Journal of Clinical Medicine

Review Prognostic Biomarkers in : A Systematic Review and Meta-Analysis

Eva Coll-de la Rubia 1 , Elena Martinez-Garcia 2, Gunnar Dittmar 2 , Antonio Gil-Moreno 1,3,* , Silvia Cabrera 1,3,* and Eva Colas 1,*

1 Biomedical Research Group in Gynecology, Vall Hebron Institute of Research, Universitat Autònoma de Barcelona, CIBERONC, 08035 Barcelona, Spain; [email protected] 2 Quantitative Biology Unit, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg; [email protected] (E.M.-G.); [email protected] (G.D.) 3 Gynecological Department, Vall Hebron University Hospital, CIBERONC, 08035 Barcelona, Spain * Correspondence: [email protected] (A.G.-M.); [email protected] (S.C.); [email protected] (E.C.); Tel.: +34-93-489-40-52 (A.G.-M., S.C. & E.C.)

 Received: 24 May 2020; Accepted: 15 June 2020; Published: 17 June 2020 

Abstract: Endometrial cancer (EC) is the sixth most common cancer in women worldwide and its mortality is directly associated with the presence of poor prognostic factors driving tumor recurrence. Stratification systems are based on few molecular, and mostly clinical and pathological parameters, but these systems remain inaccurate. Therefore, identifying prognostic EC biomarkers is crucial for improving risk assessment pre- and postoperatively and to guide treatment decisions. This systematic review gathers all biomarkers associated with clinical prognostic factors of EC, recurrence and survival. Relevant studies were identified by searching the PubMed database from 1991 to February 2020. A total number of 398 studies matched our criteria, which compiled 255 associated with the prognosis of EC. MUC16, ESR1, PGR, TP53, WFDC2, MKI67, ERBB2, L1CAM, CDH1, PTEN and MMR proteins are the most validated biomarkers. On the basis of our meta-analysis ESR1, TP53 and WFDC2 showed potential usefulness for predicting overall survival in EC. Limitations of the published studies in terms of appropriate study design, lack of high-throughput measurements, and statistical deficiencies are highlighted, and new approaches and perspectives for the identification and validation of clinically valuable EC prognostic biomarkers are discussed.

Keywords: endometrial cancer; protein ; prognostic; prognosis; risk assessment; ESMO-ESGO-ESTRO risk classification; TCGA; endometrial adenocarcinoma; uterine cancer; recurrence

1. Introduction

1.1. Endometrial Cancer Endometrial cancer (EC) is the sixth most common cancer in women worldwide and the most common tumor of the female genital tract. A total number of 382,069 new estimated cases and 89,929 estimated deaths were reported for 2018 [1]. EC incidences have been increasing in the last years as a consequence of the populations increasing life expectancy and a higher overall prevalence of obesity and metabolic syndromes. Moreover, unlike many other malignancies, EC mortality has also been increasing [2]. The number of new cases and deaths is expected to increase by 20.3% and 17.4% by 2025, respectively [1]. Mortality of EC patients is directly associated to the presence of poor prognostic factors, which drive tumor recurrence.

J. Clin. Med. 2020, 9, 1900; doi:10.3390/jcm9061900 www.mdpi.com/journal/jcm J. Clin. Med. 2020, 9, 1900 2 of 20

1.2. Current Diagnosis and Preoperative Risk Stratification of Ec Patients To date, screening tests of EC do not exist. Thus, current diagnosis relies on the presentation of symptoms, with abnormal uterine bleeding (AUB) being the most common one, since it is present in 90% of EC patients. However, this symptom is common to other and only 10–15% of women with AUB will develop EC [3,4]. In order to rule out EC from other benign diseases, patients undergo a multi-step process of diagnosis including a gynecological examination, transvaginal ultrasonography (TVUS) and a pathological examination of an endometrial biopsy. This biopsy can be obtained by different procedures, i.e., aspiration (office-based pipelle biopsy), dilatation and curettage (D&C), or hysteroscopy, depending on the clinician’s choice [5]. Pathologists use these endometrial biopsies to give a final diagnosis of EC and prognostic information. Once EC is diagnosed, and imaging tests will also provide additional prognostic information of the tumor to finally reach a presurgical stratification, which will ultimately guide the surgical treatment. A prognostic factor is a patient or characteristic that provides information about the likely outcome of the disease, independent of therapy, at the time of diagnosis [6]. The most important prognostic factors of EC patients include tumor grade, histological subtype, depth of myometrial invasion, cervical involvement, tumor size, lymphovascular space invasion (LVSI) and lymph node status [5]. Information about tumor grade and histological subtype is obtained from the histological examination of endometrial biopsies. Traditionally, EC has been classified into two different subtypes based on clinical, pathological and molecular features. The most common is the Type I or endometrioid (EEC) subtype, which mostly includes the endometrioid histology and is associated with a good prognosis; while Type II or non-endometrioid (NEEC), includes different minor histologies such as serous (~10%), clear cell (~3%), mixed cell adenocarcinoma and other rare types, and is associated with a poor prognosis [7] (Figure1A). Although this dualistic classification is broadly used in the current clinical practice for preoperative assessment and surgical planning [8], its prognostic value remains limited. Using this classification model, approximately 20% of EEC cases relapse, whereas 50% of Adaptet from Morice P et al., 2016 NEEC do not [7]. Besides the binarySCNAs classification, load: Somatic copy number 15–20% alterations (SCNAs) of EEC load tumors are classified as high-grade Sp.Mlc.alterations: specific molecular alterations MMR: miss match repair proteins lesions that do not fit in this model (FigureSerous1 B).Endometrial Intra-neoplasia (SEIN) +++: present/high; + occasional; ++ frequent; - absent/low

A RISK FACTORS MOLECULAR CHARACTERISTICS PX Clinical Genomic Hormone TP53 Precedent Histology Grade Stage Commonly mutated 5 years OS features stability Receptor mutation Low Early PTEN, PIK3CA, KRAS, Metabolic Hyperplasia, Diploid Good Type I EEC (63% G1, G2 (84% I-II Positive No FGFR2, CTNNB1, MSI and syndrome EIN (40% MSI) (85%) 37% G3) 16% III-IV) ARID1A

Late Atrophy, High TP53, ERBB2, CDH1, None NEEC (45% I-II Aneuploid Negative Yes Poor Type II SEIN (SEC, CC, etc.) (100% G3) CDK2A (55%) 55% III-IV) B RISK FACTORS MOLECULAR CHARACTERISTICS PX DNA MMR Aberrant Grade Metastasis ER/PR PTEN MKI67 / MIB1 5 years OS loss TP53 EEC – G1 Low Good Well formed Uncommon +++ + + - - (G1, G2) (>80%) glands

EEC – G3 High LN, distant Poor Solid growth ++ + + + +++ (G3) organs (63%) pattern

LN, NEEC - High Poor peritoneal, + +++ - +++ +++ (G3) (56%) Serous distant organs

NEEC – High LN, Poor - +++ + + +++ Clear Cell (G3) peritoneal (60%)

Figure 1. Principle features of the two different subtypes described in the dualistic classification model of endometrial cancer (EC) by Bokhman et al. [7](A) Risk factors, molecular characteristics Good Survival Differences Among Uterine Papillary Serous, Clear Cell and Grade 3 Endometrioid Adenocarcinoma Endometrial Cancers (82.6%) McGuindal 16. and prognosis of the dualisticNo classification. poso 82.6 perquè sinó és inferior a type (I solB i )queda Deconstruction raro. of the dualistic model according to the different histological grades that exist on endometrioid-endometrial cancers (EECs) and the two most common histological subtypes of non-endometrioid endometrial cancers (NEECs). PX: prognosis; OS: overall survival; SEC: serous EC; CC: clear cell EC; EIN: endometrial intraepithelial neoplasia; MSI: stability instable; SCNAs: somatic copy number alterations load; Sp.Mlc.alterations: specific molecular alterations; MMR: miss-match repair proteins: SEIN: serous endometrial intra-neoplasia; LN: lymph node status; +++: present/high; ++ frequent; + occasional; - absent/low. J. Clin. Med. 2020, 9, 1900 3 of 20

Imaging techniques represent a cornerstone in the preoperative evaluation of patients with EC. Myometrial invasion, tumor size, and cervical invasion are properly assessed using imaging techniques such as TVUS, or magnetic resonance imaging (MRI). Lymph node status can be studied by computed tomography (CT), MRI or positron emission tomography (PET-CT), and those techniques seem to have similar accuracies. Specificity is high but the sensitivities are moderate to low [9]. In fact, imaging cannot replace lymph-node dissection for staging purposes since the detection rate for metastatic lesions of 4mm or less is only 12% [10]. Another important prognostic factor is the LVSI since the presence of tumor cells within vascular spaces is considered an early step in the metastatic process and consequently a strong predictor of nodal metastasis, recurrence and cancer-specific death [11]. However, it cannot be assessed using imaging techniques and usually not observed in preoperative biopsies. Thus, the decision to perform lymphadenectomy can be made based on intra-operative frozen sections or preoperative risk assessment based on histology and imaging tests, where the most important criteria are myometrial invasion and tumor grading. In this regard, sentinel lymph node mapping has emerged as a new strategy to know lymph node status while sparing many lymph node dissections [12].

1.3. Risk Stratification Systems Once prognostic factors are identified in the resected tumor tissue, i.e., after surgical treatment, patients are classified according to the risk stratification system defined by the three major consortiums in EC, i.e., the ESMO-ESTRO-ESGO consensus [13]. As seen in Figure2, this system purely uses the clinical, molecular and pathological information to define groups of patients from low to metastatic, measuring the risk of recurrence to classify, treat and predict the outcome of EC patients. Although this classification has the highest power of discrimination for stratifying the risk of recurrence in patients with EC, still 9% of patients at low risk recur while 60% of patients at high risk will not recur [14]. Therefore, it is evident that an improved version of the current stratification system needs to be developed, probably by the integration of additional molecular biomarkers. In 2013, The Cancer Genome Atlas (TCGA) Research Network proposed a novel classification system exclusively based on a profound molecular characterization of the tumors [15]. According to this, EC is classified into four different subgroups: POLE ultramutated, microsatellite stability instable (MSI) hypermutated, copy-number low (microsatellite stable, MSS), and copy-number high (serous-like). The POLE ultramutated group is a small group of all grades EEC and barely a few serous EC [16], and is characterized for its excellent prognosis. The MSI hypermutated and copy-number low (MSS) groups are composed of endometrioid tumor histology and they have an intermediate prognosis. They are distinguished because MSI hypermutated subgroup includes most high-grade endometrioid cancers displaying genomic instability, while the copy-number low (MSS) subgroup has a lower rate of somatic copy number alterations. Tumors classified as copy-number high (serous-like) present the worst prognosis. They include serous EC and high-grade EEC with the highest load of somatic copy number alterations (Figure3). In order to translate this system into clinical practice, the ProMisE decision-tree and the TransPORTEC classifications emerged, mainly based on the assessment of three surrogate biomarker tests: (i) POLE sequencing; (ii) of mismatch repair proteins (MMR-IHQ); (iii) immunohistochemistry of the p53 protein [17–19]. These surrogate biomarkers categorize most ECs according to their molecular classification, but the method is limited by (i) systematic evaluation of the pathogenicity of POLE mutations is required [20]; (ii) p53 immunohistochemistry does not perfectly correlate with TP53 copy-number alterations; (iii) tumors harboring more than one classifying genomic aberration are difficult to classify; (iv) the algorithms do not include the evaluation of the significant heterogeneity seen in the copy-number-low group [21]. Despite these limitations, the scientific and clinical community supports incorporating the TCGA molecular classification in daily clinical practice for classifying EC patients. Indeed, there exists a high correlation of the TCGA classification between J. Clin. Med. 2020, 9, 1900 4 of 20 preoperative biopsies and the resected tumor tissue, thus, allowing us to incorporate this information preoperatively to guide both surgical and adjuvant treatment [22].

