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BMC Cancer

Open Access

Cyclin D1 as a therapeutic target of renal cell carcinoma- a combined transcriptomics, tissue microarray and molecular docking study from the Kingdom of Saudi Arabia

  • Sajjad Karim1Email author,
  • Jaudah A. Al-Maghrabi2, 3,
  • Hasan M. A. Farsi4,
  • Ahmad J. Al-Sayyad4,
  • Hans-Juergen Schulten1,
  • Abdelbaset Buhmeida1,
  • Zeenat Mirza5,
  • Alaa A. Al-boogmi1,
  • Fai T. Ashgan1,
  • Manal M. Shabaad1,
  • Hend F. NourEldin1,
  • Khalid B. M. Al-Ghamdi6,
  • Adel Abuzenadah1, 7,
  • Adeel G. A. Chaudhary1 and
  • Mohammed H. Al-Qahtani1Email author
BMC Cancer201616(Suppl 2):741

https://doi.org/10.1186/s12885-016-2775-2

Published: 30 September 2016

Abstract

Background

Renal cell carcinoma (RCC) is a seventh ranked malignancy with poor prognosis. RCC is lethal at metastatic stage as it does not respond to conventional systemic treatments, and there is an urgent need to find out promising novel biomarkers for effective treatment. The goal of this study was to evaluate the biomarkers that can be potential therapeutic target and predict effective inhibitors to treat the metastatic stage of RCC.

Methods

We conducted transcriptomic profiling to identify differentially expressed genes associated with RCC. Molecular pathway analysis was done to identify the canonical pathways and their role in RCC. Tissue microarrays (TMA) based immunohistochemical stains were used to validate the protein expression of cyclinD1 (CCND1) and were scored semi-quantitatively from 0 to 3+ on the basis of absence or presence of staining intensity in the tumor cell. Statistical analysis determined the association of CCND1 expression with RCC. Molecular docking analyses were performed to check the potential of two natural inhibitors, rutin and curcumin to bind CCND1.

Results

We detected 1490 significantly expressed genes (1034, upregulated and 456, downregulated) in RCC using cutoff fold change 2 and p value < 0.05. Hes-related family bHLH transcription factor with YRPW motif 1 (HEY1), neuropilin 2 (NRP2), lymphoid enhancer-binding factor 1 (LEF1), and histone cluster 1 H3h (HIST1H3H) were most upregulated while aldolase B, fructose-bisphosphate (ALDOB), solute carrier family 12 (SLC12A1), calbindin 1 (CALB1) were the most down regulated genes in our dataset. Functional analysis revealed Wnt/β-catenin signaling as the significantly activated canonical pathway (z score = 2.53) involving cyclin D1 (CCND1). CCND1 was overexpressed in transcriptomic studies (FC = 2.26, p value = 0.0047) and TMA results also showed the positive expression of CCND1 in 53 % (73/139) of RCC cases. The ligands – rutin and curcumin bounded with CCND1 with good affinity.

Conclusion

CCND1 was one of the important upregulated gene identified in microarray and validated by TMA. Docking study showed that CCND1 may act as a potential therapeutic target and its inhibition could focus on the migratory, invasive, and metastatic potential of RCC. Further in vivo and in vitro molecular studies are needed to investigate the therapeutic target potential of CCND1 for RCC treatment.

Keywords

Renal cell carcinomaCyclin D1Gene expression profilingTissue microarrayMolecular dockingTherapeutic targetSaudi Arabia

Background

Renal Cell carcinoma (RCC) is a major health problem and accounts for approximately 1.5 percent of all cancer deaths [1, 2]. It accounts for about 3 % of all cancers and 2-3 % per year increase in global incidence [1, 3]. For RCC treatment, surgery is the best option at advance stage, however, one third of patients develop metastases even after surgery [4]. At metastatic stage, prognosis is very poor because RCC patients hardly respond to conventional existing systemic treatments and leads to death [5]. RCC treatment is a big challenge without identification of new drug targets and effective remedies. Although previous studies have reported role of gene alterations, their expression and deregulation of molecular signals to be linked with cancer initiation and progression, there still lack of curative therapy for RCC [68]. Therefore, identification of a potential drug target and prediction of suitable ligand is crucial for the patients with RCC.

The cyclin D members (D1, D2 and D3) bind to CDKs and are required for the hematopoietic cells proliferation and survival and perform a rate-limiting antiapoptotic function in vivo [9]. Cyclin D1 (CCND1) overexpression is predominantly correlated with early cancer onset, tumor progression, shorter cancer patient survival and increased metastases [1012]. Induction of vascular endothelial growth factor (VEGF) production by CCND1 promotes oncogenesis by increasing growth and angiogenesis, while downregulation of death receptor, Fas by CCND1 causes chemotherapeutic and apoptosis resistance [13]. Overexpression of CCND1 has been previously reported in many cancers including lung cancers [14], esophageal squamous cell carcinoma [15], head and neck cancer [16], pancreatic cancer [17], pituitary cancer [18], and breast cancer [19].

CCND1 is a proto-oncogene and a good biomarker for tumor progression, found to be deregulated in several cancers, including RCC. CCND1 along with associated cyclins activates cyclin-dependent kinases (CDKs) - CDK4 and CDK6. G1-S phase transition during cell cycle, requires phosphorylation of retinoblastoma (Rb) by CDK4 and CDK6. Hyperphosphorylation of Rb allows expression of genes involved in DNA replication and cell division [2023]. The ability of CCND1 to exhibit oncogenic property and to regulate a critical G1-S transition checkpoint by activating CDK4/CDK6, makes it a potential therapeutic target of RCC [2428].

Alternative or synergistic anticancer therapies using natural compounds and their derivatives (polyphenols, flavonoids, alkaloids, saponins, etc.) have been extensively studied [29]. Rutin is a flavonol glycoside found in many plants, including buckwheat; tobacco; asparagus, green tea etc. and contributes to the antibacterial [30], hepatoprotective [31], neuroprotective [32] and antioxidant [33] properties of the plant. It is structurally very similar to quercitrin and has been used therapeutically to decrease capillary fragility, to protect blood capillaries, and as ingredients of multivitamin nutritional supplements and alternative herbal remedies. It can attach to iron ion, thereby averting its binding to H2O2 and free radical generation. In addition, rutin acts as an angiogenesis inhibitor and can stall the VEGF in vitro; also has potential anticancerous and antiproliferative property [34, 35].

Curcumin commonly known as turmeric is a phytopolylphenol pigment isolated from the plant Curcuma longa, and possesses a variety of pharmacologic properties like anti-inflammatory, antineoplastic, antiproliferative, anticancer, apoptosis inducer, chempreventive [36, 37]. It can inhibit the reactive-oxygen species formation, cyclooxygenases (COX) and other metabolic enzymes involved in inflammation; and can disrupt cell signal transduction via inhibition of protein kinase C. It can interact with myriad of biomolecules by covalent and non-covalent binding. The H-bonding and hydrophobic interactions, arising from the aromatic and tautomeric structures in addition to the flexible linker group owe for the non-covalent interactions [38]. Curcumin reportedly suppress cyclin D1 expression by promoting proteolysis and down-regulating its expression and causes inhibition of CDK4-mediated phosphorylation of retinoblastoma protein [39]. It has been reported that curcumin-treated cells show decreased expression of CCND1, resulting in low cell growth rate. This curcumin-induced CCND1 mRNA down-regulation is perhaps mediated by induction of BTG2 as well as inhibition of nuclear translocation of NF-kappaB [40].

