Title:

Proteomic analysis of malignant and benign endometrium according to

obesity and insulin resistance status using Reverse Phase Protein Array

Authors:

Olivia Raglan MBBS BSca,b, Nada Assi PhDc, Jaya Nautiyal PhDa, Haonan Lu MSca,

Hani Gabra PhDa,d, Marc J Gunter PhDc, Maria Kyrgiou PhD*a,b

aDepartment of Surgery and Cancer, Institute of Reproductive and Developmental

Biology, Faculty of Medicine, Imperial College London, W12 0NN, UK

bQueen Charlotte’s and Chelsea – Hammersmith Hospital, Imperial College

Healthcare NHS Trust, London, W12 0HS, UK

cSection of Nutrition and Metabolism, International Agency for Research on Cancer

(IARC); 150 Cours Albert Thomas, Lyon, France

dEarly Clinical Development, IMED Biotech Unit, AstraZeneca, Cambridge, UK

*Corresponding author:

Maria Kyrgiou, MSc, PhD, MRCOG

Room 3006, 3rd Floor, Institute of Reproductive and Developmental Biology

Department of Surgery and Cancer, Hammersmith Campus

Imperial Healthcare NHS Trust – Imperial College

Du Cane Road, W12 0NN, London

Email: m.kyrgiou@imperial.ac.uk - Tel: +44 2075942177

1

Running title: Proteomic analysis of endometrium by metabolic status

Keywords: Endometrial cancer; reverse phase protein array; RPPA; functional

proteomics

Manuscript word count: 9,665

2

ABSTRACT

Obesity and hyperinsulinaemia are known risk factors for endometrial cancer, yet the

biological pathways underlying this relationship are incompletely understood. This

study investigated protein expression in endometrial cancer and benign tissue and

its correlation with obesity and insulin resistance.

107 women undergoing hysterectomy for endometrial cancer or benign conditions

provided a fasting blood sample and endometrial tissue. We performed proteomic

expression according to body mass index (BMI), insulin resistance and serum

marker levels. We used linear regression and independent t-test for statistical

analysis. Proteomic data from 560 endometrial cancer cases from The Cancer

Genome Atlas (TCGA) databank was used to assess reproducibility of results.

127 proteins were significantly differentially expressed between 66 cancer and 26

benign patients. Protein expression involved in cell cycle progression, impacting

cytoskeletal dynamics (PAK1) and cell survival (Rab 25), were most significantly

altered. Obese women with cancer had increased PRAS40_pT246; a downstream

marker of increased PI3K-AKT signalling. Obese women without cancer had

increased mitogenic and anti-apoptotic signaling by way of upregulation of Mcl-1,

DUSP4 and Insulin Receptor-b.

This exploratory study identified a number of candidate proteins specific to

endometrioid endometrial cancer and benign endometrial tissues. Obesity and

insulin resistance in women with benign endometrium leads to specific upregulation

of proteins involved in insulin and driver oncogenic signalling pathways such as the 3

PI3K-AKT-mTOR and growth factor signaling pathways which are mitogenic and

also disruptive to metabolism. 

INTRODUCTION

Endometrial cancer is the most common gynaecological cancer in the developed

world 1, accounting for 5% of all new cancer cases in females 2. Endometrial cancer

incidence is rising, attributed primarily to lifestyle factors such as the obesity and

diabetes epidemic and to increasing population age 3 4. Obesity is a major risk factor

for endometrial cancer 5, yet the biological pathways underlying the association of

obesity with the most prevalent subtype, endometrioid endometrial cancer (EEC),

are not fully characterised. Obesity influences synthesis and bioavailability of

endogenous sex steroids, it can lead to chronic hyperinsulinaemia 6 7 and low-grade

chronic inflammation 8, and drive growth factor production 9. High levels of insulin,

insulin-like growth factor-1 (IGF-1) and oestrogens bind to endometrial tissue

receptors and interact with growth factor signalling pathways, including PI3K-Akt-

mTOR and MAPK/ERK, and can promote proliferation 9. Epidemiological data

emphasize the role of hyperinsulinaemia as a risk factor for EEC, independent of

oestradiol 10. Evidence also suggests that diabetes increases the risk of developing

endometrial cancer 11.

The advent of high-throughput, quantitative functional proteomic technologies such

as Reverse Phase Protein Array (RPPA), are mobilizing research efforts directed at

the discovery of protein biomarkers which may aid in early detection and treatment of

EEC. RPPA, through investigation of protein expression levels, enables an

exploratory analysis of signalling pathway activation that can then be linked to the 4

biology of cancer progression. This includes identifying the role of significantly

expressed proteins in fundamental cellular functions such as proliferation, growth

and survival.

While the TCGA network and other groups have investigated endometrial tumour

tissue on genomic, transcriptomic and proteomic platforms 12-14, the signature protein

expression levels of low- and high-risk women with benign endometrium and its

correlation to obesity and an insulin resistance are yet to be characterised. In this

study, we aimed to investigate the expression of protein levels in endometrioid

endometrial cancer and in benign tissue, and to assess whether protein expression

correlated with obesity and insulin resistance status across the two cohorts.

MATERIALS AND METHODS

Population

We recruited 107 women attending for gynaecological surgery that included removal

of the uterus from 2014-2016. We included women diagnosed with EEC and those

with benign gynaecological conditions (i.e. menorrhagia, endometriosis, pelvic pain,

fibroids). The following cases were excluded: non-endometrioid or mixed endometrial

cancers, different primary site tumour of the female genital tract (i.e. ovarian cancer),

concomitant malignancies, previous hysterectomy. All EEC cases were grouped into

one set, and benign cases formed a second set. The replication cohort used

archived RPPA data from 560 primary endometrial tumour tissues available in the

TCGA databank (https://portal.gdc.cancer.gov/).

5

Ethical approval was obtained the NHS West of Scotland Research Ethics Service

Committee (REC:14/WS/1098) and Imperial College London and Imperial College

Healthcare NHS Trust Joint Research Compliance Office (No.14HH2220 CSP, Ref:

154598). All experiments were performed in accordance with approved guidelines,

all patients gave informed consent.