PRIMARY FINAL DIAGNOSIS ADJUVANT PREOPERATIVE ASSESSMENT and SURGERY PLAN PROGNOSIS TREATMENT (tissue specimen obtained in surgery) TREATMENT

Worse Advanced or Recurrent EC Recurrence Chemotherapy CYTOREDUCTIVE NO YES SURGERY Advanced preoperative stage Metastatic Hormonotherapy • Lymph node infiltration Radiotherapy Anamnesis • Peritoneal spread No candidates for FIGO stage radical surgery: Clinical or Exploration RADIOTHERAPY, Palliative features I II-IV Recurrent disease CHEMOTHERAPY treatment Blood test IV Metastatic

Chemotherapy Initial EC (uterine-confined) +/- III Advanced Radiotherapy High preoperative risk +/- • Histological type: EEC COMPLETE Brachytherapy • Histological grade: G3 STAGING SURGERY • Myometrial invasion: >50% Histological type • No Cervical invasion III High or EEC NEEC • Histological type: EEC Endometrial Total hysterectomy Histological type IHQ • Histological grade: G3 biopsy • Cervical invasion: Present Histological grade EEC NEEC or Radiotherapy 1 • Histological type: NEEC High 2 3 Bilateral + adnexectomy Histological grade Brachytherapy Intermediate preoperative risk +/- 1/2 3 Chemotherapy • Histological type: EEC • Histological grade: G1, G2 Pelvic • No Cervical invasion lymphadenectomy • Myometrial invasion: >50% Myometrial invasion or <50% >50% Transvaginal • Histological type: EEC Cervical invasion Para-aortic US • Histological grade: G3 High lymphadenectomy <50% >50% • No Cervical invasion CT • Myometrial invasion: <50% High-intermediate Imaging Myometrial invasion LVSI Radiotherapy MRI <50% >50% +/- NEG POS Brachytherapy Low preoperative risk Total hysterectomy PET-CT Tumor size • Histological type: EEC High-intermediate • Histological grade: G1, G2 Myometrial invasion • No Cervical invasion Brachytherapy or • Myometrial invasion: <50% Bilateral <50% >50% no adjuvant adnexectomy Intermediate treatment (especially <60y)

No adjuvant Low treatment Better

Figure 2. The EC risk stratification system according to the ESMO-ESGO-ESTRO (European Society for Medical Oncology-European Society of Gynaecological Oncology-European SocieTy for radiotherapy & Oncology) consensus [13] and its associated primary and adjuvant treatment. Clinical, molecular and pathological characteristics used to predict EC treatment and decision tree showing the clinical and pathological features used for the final definition of EC treatment. IHQ: immunohistochemistry; Transvaginal US: transvaginal ultrasonography; CT: computed tomography; MRI: magnetic resonance imaging; PET-CT: positron emission tomography; EEC: Endometrioid endometrial cancer; NEEC: non-endometrioid endometrial cancer; LVSI: lymphovascular space invasion. The information is scaled down to provide a result on the associated risk, primary and adjuvant treatments, and prognosis.

The TCGA molecular classification promises to improve the current ESMO-ESGO-ESTRO risk stratification system since it provides additional prognostic information. Indeed, studies performed in large cohorts of patients, notably by the TCGA (additional cohort), the Vancouver and the PORTEC groups [18,19,23–25], validated its prognostic relevance and pointed out specific subsets of patients who will benefit from this classification system (Figure4). In particular, it has been reported that 7% of patients diagnosed with a good prognosis cancer (EEC-Grade 1) but with a copy-number high molecular diagnosis will now be stratified in a poorer prognosis group [15]. In contrast, all patients with POLE-hypermutated tumors (6–13% of all ECs) will now be considered good prognosis tumors, independently of the state of other prognostic factors (e.g., histological grade or FIGO stage). J. Clin. Med. 2020, 9, 1900 5 of 20 Adaptet from Morice P et al., 2016 SCNAs load: Somatic copy number alterations (SCNAs) load; IHQ: Sp.Mlc.alterations: specific molecular alterations MMR: miss match repair proteins

Copy-number low, MSS Copy-number high, high POLE-ultramutated MSI-hypermutated (endometrioid) SCNA (serous-like)

CLINICAL FEATURES

Prevalence 5-15% 25-30% 30-40% 5-15%

Associated Might be associated with Advanced stage at Younger age at presentation Higher Body Mass Index features Lynch Syndrome presentation

RISK FACTORS

Histology (24) Mainly endometrioid Serous and endometrioid

Grade G3 > G1, G2 G1 , G2 > G3 G3

Stage I , II , III , IV

Ambiguous morphology, Mucinous differentiation, Squamous differentiation, Histological tumor giant cells, prominent MELF-type invasion, notable absence of TILS, Diffuse cytonuclear atypia features TILs presence of LVSI ER/PR diffuse positive

MOLECULAR CHARACTERISTICS

Mutation load

SCNAs load

PI3K alterations

KRAS mutation

TP53 mutation 35% 5% 1% >90%

Spec. Molec. Hotspots mutation in Loss of DNA MMR TP53; ~25% ERBB2 CTNNB1 (52%) alterations POLE proteins (MLH1, MSH2,...) amplified

DIAGNOSIS

DNA – P286R, V411L, S297F, A456P, S459, F367S, L424I, M295R, POLE-wt or Non-pathogenic POLE Sequencing (25) P436R, M444K, D368Y MMR-loss: MLH1, MSH2, MMR-proficient; TP53-mut Protein - IHQ MSH6, PMS2 TP53-wt TP53-abnormal

PROGNOSIS

Progression Excellent Intermediate Intermediate Poor free survival (Stage independent) (Stage independent) (Stage independent)

TREATMENT

Treatments to PI3K/AKT/mTOR-i; Cell cycle regulators; Immune checkpoint-i explore (26) hormonal therapy PI2K/AKT/mTOR-i

Figure 3. Clinical features, risk factors, molecular characteristics, diagnosis, prognosis and treatment associated with each subgroup of the TCGA classification system [15,16,26]. MSI: microsatellite stability instable; SCNA: somatic copy number alterations load; IHQ: immunohistochemistry; Sp.Mlc.alterations: specific molecularJ. Clin. alterations; Med. 2020, 9, x MMR: FOR PEER miss-match REVIEW repair proteins; mut: mutated; wt: wild-type; -i: inhibitors.6 of 21

POLE MSI CN-low CN-high EEC - G1 7% 26% 65% 2%

POLE MSI CN-low CN-high EEC - G2 5% 31% 56% 8%

POLE MSI CN-low CN-high EEC - G3 17% 54% 9% 20%

CN-low CN-high SEC 2% 98%

0% cases100 Figure 4. AssessmentFigure 4. Assessment of TCGA of novelTCGA classification.novel classification. Itemization Itemization of of the the TCGA TCGA subgroupssubgroups in in the the dualistic model. Thedualistic data usedmodel. for The this data figure used for corresponds this figure corresponds to the TCGA to the TCGA cohort cohort [15]. [15].

2. Aim of2. the Aim Review of the Review In the age of , the detailed classification of patient subgroups is imperative In thebefore age ofand personalized after surgical treatment. medicine, For the the detailedEC, this translates classification into the ofimprovement patient subgroups of stratification is imperative before andtools, after including surgical pathologic treatment. parameters, For the imaging EC, this techniques translates and into molecular the improvement markers. The TCGA of stratification tools, includingmolecular pathologic classification parameters,offers a basis for imaging such an integrative techniques approach. and molecular markers. The TCGA molecular classificationIn this review, off weers systematically a basis for reviewed such an the integrative existing literature approach. compiling an overview of the numerous proteins which are EC prognostic factors associated or are directly related to recurrence In thisand review, survival. we Among systematically those, we highlight reviewed the prot theeins existing with an literature increased compilingpotential to become an overview of the numerousprognostic proteins biomarkers which in arethe clinical EC prognostic setting after factors prospective associated validation. or Finally, are directly we discuss related possible to recurrence improvements and new approaches not yet applied to EC biomarker research that could accelerate the identification of clinically relevant biomarkers.

3. Materials and Methods

3.1. Search strategy Literature searches were performed in MEDLINE from 1991 to February 2020 using the terms “endometrial cancer” or “endometrial neoplasms” or “endometrial carcinoma”, “biomarkers” or “markers”, and “prognosis or prognostic” or “recurrence” or “survival”.

3.2. Screening Duplicate hits were discarded. Unrelated studies were excluded through careful browsing of the title and/or abstract of each publication. Articles where only the abstract was available were also rejected.

3.3. Inclusion and exclusion criteria The inclusion criteria were (1) studies including endometrial cancer with an epithelial origin; (2) biomarker studies performed at protein level; (3) prognostic biomarker studies, i.e., studies that identify or validate biomarkers that are associated to EC risk factors, recurrence or survival; (4) studies performed on any biological human sample, but not on cultured cells or animal models; (5) studies based on the expression of biomarkers. Exclusion criteria were articles (1) not written in English; (2) based on the characterization of one specific EC subtype; (3) based on response-to- treatment biomarkers; (4) articles performed using less than 10 samples in total; (5) reviews, meta- analyses, opinion articles or case report studies.

3.4. Data extraction All selected articles were reviewed and data were compiled in a comprehensive database which contained: general information (name of the first author, country, journal, year of publication); number of patients and analytical technique used; association of the described biomarkers with

J. Clin. Med. 2020, 9, 1900 6 of 20 and survival. Among those, we highlight the proteins with an increased potential to become prognostic biomarkers in the clinical setting after prospective validation. Finally, we discuss possible improvements and new approaches not yet applied to EC biomarker research that could accelerate the identification of clinically relevant biomarkers.

3. Materials and Methods

3.1. Search Strategy Literature searches were performed in MEDLINE from 1991 to February 2020 using the terms “endometrial cancer” or “endometrial neoplasms” or “endometrial carcinoma”, “biomarkers” or “markers”, and “prognosis or prognostic” or “recurrence” or “survival”.

3.2. Screening Duplicate hits were discarded. Unrelated studies were excluded through careful browsing of the title and/or abstract of each publication. Articles where only the abstract was available were also rejected.

3.3. Inclusion and Exclusion Criteria The inclusion criteria were (1) studies including endometrial cancer with an epithelial origin; (2) biomarker studies performed at protein level; (3) prognostic biomarker studies, i.e., studies that identify or validate biomarkers that are associated to EC risk factors, recurrence or survival; (4) studies performed on any biological human sample, but not on cultured cells or animal models; (5) studies based on the expression of biomarkers. Exclusion criteria were articles (1) not written in English; (2) based on the characterization of one specific EC subtype; (3) based on response-to-treatment biomarkers; (4) articles performed using less than 10 samples in total; (5) reviews, meta-analyses, opinion articles or case report studies.

3.4. Data Extraction All selected articles were reviewed and data were compiled in a comprehensive database which contained: general information (name of the first author, country, journal, year of publication); number of patients and analytical technique used; association of the described biomarkers with different prognostic factors (histological type, histological grade, FIGO stage, myometrial invasion, lymph node status, LVSI, cervical invasion, metastasis, TCGA molecular classification, recurrence, risk, overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), progression-free survival (PFS), and recurrence-free survival (RFS)); statistical information of the identified biomarkers (e.g., p-value, adjusted p-value, fold-change, area under the receiver operating characteristic curve (AUC), etc.).

3.5. Quality Assessment The guidelines from Reporting Recommendations for Prognostic Studies (REMARK) [27,28] were used to evaluate the quality of studies that were eligible.

3.6. Functional Enrichment Analysis To investigate the potential functions of the most studied proteins regarding EC prognosis, we performed Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/home.jsp). The GO terms refer to biological processes (BP) [29]. KEGG was used to identify the most deregulated EC pathways [30]. J. Clin. Med. 2020, 9, 1900 7 of 20

3.7. Statistical Analysis A meta-analysis on OS was performed for the five most studied biomarkers. Only studies providing an estimate of the hazard ratio (HR) and the associated 95% CI for the parameter here considered were included. Since not all studies provided the same data regarding the estimation of HR, we focused the meta-analysis mainly on the unadjusted or ‘univariate’ estimates of HR. However, we used adjusted or ‘multivariate’ estimates for those articles not providing the unadjusted. HR and confidence interval (CI) were log-transformed. Pooled estimates of the HR (overall-effect model), and statistics I2 and tau-squared were computed following the guidelines of Doing Meta-Analysis in R [31]. Analysis and forest plots were created using the ‘meta’ package (Schwarzer, 2007) of the R software (R Core Team, 2019).

