Gene Expression Profiling in Gynecological ...

In Current Advances in Gynecological Oncology –Basic and Clinical aspects (2011) Eds. Chauhan SC, Kumar D, Jaggi, M and Bell MC; Research Signpost

Gene Expression Profiling in Gynecological Cancers Nyla Dil1 and Abhijit G. Banerjee1, 2,* Affiliations: 1 Department of Oral Biology; Dental Diagnostics and Surgical Sciences, University of Manitoba Health Sciences Center, Winnipeg, Canada 2 Center for Genomic Biomedicine Research & Incubation, Durg, India * Corresponding author

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Keywords: Gynecological Cancer, Gene expression, ovarian cancer, cervical cancer, uterine cancer, Biomarkers pregnancies, and fallopian tube dysplasia. Abstract Occasionally skin cancers or sarcomas can also be found in the female genitalia, termed Inherited or acquired genetic mutations contribute to the Generally, most pathogenesis of gynecological malignancies. Improved gynecological sarcomas. molecular techniques have lead to the identification of gynecological cancers are found in women many of these genetic defects. The advent of DNA- aged over 50, though the incidence rates for microarray technology has permitted comprehensive younger women have been rising. This chapter genetic profiling of gynecological cancers by allowing the simultaneous study of tens of thousands of genes. Through discusses the more common gynecological the use of this technology, multi-gene signatures have been cancer types; and will not further discuss the discovered that can highlight the mechanisms of cell rare malignancies. The lack of reliable early growth, classify tumor histological subtypes, predict detection and the absence of specific symptoms clinical end points, and also provide mechanistic of most gynecological cancer result in information on drug resistance in gynecological cancers with important implications for developing screening tests diagnosis of these cancers at advanced stage in and prognostic markers. This review discusses gene the majority of the patients. expression profiling studies of ovarian, endometrial and cervical cancers. In addition, similarities in identification of potential genetic targets, between human papilloma viruses (HPV) mediated cervical cancer and head and neck cancer progression are discussed. Applications of these expression profiles may lead to novel targeted gene therapies that are critical to genetic defects seen in gynecologic cancers.

Introduction Gynecological cancers are a group of different malignancies of the female reproductive system. The most common types of gynecologic malignancies are ovarian cancer, endometrial (uterine) cancer, and cervical cancer. There are other less common gynecological malignancies including vaginal cancer, vulvar cancer, gestational trophoblastic tumors/ Molar 65

Genetic alterations at molecular level characterize the development of human cancer. Success of the human genome project and the recent advances in molecular genetic technology has empowered investigators with new molecular tools in dissecting the cancer genome for discovery of cancer-related genes. The purpose of this review chapter is to highlight/summarize gene expression profiling studies of ovarian cancer cervical cancer, and endometrial cancer which identify important genes that may help understand the evolution from normal gynecological tissue to gynecological cancers. We conducted this review also to help find (if and) what synergies lie in the cancers of mucosal epithelial origin that bears the onslaught of various pathogen

In Current Advances in Gynecological Oncology –Basic and Clinical aspects (2011) Eds. Chauhan SC, Kumar D, Jaggi, M and Bell MC; Research Signpost infections, inflammation and as a result; progress towards malignancy from benign lesions. These would thus help reveal expression profile dependent systems biology common to both types of cancers of oral and gynecological origins, wherein HPV and bacterial infections are common etiological route to malignant transformations.

population. However, microarray technology has been used to compare gene-expression profiles in epithelial ovarian cancers with those from the surface epithelium of normal ovaries with the goal to identify genes that are differentially up regulated in ovarian cancer, and then to also determine whether these genes encode proteins detectable in the serum.

Ovarian Cancer

Expression Profile Analyses of Ovarian Cancer

Ovarian cancer is the sixth most commonly diagnosed and fifth leading cause of death from cancer in women. A woman has a lifetime risk of ovarian cancer of around 1.5%, which makes it the second most common gynecologic malignancy, following cancer of the uterine corpus, and causes more deaths per year than any other malignancy of the female reproductive system(1). Women typically present with latestage disease when the overall 5-year relative survival rate is 45% in these types of cancers.