Data collection and experiments

A comprehensive questionnaire collecting epidemiological information was collected

at recruitment from each participant. Data regarding current and past oral

contraceptive pill (OCP) and hormone replacement therapy (HRT) use were

collected. Women were categorised as ‘non-users’ of OCP or HRT if they had never

taken any in the past or had not taken any in the two months prior to time of

endometrial tissue sampling. Fasting blood samples were procured on the morning

of surgery, prior to administration of any anesthetic medication, and serum was

stored at -80ºC post sample centrifugation. Endometrial tissue samples were

collected from the operating theatre and taken to the on-site histopathologist where a

tissue section was cut for immediate storage at -80C. A 1mg tissue section was cut

from the frozen banked specimen and to confirm the histology of the research

sample, the cellularity and tumour content, four sections (10m thickness) were cut

using a cryostat and stained with haemotoxylin and eosin (H&E). Each section was

then reviewed by a Consultant histopathologist to confirm the cellular structures

present (Supplementary Figure 1). Only samples with medium to very high cellularity

and tumour content (in endometrial cancer samples only, threshold for medium (30-

50%) and high (>50%)) were included in the analysis. Benign samples were similarly

evaluated and included only if the absence of tumour content or fibroid material was 6

confirmed. Each sample was prepared for RPPA based on previously published

protocols 13 15. The samples were shipped to RPPA Core Facility at MD Anderson

Cancer Centre, USA, for processing (Supplementary Methods, Supplementary Table

1). Fasting serum was assayed by ELISA for concentrations of nine selected

markers related to metabolism, insulin regulation and obesity (Supplementary Table

2).

Statistical Analysis

Baseline characteristics were determined for patients with EEC and benign

endometrium separately. Body mass index (BMI) was calculated as weight (kg)/

height (m2). Participants with BMI 30kg/m2 were considered obese, and BMI <30

kg/m2 as non-obese16. Homeostasis model assessment-insulin resistance (HOMA-

IR) index was used to determine insulin resistance, calculated as the product of

fasting blood glucose (mmol/L) and fasting insulin (U/mL), divided by a standard

constant, 22.5 17. Based on our cohort distribution, we selected the second tertile

value (4.35) as a cut-off for insulin resistance. A calculated HOMA-IR value equal to

or above 4.35 classified that sample as insulin resistant (IR), samples with values

below that threshold were non-IR. To assess differences between benign and cancer

sample baseline characteristics, student’s t-test and chi-square test were applied as

appropriate.

RPPA analysis

A global primary assessment of protein expression profiles within our study cohort

was conducted using unsupervised hierarchical clustering analysis of genes via

centroid linkage using GeneCluster 3.0 7

(http://bonsai.hgc.jp/*mdehoon/software/cluster/software.htm). Data was analysed

using STATA software (Version 15.0, StataCorp, TX, USA).

Principal Component Partial R-squared analysis 18 was performed on raw RPPA data

to assess sources of systematic variability (Supplementary Figure 2). Factors

examined were: year of sample collection, sample provenance, patient age,

histopathology, menopausal status and diabetes status. The highest contributing

factor was year of sample collection (over 19% of variance present). Age was also a

contributing factor (over 2% of explained variability). All further analyses were

adjusted for these two variables. The high variability observed for histopathology is

consistent with the two different tissue types (benign and cancer) being investigated.

19 proteins out of the 282 RPPA protein panel were excluded due to high coefficient

of variation (CV >1.0) (Supplementary Table 3).

Protein expression according to obesity, insulin resistance and serum markers

Crude and multivariable linear models adjusted for year of sample collection and

patient age were used to relate BMI, IR, as well as serum marker levels (including

oestradiol, insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding

protein-3 (IGFBP3), sex-hormone binding globulin (SHBG), glucose, insulin, leptin,

adiponectin, c-reactive protein (CRP)) with protein expression levels in each cohort.

A sensitivity analysis excluding women who had used oral contraception (OCP) or

were taking hormone replacement therapy (HRT) was also conducted. Independent

t-tests were applied to assess the mean difference in protein levels according to

histology, obesity status and insulin resistance status.

8

A sensitivity analysis that excluded samples with both ‘low’ and ‘medium’ tumour

content was performed (Supplementary Table 4). After additional adjustment for the

sources of highest variability (patient age and year of sample collection), the analysis

for the benign cohort only containing samples which were of high or very high

cellularity retained 10 of the 23 original proteins found to be significantly expressed

(p<0.05). One out of the three proteins (annexin-I) previously found to be significant

in the linear model for insulin resistance retained significance in the new analysis. In

the linear regression analysis for the cancer cohort specifically, only the PR

(progesterone receptor) protein remained significant in the linear model for increase

in insulin resistance status.

Replication cohort

Data available from the TCGA Research Network was used as a replication set. 560

uterine cancer cases were listed, and where available, demographic data on age,

year of sample collection and BMI were included in our analysis. 138 of the proteins

measured in the TCGA dataset were common to our panel of proteins. Linear

models compared significantly expressed proteins in cancer cases from our dataset

with the TCGA cancer dataset according to obesity status only. We were unable to

conduct a similar analysis for benign samples, as there are no published proteomic

datasets available.

All statistical tests were two sided and p<0.05 considered to be statistically

significant. FDR correction was applied to control for multiple testing and q-values

reported. Analysis was conducted in R statistical software version 3.3.1.

9

RESULTS

We recruited 107 women planned for gynaecological surgery including removal of

uterus. Ninety-two of these fulfilled the inclusion criteria where initial histology

confirmed either EEC or benign endometrium. At histopathological examination,

fifteen cases were found to be non-EEC, mixed endometrial cancer types or not

primary endometrial cancer and were excluded. 66 out of the 92 cases were EEC

along with 26 benign endometrial tissues. Mean age (64 vs 53y, p=2.9e-04) and mean

BMI (33.9 vs 28.3kg/m2, p=0.008) was higher in the cancer than the benign cohort

(Table 1).

Serum markers

IGF-1 and IGFBP-3 levels were significantly lower in cancer samples (p=0.004 and

p=4.3e-04, respectively) (Figure 1), among the total cohort. SHBG was also lower in

the cancer cohort (premenopausal women only, p=0.004). In postmenopausal

women only, IGF-1 levels were significantly lower in the cancer cohort, irrespective

of HRT use (p=0.030). There was no significant difference in oestradiol expression

between the cancer or benign cohort, even when adjusted for menopause status

(premenopausal, p=0.890, postmenopausal, p=0.754). Pre- and postmenopausal

women in the cancer group had higher mean levels of oestradiol, CRP, leptin, insulin

and glucose compared to the benign group, and lower mean levels of adiponectin,

though these serum markers did not reach significance. Premenopausal women

showed the most marked differences.

Protein expression using RPPA

10

Unsupervised hierarchical clustering analysis of proteins quantified through RPPA is

shown in Supplementary Figure 3. Age, HOMA-IR and BMI levels were higher in the

benign patients clustering in the top left-hand corner of the heatmap, compared to

benign patients in the bottom left-hand corner (p=0.023, p=0.009 and p=0.054,

respectively). Of note, cancer patients which are interspersed among the benign

patients on the left of the heatmap were older and had a higher mean BMI compared

to the benign group (age, p=0.043 and BMI, p=1.60e-04, respectively).

