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?
- Adnan Merdad†1,
- Sajjad Karim†2, 3Email author,
- Hans-Juergen Schulten2,
- Manikandan Jayapal2,
- Ashraf Dallol2,
- Abdelbaset Buhmeida2,
- Fatima Al-Thubaity1,
- Mamdooh A GariI2, 3,
- Adeel GA Chaudhary2, 3,
- Adel M Abuzenadah2, 3, 4 and
- Mohammed H Al-Qahtani2, 3Email author
© MERDAD et al; licensee BioMed Central Ltd. 2015
Published: 15 January 2015
Breast cancer incidence rates are increasing at an alarming rate among Saudi Arabian females. Most molecular genetic discoveries on breast cancer and other cancers have arisen from studies examining European and American patients. However, possibility of specific changes in molecular signature among cancer patients of diverse ethnic groups remains largely unexplored. We performed transcriptomic profiling of surgically-resected breast tumors from 45 patients based in the Western region of Saudi Arabia using Affymetrix Gene 1.0 ST chip. Pathway and biological function-based clustering was apparent across the tissue samples.
Pathway analysis revealed canonical pathways that had not been previously implicated in breast cancer. Biological network analysis of differentially regulated genes revealed that Fatty acid binding protein 4, adipocyte (FABP4), adiponectin (ADIPOQ), and retinol binding protein 4 (RBP4) were most down regulated genes, sharing strong connection with the other molecules of lipid metabolism pathway. The marked biological difference in the signatures uncovered between the USA and Saudi samples underpins the importance of this study. Connectivity Map identified compounds that could reverse an observed gene expression signature
This study describes, to our knowledge, the first genome-wide profiling of breast cancer from Saudi ethnic females. We demonstrate the involvement of the lipid metabolism pathway in the pathogenesis of breast cancer from this region. This finding also highlights the need for strategies to curb the increasing rates of incidence of this disease by educating the public about life-style risk factors such as unhealthy diet and obesity.
Breast cancer (BC) is the most common cancer that affects females worldwide and is the second most frequent cause of cancer-related deaths among women in the United States . According to the National Cancer Registry in the Kingdom of Saudi Arabia (KSA), BC was ranked as the most prevalent form of cancer among females, accounting for 25.1% of all newly diagnosed female cancers (5,205) in the year 2009 . The ASR was 22.7/100,000 for the female population. While the median age at presentation is around 63 years in the United States and Western Europe, the median age at presentation in the KSA is 48 years . The genetic variability between patients and tumors drive this clinically heterogeneous disease . Much of the knowledge on the molecular genetics on BC and other cancers has been resulting from examining European and US patients. However, growing scientific knowledge suggests the likelihood of variability in molecular signature between cancers from patients of different ethnic groups. Clear differences in the patterns of p53 mutations in BC were found between Midwest US Caucasian, African- American, Austrian, and Japanese women [4, 5]. Besides, varying patterns and the occurrence of germline mutations in BRCA1 and BRCA2 between ethnic groups were also reported . Recently, National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program show that the age-adjusted BC frequency among minority groups are significantly lower than those among white women  Increase in BC mortality was also found in African American women when compared to white women . All these differences continue to be mostly unexplained . Moreover, changes in occurrence and clinical characteristics linked with ethnicity or race has got only limited consideration . The current lack of measures to aid in stratifying BC treatment indicates the necessity for a new methodology to formulate better prognostication and therapy prediction. Although the parameters such as histological grade, stage, and tumor size are accepted as prognostic markers for BC , about 50% of the patients with ER-positive cancer fail on tamoxifen treatment [12, 13].
Transcriptomics approaches that allow screening of thousands of genes in an experiment, has had a major impact on BC research over the past 10 years  Several groups have carried out gene expression profiling of BC and classified clinically distinct subclasses of tumors , hereditary BC , and treatment prediction . Many microarray studies so far reported have led to the discovery of several genes associated with BC. However, most of the gene expression profiling studies has been carried out mainly in the Caucasian population, and inclusion of the non-Caucasian population has been minimal [17, 18]. Significant risk factors of BC in the western countries such as nulliparity, low parity, first time pregnancy at late age, having no history of breast feeding, etc., are usually not practiced in the Saudi society, yet BC incidence is still high among women from the KSA . In addition, a study of Taiwanese population reported that women who had more than three full-term pregnancies; first full-term pregnancy at the age of below 30; and three or more years of breast feeding showed significantly reduced risk of BC .
These studies suggest that there is an important need to better understand the molecular mechanisms underlying BC from different populations. In view of the existence of genetic differences across regional or geographical locations, more data specific to indigenous populations are needed for comparative analysis of molecular changes in breast tumors among Saudi population. More comprehensive molecular and genomic analyses of breast cancers are important to understand the complexity and severity of the disease and to find ‘druggable’ molecular targets for the development effective treatments. In order to obtain relevant target genes associated with BC patients in the KSA, we aim to start comprehensive microarray-based gene expression profiling study of BC in the Kingdom. Transcriptomic analysis coupled with functional and pathway analysis could lead to new insight into biomarkers and signatures associated with the disease. Deregulated signaling pathways are thought to drive functional processes such as cell growth, cellular proliferation, and invasion of cancer cells. Thus, identifying such underlying driving changes will be vital for studies of tumor progression, for the identification of novel therapeutic targets.