3.8. Analyses of TCGA Data Data from The Cancer Genome Atlas (TCGA) cohort of uterine corpus endometrial carcinoma, published in Nature [15], was obtained from https://tcpaportal.org/tcpa/download.html (L4) and clinical data were retrieved using cBioPortal. Protein expression levels were plotted using data from 200 patients and R software (R Core Team, 2019).

3.9. Analyses of CPTAC Data Data used in this publication were generated by the Clinical Proteomic Tumor Analysis Consortium (NCI/NIH). Thermo RAW files and clinical data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Uterine Corpus Endometrial Carcinoma (UCEC) Discovery study published in Cell [32] were retrieved using https://cptac-data-portal.georgetown.edu. MaxQuant software package version 1.6.7.0 [33] and the human database from Uniprot [34] were used to perform the protein and peptide identification and quantification. Protein expression levels from 100 patients were plotted using the proteinGroups.txt file by using R software (R Core Team, 2019).

4. Results

4.1. Data Summary Our search retrieved 2507 hits in the initial PubMed Search, that were reduced to 1557 after the first screening step. Of those, 398 met our criteria and were included in this review (Figure5A). Biomarker research on prognostic biomarkers in EC has increased over time and the global distribution points to Asia (43%) and Europe (41%) as the main contributors. At the country level, the leading countries are Japan, China, the United States of America, Turkey and Norway (Figure5B). From the 398 reviewed studies, a total of 255 protein biomarkers were identified as potential prognostic biomarkers, defined as proteins that are associated with one or more of the known clinical prognostic factors in EC, recurrence or survival. Remarkably, only 6% of articles have categorized the recruited patients and/or analyzed their results based on the TCGA classification from 2013 to date (Figure5C). From the 255 protein biomarkers compiled in this review, only 21% were validated by using either an independent technique, an independent cohort, or in an independent study. Curiously, 60% of the studies were based on the study of a single protein (Figure5D). Regarding the clinical sample used, 79% of the studies were performed in tissue specimens, followed by 16% of studies that used samples. Other sources were plasma, imprint smears, peritoneal cytology or uterine aspirates. Additionally, six studies were performed in tissue and validated in serum samples and five articles did it viceversa (Figure5E). J. Clin. Med. 2020, 9, x FOR PEER REVIEW 8 of 21

distribution points to Asia (43%) and Europe (41%) as the main contributors. At the country level, the leading countries are Japan, China, the United States of America, Turkey and Norway (Figure 5B). From the 398 reviewed studies, a total of 255 protein biomarkers were identified as potential prognostic biomarkers, defined as proteins that are associated with one or more of the known clinical prognostic factors in EC, recurrence or survival. Remarkably, only 6% of articles have categorized the recruited patients and/or analyzed their results based on the TCGA classification from 2013 to date (Figure 5C). From the 255 protein biomarkers compiled in this review, only 21% were validated by using either an independent technique, an independent cohort, or in an independent study. Curiously, 60% of the studies were based on the study of a single protein (Figure 5D). Regarding the clinical sample used, 79% of the studies were performed in tissue specimens, followed by 16% of J. Clin.studies Med. 2020 that, 9, used 1900 serum samples. Other sources were plasma, imprint smears, peritoneal cytology or8 of 20 uterine aspirates. Additionally, six studies were performed in tissue and validated in serum samples and five articles did it viceversa (Figure 5E).

A B 41% 13% 6 4 5 % 4 43 14 ("endometrial cancer” OR "endometrial neoplasms” OR "endometrial carcinoma") 12 4 4 7 16 AND ("biomarkers" OR "markers”) AND… 1% AND (”prognosis" OR AND (”recurrence" ) AND (”survival" ) “prognostic”) 1% 1341 articles 356 articles 810 articles 1%

Identification From Jul 1991 – Feb 2020 From May 1992 – Feb 2020 From 1991 – Feb 2020 n = 2507 C 40

Articles-TCGA Records after duplicate removal 30 Articles n = 1557 20 Records excluded (other cancer types)

Screening Records screened n = 236 10

n = 1321 0 Number of published articles published of Number 4 0 9 5 8 92 9 95 96 97 98 99 0 01 03 04 05 06 07 08 0 10 11 12 13 1 16 17 1 19 9 993 9 9 9 9 9 9 0 0 002 0 0 0 0 0 0 0 0 0 0 0 014 0 0 0 0 0 020 Records excluded (only abstract available) 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Year n = 66 D 250 Full-text articles assessed for eligibility 200 150 n = 1255 80 70 Inclusion criteria: (i) EC with epithelial 60 Potential prognostic biomarkers origin articles: (ii) Protein biomarkers; (iii) 50 (n=255) prognostic biomarkers; (iv) Any biological 40 human sample; (v) Studies based on the 30 expression of biomarkers. 20 Number of articles Number Eligibility Exclusion criteria: (i) Non-English 10

articles; (ii) Studies exclusively based on 0 the characterization of ONE specific EC 123456789101112131415 subtype ; (iii) Studies on response to Number of proteins/article treatment biomarkers; (iv) Studies E

including less than 10 samples in total; (v) 300 Reviews, meta-analysis, opinion articles or case report studies 200

60 n = 857

40 Studies included in quantitative analysis

20 Included

n = 398 Numberstudies of

0

e m e a te rs ru su m e is as S Serum T pira Tissu + + Pl s Smea e A u m nt s ri s eru Ti S terine Imp U

FigureFigure 5. Search 5. Search strategy strategy and and global global overview. (A ()A Flow) Flow diagram diagram depicting depicting the steps the followed steps followed for the for the selectionselection of the studies studies included included in inthis this review; review; (B) world (B) world distribution distribution of the selected of the selected articles; ( articles;C) (C) distribution of ofthe the selected selected studies studies across years. across Ar years.ticles including Articles TCGA including classification TCGA in classificationtheir dataset in theirare dataset marked are in marked dark green; in dark (D) green;distribution (D) distribution of the number of of the protei numbern biomarkers of protein evaluated biomarkers in each evaluated of in eachthe of studies the studies included included in this review; in this review;(E) Distribution (E) Distribution of the studies of the according studies to according the clinical to sample the clinical sampleused used in the in thestudy. study.

4.2. Prognostic4.2. Prognostic Protein Protein Biomarkers Biomarkers in in EC EC As shownAs shown in Figurein Figure6, the6, the majority majority of of biomarkers biomarkers identifiedidentified in in this this systematic systematic review review were were associated with histological grade, FIGO stage and OS, with more than 100 biomarkers described for associated with histological grade, FIGO stage and OS, with more than 100 biomarkers described for each of these parameters. Other biomarkers were associated with lymph node status, histological type, myometrial invasion, LVSI, DFS, recurrence, DSS, PFS, risk, RFS, metastasis, cervical invasion and the TCGA subgroups (Figure6). The vast majority of biomarkers are related to more than one of the above-mentioned parameters, indicating that they provide relevant prognostic information but are not specifically linked to one feature in particular. In fact, those that were associated with a specific parameter (in bold in Figure6) generally corresponded to those biomarkers that have been scarcely studied. Thus, further research needs to be performed to understand whether they are truly significant as prognostic factors and specific of that parameter in particular or might be also related to other parameters. J. Clin. Med. 2020, 9, 1900 9 of 20

Number validated Prognostic factor Gene names of the validated proteins proteins

ADM AKT1 ALB ALCAM ANXA2 AR ARID1A ASRGL1 ATAD2 ATP7B AURKB BECN1 BGN BIRC5 BUB1 C1QBP CCNA2 CCNB1 CCND1 CCNE1 CD44 CD68 CD82 CDC20 CDCP1 CDH1 CDH3 CDK4 CDKN2A CHI3LI CIP2A CLU CRIP1 CRP CTNNA1 CTNNB1 CTNND1 CYP19A1 DPP10 EDIL3 EGFR EIF3A EIF4EBP1 ELF1 ENG EPHA2 ERAP1 ERBB2 ESR1 ESR2 ETV5 F2R FASN FGA FGFR2 FHIT FOXA1 FSCN1 GDF15 GGT1 GLP1R H3C1 HDAC1 HDAC2 HIF1A HIF1AN HLA-A HLA-E HSF1 HSPA5 INHA INHBA INHBB JUN KDM1A Histological 158 KHDRBS1 L1CAM LGALS3 LNPEP LRP1 MACC1 MAD2L1 MAPK1 MCM6 MCM7 MIB1 MIF MKI67 MME MMP12 MMP7 MMR MT1A MT2A MUC1 MUC16 MYCBP NAMPT NFKB1 grade NOTCH2 NOTCH3 NSD2 NTSR1 OVGP1 PARK7 PAX8 PBXIP1 PCNA PDPN PECAM1 PGR PKM PLAU PLK1 PODXL PRL PROM1 PTCH1 PTEN PTGS2 PTPN6 REPP86 RICTOR ROBO1 RRBP1 S100A4 SAA1 SATB1 SCYL3-p SERPINE1 SHH SKP2 SLC2A1 SLC7A5 SLIT1 SNAI1 SNAI2 SNCG SOX2 SPAG9 SPP1 ST6GALNAC1 STMN1 TFGA TIMP2 TMEFF2 TNC TNFAIP8 TOP2A TP53 TRA2B TRIM44 TWIST1 TYMP UCHL1 VCAN WFDC2 WNT7A

ALB ALCAM ANXA2 ASRGL1 ATAD2 BCL2 BGN BIRC5 BSG C1QBP CCNA2 CCNB1 CCND1 CD274 CD34 CD68 CD82 CDC20 CDCP1 CDH1 CDH3 CDK20 CDKN1A CDKN2A CFLAR CHI3LI CIP2A CLU CRIP1 CRP CSF1 CTNNB1 CTSB CXCL10 CYP19A1 DPP10 EGFR ELF1 ENG EPCAM EPHA2 ERBB2 ESR1 FAS FASLG FGA FGF1 FGFR2 FHIT FOXA1 GDF15 GH1 GINS3 GPER1 GSTO1 HDAC2 HDGF HIF1A HLA-E HMGA1 HSPA5 IDO1 IL31 IL33 INHA IRAK1 KDM1A KHDRBS1 L1CAM LGALS3 LNPEP LRP1 MACC1 MAPK1 MCM6 MIF FIGO stage 140 MKI67 MME MMP12 MMP2 MMP9 MMR MSI1 MSLN MTOR MUC1 MUC16 MYCBP NAMPT NOTCH1 NOTCH2 NOTCH3 NSD2 OVGP1 PARK7 PBXIP1 PDPN PGR PIGF PKM PLAU PODXL PRL PROM1 PTEN PTGS2 PTK2 PTP4A3 PTTG1 RBMX REPP86 RICTOR ROBO1 RRBP1 S100A4 S100A8 SAA1 SATB1 SCYL3-p SERPINE1 SLIT1 SNAI1 SNAI2 SNCG SPAG9 SPP1 ST6GALNAC6 STC2 STMN1 TMEFF2 TNFAIP8 TOP2A TP53 TRIM44 UCHL1 VCAN VEGFA VIM WFDC2 WNT7A