Differences in protein expression in relation to BMI and insulin level are shown in

Table 2. In cancer cases, increasing BMI was associated with increased expression

of eight proteins; proline-rich Akt substrate of 40 kDa (PRAS40_pT246) and its

binding partner 14-3-3-epsilon, Excision Repair Cross-Complementing genes XPF

and ERCC1, Octamer-binding transcription factor 4 (Oct-4), Human epidermal

growth factor receptor 2 (HER2), Cytochrome oxidase subunit 4 (Cox-IV) and

Stearoyl-CoA Desaturase (SCD). BMI was inversely associated with Ubiquitin-

associated domain-containing protein 1 (UBAC1), Inositol polyphosphate-4-

phosphatase Type II B (INPP4b), glutaminase, Src homology region 2 domain-

containing phosphatase-2 (SHP-2), Jagged1 and protein kinase A (PKA-a). In the

benign cohort, BMI was positively associated with the expression of 12 proteins:

Hairy and enhancer of split-1 (HES1), tafazzin (TAZ), myeloid cell leukemia

sequence 1 (Mcl-1), general control of amino-acid synthesis 5-like 2 (GCN5L2), b-

actin, Enhancer of yellow 2 transcription factor homolog (ENY2), di-methyl-histone

H3 (DM-K9-Histone-H3), dual specificity protein phosphatase 4 (DUSP4),

cyclophilin-F, insulin receptor-b (IR-b), solute carrier family 1 member 5 (SLC1A5)

and x-ray cross-complementing protein 1 (XRCC1). In the cancer cohort, increasing 11

insulin levels positively associated with expression of proteins: B7-H4, Jun proto-

oncogene (c-Jun_pS73), progesterone receptor (PR), B-cell lymphoma 2 (Bcl2),

SRY-Box transcription factor 2 (Sox2), signal transducer and activator of

transcription 5a (Stat5a) and estrogen receptor (ER). Within the benign cohort,

increasing insulin levels positively associated with expression of two proteins,

heregulin and c-raf, and negatively with annexin-I. There were no proteins whose

expression was positively or negatively associated with BMI or insulin that were

common to both the benign and cancer cohort.

127 proteins were significantly differentially expressed between the cancer and

benign cohorts after false discovery rate (FDR) correction (Supplementary Table 5).

Among these were multi-functioning proteins involved in five main signalling

pathways: MAPK signalling, ERBB signalling, FOXO signalling, oestrogen signalling

and apoptotic pathways (Figure 2, Figure 3). The most significantly upregulated

proteins in cancer cases were ACC1, PAK1, TFRC, Wee1, and Rab25. Among

benign cases, c-Kit, ATM pS1981, Creb, SF2 and PEA-15 expression levels were

most increased; proteins involved in cell cycle regulation, apoptosis and FOXO

signalling pathways.

Linear regression analyses examined whether incremental rise in serum marker

concentration altered protein expression level (Supplementary Table 6). Within the

cancer cohort, protein expression of progesterone receptor (PR) increased with

every 1-unit increase of glucose (mmol/L, p=1.4e-03, q=0.038). Remarkably, PAK1,

Gab2, FoxM1, stathmin-1 and PARP1 levels increased per unit rise of CRP in the

cancer cohort only. Although serum markers were significantly associated with the 12

expression of a number of proteins, none of these retained significance in the benign

cohort after FDR correction, likely as a result of small effect size relating to the

overall limited sample size. A further correlation analysis of serum biomarkers was

performed in both benign and endometrioid endometrial cancer datasets, and results

were mutually adjusted when the correlation between two biomarkers was greater

than 0.5 (Supplementary Table 7. Associations noted between serum biomarkers are

highlighted in bold, and the subsequent linear regression analysis for protein

expression in both the benign and cancer sets after adjustment for patient age, year

of sample collection and serum biomarker correlation greater than 0.5, is shown in

Supplementary Table 8. Proteins commonly expressed in original analysis

(Supplementary Table 6) versus post-adjustment for serum biomarker analysis are

highlighted in bold in Supplementary Table 8. After multiple testing correction, no

proteins retained statistical significance in the original linear regression analyses

(Supplementary Table 6), or the additional analyses presented in this Supplementary

Table 8 (for both benign and cancer sets).

PR expression levels were also significantly increased in obese women in both

cancer and benign cohorts (p=0.019 and p=0.026, respectively) (Supplementary

Table 9). No difference in protein expression was found in insulin-resistant women

(q<0.05, data not shown).

Replication of the findings in TCGA dataset

Analysis of the significantly expressed proteins in our dataset with protein expression

levels in the TCGA dataset according to BMI, revealed two proteins associated with

obesity, but with different direction of association. The expression of Stearoyl-CoA 13

Desaturase (SCD), a protein linked to obesity and integral to fatty acid biosynthesis,

increased with increasing BMI in the Imperial dataset (p=0.035), whereas in the

TCGA cohort, levels decreased (p=0.037). It is difficult to explain this inconsistency

between the expression of SCD between the two datasets and could be linked to

methods of analysis, differences in cohort sizes or could be attributed to the

complicated biology of the SCD enzyme. Despite the differences the emergence of

SCD in both the datasets does underscore the importance of the links between fatty

acid metabolism, obesity and cancer. In obese women with endometrioid

endometrial cancer, expression of inositol polyphosphate 4-phosphatase type II

(INPP4b) was significantly decreased in both datasets, replicating our findings

(Imperial, p=0.026, TCGA; p=0.014) (Imperial dataset: Supplementary Table 9,

TCGA dataset: Supplementary Table 10).

DISCUSSION

Carcinogenesis occurs as a result of signaling dysregulation and activation of

oncogenic signalling pathways leading to loss of control of cell growth. Identifying

signature protein expression in tumour cases or at-risk groups has proven

challenging. In colorectal cancer, for example, genome or mRNA-based approaches

to characterizing aberrant signalling pathways have been limited by inadequate

prediction of tumour protein expression and function 19. Functional proteomics

facilitates the study of large-scale protein modifications such as protein

phosphorylation or cleavage which enables biomarker discovery and throws light on

multiple pathway crosstalk. This in turn gives us a better understanding of

determining cancer etiology and linking it with phenotype and also cancer genome

that may translate into personalized cancer care. 14

Endometrial protein expression changes: benign versus cancer

Our results using independent t-tests (q<0.05) show that 127 proteins are

differentially expressed between the cancer and benign cohorts. This large list of

proteins encompasses a multitude of proteins involved in pathways linked with

metabolism, cell growth, cell cycle, survival and tissue remodeling. Among the most

significantly upregulated proteins in cancer were Acetyl-CoA carboxylase (ACC1;

q=7.02e-08), P21 (RAC1) Activated Kinase 1 (PAK1; q=4.24e-07), Transferrin Receptor

(TFRC; q=1.18 e-06), Wee1 (q=2.37e-06) and Rab25 (q=1.13e-05).