In this study, expression profiling of RCC (CEGMR data) identified 1490 significantly differentially expressed genes and molecular pathway analysis predicted alteration in many important cancer related pathways. However, the major finding of this study was identification and tissue microarray based validation of CCND1 as important over-expressed gene/proteins of RCC. Overexpression of CCND1 can trigger cancer by activating many pathways, including Wnt/β-catenin signaling pathway and has been shown to exhibit oncogenic property, making it a potential therapeutic target. We, therefore, attempted docking study to show the therapeutic potential of anticancerous natural ligands (rutin and curcumin) against the identified potential drug target (CCND1).

Methods

Patients and samples

The study was executed on RCC patients from Saudi Arabia and resected tissue samples were collected from collaborating hospitals of Jeddah during the period 2010–2014. For gene expression analysis, fresh surgically resected tumor and normal tissue were collected and stored in RNALater (Invitrogen/Life Technologies, NY, USA) till RNA extraction. All patients included in the present study were Saudi in origin and diagnosed with clear cell or chromophobe renal cell carcinoma without any prior chemotherapy or radiotherapy exposure.

Ethical approval

Local ethical committee has approved this study (08-CEGMR-02-ETH). Patients were included in the present study only after their prior consent.

RNA extraction and array processing

Qiagen RNeasy Mini Kit (Qiagen, Hilden, Germany) was used to extract total RNA from fresh kidney tissue, Nano Drop 1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) was used for concentration determination and RNA quality was checked with Bioanalyzer (Agilent Technologies, CA, USA). Out of 20 specimen, only 7 tumor and 5 control samples passed the selection criteria of RNA integrity number (RIN) >5 and were judged fit to be used for array expression analysis. We used Human Gene 1.0 ST GeneChip arrays (Affymetrix, Santa Clara, CA, USA) for transcriptomics studies (Life Technology, Grand Island, NY), interrogating 764,885 probes and 36,079 annotated reference sequences (NCBI build 36). We processed 250 ng RNA of 12 samples using the Ambion WT Expression Kit (Life Technologies, Austin, TX), GeneChip Hybridization, Wash and Stain Kit (Affymetrix, Santa Clara, CA) and GeneChip WT Terminal Labeling and Controls Kit (Affymetrix, Santa Clara, CA). The hybridization of 5500 ng of cDNA was done in a hybridization oven at 45 °C under rotation (60 rpm) for 17 h. After complete processing, the arrays were scanned in the GeneChip Scanner 3000 7G and GeneChip Command Console Software (AGCC) were used to generate probe cell intensity data (CEL files).

Gene expression analysis

We carried transcriptomic profiling of 12 samples, seven RCC and five normal kidney tissues. To gain confidence with our limited number samples, we performed a comparative analysis with independent expression datasets from NCBI’s GEO database (GSE781, n = 34; GSE7023, n = 47; and GSE6344, n = 40) for confirmation. Affymetrix. CEL files were imported and analyzed using Partek Genomics Suite version 6.6 (Partek Inc., MO, USA). Default settings robust multi-chip averaged (RMA) was used to log-transform data set and for normalization. Analysis of Variance (ANOVA) was applied, and differentially expressed genes (DEGs) were identified with cut off fold change > 2 and p value <0.05. Principal component analysis (PCA) was performed to assess overall expression pattern among sample groups, similar samples were grouped together.

Tissue microarray and immunohistochemistry

Tissue microarrays (TMA) were designed and constructed for 139 primary RCC and 34 normal kidney tissue as previously described [41]. Experienced pathologist reviewed hematoxylin and eosin (HE) slides of RCC and normal kidney tissue. 1.5 mm tissue cores from areas of interest were chosen from donor block(s) and transferred to recipient paraffin block of TMA Master 1.14 SP3 (3D Histech Ltd, Budapest, Hungary). HE staining of TMA slides was repeated to assess basic morphology of slide construction.

Immunohistochemical studies were performed on positive-charged leica plus slides (Leica Microsystems, Wetzler, Germany) mounted with 4 μm of TMA paraffin blocks. Deparaffinisation of sections was done using xylene, followed by rehydration in an automated BenchMark XT immunostainer (Ventana® Medical systems Inc., Tucson, AZ, USA) and pretreatment in prediluted cell conditioning 1 (CC1) solution for an hour. Immunostaining of TMA slides was done by incubating anti-CCND1 antibody at 37 °C for 16 min, followed by washing, counterstaining (with Mayer’s hematoxylin) and mounting using Ventana® Ultraview Universal DAB detection kit. For analysis and interpretation both negative (with tris-buffered saline only) and positive (with primary antibody) control slides were used. Sections were evaluated independently by the pathologist without knowing the clinicopathological characteristics of RCC patients. Immunostainings were scored semiquantitatively from 0 to 4 + .

Functional and pathway analysis

We performed pathway analyses and Gene ontology (GO) studies for differentially regulated genes in RCC to find associated biological networks and molecular processes, using Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems, Redwood City, CA). Significantly expressed genes with Affymetrix ID, expression level and p-value were uploaded into IPA software to identify the most significant altered biological functions and networks. Fisher’s exact test was used to calculate the significance of association between trancriptomic data and canonical pathways of RCC.

Molecular docking studies

The 3-D crystal structure of cyclin D1 was retrieved from RCSB’s Protein Data Bank (PDB) – PDB id: 2w96: Chain A. Structure visualization and illustration was done using PyMol (DeLano Scientific) (Fig. 1). The molecular structure of rutin and curcumin were retrieved from NCBI’s PubChem compound database with CID 5280805 and 969516 respectively (Fig. 2).
Fig. 1

Molecular structure of rutin and curcumin retrieved from NCBI’s PubChem compound database with CID 5280805 and 969516

Fig. 2

Overall cyclin D1 structure depicted as ribbon diagram (PDB: 2 W96)

Molecular docking was performed using Molecular Docking Server on [42]. The MMFF94 force field geometry optimization method was used for energy minimization of ligand molecule: rutin and curcumin using DockingServer. Gasteiger partial charges were added to the ligand atoms at pH 7.0. Non-polar hydrogen atoms were merged, and rotatable bonds were defined. Rest methodology was followed in sequential manner as previously described [2, 6, 43].

Supporting data availability

Data series (Accession No. GSE781, GSE7023, GSE6344) used in present study are available at NCBI’s Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/).

Results

This study focused on utilizing transcriptomic profiling to identify biomarkers associated with RCC and conducting molecular docking analysis to assess the interactions between potential target and drugs. We identified CCND1 as important overexpressed gene/proteins of RCC and demonstrated its potential as possible anticancer drug target.