To this end, this study aimed to survey the genes that are differentially regulated in forty five freshly frozen BC tissue samples compared to eight normal controls. The ethnic group of the cohort in this study provides unique dimension to BC research. We examined genome-wide gene expression in order to elucidate the molecular mechanisms underlying malignancy in breast tissues. Based on the differential expression signature, we found biological functions and pathways that are significantly altered in BC. Pathway-based clustering was apparent across the tissue samples. Pathway analysis revealed canonical pathways that have not been implicated in BC, previously. We also demonstrated additional utility of our BC signature by comparative analysis between the USA and the KSA samples reveals marked biological differences in signature. Furthermore, Connectivity Map (cmap) analysis offered hypotheses regarding potential treatments for differential regulation observed in the BC . Thus, our finding might open up new avenues of BC research and assist discover possible new targets for BC treatment where ethnicity plays major role.
Identification of differentially expressed genes
Functional analysis of the breast cancer-associated genes found an over expression of genes involved in cell cycle progression, DNA repair, cell death, tumor morphology and tissue developments. Specifically, genes that are known to be associated with BC, including Topoisomerase 2 alpha (TOP2A), Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), denticleless/RA-regulated nuclear matrix associated protein (DTL), H3 histone family, member A, histone 1, H3a (HIST1H3A), the targeting protein for Xklp2 (TPX2), Hepatitis C virus NS5A-transactivated protein (KIAA0101), centromere protein-F (CENPF) and ubiquitin-conjugating enzyme E2T (UBE2T) were upregulated compared to the controls. Furthermore, genes associated with immune cell trafficking and interferon signaling including chemokine (C-X-C motif) ligand 10 (CXCL10), matrix metallopeptidase 11(MMP11), interferon, gamma-inducible protein 6 (IFI6), chemokine (C-X-C motif) ligand 9 (CXCL9) and matrix metallopeptidase 9 and 13 (MMP9 and 13) were also significantly upregulated. Interestingly, the biological process, cellular movement was significantly overrepresented in both down-regulated and up-regulated gene lists pointing that the metastasis was linked to a different equilibrium of switching on and off.
Canonical pathways predicted by Ingenuity Pathway Analysis.
p-value (Fisher's Exact)
SDC1 (includes EG:20969),DGAT2,ADH1C (includes EG:11522),LIPE,GK,PNPLA2,ADH1A,GPAM,PPAPDC1A,PPAPDC1B,AADAC,ALDH2,ALDH1A1,PPAP2A,ALDH1A3,AGPAT2,ALDH1A2,LPL,ADH1B,MGLL,PPAP2C
Mitotic Roles of Polo-Like Kinase
KIF23,CDC25C,CDC20,PTTG1,PRC1 (includes EG:233406),CDC7 (includes EG:12545),CCNB2,PLK1,CDK1,CCNB1,PLK4,ANAPC5,PPP2R1B,KIF11
Cardiac β-adrenergic Signaling
FN1,MYH11,CREB5,RHOH,KRT18,RHOU,FIGF,AKT3 (includes EG:10000),ACTG2,MUC1,CFL1,FERMT2,PIK3C2G,RHOJ,CREB3L4,MYL9,RHOQ,RND3,FLNC,CFL2,ARHGEF6,MAPK10,LEF1,PPP2R1B,MMP9
Protein Kinase A Signaling
AKAP12,PHKG2,LIPE,PDE1A,CREB5,HIST2H3C (includes others),PTCH2,TGFBR2,GNG11,PDE7B,PDE3B,PPP1CA,HIST1H1B,APEX1,PDE2A,PLN,GNAI1,PPP1R14A,PYGL,CREB3L4,TTN (includes EG:22138),PDE1C,MYL9,AKAP2/PALM2-AKAP2,PRKAR2B,PYGM,ADD3,FLNC,PDE1B,ANAPC5,PDE8B,LEF1,GNG2,TCF7L2
Leukocyte Extravasation Signaling
VAV2,RAC2,CLDN11,JAM2,PIK3C2G,GNAI1,MMP13,MAPK13,CLDN7,DLC1,ITGAL,RHOH,TIMP4,CLDN4,CLDN8,EZR,CXCL12 (includes EG:20315),MAPK10,PECAM1,MMP11,ACTG2,MMP9,CLDN3
CMA1,CD36,MMP13,PLA2G2A,F3,APOC1,IL33,PLA2G4A,ALB,CXCL12 (includes EG:20315),LPL,COL10A1,PDGFD,MMP9,APOD,RBP4
PGK1,ADH1C (includes EG:11522), ALDH1L1, ALDH2, HK1,ADH1A ,ALDH1A1,ALDH1A3,ALDH1A2,PGM5,ADH1B, ACSL1,ALDOC
Agrin Interactions at Neuromuscular Junction
RAC2,ANGPT1,PIK3C2G,GNAI1,RHOJ,IRAK3,RHOH,CDH1,RHOQ,GNG11,RND3,MAPK10,RHOU,MRAS,AKT3 (includes EG:10000),FIGF,GNG2,MMP9,ITGAX,EGFR
Inhibition of Angiogenesis by TSP1
TGFBR2,SDC1 (includes EG:20969),MAPK10,CD36,AKT3 (includes EG:10000),MAPK13,MMP9
OAS1,MX1,IRF9,STAT1,TAP1,BAK1,IRF1 (includes EG:16362)
Cell Cycle: G2/M DNA Damage Checkpoint Regulation
Cell Cycle Control of Chromosomal Replication
MCM3,CDC45,CDC6 (includes EG:23834),CDC7 (includes EG:12545),MCM4,MCM7
Role of BRCA1 in DNA Damage Response
RAC2,CAPN6,TSPAN7,PIK3C2G,RHOJ,ITGAL,TTN (includes EG:22138), RHOH,MYL9,RHOQ, TLN2,PAK3, RND3,TSPAN1,CAV1,MRAS,RHOU,AKT3 (includes EG:10000), ACTG2 ,ITGA7, ITGAX
Production of Nitric Oxide and Reactive Oxygen Species in Macrophages
PIK3C2G,PPP1R14A,RHOJ,MAPK13,RHOH,IRF1 (includes EG:16362), APOC1, ALB,RHOQ, RND3,CAT,RHOU,MAPK10,AKT3 (includes EG:10000),STAT1,PPP1CA,PPP2R1B,APOD,RBP4
Hereditary Breast Cancer Signaling