ACC1 is an enzyme involved in the maintenance of fatty acid biosynthesis. Fatty acid

biosynthesis is an essential cellular process for conversion of nutrients into metabolic

intermediates for membrane biosynthesis, energy storage and generation of

signaling molecules, that are all essential for the rapidly growing cancer cells and

this lipogenic phenotype is one of the metabolic hallmarks of cancer cells20 21. PAK1,

a serine threonine kinase, has an essential role in controlling cell migration (i.e.

tumour progression and metastasis in cancer) by bringing about changes in the actin

cytoskeleton organization, cell shape and adhesion dynamics by linking a variety of

extracellular signals.

Wee1 is an oncogenic kinase that regulates cell cycle at the G2M checkpoint by

phosphorylating Tyr 15 and Thr14 of Cyclin dependent kinase (CDK1). Wee1 kinase

acts as a gatekeeper of the G2-M cell-cycle checkpoint, allowing DNA repair prior to

mitosis. It is highly expressed and active in several cancer types that depend on a

functional G2–M checkpoint for DNA repair. Along with phosphorylating CDK1, 15

Wee1 also negatively regulates Mitosis promoting factor (MPF) which causes

aberrant mitosis and resistance to DNA damage and induced apoptosis. Wee1 has

been reported to be upregulated in several cancers22.

The context-dependent role of the GTPase Rab25 has shown that it can function as

either tumour suppressor gene and oncogene. Loss of and amplification of Rab25

are thought to contribute to tumorigenesis of subtypes of breast cancer23, although

its role in endometrial cancer is less well documented.

On further analysis of the subcohort containing women with cancer who were obese

or who had insulin resistance, we identified distinct protein expression signatures.

Protein signatures of obese women with EEC include an activated PI3K-mTOR

pathway, increased Her2 expression and Activated Nucleotide Excision Repair

pathway: PRAS40_pT246, which is a substrate of AKT and also a component of

mTORC1 was increased in obese women with EEC (Table 2). PRAS40 is

phosphorylated by growth factors and regulates growth factor signalling in turn24.

Phosphorylated PRAS40 is linked with tumour progression in melanoma and

prostate cancers by regulating cellular proliferation, apoptosis, senescence and

metastasis. Interestingly, obese women with EEC also showed increased

expression of the proto-oncogene human-epidermal growth factor receptor 2

(HER2/ERBB2) which has an important role in development and progression of

certain aggressive types of breast cancer25 and higher levels are associated with a

poorer survival outcome in endometrial cancer patients26. A study analyzing breast

cancer patients showed that PRAS40_pT246 signifying activated PI3K pathway can

16

be used as a biomarker for lack of response to Her2 targeting antibody

trastuzumab27.

Together with XPF, ERCC1 forms the ERCC1-XPF enzyme complex that regulates

DNA repair and DNA recombination as part of the nucleotide excision repair

pathway. Cancer tissues are usually considered deficient in multiple DNA repair

proteins, allowing DNA damage to persist and give rise to carcinogenic mutations.

Deficiencies in DNA repair proteins ERCC1, Pms2 and XPF, when occurring

together, were found to contribute to progression of colorectal cancer 28. A

population-based case-control study found no association between downregulation

of nucleotide repair excision proteins such as ERCC1 and XPF and risk of

developing endometrial cancer 29, suggesting further evidence is needed to establish

the possible influence of these proteins in the endometrium.

In women with endometrioid endometrial cancer who were insulin resistant,

upregulation of proteins involved in cell adhesion, MAPK, PI3K-AKT and JAK-STAT

pathways was found. Progesterone receptor (PR) was upregulated in our study in

women with insulin resistance and with increasing glucose levels, suggesting

receptor expression may be a useful predictor in women with metabolic

dysregulation. In insulin resistant women with EEC, there was an upregulation of

both the progesterone receptor and estrogen receptor (ER) encoding genes.

Upregulation of the PR was also found among benign samples who were obese. PR

and ER are transcription factors belonging to the nuclear receptor superfamily.

Nuclear ER (ERα and ERβ) are expressed distinctly in the endometrium leading to

cellular proliferation and differentiation. The two isoforms of the PR (PR-A and PR-B) 17

are functionally distinct transcription factors: PR-A has oestrogen antagonistic

actions and modulates anti-proliferative effects of progesterone on the uterus, PR-B

has oestrogen agonist actions and induces cell growth in the absence of PR-A30.

Major risk factors for endometrial carcinogenesis include obesity among

postmenopausal women and unopposed exogenous oestrogen. Increased

production of oestrogen in the adipocytes of obese women could stimulate the

endometrium leading to upregulation of the anti-estrogenic PR. In endometrial

cancer, although ERα expression is reduced in both endometrial glands and stroma

31 32 the evidence for PR expression status is conflicting33. Relative over-expression

of PR-B, an endometrial oestrogen agonist, without transcriptional repression by PR-

A, may act to promote carcinogenesis, rather than prevent it. Recent evidence has

shown increased PR and ER presence in women with endometrial cancer had a

favourable prognostic outcome 26.

Pathways activated in obesity and insulin resistance that may drive cancer

Obese and insulin resistant women with benign endometrial tissue showed

upregulation of several pathways that were mitogenic and upregulation of which

would potentially lead to the development of malignancy at a later stage. Obese

women with benign endometrial tissue had increased expression of proteins involved

primarily in regulation of beta-cell development in the pancreas, insulin signalling,

NOTCH and anti-apoptotic signaling pathways. For example, there was increased

expression of Mcl-1, DUSP4 and IR-b among obese women, as compared to their

non-obese counterparts. Mcl-1 is an anti-apoptotic protein which is a member of the

Bcl-2 family, and elevated expression levels have been associated with breast

cancer34 and more recently, endometrial cancer35. DUSP4 regulates mitogenic 18

signal transduction by inactivating its target kinases by dephosphorylating both the

threonine and tyrosine residues on MAP kinases ERK1 and ERK2 and at present its

potential role in endometrial carcinogenesis has not been well investigated.