Identification of differentially expressed genes

Three-dimensional scatter plot of PCA demonstrated that RCC and control tissues are distinctly clustered (Fig. 3). We did genome-wide transcription profiling of fresh RCC specimens and identified 1490 differentially expressed genes; 1034 up-regulated and 456 down-regulated using unadjusted p value < 0.05 (Additional file 1). Number of differentially expressed genes reduced to 141 (22 up-regulated and 119 down regulated) on applying the stringent condition of false discovery rate with p value < 0.05 while keeping all other above parameter same (Fig. 4, Table 1). Hes-related family bHLH transcription factor with YRPW motif 1 (HEY1), neuropilin 2 (NRP2), lymphoid enhancer-binding factor 1 (LEF1), and histone cluster 1 H3h (HIST1H3H) were the most upregulated ones while aldolase B, fructose-bisphosphate (ALDOB), solute carrier family 12 (SLC12A1), calbindin 1 (CALB1) were the most down regulated genes in our dataset. We compared our identified differentially expressed genes list with re-analyzed GEO data series (GSE781, GSE6344 and GSE7023) and identified over-expression of CCND1 in all dataset, thus supporting our result (Table 2).
Fig. 3

Scatter plot of PCA show grouping of similar type based on genome-wide expression values, as represented as eclipse, where each ball represents one sample. Blue and red is representing RCC and normal kidney tissue

Fig. 4

Hierarchical clustering and functional analysis of significantly differentially expressed genes in kidney cancer using Affymetrix Human ST 1.0 array and Partek Genomics suite (ver 6.6)

Table 1

Differentially expressed significant genes in RCC

Gene symbol

Gene name

RefSeq

p-value

Fold-change

HEY1

hes-related family bHLH transcription factor with YRPW motif 1

NM_001040708

7.88E-06

3.64128

NRP2

neuropilin 2

ENST00000272849

0.00017

3.63215

LEF1

lymphoid enhancer-binding factor 1

NM_001130713

4.04E-05

3.54448

HIST1H3H

histone cluster 1, H3h

NM_003536

3.29E-05

2.87948

ITGAX

integrin, alpha X (complement component 3 receptor 4 subunit)

NM_000887

3.23E-05

2.69367

BUB1

BUB1 mitotic checkpoint serine

NM_001278616

3.35E-05

2.62163

MAP3K7CL

MAP3K7 C-terminal like

NM_001286620

2.81E-05

2.54523

FXYD5

FXYD domain containing ion transport regulator 5

NM_001164605

0.000158

2.45618

HIST1H2AI

histone cluster 1, H2ai

NM_003509

0.000108

2.27918

CENPK

centromere protein K

NM_001267038

6.01E-05

2.27502

CCND1

cyclin D1

NM_053056

0.004789

2.25898

DDX11

DEAD

NM_001257144

0.000178

2.24119

APOBEC3D

apolipoprotein B mRNA editing enzyme, catalytic polypeptide-li

NM_152426

2.61E-06

2.23905

ATAD2

ATPase family, AAA domain containing 2

NM_014109

0.000129

2.18905

HIST1H3F

histone cluster 1, H3f

NM_021018

0.00016

2.14196

TREM2

triggering receptor expressed on myeloid cells 2

NM_018965

0.00021

2.13092

GAL3ST4

galactose-3-O-sulfotransferase 4

NM_024637

1.50E-05

2.11736

LOC400464

uncharacterized LOC400464

AK127420

9.33E-05

2.11281

CXCL11

chemokine (C-X-C motif) ligand 11

NM_005409

0.0002

2.07422

NEIL3

nei endonuclease VIII-like 3 (E. coli)

NM_018248

2.73E-05

2.06874

EVL

Enah

ENST00000553771

5.57E-05

2.06506

SLFN12L

schlafen family member 12-like

ENST00000361112

9.07E-05

2.02832

ALDH4A1

aldehyde dehydrogenase 4 family, member A1

NM_001161504

4.94E-07

−16.1809

SLC22A12

solute carrier family 22 (organic anion

NM_001276326

9.50E-06

−16.4997

SLC47A2

solute carrier family 47 (multidrug and toxin extrusion), me

NM_001099646

5.60E-06

−17.2214

HAO2

hydroxyacid oxidase 2 (long chain)

NM_001005783

0.000192

−17.3322

SLC6A19

solute carrier family 6 (neutral amino acid transporter), me

NM_001003841

0.000195

−18.4585

XPNPEP2

X-prolyl aminopeptidase (aminopeptidase P) 2, membrane-bound

NM_003399

0.000131

−18.9386

CYP4A11

cytochrome P450, family 4, subfamily A, polypeptide 11

XR_246241

7.59E-05

−19.2237

SLC22A6

solute carrier family 22 (organic anion transporter), member 6

NM_004790

5.33E-06

−20.6522

KCNJ1

potassium inwardly-rectifying channel, subfamily J, member 1

NM_000220

8.79E-05

−22.4359

TMEM52B

transmembrane protein 52B

NM_001079815

0.000112

−23.1374

SLC12A3

solute carrier family 12 (sodium

NM_000339

1.14E-06

−27.7638

HPD

4-hydroxyphenylpyruvate dioxygenase

NM_001171993

1.88E-05

−29.7949

SLC5A12

solute carrier family 5 (sodium

XM_006718157

0.000155

−30.9613

KNG1

kininogen 1

NM_000893

4.28E-06

−34.7144

SLC13A3

solute carrier family 13 (sodium-dependent dicarboxylate tra

NM_001011554

1.93E-06

−37.6483

SLC36A2

solute carrier family 36 (proton

NM_181776

4.06E-07

−37.8957

PLG

plasminogen

NM_000301

4.87E-07

−43.0535

SLC22A8

solute carrier family 22 (organic anion transporter), member

NM_001184732

4.50E-06

−45.8474

UMOD

uromodulin

NM_001008389

2.44E-06

−68.9599

CALB1

calbindin 1, 28 kDa

NM_004929

2.29E-06

−78.3947

SLC12A1

solute carrier family 12

ENST00000330289

0.000135

−79.6698

ALDOB

aldolase B, fructose-bisphosphate

NM_000035

2.56E-05

−87.9122

Negative fold change value indicates the downregulation

*bold data shows CCND1 (Cyclin D1) was overexpressed (fold change = 2.258) and statistically significant (p-value = 0.00478)

Table 2

Expression of CCND1 in Saudi RCC patients (CEGMR dataset) and GEO dataset

Dataset

Sample size

P-value

Fold change

CEGMR (own data)

12

0.0047

2.26

GSE781

34

0.0030

2.41

GSE6344

40

1.04 × 10−9

4.82

GSE7023

47

6.55 × 10−5

3.33

Validation of CCND1

Transcriptomic profiling revealed distinct CCND1 overexpression (FC = 2.26, p value = 0.0047). Validation study based on TMA-immunohistochemistry staining showed the positive expression of CCND1 in 53 % (73/139) of RCC cases (Fig. 5).
Fig. 5

Immunohistochemistry stain for CCND1 show positive staining in RCC (original magnification × 60)