CDC25C,HDAC1,PIK3C2G,CDK1,CHEK1,CCNB1,RAD51,FANCD2,MRAS,AKT3 (includes EG:10000),POLR2H,BRCA2,BLM,UBC
Bile Acid Biosynthesis
ALDH2,ADH1A,AKR1C1/AKR1C2,ALDH1A1,ALDH1A3,ALDH1A2,ADH1C (includes EG:11522),ADH1B
Estrogen-Dependent Breast Cancer Signaling
HSD17B13,IGF1,MRAS,PIK3C2G,AKT3 (includes EG:10000),CREB3L4,CREB5,EGFR
HER-2 Signaling in Breast Cancer
CCNE2,MRAS,PIK3C2G,PARD6B,AKT3 (includes EG:10000),ERBB3,ERBB2,EGFR
Breast Cancer Regulation by Stathmin1
Pathways and networks underlying breast cancer
To understand the mechanisms by which the genes alter a wide range of physiological processes, we examined molecular networks underlying BC. Transcriptomic signatures showed significant disruption in signaling pathways associating genes of the glycerolipid metabolism, ATM signaling, ILK signaling, DNA damage and cell cycle (Table 1). Analysis by IPA shows a set of key genes that disrupt a pathway such that it then results in tumor initiation or progression. The pathway analysis revealed a strong correlation between the transcriptomic signature and the canonical glycerolipid metabolism which has not been implicated in BC before. Majority of the genes involved in the glycerolipid metabolism were downregulated. These results further support the putative role of these pathways in rendering BC susceptibility.
Therapeutic suggestions using the Connectivity Map
Therapeutic suggestions using the Connectivity Map
Alimentary tract and metabolism (Aid in fat, protein and carbohydrate metabolism)
Estrogen receptor antagonist
Potent antiatherogenic effect in type 2 diabetes
Corticosteroids for systemic use
Antineoplastic and immunomodulating agents
Sulfonylurea anti-diabetic drug
Treatment of type II diabetes
Treatment of a number of cancers including breast
Non-steroidal aromatase inhibitor for the treatment of hormonally-responsive breast cancer after surgery
Anti-diabetic drug from the sulfonylurea class
Treatment of a number of cancers including breast
Antidiabetic drug in a class of medications known as sulfonylureas
Antagonist of the estrogen receptor in breast tissue
Breast cancer therapeutic strategies today are generally based on histopathological characterization, tumor size, grading, and axillary lymph node and receptor status . Even so, patients when diagnosed with similar conditions and when treated with similar drug can go through extensive differences in the development and relapse of BC. Studies show that women, who had a full-term pregnancy at an age below 30; and who went through three full-term pregnancies, and three or more years of breast feeding, were significantly protected against BC . However, BC incidence is high among women from the KSA. BC risk factors including nulliparity or low parity, first full term pregnancy at very late age, no breast feeding etc., are not common in the Saudi society . These factors combined with the early onset of BC among women, in this ethnicity, prompted us to study the molecular mechanism underlying the malignancies.
In the present study, we identified transcriptomic signature in BC from the KSA that is associated with clinical and histological parameters. The ethnicity of this cohort renders the power to prospectively examine, the roles of lifestyle and genetic susceptibility in the onset of BC. The genome-wide expression analysis contains several overrepresented functional gene classes and has substantial overlaps with transcriptomic signatures of metastatic human BC. The commonalities found between different populations in terms of increased cell cycle regulation and DNA integrity checkpoints were observed. A similar transcriptomic signature for many of these genes has been reported for human BC before [22–26].
Genes associated with lipid metabolism and small molecule biochemistry was significantly downregulated in the BC tissues. The role of these genes on the tumorigenesis of BC has been reported previously [27–29]. Disruption of lipid metabolism has been implicated in tumorigenesis in several studies, especially in cancer of breast [30, 31]. The differential regulation of lipid metabolism between normal and cancer subjects might reveal transformation in the metabolism of the cancer patient in this ethnicity . Decreased ADIPOQ levels interrupt cellular signaling networks that are linked to cell survival, angiogenesis, proliferation, and cell-cycle regulation . Adipose tissues secrete ADIPOQ and LEP-ADIPOQ axis has been well implicated in breast cancer tumorigenesis . Levels of ADIPOQ have been inversely correlated with obesity. Interestingly, studies show that women with increased ADIPOQ concentrations possess 65% reduced risk for breast cancer . In another study, it has been shown that patients with low ADIPOQ levels had a significantly increased likelihood of cancer recurrence.