ALCAM ALDH1A1 ANXA2 AR ARID1A ATAD2 ATP7B AURKB BCL2 BECN1 BGN BIRC5 BNIP3 BSG C1QBP CBR1 CCND1 CCNE1 CD274 CD3E CD44 CD68 CD82 CD8A CDCP1 CDH1 CDH3 CDKN1A CDKN2A CHI3L1 CHI3LI CRHR1 CRHR2 CRIP1 CRP CTNNB1 CTNND1 CTSB CTSD CXCL12 CXCL8 DFFB EGFR ENG ERBB2 ESR1 FGA FGF1 FHIT FLT1 FLT4 FOSL1 GDF15 GH1 GINS3 GLP1R GPER1 GSTO1 H3C1 HDAC2 HDGF HTATIP2 IDO1 INHA IRAK1 JAG1 JUN KDM1A KDR KHDRBS1 KLRG1 L1CAM LCN2 LDHA LGALS3 Overall survival 138 MAD2L1 MCM6 MCM7 MIB1 MKI67 MMP2 MMR(*) MS4A1 MSI1 MTOR MUC1 MUC16 NAMPT NME1 NOTCH1 NOTCH2 NOTCH3 NSD2 NTSR1 PAEP PBXIP1 PDCD1 PDPN PGR PRL PROM1 PTEN PTPN6 PTTG1 REPP86 RICTOR RRBP1 S100A1 S100A4 S100A8 SATB1 SERPINE1 SKP2 SLC2A4 SNAI1 SNAI2 SPINT1 SPINT2 SPP1 TFGA TIMP2 TMEFF2 TNFAIP8 TNP TOP2A TP53 TRIM27 TRIM44 TWIST1 TYMP UCHL1 VCAN VEGFA WFDC2 WNT7A YTHDC1 YWHAQ ALCAM ALDH1A1 ANXA2 AR ASRGL1 ATAD2 BCL2 BGN BSG BUB1 C1QBP CD34 CD44 CD68 CDH1 CFLAR CHI3LI CSF1 CTNNB1 DFFB ERBB2 ESR1 ESR3 FGF1 FSCN1 GDF15 Lymph node GINS3 GSTO1 HIF1AN IDO1 IL33 IRAK1 JUN KDM1A L1CAM LGALS3 LNPEP MACC1 MAD2L1 MDK MIF MKI67 MTOR MUC1 MUC16 MYCBP NAMPT NME1 NSD2 PBXIP1 PDPN 85 status PGR PLAU PRL PTEN PTGS2 PTP4A3 RBMX RETN ROBO1 RRBP1 S100A4 SATB1 SERPINE1 SKP2 SLIT1 SPAG9 SPINT1 SPINT2 ST6GALNAC1 ST6GALNAC6 STMN1 TCGA TMEFF2 TNFAIP8 TP53 TRA2B TRIM44 TWIST1 TYMP UCHL1 VCAN VEGFA WFDC2 WNT7A ABCB1 AKT1 ALCAM ARID1A ASRGL1 ATAD2 BIRC5 BSG CAPG CAPS CCNA2 CCND1 CCNE1 CD151 CD274 CD34 CD44 CD68 CD8A CDH1 CDH3 CDKN2A CHI3L1 CIP2A CRIP1 CRP CTNNB1 DPP10 EGFR EIF4EBP1 ERBB2 ESR1 FGF1 FGFR2 FHIT FOLR1 FOXA1 GDF15 H3C1 HIF1A HSF1 INHBA INHBB L1CAM LCN2 LGALS1 LGALS3 LRP1 LTF MAPK1 Histological type 82 MKI67 MMR MTOR MUC1 MUC16 NOTCH2 NOTCH3 PARK7 PAX8 PCNA PGR PIGR PLAU PTEN PTPN6 ROBO1 S100A4 SCGB2A1 SLIT1 SNAI1 SNAI2 SNCG SPAG9 SPP1 STMN1 TIMP2 TP53 TYMP URI1 VEGFA VIM WFDC2 ANXA2 ARID1A ASRGL1 ATAD2 BCL2 BECN1 BGN BMI1 BSG C1QBP CD34 CD44 CD68 CDH1 CDH3 CFLAR CRIP1 CSF1 CTNNB1 CTSB DFFB EGFR EHMT2 EIF4EBP1 ELF1 Myometrial EPHA2 ERBB2 ESR1 FBXW7 FGFR2 FHIT GDF15 GPER1 GSTO1 HIF1AN HLA-E IDO1 IL33 INHA INHBA IRAK1 JUN KDM1A KHDRBS1 L1CAM LGALS3 LNPEP MIF MKI67 MSI1 82 invasion MUC1 MUC16 NAMPT NOTCH1 NSD2 OVGP1 PARK7 PBXIP1 PGR PTEN PTGS2 REPP86 ROBO1 RRBP1 SATB1 SLC2A1 SLIT1 SNAI1 SNAI2 SNCG SPP1 STMN1 TNFAIP8 TOP2A TP53 TRIM44 TWIST1 TYMP UCHL1 VEGFA WFDC2 WNT7A ALCAM AR ASRGL1 ATAD2 BCL2 BIRC5 BMI1 BSG C1QBP CCNA2 CCNE1 CD34 CD44 CD68 CDH1 CDH3 CDK20 CTNNB1 DFFB ERBB2 ESR1 FBXW7 GSTO1 H3C1 HLA-E IDO1 Lymphovascular 63 KDM1A KRT1 L1CAM LNPEP MACC1 MKI67 MMR MMR MSI1 MUC1 MUC16 MYCBP NAMPT NOTCH1 NSD2 PGR PROM1 PTGS2 RICTOR ROBO1 SATB1 SKP2 SLC2A1 SNAI1 space invasion SNCG SPINT1 SPINT2 ST6GALNAC6 STMN1 TNFAIP8 TP53 TYMP UCHL1 VCAN VEGFA WFDC2 WNT7A

ALB ALDH1A1 ATAD2 ATP7B BCL2 BIRC5 BSG C1QBP CDCP1 CDKN2A CRHR1 CRP CTSB CTSD CXCL8 DFFB ERBB2 ESR1 FASN FOLH1 HLA-E HTATIP2 JAG1 KDM1A KRT1 Disease-free 60 L1CAM LNM LNPEP MKI67 MMR MUC1 MUC16 NOTCH2 NOTCH3 NSD2 NUCB2 PBXIP1 PCNA PGR PROM1 PTCH1 PTEN PTGS2 RRBP1 S100A4 SATB1 SERPINE1 SPINT1 survival SPINT2 SPP1 TNFAIP8 TP53 TRIM44 UCHL1 VCAN VEGFA WFDC2 WNT7A WT1 YWHAQ

ALCAM ALDH1A1 ATAD2 BCL2 BIRC5 C1QBP CD3E CD68 CDCP1 CHI3L1 CRP DFFB ERBB2 ESR1 FGA FGF1 GDF15 GINS3 H3C1 KDM1A KLRG1 L1CAM MDK MIB1 MMR(*) Recurrence 42 MUC1 MUC16 NSD2 PGR PLAU SATB1 SERPINE1 SLIT1 SNAI2 SNCG ST6GALNAC6 STC2 STMN1 TNFAIP8 TP53 WFDC2 WNT7A

Disease-specific ALCAM ASRGL1 BCL2 CCNB1 CD151 CD274 CD8A CDH1 CXCL8 EIF3A EIF4EBP1 EPHA2 ERBB2 ESR1 FOXA1 GDF15 HLA-A HLA-E HSF1 INHA INHBA MIB1 MKI67 MUC16 34 survival NUCB2 PAEP PCNA PDCD1 PGR RBMX STMN1 TIMP2 TP53 VEGFA

Progression-free ALB CBR1 CD274 CD68 CD8A CDH1 CHI3L1 CHI3LI ERBB2 ESR1 FOLR1 GGT1 GLP1R IDO1 INHA L1CAM MCM6 MKI67 MMR MUC1 MUC16 NTSR1 PGR RBMX S100A4 SNAI1 31 survival SNAI2 TP53 TRIM27 TYMP WFDC2

BCL2 BIRC5 CCNA2 CCND1 CD44 CDKN1A CDKN1B CDKN2A CSF1 ELF1 ERBB2 FHIT H3C1 HSPA5 L1CAM MUC1 MUC16 PARK7 PKM S100A1 SLC2A1 ST6GALNAC1 STMN1 Risk 29 TFGA TMEFF2 TP53 TP63 VIM WFDC2

Recurrence-free ALCAM BCL2 CD151 CDH1 CDKN1A CXCL12 ERBB2 FGF1 GH1 GINS3 L1CAM MIB1 MMP2 MMR(*) MUC16 NAMPT PGR PLAU PRL PTGS2 REPP86 SERPINE1 SNAI2 28 survival ST6GALNAC1 STC2 STMN1 TP53 WFDC2 Metastasis 13 CCND1 CRP ESR1 FGA FGF1 IL33 L1CAM MUC1 MUC16 PGR RBMX ST6GALNAC6 UCHL1 Cervical invasion 10 BSG CD44 CIP2A CTSB ESR1 MUC16 PGR SPP1 UCHL1 WFDC2 TCGA 3 L1CAM MCM6 PTEN

Figure 6. List of proteins associated with each prognostic factor. Proteins linked only to one specific parameter are highlighted in bold.

Most of the candidate prognostic biomarkers are involved in common biological processes, such as cellular processes, biological regulation, metabolic processes, response to a stimulus, cellular component organization or biogenesis and signaling and also, proteins that are part of the basic structural and functional units of the cell. In order to become promising biomarkers, the potential of any specific protein should be validated in different cohorts, and if possible, by independent groups, and in prospective studies. According to our findings, 11 proteins have been extensively studied, i.e., in more than five independent studies (Figure7A). Importantly, all these proteins have also been described as diagnostic biomarkers and are the main drivers of the oncogenic pathways related to EC (Figure7B,C) [35,36]. ERBB2 codes for a protein tyrosine kinase. In the nucleus of the cell it is involved in transcription regulation and it enhances protein synthesis and . ERBB2 has been reported as an amplified in cancer and its protein overexpression has been associated with high grade and high-stage, NEEC histologies, the degree of tumor progression and the outcome and survival of the patients [37]. Additionally, it has demonstrated to be a potential therapeutic target in serous EC tumors overexpressing ERBB2 [38]. CDH1 codes for e-cadherin, a calcium-dependent cell adhesion protein involved in mechanisms regulating cell-cell adhesions, mobility and proliferation. In EC, it has been associated with the epithelial-mesenchymal transition (EMT) and has demonstrated to have a role in the progression of EC [39]. Its loss has been associated with worse prognostic factors and poorer survival [40]. PTEN is the most frequently mutated gene in EC. PTEN is a tumor suppressor and it plays a role in cell cycle progression and cell survival by modulating the AKT-mTOR signaling pathway. In EC, the loss of function of PTEN has been postulated as an early event in carcinogenesis and correlated to a good prognosis [41]. TP53 is a tumor suppressor protein involved in cell cycle regulation, growth arrest and apoptosis, and it is involved in activating oxidative stress-induced necrosis. As one of the guardians of the genome, it is one of the most mutated genes in a wide range of cancers. The measurement of p53 by immunohistochemistry is broadly used in EC to classify tumor subtypes since p53 overexpression J. Clin. Med. 2020, 9, 1900 10 of 20 is linked to high-grade endometrioid and serous subtypes [42]. MMR proteins are components of the post-replicative DNA mismatch repair system involved in DNA repair. Defects in these proteins resulted in the definition of a new subgroup of the TCGA molecular classification: phenotype MSI, mainly caused by methylation of the MLH1 promoter and associated to type I EC [15]. The estrogen and progesterone receptors (ESR1 and PGR) are involved in the regulation of eukaryotic and affect cellular proliferation and differentiation. In EC, the positivity of these receptors has been associated with type I and a good prognosis [43]. The loss of both proteins was reported to independently predict lymph node metastasis with a specificity of 0.84 [44]. MKI67 is a proliferation marker with a role in maintaining individual mitotic dispersed in the cytoplasm following nuclear envelope disassembly. Its labeling index is low in low-grade squamous areas of EC [43]. Its expression has been related to the early stages of the disease, as well as worse OS [45]. Besides its potential to predict lymph node metastasis with an AUC of 0.604 [46]. The neural cell adhesion (L1CAM) is involved in the dynamics of cell adhesion. It has been described as critical in EC to promote the EMT and predictive of worse outcomes among EC, including tumors diagnosed at an early stage. L1CAM was described as the best-ever published prognostic factor able to greatly predict recurrence (sensitivity of 0.74; specificity of 0.91) and death (sensitivity of 0.77; specificity of 0.89) [47]. Finally, the MUC16 and WFDC2 are two proteins widely evaluated, since their high expression demonstrated either diagnostic and prognostic value in gynecological tumors. Specifically, MUC16, an epithelial ovarian carcinoma antigen, is already used in clinics as a tool to diagnose and it has been shown to predict lymph node metastasis with 0.78 sensitivity and specificity in EC [48]. Remarkably, in EC, serum WFDC2 reported a significantly higher pooled sensitivity (0.71) in comparison to MUC16 (0.35) and has been demonstrated to be a better diagnostic tool [49]. WFDC2 has also been demonstrated to be a more sensible predictor of lymph node metastasis (sensitivity of 0.82) in relation to MUC16 (0.72) [50] and better in predicting myometrial invasion (AUC of 0.76) than MUC16 (AUC = 0.65) [12]. All the 11 proteins have been studied by immunohistochemistry in primary tissue specimens. Notably, MKI67 and PTEN were also validated in tissue samples from imprint smears [51–53], as well as CDH1, which was analyzed in uterine aspirates using -based approaches [54]. MUC16 and WFDC2 have been extensively studied in serum samples by -based techniques such as ELISA or chemiluminescence techniques [50,55–57] and L1CAM was also validated in serum but just in one study [58]. J. Clin. Med. 2020, 9, x FOR PEER REVIEW 11 of 21