Pathways and networks underlying RCC

Pathway analysis of identified DEGs revealed the biofunctions, molecular network and canonical pathways association with RCC (Table 3). Most significantly inhibited pathways were synaptic long term potentiation (z-score = −2.33), NRF2-mediated oxidative stress response (z-score = −2.33), production of nitric oxide and reactive oxygen species in macrophages (z-score = −2.324), and renin-angiotensin signaling (z-score = −2.121). Wnt/β-catenin signaling was significantly activated pathway (z-score = 2.53) involving following genes; cyclin D1 (CCND1, FC = 2.26), CD44 molecule (CD44, FC = 2.31), v-myc avian myelocytomatosis viral oncogene homolog (c-Myc, FC = 2.31), HNF1 homeobox A (TCF1, FC = −2.26), secreted frizzled-related protein 1 (SFRP1, FC = −4.45) (Fig. 6). We found over expression of CCND1 playing important role in regulation of Wnt/β-catenin signaling along with other cancer related pathways like Acute Myeloid Leukemia Signaling, Non-Small Cell Lung Cancer Signaling, PTEN Signaling, Regulation of Cellular Mechanics by Calpain Protease, ErbB2-ErbB3 Signaling, HER-2 Signaling in Breast Cancer, HER-2 Signaling in Breast Cancer, Thyroid Cancer Signaling, Endometrial Cancer Signaling etc. Further extensive molecular pathway analysis may help to better understand the mechanism of RCC initiation, invasion and metastasis.
Table 3

Canonical pathways predicted by Ingenuity Pathway Analysis for significant genes differentially expressed in kidney cancer

Ingenuity canonical pathways

-log (p-value)

z-score

Molecules

Wnt/β-catenin Signaling

0.271

2.530

CSNK1E,MYC,PPP2R4,TGFBR3,CD44,LEF1,SFRP1,UBC,CCND1,HNF1A,ACVR2A,LRP1

Synaptic Long Term Potentiation

0.481

−2.333

PLCB4,PPP1R1A,PPP1R3C,PPP3R1,PRKAR2A,CACNA1C,PLCL1,PLCD4,PRKCZ,PRKCA

NRF2-mediated Oxidative Stress Response

1.5

−2.333

GSTA3,AKR7A2,AKR7A3,GSTM1,GSTM3,NQO2,ABCC2,NQO1,DNAJC19,SOD1,PRKCZ,DNAJC11,AKR1A1,SCARB1,FMO1,GSTA1,AOX1,TXN,PRKCA,EPHX1

Production of Nitric Oxide and Reactive Oxygen Species in Macrophages

0.512

−2.324

PPARA,MAP3K15,APOE,APOM,PPP1R3C,PRKCZ,APOL1,ALB,PPP2R4,CYBA,APOC1,CHUK,APOD,RBP4,PRKCA

Sperm Motility

1.19

−2.309

PLA2G16,SLC16A10,PLCB4,PLA2R1,PRKAR2A,PNPLA3,PLCL1,PLA2G12B,PDE1A,PLCD4,PLA2G7,PRKCZ,PRKCA

Renin-Angiotensin Signaling

0.279

−2.121

ADCY9,GRB2,REN,PRKAR2A,CCL5,PRKCZ,AGT,PRKCA

Nitric Oxide Signaling in the Cardiovascular System

0.27

−1.890

KNG1,CAV1,PRKAR2A,CACNA1C,PDE1A,PRKCZ,PRKCA

Antioxidant Action of Vitamin C

3.01

1.897

PLA2G16,NAPEPLD,PLA2R1,SLC23A3,PLA2G7,GLRX,SLC2A3,PLCB4,SLC23A1,SLC2A2,PNPLA3,CHUK,TXN,PLA2G12B,PLCL1,PLCD4

Aldosterone Signaling in Epithelial Cells

1.34

−1.897

DNAJC12,DNAJC19,PDPK1,HSPD1,HSPA2,PRKCZ,HSPA12A,DNAJC11,PLCB4,SCNN1G,SLC12A1,CRYAA/LOC102724652,SCNN1B,PLCL1,PLCD4,PRKCA,AHCY

Valine Degradation I

10.11

NaN

ECHS1,ABAT,ACADSB,BCKDHB,BCAT1,HIBCH,HIBADH,AUH,DLD,DBT,EHHADH,HADHA,ALDH6A1

Ethanol Degradation II

9.4

NaN

HSD17B10,ADH6,ALDH1B1,ALDH4A1,ACSS1,ALDH9A1,ADH5,ALDH2,AKR1A1,ALDH3A2,ACSS2,ADHFE1,ACSL1,ALDH7A1,DHRS4

Fatty Acid β-oxidation I

9.4

NaN

HSD17B10,ECHS1,SLC27A2,ACAA1,ACAA2,SCP2,ECI2,AUH,ACSL4,IVD,EHHADH,ACADM,HADHA,ACSL1,HADH

FXR/RXR Activation

9.17

NaN

PPARA,KNG1,APOE,PKLR,APOH,ABCC2,SLC22A7,HNF1A,CYP8B1,MTTP,PCK2,SCARB1,SLC10A2,FGFR4,LPL,GC,AGT,APOM,SDC1,UGT2B4,CYP27A1,SERPINF2,APOL1,ALB,FABP6,APOC1,FBP1,G6PC,SLC51B,RBP4,APOD

Serotonin Degradation

8.46

NaN

ADH6,HSD17B10,ALDH4A1,ALDH1B1,UGT3A1,UGT2B4,UGT2B7,UGT1A1,ALDH9A1,ADH5,ALDH2,AKR1A1,SMOX,ALDH3A2,ADHFE1,DHRS4,ALDH7A1,MAOA

Noradrenaline and Adrenaline Degradation

7.86

NaN

ADH6,HSD17B10,ALDH4A1,ALDH1B1,ALDH9A1,ADH5,ALDH2,AKR1A1,SMOX,ALDH3A2,ADHFE1,DHRS4,ALDH7A1,MAOA

Tryptophan Degradation

7.34

NaN

ALDH4A1,ALDH1B1,ALDH2,AKR1A1,SMOX,ALDH3A2,DDC,ALDH9A1,ALDH7A1,MAOA

PXR/RXR Activation

4.04

NaN

PPARA,SCD,GSTM1,ABCB1,ABCC2,PRKAR2A,CES2,HMGCS2,UGT1A1,PCK2,ALDH3A2,GSTA1,G6PC,CYP2B6

Acute Myeloid Leukemia Signaling

0.491

0.816

RUNX1,MYC,GRB2,LEF1,CCND1,HNF1A,IDH1

Non-Small Cell Lung Cancer Signaling

0.481

−0.816

GRB2,PDPK1,EGF,ERBB2,CCND1,PRKCA

PTEN Signaling

0.662

0.905

FGFR3,GRB2,FGFR4,TGFBR3,PREX2,ITGA5,FGFR2,PDPK1,CHUK,CCND1,PRKCZ

Regulation of Cellular Mechanics by Calpain Protease

0.453

−1.000

GRB2,ITGA5,EGF,CCND1,ACTN1

ErbB2-ErbB3 Signaling

0.967

−1.890

MYC,GRB2,NRG3,PDPK1,ERBB3,ERBB2,CCND1

Aryl Hydrocarbon Receptor Signaling

2.07

NaN

GSTA3,GSTM1,ALDH4A1,ALDH1B1,GSTM3,NQO2,NQO1,ALDH8A1,CCND1,ALDH9A1,MYC,ALDH1L1,ALDH1L2,ALDH3A2,GSTA1,ALDH5A1,ALDH6A1,ALDH7A1