Unsupervised clustering analysis also revealed the gene expression signatures to be associated with histologic grade, age and triple negative status. This signature could also be used for improving stratification of patients with BC in this population. Further pathway analysis of differentially regulated genes provides novel hypotheses underlying metastatic progression of BC. In addition, cmap analysis can formulate novel therapeutics for BC, starting from a cellular approach to investigating the effects of the compound determined by cmap. It may be possible to develop stratified therapy by combining both standard chemotherapy and pathway-specific therapies.
ADIPOQ, an adipocyte secreted protein, was found to one of the most downregulated gene in our analysis and it’s lower expression levels have been shown to be associated with obesity, insulin resistance, and type 2 diabetes mellitus . It has also been shown to be reduced in pre-menopausal women with endometrial cancer, which is tightly linked with obesity and insulin resistance. A case control study found significantly inverse relationship of serum ADIPOQ with BC .
Comparison of BC data from USA with KSA further revealed distinct expression levels for LEP and other genes involved in lipid metabolism . In our analysis, glycerolipid metabolism was found to be a highly significantly altered canonical pathway. However, comparative analysis of these findings is different from the recent transcriptomic profiles of BC from the USA data, as glycerolipid metabolism was ranked 41st in the USA data . In the KSA samples, glycerolipid metabolism was one the most overrepresented pathways, whereas, PPARα / RXRα activation pathway is the most disrupted one in the USA data.
To comprehend population differences in the severity of BC, studies linking both genetic and environmental factors and examining cases from different populations are important. Researchers have been investigating issues surrounding lifestyle, circadian rhythm, obesity, and adverse exposures in an attempt to identify susceptibility factors in initiating BC. Since diet and obesity contribute to changes in circulating hormone levels, they both play a key role in the development of BC. Understanding the underlying genetics and complex interactions of lifestyle, diet, and other known risk factors will remain a key area of research.
The prevalence of obesity among Saudi patients adults was 55.56% in comparison to 33.8% in USA adults, however combined obese and overweight percentage were almost same with 68.0% in western and 73 % in Saudi BC patients . Obesity is primarily characterized by excess fat storage, adipocyte mass, and increase in certain types of lipids. Normally healthy women have 14–28% of fat mass in the body, but it may increase up to 60–70% in morbidly obese individuals . Earlier white adipose tissue function was assigned for purely an energy storage tissue, however, recent finding has uncovered the endocrine and metabolic properties that has led to several mechanisms implicated in how obesity drives cancer prevalence and cancer deaths. Many possible mechanisms have been proposed to explain the increased risk of breast cancer associated with obesity such as increased lipids and lipid signaling, inflammatory responses, insulin resistance, adipokines, altered immune responses, and oxidative stress. Study had shown that cancer cells can access lipids from neighboring adipocyte stores and can directly use these transferred lipids as an energy source, which in turn promotes tumor growth . Thus, it is critical to understand the physiological impact of obesity on breast cancer development and progression. In recent years, obesity has been identified as a significant modifiable increased risk factor for breast cancers among postmenopausal women but is unrelated or inversely related to risk among premenopausal women not in pre-menopausal women [39–43]. A study examined obesity and mortality from cancer in 495,477 US women, and found that obese women had double the death rate (relative risk, 2.12) than women with normal BMI (25 or less) .
Fat tissue of obese women are most important source of estrogen after ovary, secreting higher level of estrogen potentially leading to more rapid growth of estrogen-responsive breast tumors . Fat cells may also promote inflammation linked cancer through tumor growth regulators and tumor necrotic factors. Obesity often cause insulin resistance, which may promote the development of breast cancer [46, 47]. Adipose tissue secrete TNF-α leading to inflamation promoting cancer. Fat cells produce hormones, called adipokines, that may stimulate or inhibit cell growth . For example, adiponectin, a down regulated gene in present study, has been reported to be inhibitor for proliferetion and negative regulator of angiogenesis [37, 49, 50]. Further a significant inverse correlation between serum adiponectin levels and poor-prognosis breast cancer were confirmed. Thus, low adiponectin in obese individuals could increase risk of developing tumors with aggressive angiogenesis [34, 51].
This study described, to our knowledge, the first genome-wide profiling of BC from Saudi ethnic females. Our analysis reveals appropriate biological relevance and a number of molecular pathways that may serve as targets for novel therapeutics. Our finding of 73.3% obese vs 26.7% normal weight clearly suggest that obesity increases the risk of breast cancer occurrence. Further studies are needed to determine the relationship between lipid metabolism dysregulation and the mechanisms underlying BC. This study may open new avenues of experimental strategies for further examination in larger cohorts of all subtypes, correlating ethnicity to molecular signature and life-style.
Patients and samples
Clinicopathological characteristics of the 45 breast cancer patients.