The gene expression profiles of normal ovaries and ovaries from patients with epithelial ovarian cancer were examined by both Affymetrix U95 oligonucleotide chip array (3) and cDNA microarray platforms. The Affymetrix profile queried 68000 probe sets from 50 epithelial ovarian cancers of different histotypes as well as stages and was able to discriminate between various subtypes of ovarian cancers while cDNA array profiles specifically demarcated the differences between serous or endometroid epithelial ovarian cancers with their normal counterparts as mentioned in the sections below. The Affymetrix profile identifies genes that would represent changes in gene expression that resembles their respective normal counterpart as pathologists point towards their morphologic histotypes and thus would provide basis for clinical diagnosis. The respective subtypes mostly clustered together in the dendrogram and helped identify specific deregulated molecular signatures representing each subtype, amongst which 95% (102 out of 107) were found to be up regulated with respect to their normal epithelial origin, in sharp contrast to only 58% when they were compared with respect to only normal ovarian epithelial cells. This suggests that difference in gene expression profile of ovarian cancers may represent mostly in up regulation of genes involved in cellular differentiation, reflecting their morphologic differences. The genes identified to be of diagnostic and prognostic

Most tumors of the ovary can be classified into three major categories; surface epithelial carcinomas, sex cord-stromal tumors, and germ cell tumors, based on the cell type from which the tumors originate. Each category includes several subtypes and within each of these categories are tumors of uncertain malignant behavior with combinations of different subtypes. However, most predominant ovarian cancers are the epithelial carcinoma and can be classified into four main subtypes: serous, mucinous, endometrioid, and clear cell (2). Despite its clinical significance, the factors that regulate the development and progression of ovarian cancer are among the least understood of all major human malignancies. Gene expression profiling and sequential statistical analyses of gene subsets can identify new genes and molecular pathways affecting development of ovarian cancer. At present, there is no screening strategy for the early detection of ovarian cancer in the general 66

In Current Advances in Gynecological Oncology –Basic and Clinical aspects (2011) Eds. Chauhan SC, Kumar D, Jaggi, M and Bell MC; Research Signpost value are a group of eight genes found to be up regulated in mucinous subtypes (TFF1, TFF3, AGR2, TM4SF3, CEACAM6, LGALS4, BCMP1and CTSE) but not in others. Similarly, four candidate genes were found to be up regulated in the clear cell group (GPX3, GLRX, FXYD2, and RBP4) and the genes clearly discriminating between serous, clear and endometroid ovarian cancer types were AMY2B, RBP4 and FGF9 respectively.

The expression of certain genes has been shown to have limited prognostic or predictive value in epithelial ovarian cancer. These genes include p53, BCL-2, BAX, EGFR and c-erbB2 (also called HER2) (13-16). The advent of DNA-microarray technology has permitted comprehensive genetic profiling of cancer by allowing the simultaneous study of tens of thousands of genes in contrast to studying the expression of single genes (17, 18). Through the use of this technology, multigene signatures have been discovered that can provide insights into the mechanisms of cell growth, classify tumor histological subtypes, predict clinical end points, and also provide mechanistic information on drug resistance in cancer (19, 20)

The cDNA array based gene expression profile of serous epithelial cancers was not dependent on the stage of the disease at point of diagnosis (4). Supervised microarray data analysis identified a subset of 329 genes showing significant differential expression between cancerous and normal ovarian tissue including several new genes such as TNFalpha and activin A receptor type I. Real-time RT-PCR analysis revealed altered expression levels of few selected genes; WAP four-disulfide core domain protein HE4 (WAP, up-regulated), secreted phosphoprotein1 (osteopontin) (SPP1, upregulated), activin A receptor type I (ACVR1, down-regulated), tumor necrosis factor (TNF , up-regulated) and decorin (DCN, downregulated) in cancerous ovarian tissue (4). Oligonucleotide arrays were used to identify 275 genes predicted to encode secreted proteins with increased or decreased expression in ovarian cancers. The serum levels of four of these proteins; matrix metalloproteinase-7, osteopontin, secretory leukoprotease inhibitor, and kallikrein 10, were significantly elevated when tested in a series of 67 independent patients with serous ovarian carcinomas compared with 67 healthy controls (5). Similar microarray studies have generated a substantial number of potential biomarkers. However, only some of these potential biomarkers have been validated in small patient cohorts. These biomarkers include; osteopontin (6, 7), prostasin (8), whey acidic protein HE4 (9), epithelial cell adhesion molecule (10), kallikrein 10 (11) and brain creatine kinase (12).