J. Clin.WFDC2 Med. 2020 was, 9obtained., 1900 Remarkably, WFDC2 has higher HR and 95% CI than ESR1 and TP53, making11 of 20 this protein a good and easy-to-assess biomarker, since it has been identified in serum samples (Figure 8A).

BIOMARKERS PROGNOSTIC FACTORS A Gene name Protein ID HT HG Fs MI LNM LVSI CI M TCGA Risk R DFS DSS OS PFS RFS BIOMARKERS PROGNOSTIC FACTORS MUC16 MUC16 Q8WXI7 2 102720189 6 7 1 5 7 3 112 2 (Q8WXI7) ESR1 ESR1 P03372 12 21 17 11 9 4 2 3 2 6 5 7 2 (P03372) PGR PGR P06401 11 21 13 11 7 3 1 1 2 6 3 7 2 1 (P06401) TP53 TP53 P04637 151810371 1 2351143 (P04637) Gene name Protein ID HT HG Fs MI LNM LVSI CI M TCGA Risk R DFS DSS OS PFS RFS WFDC2 WFDC2 Q14508 3 8 14 14 8 5 3 2 5 4 8 2 2 (Q14508) MKI67 MKI67 P46013 6181484 2 2 3 6 1 (P46013) ERBB2 ERBB2 P04626 479952 1 225431 (P04626) L1CAM L1CAM P32004 685333 11343 211 (P32004) MUC16 Q8WXI7 2 102720189 6 7 1 5 7 3 112 2 CDH1 CDH1 P12830 665512 1421 (P12830) MMR (MSH2 MMR(*) MMR(*) 356 2 1 23 531 (P43246), PTEN PTEN P60484 27523 24 (P60484) MUC1 MUC1 P15941 344122 1 111 31 (P15941) STMN1 344121 11 1 1 ESR1 P03372 12 21 17 11 9 4 2 3 2 6 5 7 2 STMN1 P16949 (P16949) CD44 CD44 P16070 32 1211 1 2 (P16070) CD68 CD68 P34810 122111 1 31 (P34810) CTNNB1 CTNNB1 P35222 222111 13 (P35222) VEGFA VEGFA P15692 32121 112 PGR P06401 112113117311 263721 (P15692) AT AD2 ATAD2 Q6PL18 122111 11 2 (Q6PL18) GDF15 GDF15 Q99988 22212 1 11 (Q99988) ALCAM ALCAM Q13740 121 11 1 21 1 (Q13740) ASRGL1 ASRGL1 Q7L266 122211 2 (Q7L266) TP53 P04637 15 18 10 3 7 1 1 2 3 5 11 4 3 CDKN2A CDKN2A P42771 222 1 1 3 (P42771) INHA INHA P05111 221 222 (P05111) BCL2 BCL2 P10415 1111 11111 1 (P10415) BIRC5 BIRC5 O15392 121 1 111 2 (O15392) SERPINE1 WFDC2 Q14508 381414853 254 822 SERPINE1 P05121 22 1 11 2 1 (P05121) CRP CRP P02741 112 1 11 2 (P02741) EGFR EGFR P00533 2222 1 (P00533) PLAU PLAU P00749 122 1 2 1 (P00749) SNAI2 SNAI2 O43623 1111 1211 MKI67 P46013 61814842 2361 (O43623) TYMP TYMP P19971 11 211 21 (P19971) UCHL1 UCHL1 P09936 1111111 1 1 (P09936) ARID1A ARID1A O14497 33 1 1 (O14497) BSG BSG P35613 111111 11 (P35613) ERBB2 P04626 479952 1225431 C1QBP C1QBP Q07021 11111 11 1 (Q07021) EPHA2 EPHA2 P29317 222 2 (P29317) KDM1A KDM1A O60341 11111 11 1 (O60341) NAMPT NAMPT P43490 11211 11 (P43490) L1CAM P32004 685333 11343 211 NSD2 NSD2 O96028 11111 11 1 (O96028) PTGS2 PTGS2 P35354 21111 1 1 (P35354) SATB1 SATB1 Q01826 11111 11 1 (Q01826) TNFAIP8 TNFAIP8 O95379 11111 11 1 (O95379) TOP2A 222 2 CDH1 P12830 665512 1421 TOP2A P11388 (P11388) WNT7A WNT7A O00755 11111 11 1 (O00755) CCND1 CCND1 P24385 112 1 2 (P24385) FGF1 FGF1 P05230 1111111 (P05230) LGALS3 LGALS3 P17931 21111 1 MMR(*) MMR(*) 356 2 1 23 531 (P17931) MIB1 MIB1 Q86YT6 1 2112 (Q86YT6) S100A4 S100A4 P26447 111 1 111 (P26447) SLC2A1 SLC2A1 P11166 311 1 (P11166) SNAI1 SNAI1 O95863 1111 1 11 (O95863) PTEN P60484 27523 2 4 SPP1 SPP1 P10451 1111 1 1 1 (P10451) CDH3 CDH3 P22223 1111 1 1 (P22223) DFFB DFFB O76075 111 11 1 (O76075) FHIT FHIT P49789 1111 1 1 (P49789) H3C1 MUC1P15941344122 1 111 31 H3C1 P68431 111111 (P68431) HLA-E HLA-E P13747 111 1 11 (P13747) IDO 1 IDO1 P14902 1111 11 (P14902) LNPEP LNPEP Q9UIQ6 11111 1 (Q9UIQ6) MME MME P08473 11 STMN1 P16949 3 4 4 1 2 1 1 1 1 1 (P08473) NOTCH3 NOTCH3 Q9UM47 111 12 (Q9UM47) PARK7 PARK7 Q99497 1211 1 (Q99497) PBXIP1 PBXIP1 Q96AQ6 1111 11 (Q96AQ6) PCNA PCNA P12004 22 11 (P12004) CD44 P16070 3 2 1 2 1 1 1 2 PROM1 PROM1 O43490 11 1 1 2 (O43490) ROBO1 ROBO1 Q9Y6N7 111111 (Q9Y6N7) RRBP1 RRBP1 Q9P2E9 1111 11 (Q9P2E9) SLIT1 SLIT1 O75093 11111 1 (O75093) CD68 P34810 1 2 2 1 1 1 1 3 1 SNCG SNCG O76070 1111 1 1 (O76070) TRIM44 TRIM44 Q96DX7 1111 11 (Q96DX7) VCAN VCAN P13611 11 11 1 1 (P13611) ALD H1A1 ALDH1A1 P00352 1112 (P00352) BGN 1111 1 CTNNB1 P35222 2 2 2 1 1 1 1 3 BGN P21810 (P21810) CCNA2 CCNA2 P20248 111 1 1 (P20248) CCNE1 CCNE1 P24864 21 1 1 (P24864) CD274 CD274 Q9NZQ7 11 111 (Q9NZQ7) CD34 CD34 P28906 11111 VEGFA P15692 3 2 1 2 1 1 1 2 (P28906) CD8A CD8A P01732 1 112 (P01732) CDCP1 CDCP1 Q9H5V8 11 11 1 (Q9H5V8) CHI3LI CHI3LI P36222 11 1 11 (P36222) CRIP1 CRIP1 P50238 1111 1 (P50238) ATAD2Q6PL18122111 11 2 CSF1 CSF1 P09603 212 (P09603) CTSB CTSB P07858 11 1 1 1 (P07858) EIF4EBP1 EIF4EBP1 Q 13541 12 1 1 (Q13541) FGA FGA P02671 11 1 1 1 (P02671) GINS3 GDF15Q9998822212 1 11 GINS3 Q9BRX5 11 1 11 (Q9BRX5) GSTO1 GSTO1 P78417 1111 1 (P78417) NOTCH2 NOTCH2 Q04721 111 11 (Q04721) PRL PRL P01236 11 1 11 (P01236) RBMX RBMX P38159 11 1 11 ALCAMQ1374012111 1211 (P38159) REPP86 REPP86 NA 111 11 (NA) ST6GALNA ST6GALNAC C1 Q9NSC7 21 1 1 1 S(TQ69GNASLCN7A) ST6GALNAC C6 Q969X2 1111 1 6 (Q969X2) TMEFF2 TMEFF2 Q9UIK5 11 1 1 1 (Q9UIK5) ASRGL1 Q7L266 1 2 2 2 1 1 2 TWIST1 TWIST1 Q15672 112 1 (Q15672) ALB ALB P02768 11 11 (P02768) ANXA2 ANXA2 P07355 11 1 1 (P07355) AR AR P10275 111 1 (P10275) CDKN2A P42771 2 2 2 1 1 3 CDKN1A CDKN1A P38936 1111 (P38936) CHI3L1 CHI3L1 P36222 1 111 (P36222) CIP2A CIP2A Q8TCG1 111 1 (Q8TCG1) ELF1 ELF1 P32519 111 1 (P32519) ENG 11 2 INHA P05111 2 2 1 2 2 2 ENG P17813 (P17813) FGFR2 FGFR2 P21802 1111 (P21802) FOXA1 FOXA1 P55317 111 1 (P55317) IL33 IL33 O95760 111 1 (O95760) INHBA INHBA P08476 11 1 1 BCL2 P10415 1 1 1 1 1 1 1 1 1 1 (P08476) IRAK1 IRAK1 P51617 111 1 (P51617) JUN JUN P05412 111 1 (P05412) KHDRBS1 KHDRBS1 Q07666 111 1 (Q07666) MACC1 MACC1 Q6ZN28 11 11 (Q6ZN28) BIRC5 O15392 1 2 1 1 1 1 1 2 MCM6 MCM6 Q14566 11 11 (Q14566) MIF MIF P14174 1111 (P14174) MSI1 MSI1 O43347 11 1 1 (O43347) MTOR MTOR P42345 111 1 (P42345) MYCBP MMR(*): MSH2 (P43246), MLH1 (P40692), MSH6 (P52701), PMS2 (P54278) MYCBP Q99417 11 11 (Q99417) NOTCH1 NOTCH1 P46531 11 1 1 (P46531) PDPN PDPN Q86YL7 11 1 1 (Q86YL7) RICTOR RICTOR Q6R327 11 1 1 (Q6R327) SKP2 SKP2 Q 13309 111 1 (Q13309) SPAG9 SPAG9 O60271 111 1 MUC16 CRP CDH3 CDCP1 FOXA1 CFLAR CD3E MMP12 EDIL3 NFKB1 (O60271) SPINT1 SPINT1 O43278 11 1 1 (O43278) B SPINT2 SPINT2 O43291 11 1 1 ESR1 EGFR DFFB CHI3LI IL33 CXCL8 CDC20 NME1 EHMT2 PECAM1 (O43291) TIMP2 TIMP2 P16035 11 11 (P16035) AKT1 AKT1 P31749 21 (P31749) PGR PLAU FHIT CRIP1 INHBA DPP10 CDK20 NUCB2 EPCAM PIGF AT P7B ATP7B P35670 1 11 (P35670) BECN1 BECN1 Q14457 11 1 (Q14457) TP53 SNAI2 H3C1 CSF1 IRAK1 GH1 CRHR1 PAEP ERAP1 PIGR CCNB1 CCNB1 P14635 11 1 (P14635) CD151 CD151 P48509 1 11 (P48509) WFDC2 TYMP HLA-E CTSB JUN GLP1R CTNNA1 PAX8 ESR2 PLK1 CD82 CD82 P27701 11 1 (P27701) CFLAR CFLAR O15519 111 (O15519) CXCL8 MKI67 UCHL1 IDO1 EIF4EBP1 KHDRBS1 GPER1 CTNND1 PDCD1 ESR3 PTK2 CXCL8 P10145 111 (P10145) DPP10 DPP10 Q8N608 111 (Q8N608) GH1 1 11 ERBB2 ARID1A LNPEP FGA MACC1 HDAC2 CTSD PODXL ETV5 RETN GH1 P01241 (P01241) GLP1R GLP1R P43220 1 11 (P43220) GPER1 GPER1 Q99527 11 1 L1CAM BSG MME GINS3 MCM6 HIF1A CXCL12 PTCH1 F2R SCGB2A1 (Q99527) HDAC2 HDAC2 Q92769 11 1 (Q92769) HIF1A HIF1A Q16665 111 (Q16665) CDH1 C1QBP NOTCH3 GSTO1 MIF HIF1AN CYP19A1 PTP4A3 FAS SHH HIF1AN HIF1AN Q9NWT6 111 (Q9NWT6) HSF1 HSF1 Q00613 11 1 (Q00613) MMR(*) EPHA2 PARK7 NOTCH2 MSI1 HSF1 EIF3A PTTG1 FASLG SLC2A4 HSPA5 HSPA5 P11021 11 1 (P11021) LRP1 LRP1 Q07954 111 (Q07954) PTEN KDM1A PBXIP1 PRL MTOR HSPA5 FASN S100A1 FLT4 SOX2 MAD2L1 MAD2L1 Q13257 11 1 (Q13257) MAPK1 MAPK1 P28482 111 (P28482) MUC1 NAMPT PCNA RBMX MYCBP LRP1 FBXW7 S100A8 FOLH1 TNC MMP2 MMP2 P08253 1 11 (P08253) NTSR1 NTSR1 P30989 1 11 (P30989) OVGP1 111 STMN1 NSD2 PROM1 REPP86 NOTCH1 MAD2L1 FLT1 SAA1 FOSL1 TP63 OVGP1 Q12889 (Q12889) PKM PKM P14618 11 1 (P14618) PT PN6 PTPN6 P29350 11 1 CD44 PTGS2 ROBO1 ST6GALNAC1 PDPN MAPK1 FOLR1 SCYL3-p HDAC1 URI1 (P29350) STC2 STC2 O76061 1 11 (O76061) TFGA TFGA P01135 1 11 (P01135) CD68 SATB1 RRBP1 ST6GALNAC6 RICTOR MMP2 FSCN1 TRA2B HMGA1 WT1 VI M VIM P08670 11 1 (P08670) TN phenot. x3[-](*) x3[-](*) 3 (*) CTNNB1 TNFAIP8 SLIT1 TMEFF2 SKP2 NTSR1 GGT1 TRIM27 IL31 YTHDC1 AURKB AURKB Q96GD4 1 1 (Q96GD4) BMI1 BMI1 P35226 11 (P35226) VEGFA TOP2A SNCG TWIST1 SPAG9 OVGP1 HDGF YWHAQ KDR ABCB1 BUB1 BUB1 O43683 11 (O43683) CBR1 CBR1 P16152 11 (P16152) ATAD2 WNT7A TRIM44 ALB SPINT1 PKM HLA-A ADM LDHA SLC7A5 CD3E CD3E P07766 11 (P07766) CDC20 CDC20 Q12834 11 (Q12834) CDK20 GDF15 CCND1 VCAN ANXA2 SPINT2 PTPN6 HTATIP2 BNIP3 LGALS1 CDK20 Q8IZL9 11 (Q8IZL9) CRHR1 CRHR1 P34998 11 (P34998) CTNNA1 CTNNA1 P35221 1 1 ALCAM FGF1 ALDH1A1 AR TIMP2 STC2 INHBB CAPG LTF (P35221) CTNND1 CTNND1 O60716 1 1 (O60716) CTSD CTSD P07339 11 ASRGL1 LGALS3 BGN CDKN1A AKT1 TFGA JAG1 CAPS MMP7 (P07339) CXCL12 CXCL12 P48061 11 (P48061) CYP19A1 CYP19A1 P11511 11 (P11511) CDKN2A MIB1 CCNA2 CHI3L1 ATP7B VIM KLRG1 CDK4 MMP9 EIF3A EIF3A Q14152 1 1 (Q14152) FASN FASN P49327 1 1 (P49327) INHA S100A4 CCNE1 CIP2A BECN1 AURKB KRT1 CDKN1B MS4A1 FBXW7 FBXW7 Q969H0 11 (Q969H0) FLT1 FLT1 P17948 2 (P17948) BCL2 SLC2A1 CD274 ELF1 CCNB1 BMI1 LCN2 CLU MSLN FOLR1 FOLR1 P15328 1 1 (P15328) FSCN1 FSCN1 Q16658 11 (Q16658) GGT1 BIRC5 SNAI1 CD34 ENG CD151 BUB1 MCM7 CRHR2 MT1A GGT1 P19440 1 1 (P19440) HDGF HDGF P51858 1 1 (P51858) HLA-A HLA-A P04439 1 1 SERPINE1 SPP1 CD8A FGFR2 CD82 CBR1 MDK CXCL10 MT2A (P04439) HTATIP2 HTATIP2 Q9BUP3 11 (Q9BUP3) INHBB INHBB P09529 11 (P09529) JAG1 JAG1 P78504 11 (P78504) KLRG1 KLRG1 Q96E93 11 (Q96E93) KRT1 KRT1 P04264 11 (P04264) LCN2 LCN2 P80188 1 1 (P80188) MCM7 MCM7 P33993 1 1 (P33993) MDK MDK P21741 11 C (P21741) MMP12 MMP12 P39900 11 (P39900) NME1 NME1 P15531 1 1 (P15531) NUCB2 NUCB2 P80303 11 (P80303) PAEP PAEP P09466 11 (P09466) PAX8 PAX8 Q 06710 11 (Q06710) PDCD1 PDCD1 Q15116 11 (Q15116) PODXL PODXL O00592 11 (O00592) PTCH1 PTCH1 Q13635 1 1 (Q13635) PTP4A3 PTP4A3 O75365 11 (O75365) PTTG1 PTTG1 O95997 1 1 (O95997) S100A1 S100A1 P23297 11 (P23297) S100A8 S100A8 P05109 1 1 (P05109) SAA1 SAA1 P0DJI8 11 (P0DJI8) SCYL3-p SCYL3-p Q 8IZE3 11 (Q8IZE3) TRA2B TRA2B P62995 11 (P62995) TRIM27 TRIM27 P14373 11 (P14373) YWHAQ YWHAQ P27348 11 (P27348) ADM ADM P35318 1 (P35318) BNIP3 BNIP3 Q12983 1 (Q12983) CAPG CAPG P40121 1 (P40121) CAPS CAPS Q13938 1 (Q13938) CDK4 CDK4 P11802 1 (P11802) CDKN1B CDKN1B P46527 1 (P46527) CLU CLU P10909 11 (P10909) CRHR2 CRHR2 Q13324 1 (Q13324) CXCL10 CXCL10 P02778 1 (P02778) EDIL3 EDIL3 O43854 1 (O43854) EHMT2 EHMT2 Q96KQ7 1 (Q96KQ7) EPCAM EPCAM P16422 1 (P16422) ERAP1 ERAP1 Q9NZ08 1 (Q9NZ08) ESR2 ESR2 Q92731 1 (Q92731) ESR3 ESR3 P62508 1 (P62508) ETV5 ETV5 P41161 1 (P41161) F2R F2R P25116 1 (P25116) FAS FAS P25445 1 (P25445) FASLG FASLG P48023 1 (P48023) FLT4 FLT4 P35916 1 (P35916) FOLH1 FOLH1 Q04609 1 (Q04609) FOSL1 FOSL1 P15407 1 (P15407) HDAC1 HDAC1 Q13547 1 (Q13547) HMGA1 HMGA1 P17096 1 (P17096) IL31 IL31 Q6EBC2 1 (Q6EBC2) KDR KDR P35968 1 (P35968) LDHA LDHA P00338 1 (P00338) LGALS1 LGALS1 P09382 1 (P09382) LTF LTF P02788 1 (P02788) MMP7 MMP7 P09237 1 (P09237) MMP9 MMP9 P14780 1 (P14780) Model 18 M18p(*) M18p(*) 1 prots (*) MS4A1 MS4A1 P11836 1 (P11836) MSLN MSLN Q13421 1 (Q13421) MT1A MT1A P04731 1 (P04731) MT2A MT2A P02795 1 (P02795) NFKB1 NFKB1 P19838 1 (P19838) PECAM1 PECAM1 P16284 1 (P16284) PIGF PIGF Q07326 1 (Q07326) PIGR PIGR P01833 1 (P01833) PLK1 PLK1 P53350 1 (P53350) PTK2 PTK2 Q05397 1 (Q05397) RETN RETN Q9HD89 1 (Q9HD89) SCG B2A1 SCGB2A1 O75556 1 (O75556) SHH SHH Q15465 1 (Q15465) SLC2A4 SLC2A4 P14672 1 (P14672) SOX2 SOX2 P48431 1 (P48431) TNC TNC P24821 1 (P24821) TP63 TP63 Q9H3D4 1 (Q9H3D4) URI1 URI1 O94763 1 (O94763) WT1 WT1 P19544 1 (P19544) YTHDC1 YTHDC1 Q96MU7 1 (Q96MU7) ABCB1 ABCB1 P08183 1 (P08183) SLC7A5 SLC7A5 Q01650 1 (Q01650) FigureFigure 7. 7.Overview Overview ofof thethe validated biomarkers. biomarkers. (A ()A Full) Full perspective perspective of all of validated all validated biomarkers: biomarkers: (i) (i)dark dark red red when when protein protein was was validated validated five five or more or more times times for that for parameter; that parameter; (ii) red: (ii) when red: protein when protein was wasvalidated validated in more in more than thanone study; one study;(iii) (iii) red: light protein red: validated protein in validated one study. in List one of study. the top-25 List most of the top-25studied most proteins studied as prognostic proteins as factor prognostic biomarkers factor is zoomed biomarkers in. For is each zoomed protein, in. the For number each protein,of studies the numberin which of it studies was validated in which appears; it was validated(B) list of proteins appears; validated, (B) list of at proteinsleast in one validated, study, for at one least of in the one study,considered for one parameters. of the considered Ordered parameters. regarding the Ordered number regarding of independent the number studies of independent where they were studies wherevalidated. they wereIn bold, validated. the top-25 In most bold, studied the top-25 proteins; most (C studied) EC disease proteins; Pathway (C) EC Map disease obtained Pathway from the Map obtainedKEGG fromDISEASE the KEGGdatabase DISEASE [30,59,60]. database The [30proteins,59,60]. from The proteinsthe 11 most from thestudied 11 most proteins studied list proteins are listhighlighted are highlighted by yellow by yellowstars, while stars, the while top-25 the are top-25 highlighted are highlighted by blue stars. by blueHT: histological stars. HT: type; histological HG: type; HG: histological grade; Fs: FIGO stage; MI: myometrial invasion; LNS: lymph node status; LVSI:

lymphovascular space invasion; CI: cervical invasion; M: metastasis; TCGA: TCGA classification; R: recurrence; DFS: disease-free survival; DSS: disease-specific survival; OS: overall survival; PFS: progression-free survival; RFS: recurrence-free survival. J. Clin. Med. 2020, 9, 1900 12 of 20