Age, mean (range) years
Obese (≥30 Kg/m2)
Over weight (≥25-29.9 Kg/m2)
Normal weight (≥18- 24.9 Kg/m2)
Under weight (≤18 Kg/m2)
Tumor size: mean (sd) (cm)
All patients included in the study provided written informed consent. The study was reviewed and approved by the Center of Excellence in Genomic Medicine Research (CEGMR) ethical committee (approval number 08-CEGMR-02-ETH).
RNA extraction and array processing
Total RNA was extracted from fresh breast tissue specimens with the Qiagen RNeasy Mini Kit (Qiagen, Hilden, Germany) including an on-column DNAse treatment according to manufacturer’s recommendations. Quality of the purified RNA was verified on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). Mean value of RNA integrity number (RIN) for all 50 processed samples was 8.0. RNA concentrations were determined using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). 300 ng of each RNA sample was processed according to the manufacturer`s recommendation (Affymetrix, Santa Clara, CA, USA). After fragmentation and labeling, the samples were hybridized at 45°C for 17 hours to Human Gene 1.0 ST GeneChip arrays (Affymetrix, Santa Clara, CA, USA). These arrays are conceptually based on the Human Genome sequence assembly UCSC hg18, NCBI Build 36 and interrogate with a set of 764,885 probes 28,869 annotated genes.
Gene Expression Analysis
Affymetrix .CEL files and were imported to Partek Genomics Suite version 6.5 (Partek Inc., MO, USA). The data was normalized using RMA normalization. Principal component analysis (PCA) was performed on all probes to visualize high dimensional data. PCA was used to assess quality control as well as overall variance in gene expression between the disease states. Analysis of Variance (ANOVA) was applied on the complete data set and differentially expressed gene list was then generated using an FDR (Benjamini Hochberg) of 0.05 with 2 fold change cut off. Unsupervised two dimensional average linkage hierarchical clustering was performed using Spearman’s correlation as a similarity matrix. The microarray data generated in this study are in compliance with MIAME (http://www.mged.org/Workgroups/MIAME/miame.html) guidelines.
Functional and Pathway analysis
To define biological networks, interaction and functional analysis among the differentially regulated genes in BC, pathway analyses were performed using Ingenuity Pathways Analysis software (IPA) (Ingenuity Systems, Redwood City, CA). Statistically differentially expressed dataset containing 1159 genes and their corresponding probesets ID, Gene symbol, Entrez gene ID as clone identifier, p-value and fold change values were uploaded into the IPA. The functional/pathway analysis of IPA identifies the biological functions and/or diseases and pathways that are most significantly altered for the differentially expressed gene set. The significance of the connection between the expression data and the canonical pathway were calculated by ratio and/or Fisher’s exact.
Connectivity Map analysis
The differentially expressed genes identified were analyzed using the cmap in an effort to link genes associated with a phenotype with potential drug molecules. Cmap analyzes the correlation between our gene expression signature and predefined signatures of therapeutic compounds. A ‘‘connectivity score” (+1 to -1), signifying relative similarity to our differentially expressed genes, was generated using a metric based on the Kolmogorov-Smirnov statistic, as described . A compound with negative connectivity scores represents compounds that potentially would reverse the molecular phenotype.
Availability of supporting data
The clinicopathological information and datasets (.CEL file) supporting the results of this article were submitted to NCBI’s Gene Expression Omnibus (GEO) under accession number GSE36295. (http://www.ncbi.nlm.nih.gov/geo/).
Conflicts of interest
The authors declare that they have no potential conflicts of interest.
This work was supported by the research grants from the Center of Excellence in Genomic Medicine Research (CEGMR-N08-14), Deanship of Research (HiCi-1434-117-2), King Abdulaziz University, Jeddah, and King Abdulaziz City for Science and Technology, (KACST- Strategic Project ID: 10-BIO1073-03 and 10-BIO1258-03) Riyadh, Saudi Arabia. The authors would like to thank Ms. Manal Shaabad, Mrs. Manar Ata, Mrs. Nuha Alansari, Ms. Alaa Albogmi and Ms. Maha Al-Quaiti for performing RNA extraction, bioanalyzer assays and microarray experiments. We thank the patients, and the contributions of physicians, nurses, and pathologists of the King Abdulaziz University Hospital, King Faisal Specialist Hospital and Research Center, and Dr. Bakhsh Hospital, Jeddah, the Kingdom of Saudi Arabia.