HER-2 Signaling in Breast Cancer

1.01

NaN

GRB2,PARD6B,EGF,ERBB3,ITGB8,ERBB2,CCND1,PRKCZ,PRKCA

Thyroid Cancer Signaling

0.828

NaN

CXCL10,MYC,LEF1,CCND1,HNF1A

Endometrial Cancer Signaling

0.765

NaN

MYC,GRB2,PDPK1,LEF1,ERBB2,CCND1

Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis

0.615

NaN

CXCL8,FN1,IL1RL1,CXCL12,CCL5,HNF1A,CCND1,FCGR1A,PRKCZ,C5,MYC,PLCB4,F2RL1,PPP3R1,LEF1,CHUK,SFRP1,PLCL1,PLCD4,FCGR3A/FCGR3B,TNFSF13B,LRP1,PRKCA,ADAMTS4

Estrogen-mediated S-phase Entry

0.278

NaN

MYC,CCND1

*bold data shows presence and importance of CCND1 among identified canonical pathways

Fig. 6

Wnt-β catenin signaling was significantly activated (z-score = 2.53) in RCC based on following differentially expressed genes: cyclin D1 (CCND1, FC = 2.25898), CD44 molecule (CD44, FC = 2.31667), v-myc avian myelocytomatosis viral oncogene homolog (c-Myc, FC = 2.31324), HNF1 homeobox A (TCF1, FC = −2.26661), secreted frizzled-related protein 1 (SFRP1, FC = −4.45838). Red represents overexpression and green underexpression

Docking studies

We made a structural attempt to study possible binding of two natural famed ligands with the potential therapeutic drug target, Cyclin D1 for cancer therapeutics. CCND1 protein has a classical double cyclin box domain fold, comprising of 11 alpha-helices [44].

Molecular docking studies predicted good interactions between three dimensional structure of drug target (CCND1, PDBID: 2w96) and selected ligands; rutin and curcumin. Molecular docking revealed that both the compounds are able to bind in the ligand binding domain. In silico docking studies revealed interaction of two active compounds with the common vital ligand binding site residues (Leu91, Lys149, Asn151) of cyclin D1. Both rutin and curcumin docked at a common ligand binding site of CCND1 slightly varied intensity as estimated by their size, structure, stereochemistry (Figs. 7 and 8; Table 4). We also examined their complete interaction profile including hydrogen bonds, HB plot, polar, hydrophobic, pi-pi and cation-pi interactions. The estimated free energy of binding with Cyclin D1 for rutin was −4.26 kcal/mol and for curcumin was −4.67 kcal/mol which is very similar, however, the estimated inhibition constant (Ki) was 757.57 μM and 380.02 μM respectively.
Fig. 7

Molecular docking conformation and interactions of rutin and curcumin with Cyclin D1

Fig. 8

Two-dimensional plot showing the primary interacting residues of Cyclin D1

Table 4

Docking features and values for rutin and curcumin

Features

RUTIN

CURCUMIN

Est. Free energy of binding

−4.26 kcal/mol

−4.67 kcal/mol

Est. Inhibition Constant, Ki

757.57 μM

380.02 μM

vdW + Hbond + desolv Energy

−5.43 kcal/mol

−6.37 kcal/mol

Electrostatic energy

−0.01 kcal/mol

−0.12 kcal/mol

Total intermolecular energy

−5.44 kcal/mol

−6.49 kcal/mol

Interaction surface

653.668

684.416

Discussion

RCC is a complex heterogeneous tumors involving altered genes and proteins. We performed a transcriptional profiling and functional analysis of RCC to understand the role of identified significant genes in regulation of physiological processes through biological pathways/networks. We found CCND1 as one of the significantly expressed gene and potential biomarker RCC.

HEY1, an upregulated gene, has been reported to be mediator of notch signaling, showing pro-oncogenic function and promotes cancer progression [45, 46]. Neuropilin-2 (Nrp2) is a well known receptor for the vascular endothelial growth factor-C (VEGF-C) and activates lymph nodes as well as promotes tumor metastasis by lymphangiogenesis [47, 48]. LEF1 interacts with β-catenin and plays critical role in proliferation of RCC by activating downstream target genes [49, 50]. Wnt/β-catenin signaling, found activated, regulates embryonic development and is involved in many diseases including cancer, polycystic kidney disease [5154]. WNT signal and its paracrine mode to growth of cancer cells makes it clinically important to understand the metastasis of tumor cells [53, 55, 56]. HIST1H3H is frequently altered chromatin factors in many cancers [57, 58]. Aldolase, a family member of glycolysis enzymes, was found to be significantly affecting RCC. Aldolase-A was reportedly upregulated while aldolase-B was downregulated in RCC and human primary hepatocellular carcinoma [5962]. SLC12 family members are involved in regulation of cell volume, blood pressure and chloride concentration, and play a critical role in diseases like cancer, epilepsy and osteoporosis [63]. In the present study, SLC12 was down regulated that is in accordance to other findings [64]. CALB1 is reported to be altered in RCC and found to be negatively stained compared to normal tissue [61, 65].

CCND1 was overexpressed in our as well as other transcriptomics studies [6669]. We validated CCND1 overexpression by using tissue microarray platform and in silico docking analysis was done to check its therapeutic potential as it plays a key role in G1-S phase transition of cell cycle. There are reports of anti-proliferative, apoptosis inducing and chemopreventive effects of natural bioactive flavonoids like baicalein, catechin, genistein, quercetin, and rutin. Docking analysis showed that rutin and curcumin binds to CCND1 and can potentially inhibit downstream CCND1/CDK4/CDK6 complex formation, required for G1-S phase transition. Our finding demonstrate the anticancer drug targets potential of CCND1 and rutin and curcumin as potential inhibitors, however, this in silico docking study has to be validated further.

Conclusion

Our microarray and immunohistochemistry results suggest significantly high levels of cyclin D1 expression in RCC. Distinct transcriptomic signatures identified for RCC needs verification at larger dataset and additional significant genes need to be further validated for identification of novel biomarkers. The critical role of CCND1 in RCC metastasis by activating G1-S transition of cell cycle has drawn our attention to examine its potential as anticancer drug target. Our in silico docking study shown CCND1 protein as an attractive anticancer target and natural flavanoids rutin and curcumin as potential anticancer drug of RCC and they may be promising in the prevention of kidney cancer too. Quantitative structure-activity relationship studies, ligand binding, efficacy and toxicity should be further investigated before clinical trials. Clinical and therapeutic applications of these natural ligands were initially limited by their low solubility and bioavailability but combination with adjuvant and nano-technology based delivery vehicles can immensely improve their potential. Moreover, these are reported to act in synergism with several other natural compounds or synthetic agents routinely used in chemotherapy and can assist in cancer prevention and treatment when used alone or in combination with other conventional treatments.