4.3. Meta-Analysis of the Top-5 Most Studied Biomarkers Results of the meta-analysis on OS of the five most studied proteins are shown in Figure8. The number of investigations meeting our criteria for the estimation of HR was low, ranging from five to seven articles per protein. Substantial heterogeneity in the HRs across studies was observed for MUC16 and PGR, where the point estimates of the pooled HR and the 95% CI from the fixed and random effects model were wide. On the contrary, in ESR1, TP53, and WFDC2 the point estimates and the error margins were similar. Focusing on the data available, there is not enough evidence to affirm that MUC16 and PGR are useful EC prognostic biomarkers. However, articles studying ESR1, TP53 and WFDC2 point out these biomarkers as promising to be prognosticators of OS. Specifically, a pooled HR estimation of 3.51 [2.22; 5.57] for ESR1, 2.80 [2.00; 3.92] for TP53, and 4.56 [2.32; 9.00] for WFDC2 was obtained. Remarkably, WFDC2 has higher HR and 95% CI than ESR1 and TP53, making this protein a good and easy-to-assess biomarker, since it has been identified in serum samples (Figure8A). J. Clin. Med. 2020, 9, x FOR PEER REVIEW 13 of 21

ABCMUC16 MUC16 ● p = 0.48 18

Source HR (9 5 % CI) ession r Mutz−Dehbalaie I, 2012 0.77 [0.38; 1.56] 17 Li J, 2015 1.07 [0.38; 3.01]

Zanotti L, 2012 2.64 [1.18; 5.89] otein exp r 16 Abbink K, 2018 3.41 [1.99; 5.83] Cetinkaya N, 2016 4.82 [1.42; 16.38] Kim HS, 2010 6.60 [1.09; 39.82] 15 Overall effect 2.23 [0.96; 5.23] Prediction interval [0.27; 18.27]

χ2 2 of p intensity Log2 14 Heterogeneity: 5 = 16.08 (P = .007), I = 69% 0.1 0.5 1 2 10 (95% CI) D eceased Living

ESR1 ESR1 ESR1 p = 0.054 p = 0.037 2 ession ession Source HR (95% CI) r r 0 Tomica D, 2017 2.57 [1.09; 6.06] 15

● ● Sho T, 2014 2.72 [1.00; 7.40] ● ● ●

otein exp otein ● exp otein ● Wik E, 2013 3.30 [1.89; 5.77] r r

Smith D, 2017 3.88 [1.62; 9.30] −2 ● Guan J, 2019 7.59 [2.55; 22.60] ● Overall effect 3.51 [2.22; 5.57] 14

● ● Prediction interval [1.32; 9.36] −4 2 2 χ of p Log2 intensity of p Log2 intensity Heterogeneity: 4 = 2.77 (P = .60), I = 0%

0.1 0.5 1 2 10 ● ● (95% CI) D eceased Living D eceased Living PGR PGR PGR PGR;PR p = 0.018 17 p = 0.36 0.0 Source HR (95% CI) ession ession r r Fukuda K, 1998 0.08 [0.01; 0.74] 16 −2.5 Saito S, 2006 0.44 [0.10; 1.94] otein exp otein otein exp otein r

Tomica D, 2017 2.15 [0.95; 4.85] r Smith D, 2017 3.87 [1.63; 9.20] −5.0 15 Guan J, 2019 7.59 [2.55; 22.60]

● Overall effect 1.41 [0.17; 11.90] ● ●

● Prediction interval [0.01; 390.66] −7.5

2 2 ● 14

χ of p Log2 intensity Heterogeneity: 4 = 19.76 (P < .001), I = 80% of p Log2 intensity

0.01 0.1 1 10 100 ● ● (95% CI) D eceased Living D eceased Living TP53 TP53 2.5 TP53 p = 0.41 p = 0.54

Source HR (95% CI) ● 0.0

Silverman MB, 2000 1.14 [0.38; 3.41] ession ession r r 14 Jeon YT, 2004 2.80 [0.75; 10.44] Heckl M, 2018 2.83 [1.59; 5.04]

Polychronidou G, 2018 2.83 [1.59; 5.04] exp otein 2.5 exp otein

r − r Steinbakk A, 2011 3.30 [1.10; 9.90] Appel ML, 2008 3.36 [1.39; 8.13] 12 Soong R, 1996 4.90 [1.33; 18.03] Overall effect 2.80 [2.00; 3.92] −5.0 ● Prediction interval [1.32; 5.91] ● 2 2 χ 10 Heterogeneity: 6 = 3.54 (P = .74), I = 0% of p Log2 intensity ● of p Log2 intensity

0.1 0.5 1 2 10 −7.5 ● (95% CI) D eceased Living D eceased Living

W FDC2

p = 0.13 ● WFDC2 17

ession 16 Source HR (9 5 % CI) r Mutz−Dehbalaie I, 2012 2.41 [1.17; 4.96] Zanotti L, 2012 3.43 [1.50; 7.84] 15 otein exp otein Bignotti E, 2011 5.35 [0.80; 35.72] r Stiekema A, 2017 7.12 [2.37; 21.39] 14 Abbink K, 2018 7.77 [4.00; 15.08] Overall effect 4.56 [2.31; 9.00] Prediction interval [1.11; 18.69] 13 2 2

χ intensityp of Log2 Heterogeneity: 4 = 6.59 (P = .16), I = 39% 0.1 0.5 1 2 10 12 (95% CI) D eceased Living Figure 8. FigureMeta-analysis 8. Meta-analysis on OS on ofOS theof the most most studied studied biomarkers biomarkers regarding regarding prognosis prognosis in EC (MUC16, in EC (MUC16, ESR1, PGR,ESR1, TP53, PGR, andTP53, WFDC2, and WFDC2, respectively). respectively). (A ()A Forest) Forest plots. plots. Diamond Diamond in light blue in represents light blue the represents point estimate and confidence intervals when combining all studies; (B) expression boxplots using the pointthe estimate RPPA data and of the confidence TCGA cohort intervals (n = 200:20 when deceased– combiningplotted in red all; 178 studies; living–plotted (B) expression in light red) boxplots using the RPPA[15]; (C) data Expression of the boxplots TCGA cohortusing the (n mass= 200:20 spectrometry deceased– data plottedof the CPTAC in red; 178cohort living– (n = 100:7plotted in light red)[15]; (deceased–C) Expressionplotted in boxplots red; 38 living– usingplotted the in light mass red spectrometry) [32]. data of the CPTAC cohort (n = 100:7 deceased–plotted in red; 38 living–plotted in light red)[32]. 5. Discussion This systematic review and metanalysis underline the lack of potential prognostic biomarkers of EC. Among the 2507 articles identified in this review, 398 were deeply analyzed. As a result, this review compiles information of 255 potential biomarkers, which are related to one or more of the clinical prognostic factors in EC, recurrence or survival. Although a large list of biomarkers is described, there are critical issues that hamper their clinical application and that are discussed in this

J. Clin. Med. 2020, 9, 1900 13 of 20

Additionally, the protein expression of these biomarkers was investigated in the data provided by the TCGA and CPTAC high-throughput studies. The same expression pattern is observed for the ESR1 and PGR genes in both studies, showing that the loss of these proteins is related to a worse prognosis. Regarding the TP53 expression, there is an inconsistency between the TCGA and CPTAC studies. Whilst the expression of TP53 is related to low OS in the TCGA study, the contrary is observed in the CPTAC study. TP53 has been widely reported in the literature as a poor EC prognostic factor and has been associated with histological type, histological grade, FIGO stage, myometrial invasion, lymph node status, LVSI, DSS, DFS, and PFS in addition to OS [61–64]. Thus, the CPTAC study should be interrogated to understand this discrepancy, which could be due to the clinicopathological characteristics of the patients or the limited number of cases included in the deceased group (n = 7 vs. n = 38 of the living group) [65]. MUC16 and WFDC2 were not tested by RPPA in the TCGA study. However, the expression of these proteins in the CPTAC study was reported and both proteins are likely to be upregulated in deceased patients (Figure8B,C). These results obtained from the analysis of tissue specimens are in line with the observations obtained in serum samples [50,55–57].

5. Discussion This systematic review and metanalysis underline the lack of potential prognostic biomarkers of EC. Among the 2507 articles identified in this review, 398 were deeply analyzed. As a result, this review compiles information of 255 potential biomarkers, which are related to one or more of the clinical prognostic factors in EC, recurrence or survival. Although a large list of biomarkers is described, there are critical issues that hamper their clinical application and that are discussed in this section, from a conceptual, methodological and analytical point of view. Additionally, new strategies in biomarker research are exposed. The main points of this section are summarized in Figure9. The REMARK guidelines [27] should be followed to assess the quality of the results derived from biomarker studies. We noted that many articles do not follow these guidelines and important information is missing, limiting the possibilities to reach definitive conclusions of the usefulness of the reported biomarkers. Study design is of key importance to successfully achieve robust biomarkers. Hence, the investigation of the unresolved problem has to guide the study design. Patients need to be selected according to well-defined inclusion and exclusion criteria, compiling all information possible and in a proper proportion and number to reach the required statistical power. Contrary to this, most of the assessed studies treated the usefulness of prognostic biomarkers as a secondary objective in the study. As a consequence, careful selection of patients is not performed, generating highly heterogenous groups leading to false-positive results. Additionally, statistical power is clearly affected either by a small number of patients per group or unbalanced comparison groups. In order to solve these misrepresentations, some considerations should be taken. In the initial phases (discovery) of the biomarker pipeline the aim will be to have the most balanced and less variable group of patients in order to avoid side effects, while in late phases (clinical) the assessment would be multicentric, prospective and including a wide range of patients covering the heterogeneity of the disease [66]. Another important aspect of the study design is the clinical sample that is selected to perform the study. As shown, most of EC studies were performed using tissue samples (79%), whereas 16% of the studies used serum as a potential source, and only the remaining 5% of the articles used other sources such as imprint smears, peritoneal cytology or uterine aspirates. A convenient and effective biomarker should not only discriminate two groups of patients with high accuracy but if possible, be easily implemented in clinical practice. An ideal prognostic biomarker should be identified preoperatively since this information is relevant to guide the treatment of the patient, which is mainly surgical and can vary from minimal to extensive surgery. To achieve that, a prognostic biomarker in EC should be measurable in noninvasive samples collected at the early steps of the diagnostic process. Therefore, the aim of investigators should be translating the results in easy-to-access biofluids such as blood or proximal biofluids. However, this has not been a priority yet, since only 21% (84/398) of J. Clin. Med. 2020, 9, 1900 14 of 20 the articles were assessing proteins in non-tissue samples, and remarkably, almost half of them only J.assessed Clin. Med. two2020, proteins,9, x FOR PEER i.e., REVIEW MUC16 and WFDC2. 16 of 21

Observation Alternative / Improvement Preanalytical factors Unclear clinical question/multiple hypothesis testing Clearly define the clinical question to be addressed Evident need of discovery studies and need to go through the different Almost no discovery studies. Mostly validation studies steps of the biomarker pipeline: discovery, verification and subsequent Study design validations Sample size must be adequated to ensure adequate statistical power Small sample size at every step of the process Patients not classified according to TCGA classification Need to classify patients in the new TCGA molecular classification No consensus on which parameters need to be reported Define inclusion/exclusion criteria for patient selection and Patient selection and detailed information about patients is often absent clinicopathological characteristics of patients Use of liquid biopsies is necessary to develop prognostic biomarkers Clinical sample Most studies were performed using tissue samples for preoperative implementation. It is highly recommended the use of selection minimally invasive proximal fluids Analytical factors