Increased DUSP4 expression has been found to be significantly higher in malignant

tumours than in benign lesions in colorectal adenocarcinoma, and may be a marker

of adverse prognosis 36. The insulin receptor in encoded by a single gene INSR, from

which two isoforms, IR-A or IR-B are produced. Phosphorylation of insulin receptor

substrates leads to activation of two main signalling pathways: the PI3K-Akt

pathway, responsible for most of the metabolic actions of insulin, and the Ras-MAPK

pathway, which cooperates with the PI3K pathway to control cell growth and

proliferation associated with endometrial cancer development. Upregulation of IR-a

and IR-b in cancer tissues is suggestive of IR-mediated signalling pathways having

important implications for carcinogenesis37. IR-b specifically has a modestly higher

affinity for mitogenic insulin, providing a selective growth advantage to tumour cells

when exposed to conditions of hyperinsulinaemia 38. The Notch signalling pathway

proteins HES1 and GCN5L2 were also increased among obese women. Notch

signalling pathway activity has been found to both promote tissue growth and

endometrial cancers and also act as a tumour suppressor in endometrial

carcinogenesis 39 40. This confusion may in part be due to Notch’s four paralogues

(Notch 1-4) acting to coordinate diverse and alternating biological outcomes 41.

Moreover, obese women with benign endometrial tissue downregulated expression

of proteins involved in innate immunity and cytokine signalling, G1/S phase cell cycle

transition and proteins encoding tumour suppressor genes.

19

In women with benign endometrium, the subset with insulin resistance had

overexpression of heregulin and c-raf when compared to women without

hyperinsulinaemia. Heregulin is a mitogen, known to induce phosphorylation and

activation of oncogenic signalling pathways MAPK3/ERK1 and Akt, both involved in

endometrial malignant transformation42. C-Raf is part of the Ras-MAPK signalling

cascade which results in cell growth, proliferation and survival and is associated with

endometrial cancer formation43 44. We also found downregulation of annexin-1 in this

subset of hyperinsulinaemic women, a membrane-localized protein which binds

phospholipids and is known to have anti-inflammatory properties through its

inhibition of phospholipase A2. This may promote a proinflammatory state making

them susceptible to carcinogenesis.

In the total cohort, the benign subgroup had increased fasting serum concentrations

in siz out of nine serum markers measured (oestradiol, IGF-1, glucose, insulin,

SHBG, IGFBP-3 and adiponectin), compared to the cancer subgroup, despite

adjustment for OCP and HRT users. Of these, IGF-1 and IGFBP-3 reached

statistical significance. IGFBP-3 is known to have dual stimulatory and inhibitory

roles, primarily binding to IGF-1 with high affinity, preventing it from activating the cell

proliferation-stimulating IGF-1 receptor. Downregulation of IGFBP-3 expression in

some cancers such as hepatoma 45 and non-small cell lung cancer 46 are associated

with poor patient outcomes. Of note, increasing levels of glucose in women with

endometrioid endometrial cancer and higher BMI in both cancer and benign cohorts

was linked with increase in the expression of progesterone receptor.

20

Women with benign endometrial tissue reported a significantly higher daily coffee

intake compared to those with cancer (p=9.7e-04). It has been previously suggested

that daily coffee consumption and reduces endometrial cancer incidence 5 47-49.

Coffee contains multiple biologically active components including caffeine, cafestol,

kahweol and chlorogenic acid, which can prevent oxidative DNA damage, modify the

apoptotic response and reverse the cell cycle checkpoint function 50 51.

The proteogenomic analysis by the TCGA network proposed a new classification of

endometrial cancer subtypes, although benign endometrial samples were not

included. The TCGA endometrial cancer dataset was used to replicate this study’s

findings of proteins that were significantly expressed in cancer cases according to

obesity status, after adjustment for patient age and year of sample collection. Two

proteins, Stearoyl-CoA desaturase (SCD1) and inositol polyphosphate 4-

phosphatase type II (INPP4b), were found to be associated with obese women with

endometrioid endometrial cancer across both of the datasets. SCD1 is responsible

for fatty acid biosynthesis as a rate-limiting enzyme in the conversion of saturated

fatty acids to mono-unsaturated fatty acids. The exact role of SCD1 in endometrial

carcinogenesis remains unclear52, although upregulation of this protein has also

been associated with obesity and its metabolic complications 53 54, which is in line

with our findings. It has also been highlighted as an established molecular target in

primary tumours of the breast, lung, pancreas and colon55. These are in line with the

findings in our cohort. The difference in the findings from the TCGA data suggesting

a negative correlation between SCD1 and BMI are difficult to explain and may be

partly explained by small sample size. Malignant transformation can have a number

of consequences to the cell metabolism and SCD1 is frequently modified in cancer. 21

This pathway certainly warrants further attention. More and larger studies with

patients with similar stage cancer as well as benign tissues are required to

investigate its role in obesity and endometrial cancer development.

INPP4b is an enzyme integral to the phosphatidylinositol pathway. A phosphatase

which is similar to PTEN, both PTEN and INPP4b have been proposed to act as

tumour suppressors by antagonizing the PI3K-Akt signalling pathway and are

frequently dysregulated in cancer. Loss of PTEN is well established as an early step

in the progression to endometrial cancer 56 57. Little is known however about the

underlying mechanisms through which INPP4b exerts its tumour suppressive role

but its downregulation in both datasets suggests an increased phosphatidylinositol

signaling from the membrane58. In conclusion, the association of both SCD1 gene

and INPP4b with obesity and endometrial cancer underscore the role of metabolic

dysregulation through perturbances in the fatty acid metabolism and signaling

through the PI3K-AKT-mTOR pathway which could potentially also be driver

pathways for obese patients who later develop cancer.

Strengths and Weaknesses

This is the first study to compare functional proteomic analysis in the endometrial

tissue of women with endometrioid endometrial cancer and benign controls. Studies

have explored the difference in gene expression profiles from the TCGA Project 59

and progesterone receptor levels with immunohistochemistry60 for obese versus non-

obese women with endometrial cancer. Differences in benign and cancer tissues

also stratified by obesity status have not been previously explored; this is a major

strength of the study. This study reports a large array of proteins and signaling 22

molecules with difference in expression between benign and endometrial cancer

tissues that will form a significant resource for the design of future mechanistic

studies in cell line models, primary cultures or endometrial organoids61.

Furthermore, it is the first study to explore the correlation of changes in protein

expression according to both BMI status and insulin resistance status. Proteomic

analysis according to insulin resistance status (defined by HOMA-IR cut-off above

4.35 in prospectively collected fasting serum samples) has not been previously

reported in any cohort as most analysed proteins based on the presence or absence

of self-reported diabetes. We found that at our defined cut-off of insulin resistance

(calculated using fasting serum glucose and insulin values, HOMA-IR >4.35), a total

of 24 women were above the threshold of insulin resistance, despite only 8 self-

reporting as having a diagnosis of diabetes. This raises the possibility that a

significant proportion of our cohort are currently in a high-risk pre-diabetes state of

hyperinsulinaemia, a known risk factor for development of endometrial cancer. This

study is therefore able to capture proteomic changes induced by hyperinsulinaemia

which well predates the diagnosis of clinical diabetes.