Publication charges for this article have been funded by Center of Excellence in Genomic Medicine Research, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
This article has been published as part of BMC Genomics Volume 16 Supplement 1, 2015: Selected articles from the 2nd International Genomic Medical Conference (IGMC 2013): Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/16/S1
- American Cancer Society: Cancer Facts and Figures. 2012Google Scholar
- Cancer Incidence Report Saudi Arabia-2009. 2012, [http://www.scr.org.sa]
- Morris SR, Carey LA: Molecular profiling in breast cancer. Reviews in endocrine & metabolic disorders. 2007, 8 (3): 185-198. 10.1007/s11154-007-9035-3.View ArticleGoogle Scholar
- Hartmann A, Blaszyk H, Saitoh S, Tsushima K, Tamura Y, Cunningham JM, McGovern RM, Schroeder JJ, Sommer SS, Kovach JS: High frequency of p53 gene mutations in primary breast cancers in Japanese women, a low-incidence population. British journal of cancer. 1996, 73 (8): 896-901. 10.1038/bjc.1996.179.PubMed CentralView ArticlePubMedGoogle Scholar
- Hartmann A, Rosanelli G, Blaszyk H, Cunningham JM, McGovern RM, Schroeder JJ, Schaid DJ, Kovach JS, Sommer SS: Novel pattern of P53 mutation in breast cancers from Austrian women. The Journal of clinical investigation. 1995, 95 (2): 686-689. 10.1172/JCI117714.PubMed CentralView ArticlePubMedGoogle Scholar
- Liede A, Narod SA: Hereditary breast and ovarian cancer in Asia: genetic epidemiology of BRCA1 and BRCA2. Human mutation. 2002, 20 (6): 413-424. 10.1002/humu.10154.View ArticlePubMedGoogle Scholar
- Siegel R, Naishadham D, Jemal A: Cancer statistics, 2013. CA: a cancer journal for clinicians. 2013, 63 (1): 11-30. 10.3322/caac.21166.Google Scholar
- Newman LA, Mason J, Cote D, Vin Y, Carolin K, Bouwman D, Colditz GA: African-American ethnicity, socioeconomic status, and breast cancer survival: a meta-analysis of 14 studies involving over 10,000 African-American and 40,000 White American patients with carcinoma of the breast. Cancer. 2002, 94 (11): 2844-2854. 10.1002/cncr.10575.View ArticlePubMedGoogle Scholar
- Weir HK, Thun MJ, Hankey BF, Ries LA, Howe HL, Wingo PA, Jemal A, Ward E, Anderson RN, Edwards BK: Annual report to the nation on the status of cancer, 1975-2000, featuring the uses of surveillance data for cancer prevention and control. Journal of the National Cancer Institute. 2003, 95 (17): 1276-1299. 10.1093/jnci/djg040.View ArticlePubMedGoogle Scholar
- Vastag B: Breast cancer racial gap examined: no easy answers to explain disparities in survival. JAMA : the journal of the American Medical Association. 2003, 290 (14): 1838-1842.View ArticlePubMedGoogle Scholar
- Winer E, Morrow M, Osborne C, Harris J: Malignant tumors of the breast. 2001, Lippincott Williams & WilkinsGoogle Scholar
- Clarke R, Liu MC, Bouker KB, Gu Z, Lee RY, Zhu Y, Skaar TC, Gomez B, O'Brien K, Wang Y, et al: Antiestrogen resistance in breast cancer and the role of estrogen receptor signaling. Oncogene. 2003, 22 (47): 7316-7339. 10.1038/sj.onc.1206937.View ArticlePubMedGoogle Scholar
- Osborne CK, Schiff R: Growth factor receptor cross-talk with estrogen receptor as a mechanism for tamoxifen resistance in breast cancer. Breast. 2003, 12 (6): 362-367. 10.1016/S0960-9776(03)00137-1.View ArticlePubMedGoogle Scholar
- Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, et al: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proceedings of the National Academy of Sciences of the United States of America. 2003, 100 (14): 8418-8423. 10.1073/pnas.0932692100.PubMed CentralView ArticlePubMedGoogle Scholar
- Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Kallioniemi OP, et al: Gene-expression profiles in hereditary breast cancer. The New England journal of medicine. 2001, 344 (8): 539-548. 10.1056/NEJM200102223440801.View ArticlePubMedGoogle Scholar
- Chang JC, Wooten EC, Tsimelzon A, Hilsenbeck SG, Gutierrez MC, Elledge R, Mohsin S, Osborne CK, Chamness GC, Allred DC, et al: Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet. 2003, 362 (9381): 362-369. 10.1016/S0140-6736(03)14023-8.View ArticlePubMedGoogle Scholar
- Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA: Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. The Journal of clinical investigation. 2011, 121 (7): 2750-2767. 10.1172/JCI45014.PubMed CentralView ArticlePubMedGoogle Scholar
- Sabatier R, Finetti P, Adelaide J, Guille A, Borg JP, Chaffanet M, Lane L, Birnbaum D, Bertucci F: Down-regulation of ECRG4, a candidate tumor suppressor gene, in human breast cancer. PloS one. 2011, 6 (11): e27656-10.1371/journal.pone.0027656.PubMed CentralView ArticlePubMedGoogle Scholar
- Ezzat AA, Ibrahim EM, Raja MA, Al-Sobhi S, Rostom A, Stuart RK: Locally advanced breast cancer in Saudi Arabia: high frequency of stage III in a young population. Med Oncol. 1999, 16 (2): 95-103. 10.1007/BF02785842.View ArticlePubMedGoogle Scholar
