcinomaAnticancer Res20505510Year: 200010769714
56. Zhou Q,Su Y,Bai M. Effect of antisense RNA targeting polo-like kinase 1 on cell growth in A549 lung cancer cellsJ Huazhong Univ Sci Technolog Med Sci282226Year: 200818278450
57. Spnkuch-Schmitt B,Wolf G,Solbach C,Loibl S,Knecht R,Stegmller M,et al. Downregulation of human polo-like kinase activity by antisense oligonucleotides induces growth inhibition in cancer cellsOncogene2131623171Year: 200212082631
58. Tan DF,Huberman JA,Hyland A,Loewen GM,Brooks JS,Beck AF,et al. MCM2-a promising marker for premalignant lesions of the lung: a cohort studyBMC Cancer16Year: 200111472637
59. Thompson D,Seal S,Schutte M,McGuffog L,Barfoot R,Renwick A,et al. A multicenter study of cancer incidence in CHEK2 1100delC mutation carriersCancer Epidemiol Biomarkers Prev1525422545Year: 200617164383
60. Cybulski C,Masojc B,Oszutowska D,Jaworowska E,Grodzki T,Waloszczyk P,et al. Constitutional CHEK2 mutations are associated with a decreased risk of lung and laryngeal cancersCarcinogenesis29762765Year: 200818281249
61. Volm M,Koomgi R,Rittgen W. Clinical implications of cyclins, cyclin-dependent kinases, RB and E2F1 in squamous-cell lung carcinomaInt J Cancer79294299Year: 19989645354
62. Fujioka S,Shomori K,Nishihara K,Yamaga K,Nosaka K,Araki K,et al. Expression of minichromosome maintenance 7 (MCM7) in small lung adenocarcinomas (pT1): prognostic implicationLung Cancer65223229Year: 200919144445
63. Neville-Webbe HL,Evans CA,Coleman RE,Holen I. Mechanisms of the synergistic interaction between the bisphosphonate zoledronic acid and the chemotherapy agent paclitaxel in breast cancer cells in vitroTumour Biol2792103Year: 200616582586
64. Gunning WT,Kramer PM,Lubet RA,Steele VE,End DW,Wouters W,Pereira MA. Chemoprevention of benzo(a)pyrene-induced lung tumors in mice by the farnesyltransferase inhibitor R115777Clin Cancer Res919271930Year: 200312738751
65. Morgan C,Lewis PD,Jones RM,Bertelli G,Thomas GA,Leonard RC. The in vitro anti-tumour activity of zoledronic acid and docetaxel at clinically achievable concentrations in prostate cancerActa Oncol46669677Year: 200717562444
66. Doisneau-Sixou SF,Cestac P,Faye JC,Favre G,Sutherland RL. Additive effects of tamoxifen and the farnesyl transferase inhibitor FTI-277 on inhibition of MCM-7 breast cancer cell-cycle progressionInt J Cancer106789798Year: 200312866041
67. Bruemmer D,Yin F,Liu J,Kiyono T,Fleck E,Herle AV,et al. Atorvastatin inhibits expression of minichromosome maintenance proteins in vascular smooth muscle cellsEur J Pharmacol4621523Year: 200312591091
68. Fan W,Jin S,Tong T,Zhao H,Fan F,Antinore MJ,et al. BRCA1 regulates GADD45 through its interactions with the OCT-1 and CAAT motifsJ Biol Chem27780618067Year: 200211777930
69. Chae HD,Yun J,Bang YJ,Shin DY. Cdk2-dependent phosphorylation of the NF-Y transcription factor is essential for the expression of the cell cycle-regulatory genes and cell cycle G1/S and G2/M transitionsOncogene2340844088Year: 200415064732
70. Salsi V,Caretti G,Wasner M,Reinhard W,Haugwitz U,Engeland K,et al. Interactions between P300 and multiple NF-Y trimers govern cyclin b2 promoter functionJ Biol Chem27866426650Year: 200312482752
71. Goodarzi H,Elemento O,Tavazoie S. Revealing global regulatory perturbations across human cancersMol Cell36900911Year: 200920005852
72. Yamanaka K,Mizuarai S,Eguchi T,Itadani H,Hirai H,Kotani H. Expression levels of NF-Y target genes changed by CDKN1B correlate with clinical prognosis in multiple cancersGenomics94219227Year: 200919559782

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Keywords: lung cancer, microarray, lipid metabolism.

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