Although hyperinsulinaemia in pre-diagnostic samples has been previously

correlated with the occurrence of endometrial cancer 62, future studies should explore

whether weight loss and correction of hyperinsulinaemia induced by interventions

such as bariatric surgery could reverse upregulated proteins and carcinogenic

pathways.

23

However, our study’s findings are limited by a small sample size and the challenge

facing proteomic analysis of finding reproducible protein expression measurement

which addresses the inherently high biological variability across a single tissue

section, or subanalysis according to tumour grade. A consultant histopathologist

reviewed and categorised individual tissue sections into low, medium or high

abundance of tumour content (%) (for endometrial cancer samples, or ‘none’ to

confirm benign samples). ‘Low’ tumour content samples were excluded from the

analysis, and a further sensitivity analysis excluding ‘medium’ tumour content

samples was also conducted. This found that ten of the twelve proteins which

remained significantly expressed despite a smaller sample size after exclusion of

‘medium’ tumour content samples had the same direction of association according to

obesity and insulin resistance status. In addition, many of the proteins found to be

significantly over- or under-expressed did not retain statistical significance after

multiple tests correction due to small sample size, resulting in a lack of power to

detect associations. This may have affected our ability to identify additional proteins

that were significantly over- or under-expressed according to obesity across both our

dataset and the TCGA cohort. We were also limited in reproducing our results on the

protein expression according to the insulin resistance status as the TCGA and other

existing datasets only include data on the presence of self-reported diabetes.

Current insulin resistance status can be more accurately ascertained by

measurement of fasting serum insulin and glucose at time of patient recruitment into

the study. It is also known that pre-diabetic metabolic changes, characterised by

impaired fasting glucose levels, occur in tissues prior to the development of complete

insulin resistance seen in type 2 diabetes. Subgroups analyses for different clinical

subgroups (i.e. menopause status, menstrual cycle etc.) was limited by small 24

numbers. There was also significant variance according to the year of collection. All

samples were collected by a single researcher in a single institution and were

analysed in one batch. We adjusted for the year of sample collection in multivariate

analyses.

Future RPPA analyses in endometrial tissues should validate this study’s findings

and could further explore the relationship between the analysed proteins by

integrating the newly generated protein data with prior transcriptomic data, using

more recently developed techniques, or endometrial cancer cell lines 63. Future

mechanistic studies should involve in vitro models of endometrial cancer with cells

lines, primary cultures or endometrial organoids that are currently under

development61. The exposure of these in vitro endometrial models to

hyperinsulinaemia with analysis of the expression of major proteins in major

pathways (i.e. mTOR) as well as over expression or knock down experiments will

allow the validation of the relevance of these proteins to occurrence of EEC in the

context of cell cycle, apoptosis and cell adhesion, migration etc. Although

hyperinsulinaemia in pre-diagnostic samples has been previously correlated with the

occurrence of endometrial cancer 64, future studies should explore whether weight

loss and correction of hyperinsulinaemia induced by interventions such as bariatric

surgery from interventions such as bariatric surgery could reverse upregulated

proteins and carcinogenic pathways.

Conclusions

This exploratory analysis has identified a number of candidate proteins with altered

expression in either endometrioid endometrial cancer or benign endometrial tissues. 25

We have found that obesity and insulin resistance in women with benign endometrial

tissue leads to a specific upregulation of proteins involved in insulin signalling, fatty

acid metabolism, hormone pathways and oncogenic signalling pathways such as the

PI3K-AKT-mTOR pathways. Subsequent validation in an independent dataset of

obese or insulin resistant women with benign endometrial tissue is needed to confirm

our findings and exclude the possibility of false discoveries from multiple parameter

testing.

Author contributions

OR: Data curation, formal analysis, investigation, methodology, project

administration, software, validation, writing – original draft, writing – review and

editing.

NA: Data curation, formal analysis, investigation, software, writing – review and

editing.

JN: Conceptualization, data curation, investigation, methodology, supervision,

writing – review and editing.

HL: Data curation, investigation, methodology, software, validation, supervision,

writing – review and editing.

HG: Conceptualization, formal analysis, investigation, methodology, writing –

supervision, review and editing.

MG: Conceptualization, formal analysis, investigation, methodology, supervision,

writing – review and editing.

MK: Conceptualization, formal analysis, investigation, methodology, project

administration, validation, supervision, writing – original draft, writing – review and

editing. 26

Acknowledgements

None of the authors have any conflicts of interest to report, and all authors confirm

they have read the journal’s policy on disclosure of potential conflicts of interest.

None of the funders have had any influence over: study design, collection, analysis

and interpretation of the data, in writing the report and in the decisions to submit this

article for publication. There are no sources of editorial support for preparation of the

manuscript to disclose. All authors have read the journal’s authorship agreement and

the manuscript has been reviewed by and approved by all named authors.

Funding

This work was supported by Genesis Research Trust (Garfield Weston Foundation,

Grant number P63522 to MK); Ovarian Cancer Action (Grant number PS5827 and

PSA601to MG and MK); the Imperial Experimental Cancer Medicine Centre, the

Cancer Research UK Imperial Centre, Imperial Healthcare NHS Trust NIHR BRC

(Grant number P45272). None of the funders have had any influence over: study

design, collection, analysis and interpretation of the data, in writing the report and in

the decisions to submit this article for publication.

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34

FIGURE LEGENDS

Figure 1. Expression levels (log10) of nine serum markers measured from patients with benign

endometrium (green) and endometrioid endometrial cancer (red). A) Total cohort included (benign,

n=26; cancer, n=66). B) Premenopausal women only (benign, n=7; cancer, n=8), C) premenopausal

women excluding OCP users (benign, n=3; cancer, n=7). D) Postmenopausal women only (benign,

n=19; cancer, n=58), E) postmenopausal women excluding HRT users (benign, n=13; cancer, n=53).

F) Total cohort, excluding all OCP/HRT users (benign, n=11; cancer, n=38). CRP, c-reactive protein;

35

IGF-1, insulin-like growth factor-1; IGFBP3, insulin-like growth factor binding protein-3; HRT, hormone

replacement therapy; OCP, oral contraceptive use, SHBG, sex hormone binding globulin.

Figure 2. A schematic of the signalling pathways upregulated in women with benign endometrial

tissue as compared to women with endometrial cancer. Statistically significant proteins (q<0.05) are

highlighted with the corresponding color of the signalling pathway they belong to.