- Lai FM, Chen P, Ku HC, Lee MS, Chang SC, Chang TM, Liou SH: A case-control study of parity, age at first full-term pregnancy, breast feeding and breast cancer in Taiwanese women. Proceedings of the National Science Council, Republic of China Part B, Life sciences. 1996, 20 (3): 71-77.PubMedGoogle Scholar
- Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, et al: The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006, 313 (5795): 1929-1935. 10.1126/science.1132939.View ArticlePubMedGoogle Scholar
- Knoop AS, Knudsen H, Balslev E, Rasmussen BB, Overgaard J, Nielsen KV, Schonau A, Gunnarsdottir K, Olsen KE, Mouridsen H, et al: retrospective analysis of topoisomerase IIa amplifications and deletions as predictive markers in primary breast cancer patients randomly assigned to cyclophosphamide, methotrexate, and fluorouracil or cyclophosphamide, epirubicin, and fluorouracil: Danish Breast Cancer Cooperative Group. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2005, 23 (30): 7483-7490. 10.1200/JCO.2005.11.007.View ArticleGoogle Scholar
- Ueki T, Nishidate T, Park JH, Lin ML, Shimo A, Hirata K, Nakamura Y, Katagiri T: Involvement of elevated expression of multiple cell-cycle regulator, DTL/RAMP (denticleless/RA-regulated nuclear matrix associated protein), in the growth of breast cancer cells. Oncogene. 2008, 27 (43): 5672-5683. 10.1038/onc.2008.186.View ArticlePubMedGoogle Scholar
- Bieche I, Vacher S, Lallemand F, Tozlu-Kara S, Bennani H, Beuzelin M, Driouch K, Rouleau E, Lerebours F, Ripoche H, et al: Expression analysis of mitotic spindle checkpoint genes in breast carcinoma: role of NDC80/HEC1 in early breast tumorigenicity, and a two-gene signature for aneuploidy. Molecular cancer. 2011, 10: 23-10.1186/1476-4598-10-23.PubMed CentralView ArticlePubMedGoogle Scholar
- Kais Z, Barsky SH, Mathsyaraja H, Zha A, Ransburgh DJ, He G, Pilarski RT, Shapiro CL, Huang K, Parvin JD: KIAA0101 interacts with BRCA1 and regulates centrosome number. Molecular cancer research : MCR. 2011, 9 (8): 1091-1099. 10.1158/1541-7786.MCR-10-0503.PubMed CentralView ArticlePubMedGoogle Scholar
- O'Brien SL, Fagan A, Fox EJ, Millikan RC, Culhane AC, Brennan DJ, McCann AH, Hegarty S, Moyna S, Duffy MJ, et al: CENP-F expression is associated with poor prognosis and chromosomal instability in patients with primary breast cancer. International journal of cancer Journal international du cancer. 2007, 120 (7): 1434-1443. 10.1002/ijc.22413.View ArticlePubMedGoogle Scholar
- Korner A, Pazaitou-Panayiotou K, Kelesidis T, Kelesidis I, Williams CJ, Kaprara A, Bullen J, Neuwirth A, Tseleni S, Mitsiades N, et al: Total and high-molecular-weight adiponectin in breast cancer: in vitro and in vivo studies. The Journal of clinical endocrinology and metabolism. 2007, 92 (3): 1041-1048. 10.1210/jc.2006-1858.View ArticlePubMedGoogle Scholar
- Hammamieh R, Chakraborty N, Barmada M, Das R, Jett M: Expression patterns of fatty acid binding proteins in breast cancer cells. Journal of experimental therapeutics & oncology. 2005, 5 (2): 133-143.Google Scholar
- Boneberg EM, Legler DF, Hoefer MM, Ohlschlegel C, Steininger H, Fuzesi L, Beer GM, Dupont-Lampert V, Otto F, Senn HJ, et al: Angiogenesis and lymphangiogenesis are downregulated in primary breast cancer. British journal of cancer. 2009, 101 (4): 605-614. 10.1038/sj.bjc.6605219.PubMed CentralView ArticlePubMedGoogle Scholar
- Pauwels EK, Kairemo K: Fatty acid facts, part II: role in the prevention of carcinogenesis, or, more fish on the dish?. Drug news & perspectives. 2008, 21 (9): 504-510. 10.1358/dnp.2008.21.9.1290819.View ArticleGoogle Scholar
- Escrich E, Solanas M, Moral R, Escrich R: Modulatory effects and molecular mechanisms of olive oil and other dietary lipids in breast cancer. Current pharmaceutical design. 2011, 17 (8): 813-830. 10.2174/138161211795428902.View ArticlePubMedGoogle Scholar
- Chen DC, Chung YF, Yeh YT, Chaung HC, Kuo FC, Fu OY, Chen HY, Hou MF, Yuan SS: Serum adiponectin and leptin levels in Taiwanese breast cancer patients. Cancer letters. 2006, 237 (1): 109-114. 10.1016/j.canlet.2005.05.047.View ArticlePubMedGoogle Scholar
- Stefan N, Vozarova B, Funahashi T, Matsuzawa Y, Weyer C, Lindsay RS, Youngren JF, Havel PJ, Pratley RE, Bogardus C, et al: Plasma adiponectin concentration is associated with skeletal muscle insulin receptor tyrosine phosphorylation, and low plasma concentration precedes a decrease in whole-body insulin sensitivity in humans. Diabetes. 2002, 51 (6): 1884-1888. 10.2337/diabetes.51.6.1884.View ArticlePubMedGoogle Scholar
- Mantzoros C, Petridou E, Dessypris N, Chavelas C, Dalamaga M, Alexe DM, Papadiamantis Y, Markopoulos C, Spanos E, Chrousos G, et al: Adiponectin and breast cancer risk. The Journal of clinical endocrinology and metabolism. 2004, 89 (3): 1102-1107. 10.1210/jc.2003-031804.View ArticlePubMedGoogle Scholar