Endometrial Cancer Endometrial carcinoma, commonly referred to as uterine cancer, is the most common malignancy of the female genital tract with overall survival rate of 85% at 5 years (21). Increasing evidence suggests that the majority of endometrial cancers can be divided into two different types based on clinico-pathological and molecular characteristics. Type I is associated with an endocrine milieu of estrogen predominance. These tumors develop from endometrial hyperplasia and are of endometroid histology. They have good prognosis and are sensitive to hormonal treatment. Unlike type I cancers, type II endometrial cancers are not associated with a history of unopposed estrogens and develop from the atrophic endometrium of elderly women. These tumors are of serous papillary or clear cell morphology, do not react to hormonal treatment and have a poor prognosis. Both types of endometrial cancer differ markedly in molecular mechanisms of transformation. The transition from normal endometrium to a malignant tumor is thought to involve a stepwise accumulation of alterations in cellular mechanisms leading to dysfunctional cell 67

In Current Advances in Gynecological Oncology –Basic and Clinical aspects (2011) Eds. Chauhan SC, Kumar D, Jaggi, M and Bell MC; Research Signpost distinguish serous from endometroid cancers, the two most common subgroups (25). These arrays also revealed previously unrecognized novel pathways in endometrial cancers, such as the down regulation of SOCS2 that is a member of the suppressors of cytokine signaling family of intracellular proteins that are involved in the negative regulation of cytokine signal transduction. They have also generated a list of candidate biomarker genes that provide discrimination between normal and malignant tissues, and different tumour types. Endometrial cancers show mutations of PTEN, KRAS2 and CTNNB1 (type I) or TP53 and Her-2/neu (type II). However, 50% of endometrial cancers lack these mutations.

growth (22, 23).

Expression Profile Analyses of Endometrial Cancer One of the most valuable potential uses of microarray analyses is their use in the classification of endometrial cancers. Studies have shown that microarrays provide a powerful approach for identifying biomarkers in these cancer types. Global expression changes of constitutive and hormonally regulated genes during endometrial neoplastic transformation was found when largescale messenger RNA expression analysis was conducted in 4 normal (2 proliferative, 2 secretory) and 10 malignant endometria using Affymetrix Gene Chip probe arrays (24). Fifty out of the 6000 tested genes were differentially expressed between normal and malignant groups and were predominantly characterized by diminished expression levels in the malignant tissue. These genes include cell cycle, cell-cell interaction, cytoskeleton, adhesion, extracellular matrix, metabolism, toxin scavenger/activator, tumor suppressor/oncogenes and several other genes. Hormonally regulated genes in normal tissues were expressed in a disordered and heterogeneous fashion in cancers, with tumors resembling proliferative more than secretory endometrium.

Peroxisome proliferator-activated receptor (PPAR) was explored as a potential therapeutic target in endometrial cancer treatment using microarrays. Treatment of endometrial cancer cells with PPAR agonist, significantly reduced proliferation and increased cell death, suggesting that altered expression of nuclear hormone receptors involved with fatty acid metabolism may lead to deregulated cellular proliferation and apoptosis. Progesterone is known to reduce the risk of developing endometrial cancer. Microarrays have been used to investigate the molecular effects of progesterone in endometrial tumorigenesis (26-28). After treatment of normal, non-transformed endometrial epithelial cells with oestradiol and the synthetic progestogen norethisterone acetate Wnt-7a (a part of Wnt family of secreted signaling molecules) was shown to be up regulated (26). Therefore, Wnt signaling may be involved in the anti-neoplastic, endometrial protective effects of progestogens.

Risinger et al examined global expression patterns of 16 non-endometroid cancers (13 serous papillary and 3 clear cell), 19 endometrioid cancers, and 7 age-matched normal endometria to elucidate molecular events involved in endometrial carcinogenesis. Unsupervised analysis of gene expression identified 191 genes that exhibited more than 2fold differences between the histological groups. Many genes were similarly dysregulated in both nonendometroid and endometroid cancers relative to normal endometria. Gene expression differences in only 24 transcripts could

Dai et al and Smid-Koopman et al have also examined the functional difference between the two human progesterone receptor (hPR) isoforms in human endometrial cancer (27), 68