Figure 3. Gene set enrichment analysis58 using the KEGG database for differentially enriched

pathways in endometrioid endometrial cancer and benign tissue. There was significant (FDR q =

0.078, FWER p =0.043) enrichment of A) MAPK signalling and B) ERBB signalling pathways (FDR

q=0.397, FWER p = 0.368). In the enrichment plots (A, B), genes are ranked by signal/noise ratio

according to their differential expression between the two tissue types. Enrichment plots for two

pathways upregulated in the tumour cohort are shown, with genes from the MAPK and ERBB gene

set marked with vertical bars under each graph. Vertical lines which are clustered to the left represent

higher ranked genes in the ranked list. C) KEGG pathway enrichment analysis was performed by

Fisher exact test. For each KEGG pathway, the bar shows the fold-enrichment of the pathway.

Enrichment scores were calculated once genes had been ranked by their expression difference and a

cumulative sum of the ranked genes determined. The maximum deviation from zero was recorded as

the enrichment score for each pathway. The normalized Enrichment Score (ES) accounts for

differences in gene set size. It can be used to compare analysis results across gene sets. It is

calculated as the actual enrichment score (ES) divided by the mean (enrichment scores against all

permutations of the dataset).

Table 1. Baseline demographics of patient cohort

Patient characteristics Cancer (n/N)(n=66)

% Benign (n/N) (n=26)

% Total (n/N)(n=92)

% p value

Age (years)Mean (range) 64 (29-90) - 53 (26-70) - 61 (26-90) - 2.9 E-04Ethnicity, n/N (%)White 35/66 53.0 18/26 69.2 53/92 58 0.054Black 3/66 4.5 4/26 15.4 7/92 8Asian 15/66 22.7 2/26 7.7 17/92 18

36

Other 3/66 4.5 2/26 7.7 5/92 58Unknown 10/66 15.2 0/26 0 10/92 11EducationNone 18/66 27.3 1/26 3.8 19/92 21 0.008GCSE's (or equivalent, aged 16yrs) 20/66 30.3 5/26 19.2 25/92 27A-Levels (or equivalent, aged 18yrs)

7/66 10.6 2/26 7.7 9/92 10

Higher degree 12/66 18.2 13/26 50.0 25/92 27Other 0/66 0 1/26 3.8 1/92 1Unknown 9/66 13.6 4/26 15.4 13/92 14Marital statusSingle 16/66 24.2 3/26 11.5 19/92 21 0.729Married/civil partnership/living together

24/66 36.4 11/26 42.3 35/92 38

Divorced/separated 11/66 16.7 5/26 19.2 16/92 17Widowed 5/66 7.6 3/26 11.5 8/92 9Unknown 10/66 15.2 4/26 15.4 14/92 15BMI distribution, n/N (%)BMI (kg/m2), mean (range) 33.9 (19.8-55.0) - 28.3 (19.6-46.0) - 32.3 (19.6-55.0) - 0.008Normal (18.5-24.9) 11/66 16.7 11/26 42.3 22/92 24 0.078Overweight (25.0-29.9) 18/66 27.3 7/26 26.9 25/92 27Obesity Class I (30.0-34.9) 12/66 18.2 4/26 15.4 16/92 17Obesity Class II (35.0-39.9) 10/66 15.2 1/26 3.8 11/92 12Obesity Class III (≥40.0) 15/66 22.7 3/26 11.5 18/92 20Menarche, n/N (%)Age at menarche (years), mean (range)

12.9 (9.0-16.0) - 12.9 (10.0-16.0) - 12.9 (9.0-16.0) - 0.916

≤10 years old 2/66 3.0 1/26 3.8 3/92 38 0.45811-12 years old 13/66 19.7 7/26 26.9 20/92 22>13 years old 24/66 36.4 12/26 46.2 36/92 39Unknown age 27/66 40.9 6/26 23.1 33/92 36Parity, n/N (%) Parity, median (range) 2 (0-6) - 1 (0-5) - 2 (0-6) -Parity, mean (SD) 2.2 (1.75) - 2.2 (1.81) - 2.2 (1.75) - 0.454 Never pregnant, n/N (%) 13/66 19.7 6/26 23.1 19/92 21 0.168 Never had term pregnancy, n/N(%)

1/66 1.5 3/26 11.5 23/92 25

1 6/66 9.1 2/26 7.7 12/92 13 ≥2 36/66 54.5 14/26 53.8 46/92 50 Unknown 10/66 15.2 1/26 3.8 11/92 12Menopause status, n/N (%)Age at menopause (mean, range) 50.7 (40.0-59.0) - 47.1 (38.0-51.0) - 49.9 (38.0-59.0) - 0.010Premenopausal 8/66 12.1 7/26 26.9 15/92 16 0.156Postmenopausal 58/66 87.9 19/26 73.1 77/92 84Use of oral contraceptive (PrMP) n/N (%)Current use of OCP 1/8 12.5 4/7 57.1 5/15 33 0.306No OCP use 7/8 87.5 1/7 14.3 8/15 53Unknown 0/8 0 2/7 28.6 2/15 13

Table 1. Baseline demographics of patient cohort (continued)

Use of hormone replacement therapy (PoMP) n/N (%)Hormone therapy use 5/58 8.6 6/19 31.6 11/77 14 0.006No hormone therapy use 53/58 91.4 13/19 68.4 66/77 86Smoking, n/N (%)Non-smoker 39/66 59.1 15/26 57.7 54/92 59 0.207Former smoker 10/66 15.2 6/26 23.1 156/92 17

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Current smoker 5/66 7.6 4/26 15.4 9/92 10Unknown 12/66 18.2 1/26 3.8 13/92 14Diabetes status:Non-diabetic or diabetic, n/N (%)Non-diabetic 49/66 74.2 25/26 96.2 74/92 80 0.036Diabetic 17/66 25.8 1/26 3.8 18/92 20Diabetes treatment:Diet control, metformin, metformin with 2nd antidiabetic, insulin, n/N (%)Diet control only 6/17 35.3 0/1 0 6/18 33 0.116Metformin alone 9/17 52.9 1/1 100 10/18 56Metformin combined with 2nd antidiabetic medication

2/17 11.8 0/1 0 2/18 44

Insulin use (alone or with oral medication)

0/17 0 0/1 0 0/18 0

HOMA-IR (mean (range))HOMA-IR 8.82 (0.44-52.03) - 3.32 (0.48-10.88) - 7.17 (0.44-

52.03)- 0.008

Coffee intake, n/N (%)None 23/66 34.8 7/26 26.9 30/92 33 0.003*1 cup/day 2/66 3.0 5/26 19.2 7/92 82 cups/day 2/66 3.0 6/26 23.1 8/92 9≥3 cups/day 8/66 12.1 1/26 3.8 9/92 10Unknown 31/66 47.0 7/26 26.9 38/92 41Family history, n/N (%)No family history or cancer, in first degree relative