The largest proteomic study was performed using RPPA Apply recent advances in MS to increase coverage

Analytical platform Apply recent advances in multiplexed antibody-based or MS-based Most studies use one or few proteins for validation techniques to accurately measure higher number of proteins (e.g. Multiplexed ELISA, RPPA, LC-SRM, LC-PRM) Post analytical factors Provide descriptive statistics such as fold change ratios and ROC Lack of robust statistics analysis Most studies focus on single biomarkers Search for biomarker signatures in order to increase prognostic value Multivariate analysis adjusted for prognostic factors and relevant Statistical anaysis Regression analysis are not always calculated in an patient characteristics would be the desirable testing. Reporting optimal manner hazard ratios and confidence intervals Validation is always needed. The TCGA (RPPA) or CPTAC (MS) Lack of external and/or independent validation studies cohorts are publicly available. Implementation in clinics Studies should incorporate data to measure the benefit of the results Clinical relevance Insufficient data to provide clinical relevance over clinical tools that are currently being used Feasibility to Easy and straightforward assays, need of assays with high-level of Most of the studies are based in IHQ technique implement the automotion and reproducibility Comercialization an The path to clinical implementation should consider protection of the - intellectual property results (if needed), to understand the freedom-to-operate, etc

FDA approval - Seek FDA guidance as early as possible in the development phases

Figure 9. Outline of the preanalytical, analytical, and post-analytical factors detected in the articles Figure 9. Outline of the preanalytical, analytical, and post-analytical factors detected in the articles reviewed and recommendations of alternatives to consider for future studies. reviewed and recommendations of alternatives to consider for future studies. EC study design should include the TCGA classification as an additional parameter to either 6.recruit Conclusions patients or evaluate the results. In this review, we only identified 11 articles including the TCGA classification.The integration The incorporation of clinical, molecular of the TCGA and molecular pathological classification data may in improve research the and current clinical EC practice risk stratificationfor classifying system, EC patients which shouldis crucial be to promoted, select the especially most optimal when treatment studying fo prognosticr each patient. biomarkers. Here, we systematicallyThe rapid reviewed advances the in literature medical and for EC biomedical prognostic sciences biomarkers have aat huge the protein impact level on the and outcome compiled for apatients. list of 255 This proteins, is possible although thanks 79% to the of tightthose relation proteins between would medical require identificationfurther validation. of clinical The needsonly proteinsand the consequentthat have been solution extensively from the studied research are side. carbohydrate However, antigen regarding 125 EC (CA125 disease, or evenMUC16), if the human clinical epididymisgaps are well-known, protein 4 (HE4 more or research WFDC2), is needed estrogen toprovide receptor solutions (ESR1) and to all proges of them.terone Based receptor on our (PGR), review mismatchwe identified repair the proteins lack of (MMR discovery proteins: studies MSH2, as one MSH6, of the MHL1, main PMS2), causes. the Discovery tumor suppressors studies allow PTEN for andthe identificationTP53, the cell of adhesion de novo biomarkersmolecules E-cadherin since they screen(CDH1) for and the neural whole orcell at adhesion least, an abundantmolecule partL1 (L1CAM),of the proteome the proliferation of the samples marker that protein are being Ki-67 studied. (KI67),Following and the Erb-B2 our search Receptor criteria, Tyrosine we could Kinase only 2 (ERBB2).identify twoOn the discovery basis of studies our meta-analysis, on prognostic ESR1 factors, TP53 in and EC. TengWFDC2 et al. may unveiled be useful the prognosticators potential use of forPKM2 OS of and EC. HSPA5 We also as biomarkersidentified critical of high-risk conceptual, EC by usingmethodological two-dimensional and analytical gel electrophoresis factors that (2-DE)need toand be aimproved liquid chromatography in further research. electrospray Consequently, ionization we encourage tandem mass the scientific spectrometry community (LC-ESI-MS to follow/MS) theseproteomics considerations approach in [ 67order]. More to successfully recently, Yang identify et al. clinically used reverse-phase valuable EC protein prognostic arrays biomarkers: (RPPA) to (i)study design 186 studies proteins whose and phosphoproteinsprimary aim is the in identification tissue samples of inprognostic a training biomarkers. (n = 183) andThus, validation patient selection(n = 333) should cohort ofbe patients. balanced Their and resultscontrolled yielded to achieve an algorithm this objective, that combined and the two sample clinical of variables the study and source18 protein of biomarkers markers to should improve be riskcarefully classification chosen; in(ii) early-EC include high-throughput [68]. We cannot exclude technologies the possibility such as MSthat to other have search a broad criteria analysis might of yieldbiomarkers additional and discoveryto have the studies, feasibility but probably,to develop they biomarker were not panels; labeled (iii) the statistical analysis in every step of the biomarker pipeline should be thoughtfully performed.

Author Contributions: Conceptualization, E.C.-d.l.R., S.C. and E.C.; methodology, E.C.-d.l.R.; software, E.C.- d.l.R.; validation, G.D., E.M.-G. and A.G.-M.; formal analysis, E.C.-d.l.R.; resources, G.D.; data curation, E.C.- d.l.R.; writing—original draft preparation, E.C.-d.l.R.; writing—review and editing, E.C.-d.l.R., S.C. and E.C.;

J. Clin. Med. 2020, 9, 1900 15 of 20 as biomarker research studies. Therefore, discovery studies focused on the identification of prognostic biomarkers should be fostered in the near future. Most of the included studies are focused on the analysis and validation of one or a few proteins. This raises another important issue, which is the lack of biomarker panels. Since cancer is a multifactorial disease, a single biomarker is unlikely to have high accuracy on its own [69]. Related to this, new advances in MS instrumentation, acquisition methods, and associated informatics tools for data processing benefit the scientific community through all phases of the biomarker pipeline, i.e., discovery, verification and validation phases. MS instrumentations are becoming considerably more sensitive and faster, able to achieve higher selectivity and confidence in the peptide identification process [70]. Therefore, it is a great opportunity to screen clinically relevant samples and generate comprehensive lists of candidate biomarkers using different acquisition methods such as data-dependent acquisition (DDA) or the more recently emerged data-independent acquisition (DIA) [71]. Additionally, MS technology can be the perfect platform to further validate these lists of candidate biomarkers in a larger number of patients, since it allows us to highly multiplex and monitor a hundred proteins at once by using methods such as selected reaction monitoring (SRM) or parallel reaction monitoring acquisition (PRM). These techniques have already demonstrated their potential in EC diagnosis either in tissue and biofluids [72]. In our revision, only Martinez-Garcia et al. used a targeted MS approach to assess prognostic biomarkers [54]. In this study, 52 proteins were evaluated in uterine aspirates of a cohort of 116 patients and the results pointed to a 3-protein panel that differentiated endometrioid vs. serous EC histologies with 95% sensitivity and 96% specificity. As seen in this study, combinations of biomarkers provide improved discrimination power over molecular tests based on single markers. Additionally, the integration of molecular biomarkers with clinicopathological features would probably be the most convenient approach to develop more sensitive and specific tests, as we observed in some studies such as in Yang et al. [68]. To achieve clinical implementation of biomarkers is another relevant point of discussion. Once validated, biomarkers should be ideally transferred to a standardized, economic, simple and fast analytical platform and should be prospectively validated following all the regulatory requirements to become an in vitro diagnostic test. Currently, the assessment of a combination of biomarkers would be easy to implement by using a multiplex immunoassay, although another possibility is to perform each biomarker independently through a standard ELISA or immunohistochemistry assay. A new trend to implement multiple biomarkers in the clinical routine is clinical MS, which seems to advance faster in the last years with the commercialization of a new generation of instruments that are fully automated, such as the Thermo Scientific™ Cascadion™ SM Clinical Analyzer (Thermo Fisher). Finally, a robust statistical analysis of the results is essential to decide whether a protein has sufficient power to become a biomarker. In this systematic review, the vast majority of articles only use the p-value to assess the clinical utility of the candidate biomarkers. However, this parameter is not sufficient [73], and it should be supported by other descriptive statistics such as fold-change ratios and parameters used in evaluating clinical validity of a biomarker (e.g., sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), AUC value, among others). Additionally, biomarkers should be validated in large and external cohorts of patients. In this respect, an important step in the biomarker field is publicly available data produced by the large consortiums of the CPTAC and the TCGA. These studies include not only proteomic but also genomic and transcriptomic data, all of them associated with clinical features of carefully recruited cohorts [32]. These resources will permit assessing the potential of biomarkers and prioritizing biomarkers in the subsequent phases of the biomarker pipeline. As far as prognostic factors are concerned, applying multiple Cox regression or similar predictive modeling (‘multivariate’ analysis) to adjust for clinical parameters would be the desirable testing. This method would allow us to report the estimated HR and its CI, as suggested by other authors [27]. However, often limited cohort size does not allow to conduct a regression analysis of good predictive performance, which may affect the validity of the estimation results. Indeed, J. Clin. Med. 2020, 9, 1900 16 of 20 it has been reported that at least 20 individuals per event would be needed for reliable modeling [74]. Moreover, despite in many studies OS being used to predict mortality, DSS should be considered.

6. Conclusions The integration of clinical, molecular and pathological data may improve the current EC risk stratification system, which is crucial to select the most optimal treatment for each patient. Here, we systematically reviewed the literature for EC prognostic biomarkers at the protein level and compiled a list of 255 proteins, although 79% of those proteins would require further validation. The only proteins that have been extensively studied are carbohydrate antigen 125 (CA125 or MUC16), human epididymis protein 4 (HE4 or WFDC2), estrogen receptor (ESR1) and progesterone receptor (PGR), mismatch repair proteins (MMR proteins: MSH2, MSH6, MHL1, PMS2), the tumor suppressors PTEN and TP53, the cell adhesion E-cadherin (CDH1) and neural cell adhesion molecule L1 (L1CAM), the proliferation marker protein Ki-67 (KI67), and the Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2). On the basis of our meta-analysis, ESR1, TP53 and WFDC2 may be useful prognosticators for OS of EC. We also identified critical conceptual, methodological and analytical factors that need to be improved in further research. Consequently, we encourage the scientific community to follow these considerations in order to successfully identify clinically valuable EC prognostic biomarkers: (i) design studies whose primary aim is the identification of prognostic biomarkers. Thus, patient selection should be balanced and controlled to achieve this objective, and the sample of the study source of biomarkers should be carefully chosen; (ii) include high-throughput technologies such as MS to have a broad analysis of biomarkers and to have the feasibility to develop biomarker panels; (iii) the statistical analysis in every step of the biomarker pipeline should be thoughtfully performed.

Author Contributions: Conceptualization, E.C.-d.l.R., S.C. and E.C.; methodology, E.C.-d.l.R.; software, E.C.-d.l.R.; validation, G.D., E.M.-G. and A.G.-M.; formal analysis, E.C.-d.l.R.; resources, G.D.; data curation, E.C.-d.l.R.; writing—original draft preparation, E.C.-d.l.R.; writing—review and editing, E.C.-d.l.R., S.C. and E.C.; visualization, E.C.-d.l.R., E.C. and S.C.; supervision, E.C., S.C. and A.G.-M.; project administration, E.C.; funding acquisition, E.C. and A.G.-M. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by CIBERONC (CB16/12/00328), the “Fondo Europeo de Desarrollo Regional” FEDER (RTC-2015-3821-1), the Asociación Española Contra el Cáncer (GCTRA1804MATI), Grups consolidats de la Generalitat de Catalunya (2017 SGR-1661) and the Instituto de Salud Carlos III (DTS17/00146, PI17/02071 and PI17/02155; and the IFI19/00029 to E.C.-d.l.R), and a PERIS grant funded EColas (SLT002/16/00315) from Generalitat de Catalunya. EM-G was supported by a grant from Télévie awarded to G.D. (F5/20/5-TLV/DD). Acknowledgments: The authors are thankful to Alfonso Parrilla, Antoine Lesur and Daniel Perez for helpful discussions and ideas. Conflicts of Interest: The authors declare no conflict of interest.

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