In Current Advances in Gynecological Oncology –Basic and Clinical aspects (2011) Eds. Chauhan SC, Kumar D, Jaggi, M and Bell MC; Research Signpost likely due to the lack of screening and infectious cofactors. There are 2 main types of cervical cancers: squamous cell carcinoma (SCC) and adenocarcinoma. About 80% to 85% of cervical cancers are squamous cell carcinomas from the squamous cells that cover the surface where the exocervix joins the endocervix. The remaining 10% of cervical cancers are adenocarcinomas which develop from the mucus-producing gland cells of the endocervix. Less commonly, cervical cancers have features of both squamous cell carcinomas and adenocarcinomas, termed adenosquamous carcinomas or mixed carcinomas (32, 33). Almost all squamous cell carcinomas and majority of adenocarcinomas of the cervix are human papilloma virus (HPV) positive. HPV integration in the genome leads to inactivation of the cell progression regulators; p53 pathway and the retinoblastoma gene product (Rb) pathway. Integration is essential for the initiation of cervical carcinogenesis but is probably not sufficient for progression to invasive cervical cancers (34).

(28). Endometrial malignant cell lines were stably transfected with either hPR-A or hPR-B and were treated with progestins and gene expression was analyzed using cDNA arrays. The data showed distinctive differences in target gene regulation between the two hPR isoforms. Cells expressing hPRB were growth responsive to progesterone. Expression of five different genes, insulin-like growth factor-binding protein3, fibronectin, replication protein A, fibronectin, and integrin3ß1 were down-regulated in hPRBexpressing cells. The results suggest that the relative distribution of hPR-A and hPR-B in endometrial cancer cells may have high impact on the behavior of human endometrial tumors. Down-regulation of adhesion molecules in the presence of the hPRB isoform suggests that progesterone acts primarily through hPRB receptors to inhibit cancer cell invasiveness mediated by adhesion molecules (27). Total RNA isolated from four human endometrial carcinoma samples (two cell-lines and two tissue samples), one benign endometrial tissue sample and a human breast cancer cell-line, was subjected to cDNA array analysis. Three genes; Decorin, TIMP3 and Cyclin D1 were identified to be differentially expressed between the benign endometrial tissue sample and the endometrial carcinoma samples comprising of tissue and celllines (29).

Expression Profile Analyses of Cervical Cancer In case of cervical cancers, both mRNA and micro RNA profiling has been carried out previously that encompasses transcriptomics and regulatory genome changes collectively. Further gene expression changes pertaining to specific intervention have also been documented. Profiling of primary cervical cancers by cDNA microarray analysis was characterized as early as 1998 by Shim et al (35) followed by profiling of ionizing resistant and sensitive cervical cancers by Kitahara et al in 2002(36). Cheng Q et al used 38 cervical patient samples and compared with 5 different non-cervical cancer samples to identify genes associated with multi-step progression of squamous carcinoma of cervix (37). They subsequently suggest that ribosomal protein S12 and NADH hydogenase4 genes may be useful as early diagnostic markers of

A comprehensive review of early events of pathogenesis in ovarian cancers has been carried out earlier that emphasize important role of tumor cell mutations and factors of host tumor environment in initiation and progression and therefore not being discussed further in this review (30).

Cervical Cancer Cervical cancer is one of the leading cancers worldwide (31). In developed countries, there is a continuous decline in incidence and mortality; however, in developing countries, there is a more stable or even increasing pattern which is more 69

In Current Advances in Gynecological Oncology –Basic and Clinical aspects (2011) Eds. Chauhan SC, Kumar D, Jaggi, M and Bell MC; Research Signpost molecules responsible for DNA replication (MCM 4 & 6), stromal matrix modulators (MMPs, Osteonectin, urokinase), ECM proteins (Collagen A1, Fibronectin and Laminin C1) and genes known to be associated with other cancers as well (Topoisomerase 2A, Aurora Kinase 2, Claudin 1, Mucin 1, Integrin B6, Mesothelin and Oncogene B-Myb). This study uniquely also confirms that genes found to be over expressed in dysplastic and invasive squamous cell carcinomas were also common to low frequency (10-20%) adenocarcinomas of cervical origin representing common tumorigenesis associated molecular profiles. Ingenuity pathway analysisTM (Ingenuity Systems Inc., USA) software based data mining of gene expression profiles involving top 400 deregulated genes in cervical cancer is represented in Table I.