30/66 45.5 15/26 57.7 45/92 49 0.502

Family history of cancer, in first degree relative

25/66 37.9 8/26 30.8 33/92 36

Unknown family history 11/66 16.7 3/26 11.5 14/92 15Cancer grade (EEC only)1 22/66 33.3 - - 22/66 100 - 2 25/66 37.9 - - 25/66 100 - 3 19/66 28.8 - - 19/66 100 - Cancer stage (EEC only) 1A 30/66 45.5 - - 30/66 46 - 1B 17/66 25.8 - - 17/66 26 - 2 5/66 7.6 - - 5/66 8 - 3A 6/66 9.1 - - 6/66 9 - 3B 2/66 3.0 - - 2/66 3 - 3C1 4/66 6.1 - - 4/66 6 - 3C2 0/66 0 - - 0/66 0 - 4A 1/66 1.5 - - 1/66 2 - 4B 1/66 1.5 - - 1/66 2 -

BMI, body mass index; EEC, endometrioid endometrial cancer; GCSE, general certificate of secondary education; HOMA, homeostatic model assessment; IR, insulin resistance; OCP, oral contraceptive use; PoMP, postmenopausal; PrMP, premenopausal; SD, standard deviation.

*Of the sampled patients, a high proportion (47.0% in the cancer cohort, and 26.9% in the benign cohort) have missing data on coffee intake, categorised as ‘unknown’. To avoid the possibility for bias when calculating the p value for coffee intake between the two groups, we have excluded the ‘unknown’ group data and present the p value for the remaining categories of coffee intake where the data is known. Table 2. Linear regression analysis showing protein expression that is significantly increased or decreased with increasing BMI or insulin resistance for the cancer and benign cohort, separately. Analysis has been adjusted for age and year of sample collection.

Protein name Estimate Std Error p value q valueCancer BMI

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PRAS40_pT246 0.045 0.014 0.003 0.433Oct-4 0.043 0.014 0.004 0.433XPF 0.042 0.014 0.005 0.433UBAC1 -0.038 0.014 0.009 0.490ERCC1 0.038 0.014 0.010 0.490INPP4b -0.034 0.013 0.011 0.49014-3-3-epsilon 0.037 0.015 0.014 0.543Glutaminase -0.035 0.015 0.020 0.561HER2 0.035 0.015 0.021 0.561Cox-IV 0.034 0.015 0.021 0.561SHP-2_pY542 -0.032 0.014 0.028 0.627Jagged1 -0.033 0.015 0.029 0.627SCD 0.031 0.015 0.035 0.712PKA-a -0.031 0.015 0.045 0.847

Insulin resistance B7-H4 0.039 0.012 0.002 0.448c-Jun_pS73 0.030 0.011 0.007 0.863PR 0.029 0.011 0.014 1.000Bcl2 0.021 0.010 0.039 1.000Sox2 0.025 0.012 0.042 1.000Stat5a 0.024 0.012 0.044 1.000ER 0.024 0.011 0.045 1.000

Benign

BMIHES1 0.101 0.024 0.000 0.055TAZ 0.103 0.025 0.000 0.055Mcl-1 0.088 0.023 0.001 0.089GCN5L2 0.089 0.025 0.002 0.143b-Actin 0.088 0.027 0.004 0.176ENY2 0.085 0.026 0.004 0.176Rictor_pT1135 -0.080 0.025 0.005 0.176DM-K9-Histone-H3 0.078 0.027 0.008 0.271DUSP4 0.069 0.025 0.012 0.362HSP27 -0.073 0.028 0.017 0.418Cyclophilin-F 0.066 0.026 0.017 0.418IR-b 0.072 0.029 0.022 0.473ADAR1 -0.053 0.024 0.034 0.541SLC1A5 0.064 0.028 0.035 0.541XRCC1 0.062 0.028 0.039 0.5414E-BP1 -0.058 0.027 0.040 0.541GATA3 -0.066 0.030 0.041 0.541EGFR_pY1173 -0.057 0.026 0.041 0.541Ubq-Histone-H2B -0.061 0.028 0.042 0.541Cyclin-D1 -0.057 0.027 0.045 0.541E2F1 -0.058 0.027 0.045 0.541Collagen-VI -0.045 0.021 0.046 0.5414E-BP1_pS65 -0.047 0.022 0.048 0.541

Insulin resistance Annexin-I -0.106 0.038 0.012 0.999Heregulin 0.242 0.097 0.024 0.999C-Raf 0.214 0.096 0.040 0.999

BMI, body mass index; HOMA, homeostatic model of assessment; IR, insulin resistance; Std, standard.

Table 3. Replication analysis of the results of our cohort against the TCGA dataset. All proteins found to be significantly over- or under-expressed according to obesity status in cancer samples were validated against the TCGA dataset. We note with bold and asterisks the proteins found to be significantly over- or under-expressed in both cohorts. We present unadjusted as well as adjusted results for age, year and sample of collection using linear regression.

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Protein name Source

of data

Type of analysis Estimate Standard

error

p value q value

Unadjusted

*SCD TCGA Linear regression  -0.013774 0.006338 0.030568 0.244547

HER2 TCGA Linear regression  -0.010422 0.006410 0.105081 0.378277

INPP4b TCGA Linear regression  0.009381 0.006425 0.145335 0.378277

CD26 TCGA Linear regression  -0.008373 0.006361 0.189139 0.378277

SHP-2_pY542 TCGA Linear regression  0.006551 0.006454 0.310882 0.497411

ERCC1 TCGA Linear regression  -0.004619 0.006519 0.479152 0.638869

PEA-15_pS116 TCGA Linear regression  0.003691 0.006326 0.560049 0.640056

PRAS40_pT246 TCGA Linear regression  0.000060 0.006458 0.992615 0.992615

Adjusted for age, year of sample collection

*SCD TCGA Linear regression -0.013477 0.006430 0.036957 0.208880

HER2 TCGA Linear regression -0.010694 0.006555 0.103904 0.208880

INPP4b TCGA Linear regression 0.009964 0.006564 0.130122 0.208880

SHP-2_pY542 TCGA Linear regression 0.009567 0.006453 0.139254 0.208880

ERCC1 TCGA Linear regression -0.004417 0.006681 0.509115 0.610938

PRAS40_pT246 TCGA Linear regression -0.000168 0.006528 0.979477 0.979477

*Significant at p<0.05.

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Figure 1

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Figure 2

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A B C

Figure 3

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