Declarations

Acknowledgment

This project and publication was supported by the NSTIP strategic technologies program in the Kingdom of Saudi Arabia – Project No (10-BIO1258-03, 09-BIO1073-03, 08-MED120-03). The authors also acknowledge with thanks Science and Technology Unit, King Abdulaziz University for technical support. Authors would like to acknowledge the Deanship of Scientific Studies, King Abdulaziz University, Jeddah, Saudi Arabia for funding the research (HiCi-1434-117-2). We also thank the patients, physicians, nurses, and pathologists of the King Abdulaziz University Hospital, and King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia.

Declaration

This article has been published as part of BMC Cancer Volume 16 Supplement 2, 2016: Proceedings of the 3rd International Genomic Medicine Conference: cancer. The full contents of the supplement are available online at http://bmccancer.biomedcentral.com/articles/supplements/volume-16-supplement-2.

Availability of data and material

Data series (Accession No. GSE781, GSE7023, GSE6344) used in present study are available atNCBI’s Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/).

Authors’ contributions

SK, JM and HF participated in the study design. HJS, AAA, FA and MS performed data collection, DNA extraction and microarray studies. AB and JM did tissue microarray, immunohistochemistry and pathological studies. SK, ZM and HN analyzed data, interpreted the results and drafted the manuscript. KA, AMA, AC, AS and MHQ participated in critical review, editing and finalization of manuscript. All authors have read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Ethical approval

Local ethical committee has approved this study (08-CEGMR-02-ETH). Patients were included in the present study only after their prior consent.

Ethical approval information is included in the materials and methods section. Local ethical committee has approved this study under the approval number 08-CEGMR-02-ETH.

Consent for publication

Patients’ consent information are included in the material and methods section. Patients were only included in the present study after their prior consent.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Center of Excellence in Genomic Medicine Research, Faculty of Applied Medical Sciences, King Abdulaziz University
(2)
Department of Pathology, King Abdulaziz University
(3)
Department of Pathology, King Faisal Specialist Hospital and Research Center
(4)
Department of Urology, Faculty of Medicine, King Abdulaziz University
(5)
King Fahd Medical Research Center, King Abdulaziz University
(6)
Department of Otorhinolaryngology and Head and Neck Surgery, King Abdulaziz University
(7)
KACST Innovation Center for Personalized Medicine, King Abdulaziz University