potentially malignant cervical cancer tissues. In another study conducted at Chinese university of Hong Kong, expression profiles of 26 cervical cancer tissues were compared with normal cervical tissues using DNA microarrays on nylon membrane filters containing approximately 11,000 features that correspond to either human transcripts with known function or anonymous expressed sequence tags (38). In this study, about 40 genes were found to be differentially expressed between normal and cervical cancer specimens, of which 4 genes could even classify different clinical progressive stages like Ib and IIb completely; which correlates to FIGO staging system of cervical cancers. Further about 300 genes clustered together were able to identify between radiotherapy-resistant and radiotherapysensitive specimens (n=13) from this study thus predicting treatment response related to particular expression profiles with discrimination accuracy of 96% and p value of less than 0.00001. Such gene clusters may eventually help in selecting treatment options from diagnostic biopsy specimens obtained at earlier stages. The genes included belong to functional group of DNA damage response (DNA damage binding protein1), Transcription factors (T-box19 and ZNF33a), serum response factor (nuclear receptor co-repressor1) and immune modulatory factors like IRF2, IFI6-16, IL-10Rβ and Ras family protein, Rho-Rac GEF. A cDNA microarray based expression profiling study by Chen Y et al in 2003 using more than 30000 unigene clones was used to classify different stages of 34 cervical cancer tissue specimens(39). The gene expression profile was able to discriminate between low- and high-grade squamous cervical epithelial lesions, amongst which 35 of top 62 differentially expressed genes or expressed sequence tags in premalignant and invasive lesions were also validated by in situ hybridization on cervical cancer tissue microarrays. Amongst the top 62 genes/ESTs chosen as molecular markers based on a permutation testing & scoring algorithm and those that lie outside 95th percentile, included

Expression profiles associated cervical cancers with HPV positivity and in comparison to those from Head and Neck region were also reviewed as these are the two most etiologically linked cancers of mucosal epithelial tissues. HPV types that are common to mucosal epithelial tissues causing benign lesions are HPV16, 18 & 31. While there is almost 100% positivity reported for cervical cancers, cancers of mucosal origin from head and neck region only comprise of 20 to 30%. A recent gene expression profiling data by Pyeon et al; in HPV positive and negative cancers of both cervical (n=20, with 8 control) and Head and neck (n=42, with 14 control) origin using more than 54000 Affymetrix probe sets, depicts specific expression pattern and identifies gene subgroups that associates HPV+ cancer in both types with cell proliferation related genes (40). These specific profiles may now help explain better clinical response to radiotherapy in HPV positive cancers and better treatment planning. The most common HPV type found in the cancer specimens were HPV16 (13 in Head & Neck versus 8 in Cervical) and thus implicates HPV related 70

In Current Advances in Gynecological Oncology –Basic and Clinical aspects (2011) Eds. Chauhan SC, Kumar D, Jaggi, M and Bell MC; Research Signpost already discussed in our previous sections. We present here a canonical pathways based on top functions and gene network analysis of common up regulated genes in cervical cancers in Fig. 1.

molecular mechanisms especially virus protein E6 and E7 mediated blockage of tumor suppressor proteins and induction of cell cycle related genes such as CCNE and MCMs in specific behavior of these cancers. The common up regulated genes of cell cycle regulation and proliferation included BUB1B, CDC20, CDC 2, CDC 7, CCNB1, ORC, CDKN2A, KNTC1, PCNA and MCM6. The down regulated genes included CCND1, CCNA1 and CDK4/6. In comparison between cervical and head and neck cancers alone, the differentiating gene sets included increased gene sets ESR1, KRT19, XIST and ZNF367 for cervical cancers as opposed to DPT, DSC1, MAGEA12, CYorf15B for head and neck cancers. Interestingly, previously thought to be testis–specific genes SCYP1 and TCAM were shown to be induced by HPV16 and specifically and synergistically up regulated by expression of both viral genes E6/E7. A separate study involving HPV positive head and neck cancers have been reported as well (41) and metaanalysis of these studies would further help us in identifying predictive genes and better prognostic indicators of response to therapy. Meta-analysis of gene expression profiles of cervical cancers obtained from studies of Biewenga et al (42), Santin et al (43) and Wong et al (44) have been

Concluding Remarks Inherited or acquired genetic defects contribute to the initiation and progression of cancer. Improved molecular techniques have lead to the identification of many of these genetic mutations in gynecologic malignancies. The molecular characterization of cancer has provided a better understanding of tumor formation and the clinical behavior of different tumor types, with important implications for developing screening tests and prognostic markers. Applications of these findings will lead to novel targeted gene therapies that correct the critical genetic defects seen in gynecologic cancers. The studies outlined above have discussed a small subset of genes regulating gynecological malignancies. They may lead to several new therapeutic approaches in gynecological cancer treatment.