- Hawthorn L, Luce J, Stein L, Rothschild J: Integration of transcript expression, copy number and LOH analysis of infiltrating ductal carcinoma of the breast. BMC cancer. 2010, 10: 460-10.1186/1471-2407-10-460.PubMed CentralView ArticlePubMedGoogle Scholar
- Flegal KM, Carroll MD, Kit BK, Ogden CL: Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. JAMA : the journal of the American Medical Association. 2012, 307 (5): 491-497. 10.1001/jama.2012.39.View ArticlePubMedGoogle Scholar
- Lorincz AM, Sukumar S: Molecular links between obesity and breast cancer. Endocrine-related cancer. 2006, 13 (2): 279-292. 10.1677/erc.1.00729.View ArticlePubMedGoogle Scholar
- Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, Romero IL, Carey MS, Mills GB, Hotamisligil GS, et al: Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nature medicine. 2011, 17 (11): 1498-1503. 10.1038/nm.2492.PubMed CentralView ArticlePubMedGoogle Scholar
- Nichols HB, Trentham-Dietz A, Egan KM, Titus-Ernstoff L, Holmes MD, Bersch AJ, Holick CN, Hampton JM, Stampfer MJ, Willett WC, et al: Body mass index before and after breast cancer diagnosis: associations with all-cause, breast cancer, and cardiovascular disease mortality. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2009, 18 (5): 1403-1409. 10.1158/1055-9965.EPI-08-1094.View ArticleGoogle Scholar
- Bianchini F, Kaaks R, Vainio H: Overweight, obesity, and cancer risk. The lancet oncology. 2002, 3 (9): 565-574. 10.1016/S1470-2045(02)00849-5.View ArticlePubMedGoogle Scholar
- van den Brandt PA, Spiegelman D, Yaun SS, Adami HO, Beeson L, Folsom AR, Fraser G, Goldbohm RA, Graham S, Kushi L, et al: Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. American journal of epidemiology. 2000, 152 (6): 514-527. 10.1093/aje/152.6.514.View ArticlePubMedGoogle Scholar
- Key TJ, Appleby PN, Reeves GK, Roddam A, Dorgan JF, Longcope C, Stanczyk FZ, Stephenson HE, Falk RT, Miller R, et al: Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women. Journal of the National Cancer Institute. 2003, 95 (16): 1218-1226.View ArticlePubMedGoogle Scholar
- Feigelson HS, Jonas CR, Teras LR, Thun MJ, Calle EE: Weight gain, body mass index, hormone replacement therapy, and postmenopausal breast cancer in a large prospective study. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2004, 13 (2): 220-224. 10.1158/1055-9965.EPI-03-0301.View ArticleGoogle Scholar
- Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ: Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. The New England journal of medicine. 2003, 348 (17): 1625-1638. 10.1056/NEJMoa021423.View ArticlePubMedGoogle Scholar
- Goss PE, Ingle JN, Ales-Martinez JE, Cheung AM, Chlebowski RT, Wactawski-Wende J, McTiernan A, Robbins J, Johnson KC, Martin LW, et al: Exemestane for breast-cancer prevention in postmenopausal women. The New England journal of medicine. 2011, 364 (25): 2381-2391. 10.1056/NEJMoa1103507.View ArticlePubMedGoogle Scholar
- Samuel VT, Petersen KF, Shulman GI: Lipid-induced insulin resistance: unravelling the mechanism. Lancet. 2010, 375 (9733): 2267-2277. 10.1016/S0140-6736(10)60408-4.PubMed CentralView ArticlePubMedGoogle Scholar
- Rose DP, Komninou D, Stephenson GD: Obesity, adipocytokines, and insulin resistance in breast cancer. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2004, 5 (3): 153-165. 10.1111/j.1467-789X.2004.00142.x.View ArticleGoogle Scholar
- Housa D, Housova J, Vernerova Z, Haluzik M: Adipocytokines and cancer. Physiological research / Academia Scientiarum Bohemoslovaca. 2006, 55 (3): 233-244.PubMedGoogle Scholar
- Arita Y, Kihara S, Ouchi N, Maeda K, Kuriyama H, Okamoto Y, Kumada M, Hotta K, Nishida M, Takahashi M, et al: Adipocyte-derived plasma protein adiponectin acts as a platelet-derived growth factor-BB-binding protein and regulates growth factor-induced common postreceptor signal in vascular smooth muscle cell. Circulation. 2002, 105 (24): 2893-2898. 10.1161/01.CIR.0000018622.84402.FF.View ArticlePubMedGoogle Scholar
- Brakenhielm E, Veitonmaki N, Cao R, Kihara S, Matsuzawa Y, Zhivotovsky B, Funahashi T, Cao Y: Adiponectin-induced antiangiogenesis and antitumor activity involve caspase-mediated endothelial cell apoptosis. Proceedings of the National Academy of Sciences of the United States of America. 2004, 101 (8): 2476-2481. 10.1073/pnas.0308671100.PubMed CentralView ArticlePubMedGoogle Scholar
- Miyoshi Y, Funahashi T, Kihara S, Taguchi T, Tamaki Y, Matsuzawa Y, Noguchi S: Association of serum adiponectin levels with breast cancer risk. Clinical cancer research : an official journal of the American Association for Cancer Research. 2003, 9 (15): 5699-5704.Google Scholar
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