References

  1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63(1):11–30.View ArticlePubMedGoogle Scholar
  2. Mirza Z, Schulten HJ, Farsi HM, Al-Maghrabi JA, Gari MA, Chaudhary AG, Abuzenadah AM, Al-Qahtani MH, Karim S. Molecular interaction of a kinase inhibitor midostaurin with anticancer drug targets, S100A8 and EGFR: transcriptional profiling and molecular docking study for kidney cancer therapeutics. PLoS One. 2015;10(3):e0119765.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Bhatt JR, Finelli A. Landmarks in the diagnosis and treatment of renal cell carcinoma. Nat Rev Urol. 2014;11(9):517–25.View ArticlePubMedGoogle Scholar
  4. Rini BI, Campbell SC, Escudier B. Renal cell carcinoma. Lancet. 2009;373(9669):1119–32.View ArticlePubMedGoogle Scholar
  5. Xue S, Li QW, Che JP, Guo Y, Yang FQ, Zheng JH. Decreased expression of long non-coding RNA NBAT-1 is associated with poor prognosis in patients with clear cell renal cell carcinoma. Int J Clin Exp Pathol. 2015;8(4):3765–74.PubMedPubMed CentralGoogle Scholar
  6. Mirza Z, Schulten HJ, Farsi HM, Al-Maghrabi JA, Gari MA, Chaudhary AG, Abuzenadah AM, Al-Qahtani MH, Karim S. Impact of S100A8 expression on kidney cancer progression and molecular docking studies for kidney cancer therapeutics. Anticancer Res. 2014;34(4):1873–84.PubMedGoogle Scholar
  7. Merdad A, Karim S, Schulten HJ, Jayapal M, Dallol A, Buhmeida A, Al-Thubaity F, Gari IM, Chaudhary AG, Abuzenadah AM, et al. Transcriptomics profiling study of breast cancer from kingdom of Saudi Arabia revealed altered expression of adiponectin and fatty acid binding Protein4: is lipid metabolism associated with breast cancer? BMC Genomics. 2015;16 Suppl 1:S11.View ArticlePubMedGoogle Scholar
  8. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.View ArticlePubMedGoogle Scholar
  9. Choi YJ, Saez B, Anders L, Hydbring P, Stefano J, Bacon NA, Cook C, Kalaszczynska I, Signoretti S, Young RA, et al. D-cyclins repress apoptosis in hematopoietic cells by controlling death receptor Fas and its ligand FasL. Dev Cell. 2014;30(3):255–67.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Diehl JA. Cycling to cancer with cyclin D1. Cancer Biol Ther. 2002;1(3):226–31.View ArticlePubMedGoogle Scholar
  11. Jares P, Colomer D, Campo E. Genetic and molecular pathogenesis of mantle cell lymphoma: perspectives for new targeted therapeutics. Nat Rev Cancer. 2007;7(10):750–62.View ArticlePubMedGoogle Scholar
  12. Thomas GR, Nadiminti H, Regalado J. Molecular predictors of clinical outcome in patients with head and neck squamous cell carcinoma. Int J Exp Pathol. 2005;86(6):347–63.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Shintani M, Okazaki A, Masuda T, Kawada M, Ishizuka M, Doki Y, Weinstein IB, Imoto M. Overexpression of cyclin DI contributes to malignant properties of esophageal tumor cells by increasing VEGF production and decreasing Fas expression. Anticancer Res. 2002;22(2A):639–47.PubMedGoogle Scholar
  14. Yamanouchi H, Furihata M, Fujita J, Murakami H, Yoshinouchi T, Takahara J, Ohtsuki Y. Expression of cyclin E and cyclin D1 in non-small cell lung cancers. Lung Cancer (Amsterdam, Netherlands). 2001;31(1):3–8.View ArticleGoogle Scholar
  15. Ikeguchi M, Sakatani T, Ueta T, Kaibara N. Cyclin D1 expression and retinoblastoma gene protein (pRB) expression in esophageal squamous cell carcinoma. J Cancer Res Clin Oncol. 2001;127(9):531–6.View ArticlePubMedGoogle Scholar
  16. Izzo JG, Papadimitrakopoulou VA, Li XQ, Ibarguen H, Lee JS, Ro JY, El-Naggar A, Hong WK, Hittelman WN. Dysregulated cyclin D1 expression early in head and neck tumorigenesis: in vivo evidence for an association with subsequent gene amplification. Oncogene. 1998;17(18):2313–22.View ArticlePubMedGoogle Scholar
  17. Gansauge S, Gansauge F, Ramadani M, Stobbe H, Rau B, Harada N, Beger HG. Overexpression of cyclin D1 in human pancreatic carcinoma is associated with poor prognosis. Cancer Res. 1997;57(9):1634–7.PubMedGoogle Scholar
  18. Simpson DJ, Frost SJ, Bicknell JE, Broome JC, McNicol AM, Clayton RN, Farrell WE. Aberrant expression of G(1)/S regulators is a frequent event in sporadic pituitary adenomas. Carcinogenesis. 2001;22(8):1149–54.View ArticlePubMedGoogle Scholar
  19. Arnold A, Papanikolaou A. Cyclin D1 in breast cancer pathogenesis. J Clin Oncol. 2005;23(18):4215–24.View ArticlePubMedGoogle Scholar
  20. Trimarchi JM, Lees JA. Sibling rivalry in the E2F family. Nat Rev Mol Cell Biol. 2002;3(1):11–20.View ArticlePubMedGoogle Scholar
  21. Mundle SD, Saberwal G. Evolving intricacies and implications of E2F1 regulation. FASEB J. 2003;17(6):569–74.View ArticlePubMedGoogle Scholar
  22. Nevins JR. The Rb/E2F pathway and cancer. Hum Mol Genet. 2001;10(7):699–703.View ArticlePubMedGoogle Scholar
  23. Harbour JW, Dean DC. The Rb/E2F pathway: expanding roles and emerging paradigms. Genes Dev. 2000;14(19):2393–409.View ArticlePubMedGoogle Scholar
  24. Ho A, Dowdy SF. Regulation of G(1) cell-cycle progression by oncogenes and tumor suppressor genes. Curr Opin Genet Dev. 2002;12(1):47–52.View ArticlePubMedGoogle Scholar
  25. Shapiro GI. Cyclin-dependent kinase pathways as targets for cancer treatment. J Clin Oncol. 2006;24(11):1770–83.View ArticlePubMedGoogle Scholar
  26. Lapenna S, Giordano A. Cell cycle kinases as therapeutic targets for cancer. Nat Rev Drug Discov. 2009;8(7):547–66.View ArticlePubMedGoogle Scholar
  27. Amin HM, McDonnell TJ, Medeiros LJ, Rassidakis GZ, Leventaki V, O’Connor SL, Keating MJ, Lai R. Characterization of 4 mantle cell lymphoma cell lines. Arch Pathol Lab Med. 2003;127(4):424–31.PubMedGoogle Scholar
  28. Musgrove EA. Cyclins: roles in mitogenic signaling and oncogenic transformation. Growth Factors (Chur, Switzerland). 2006;24(1):13–9.View ArticleGoogle Scholar
  29. Ali R, Mirza Z, Ashraf GM, Kamal MA, Ansari SA, Damanhouri GA, Abuzenadah AM, Chaudhary AG, Sheikh IA. New anticancer agents: recent developments in tumor therapy. Anticancer Res. 2012;32(7):2999–3005.PubMedGoogle Scholar
  30. van der Watt E, Pretorius JC. Purification and identification of active antibacterial components in Carpobrotus edulis L. J Ethnopharmacol. 2001;76(1):87–91.View ArticlePubMedGoogle Scholar
  31. Domitrović R, Jakovac H, Vasiljev Marchesi V, Vladimir-Knežević S, Cvijanović O, Tadić Ž, Romić Ž, Rahelić D. Differential hepatoprotective mechanisms of rutin and quercetin in CCl(4)-intoxicated BALB/cN mice. Acta Pharmacol Sin. 2012;33(10):1260–70.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Hao G, Dong Y, Huo R, Wen K, Zhang Y, Liang G. Rutin inhibits neuroinflammation and provides neuroprotection in an experimental rat model of subarachnoid hemorrhage, possibly through suppressing the RAGE-NF-kappaB inflammatory signaling pathway. Neurochem Res. 2016.Google Scholar
  33. Shahid A, Ali R, Ali N, Kazim Hasan S, Rashid S, Majed F, Sultana S. Attenuation of genotoxicity, oxidative stress, apoptosis and inflammation by rutin in benzo(a)pyrene exposed lungs of mice: plausible role of NF-kappaB, TNF-alpha and Bcl-2. Journal of Complementary & Integrative Medicine. 2016.Google Scholar
  34. Luo H, Jiang BH, King SM, Chen YC. Inhibition of cell growth and VEGF expression in ovarian cancer cells by flavonoids. Nutr Cancer. 2008;60(6):800–9.View ArticlePubMedGoogle Scholar
  35. Yu SH, Yu JM, Yoo HJ, Lee SJ, Kang DH, Cho YJ, Kim DM. Anti-proliferative effects of rutin on OLETF Rat vascular smooth muscle cells stimulated by glucose variability. Yonsei Med J. 2016;57(2):373–81.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Cheng AL, Hsu CH, Lin JK, Hsu MM, Ho YF, Shen TS, Ko JY, Lin JT, Lin BR, Ming-Shiang W, et al. Phase I clinical trial of curcumin, a chemopreventive agent, in patients with high-risk or pre-malignant lesions. Anticancer Res. 2001;21(4B):2895–900.PubMedGoogle Scholar