Acknowledgements: We acknowledge the contributions of many more researchers in the field whose work we could not include for sake of brevity of this review article or due to specific focus of such studies involved with directed interventions and responses in gynecological cancers. One of the authors (ND) is supported by a post-doctoral fellowship from the MHRC funds granted to the Principal Investigator and the senior author (AGB) of this review article.

*

Address correspondences to:

Dr. Abhijit G. Banerjee, Laboratory of Molecular Oncology & Medicine (D-303 & 304), Department of Oral Biology, University of Manitoba Health Sciences Center, Winnipeg, Canada, MB R3E 0W2 Email: [email protected]

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Table I: Top functions derived from 400 up - and down- regulated genes in cervical cancers and in common with HPV related oral cancer and involved molecules in their network. Category

Functional Annotation

P-value

Molecules

Cell Cycle

cell division process of cells

8.25E-15

ANLN, ASPM, BIRC5, BLM, BUB1, BUB1B, CCNB1, CDC2, CDC6, CDCA8, CDKN2A, CDT1, CENPE, CENPF, DBF4, DLGAP5, E2F3, ECT2, EZH2, FANCA, FANCD2, IFI16, KIF23, KIF4A, KNTC1, KPNA2, MCM5, NUF2, NUSAP1, PARP1, PKMYT1, PLAU, PLK1, PTTG1, RACGAP1, SGOL1, TMPO, TOP2A, TYMS, ZWINT (includes EG:11130)

Cell Cycle

cell stage

1.40E-25

ANLN, BIRC5, BLM, BUB1, BUB1B, CCNB1, CDC2, CDC6, CDC45L, CDCA8, CDKN2A, CDT1, CENPE, CENPF, DBF4, DLGAP5, E2F3, ECT2, FANCA, FANCD2, IFI16, KIF11, KIF23, KIF2C, KIF4A, KNTC1, KPNA2, NUF2, NUSAP1, PARP1, PKMYT1, PLAU, PLK1, PRC1, PTTG1, RACGAP1, RAD51, RAD54L, SGOL1, TMPO, TOP2A, TPX2, TYMS, UBE2C, VCPIP1, ZWINT (includes EG:11130)

46

Cell Cycle

cell stage of cells

2.14E-18

ANLN, BIRC5, BLM, BUB1, BUB1B, CCNB1, CDC2, CDC6, CDCA8, CDKN2A, CDT1, CENPE, CENPF, DBF4, DLGAP5, E2F3, ECT2, FANCA, FANCD2, IFI16, KIF23, KIF4A, KNTC1, KPNA2, NUF2, NUSAP1, PARP1, PKMYT1, PLAU, PLK1, PTTG1, RACGAP1, SGOL1, TMPO, TOP2A, TYMS, ZWINT (includes EG:11130)

37

Cell Cycle

cell progression

3.53E-15

ANLN, BIRC5, BUB1, BUB1B, CCNB1, CDC2, CDCA8, CDKN2A, CENPE, CENPF, DBF4, DLGAP5, E2F3, ECT2, EZH2, FANCA, FANCD2, IFI16, KIF11, KIF2C, KNTC1, MCM2, MCM5, NUF2, NUSAP1, PKMYT1, PLK1, PTTG1, SGOL1, TOP2A, TPX2, UBE2C, VCPIP1, ZWINT (includes EG:11130)

34

Cell Cycle

mitosis

1.55E-21

ANLN, BIRC5, BUB1, BUB1B, CCNB1, CDC2, CDCA8, CDKN2A, CENPE, DBF4, DLGAP5, E2F3, ECT2, KIF11, KIF2C, KNTC1, NUF2, NUSAP1, PKMYT1, PLK1, PTTG1, SGOL1, TOP2A, TPX2, UBE2C, VCPIP1, ZWINT (includes EG:11130)

27

cycle

72

# Molecule s 40

In Current Advances in Gynecological Oncology –Basic and Clinical aspects (2011) Eds. Chauhan SC, Kumar D, Jaggi, M and Bell MC; Research Signpost Figure 1. Canonical pathways represented by top 400 deregulated genes (Top-A) and merged networks (Bottom B) from meta-analysis of common cervical cancer associated up regulated genes (n=30) from at least 3 out of 4 studies is illustrated below.

A]

B]

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