  37. Goel A, Kunnumakkara AB, Aggarwal BB. Curcumin as “Curecumin”: from kitchen to clinic. Biochem Pharmacol. 2008;75(4):787–809.View ArticlePubMedGoogle Scholar
  38. Priyadarsini KI. Chemical and structural features influencing the biological activity of curcumin. Curr Pharm Des. 2013;19(11):2093–100.PubMedGoogle Scholar
  39. Mukhopadhyay A, Banerjee S, Stafford LJ, Xia C, Liu M, Aggarwal BB. Curcumin-induced suppression of cell proliferation correlates with down-regulation of cyclin D1 expression and CDK4-mediated retinoblastoma protein phosphorylation. Oncogene. 2002;21(57):8852–61.View ArticlePubMedGoogle Scholar
  40. Kwon YK, Jun JM, Shin SW, Cho JW, Suh SI. Curcumin decreases cell proliferation rates through BTG2-mediated cyclin D1 down-regulation in U937 cells. Int J Oncol. 2005;26(6):1597–603.PubMedGoogle Scholar
  41. Gomaa W, Ke Y, Fujii H, Helliwell T. Tissue microarray of head and neck squamous carcinoma: validation of the methodology for the study of cutaneous fatty acid-binding protein, vascular endothelial growth factor, involucrin and Ki-67. Virchows Arch. 2005;447(4):701–9.View ArticlePubMedGoogle Scholar
  42. Bikadi Z, Hazai E. Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock. J Cheminformatics. 2009;1:15.View ArticleGoogle Scholar
  43. Gueguen N, Desquiret-Dumas V, Leman G, Chupin S, Baron S, Nivet-Antoine V, Vessières E, Ayer A, Henrion D, Lenaers G, et al. Resveratrol directly binds to mitochondrial complex I and increases oxidative stress in brain mitochondria of aged mice. PLoS One. 2015;10(12):e0144290.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Day PJ, Cleasby A, Tickle IJ, O’Reilly M, Coyle JE, Holding FP, McMenamin RL, Yon J, Chopra R, Lengauer C, et al. Crystal structure of human CDK4 in complex with a D-type cyclin. Proc Natl Acad Sci U S A. 2009;106(11):4166–70.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Alana L, Sese M, Canovas V, Punyal Y, Fernandez Y, Abasolo I, de Torres I, Ruiz C, Espinosa L, Bigas A, et al. Prostate tumor OVerexpressed-1 (PTOV1) down-regulates HES1 and HEY1 notch targets genes and promotes prostate cancer progression. Mol Cancer. 2014;13:74.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Belandia B, Powell SM, Garcia-Pedrero JM, Walker MM, Bevan CL, Parker MG. Hey1, a mediator of notch signaling, is an androgen receptor corepressor. Mol Cell Biol. 2005;25(4):1425–36.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Yasuoka H, Kodama R, Tsujimoto M, Yoshidome K, Akamatsu H, Nakahara M, Inagaki M, Sanke T, Nakamura Y. Neuropilin-2 expression in breast cancer: correlation with lymph node metastasis, poor prognosis, and regulation of CXCR4 expression. BMC Cancer. 2009;9:220.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Caunt M, Mak J, Liang WC, Stawicki S, Pan Q, Tong RK, Kowalski J, Ho C, Reslan HB, Ross J, et al. Blocking neuropilin-2 function inhibits tumor cell metastasis. Cancer Cell. 2008;13(4):331–42.View ArticlePubMedGoogle Scholar
  49. Niehrs C. Function and biological roles of the Dickkopf family of Wnt modulators. Oncogene. 2006;25(57):7469–81.View ArticlePubMedGoogle Scholar
  50. Katoh M, Katoh M. WNT signaling pathway and stem cell signaling network. Clin Cancer Res. 2007;13(14):4042–5.View ArticlePubMedGoogle Scholar
  51. Clevers H, Nusse R. Wnt/beta-catenin signaling and disease. Cell. 2012;149(6):1192–205.View ArticlePubMedGoogle Scholar
  52. Kinzler KW, Nilbert MC, Su LK, Vogelstein B, Bryan TM, Levy DB, Smith KJ, Preisinger AC, Hedge P, McKechnie D, et al. Identification of FAP locus genes from chromosome 5q21. Science (New York, NY). 1991;253(5020):661–5.View ArticleGoogle Scholar
  53. Rodova M, Islam MR, Maser RL, Calvet JP. The polycystic kidney disease-1 promoter is a target of the beta-catenin/T-cell factor pathway. J Biol Chem. 2002;277(33):29577–83.View ArticlePubMedGoogle Scholar
  54. Rubinfeld B, Souza B, Albert I, Muller O, Chamberlain SH, Masiarz FR, Munemitsu S, Polakis P. Association of the APC gene product with beta-catenin. Science (New York, NY). 1993;262(5140):1731–4.View ArticleGoogle Scholar
  55. Malanchi I, Huelsken J. Cancer stem cells: never Wnt away from the niche. Curr Opin Oncol. 2009;21(1):41–6.View ArticlePubMedGoogle Scholar
  56. Nguyen DX, Chiang AC, Zhang XH, Kim JY, Kris MG, Ladanyi M, Gerald WL, Massague J. WNT/TCF signaling through LEF1 and HOXB9 mediates lung adenocarcinoma metastasis. Cell. 2009;138(1):51–62.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Coulombe-Huntington J, Lam KC, Dias C, Majewski J. Fine-scale variation and genetic determinants of alternative splicing across individuals. PLoS Genet. 2009;5(12):e1000766.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Wang L, Sun L, Huang J, Jiang M. Cyclin-dependent kinase inhibitor 3 (CDKN3) novel cell cycle computational network between human non-malignancy associated hepatitis/cirrhosis and hepatocellular carcinoma (HCC) transformation. Cell Prolif. 2011;44(3):291–9.View ArticlePubMedGoogle Scholar
  59. Unwin RD, Craven RA, Harnden P, Hanrahan S, Totty N, Knowles M, Eardley I, Selby PJ, Banks RE. Proteomic changes in renal cancer and co-ordinate demonstration of both the glycolytic and mitochondrial aspects of the Warburg effect. Proteomics. 2003;3(8):1620–32.View ArticlePubMedGoogle Scholar
  60. Mathupala SP, Rempel A, Pedersen PL. Aberrant glycolytic metabolism of cancer cells: a remarkable coordination of genetic, transcriptional, post-translational, and mutational events that lead to a critical role for type II hexokinase. J Bioenerg Biomembr. 1997;29(4):339–43.View ArticlePubMedGoogle Scholar
  61. Perroud B, Lee J, Valkova N, Dhirapong A, Lin PY, Fiehn O, Kultz D, Weiss RH. Pathway analysis of kidney cancer using proteomics and metabolic profiling. Mol Cancer. 2006;5:64.View ArticlePubMedPubMed CentralGoogle Scholar
  62. Song H, Xia SL, Liao C, Li YL, Wang YF, Li TP, Zhao MJ. Genes encoding Pir51, Beclin 1, RbAp48 and aldolase b are up or down-regulated in human primary hepatocellular carcinoma. World J Gastroenterol. 2004;10(4):509–13.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Arroyo JP, Kahle KT, Gamba G. The SLC12 family of electroneutral cation-coupled chloride cotransporters. Mol Aspects Med. 2013;34(2–3):288–98.View ArticlePubMedGoogle Scholar
  64. Skubitz KM, Skubitz AP. Differential gene expression in renal-cell cancer. J Lab Clin Med. 2002;140(1):52–64.View ArticlePubMedGoogle Scholar
  65. Cross SS, Hamdy FC, Deloulme JC, Rehman I. Expression of S100 proteins in normal human tissues and common cancers using tissue microarrays: S100A6, S100A8, S100A9 and S100A11 are all overexpressed in common cancers. Histopathology. 2005;46(3):256–69.View ArticlePubMedGoogle Scholar
  66. Lenburg ME, Liou LS, Gerry NP, Frampton GM, Cohen HT, Christman MF. Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of microarray data. BMC Cancer. 2003;3:31.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Gumz ML, Zou H, Kreinest PA, Childs AC, Belmonte LS, LeGrand SN, Wu KJ, Luxon BA, Sinha M, Parker AS, et al. Secreted frizzled-related protein 1 loss contributes to tumor phenotype of clear cell renal cell carcinoma. Clin Cancer Res. 2007;13(16):4740–9.View ArticlePubMedGoogle Scholar
  68. Tun HW, Marlow LA, von Roemeling CA, Cooper SJ, Kreinest P, Wu K, Luxon BA, Sinha M, Anastasiadis PZ, Copland JA. Pathway signature and cellular differentiation in clear cell renal cell carcinoma. PLoS One. 2010;5(5):e10696.View ArticlePubMedPubMed CentralGoogle Scholar
  69. Furge KA, Chen J, Koeman J, Swiatek P, Dykema K, Lucin K, Kahnoski R, Yang XJ, Teh BT. Detection of DNA copy number changes and oncogenic signaling abnormalities from gene expression data reveals MYC activation in high-grade papillary renal cell carcinoma. Cancer Res. 2007;67(7):3171–6.View ArticlePubMedGoogle Scholar

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© The Author(s). 2016

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