s2fieldsofstudy: Biology Medicine

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Diagnostic Accuracy of Liquid Biomarkers for the Non-Invasive Diagnosis of Endometrial Cancer: A Systematic Review and Meta-Analysis

Simple Summary Endometrial cancer is common amongst women and rates are increasing annually. The diagnosis of this condition for women with bleeding after the menopause is invasive and often painful with many more women undergoing investigation than needed. A simple, non-invasive blood or urine test for the diagnosis of endometrial cancer is being sought. This review summarizes the current research on blood and urine tests and their diagnostic accuracy for detecting endometrial cancer. Whilst many blood and urine tests have been assessed there is currently no test that has a similar accuracy to biopsy of the uterine lining. However, this review demonstrates that there are some potential candidates which need to be explored by larger studies and on bigger groups of women. Abstract Endometrial cancer rates are increasing annually due to an aging population and rising rates of obesity. Currently there is no widely available, accurate, non-invasive test that can be used to triage women for diagnostic biopsy whilst safely reassuring healthy women without the need for invasive assessment. The aim of this systematic review and meta-analysis is to evaluate studies assessing blood and urine-based biomarkers as a replacement test for endometrial biopsy or as a triage test in symptomatic women. For each primary study, the diagnostic accuracy of different biomarkers was assessed by sensitivity, specificity, likelihood ratio and area under ROC curve. Forest plots of summary statistics were constructed for biomarkers which were assessed by multiple studies using data from a random-effect models. All but one study was of blood-based biomarkers. In total, 15 studies reported 29 different exosomal biomarkers; 34 studies reported 47 different proteomic biomarkers. Summary statistic meta-analysis was reported for micro-RNAs, cancer antigens, hormones, and other proteomic markers. Metabolites and circulating tumor materials were also summarized. For the majority of biomarkers, no meta-analysis was possible. There was a low number of small, heterogeneous studies for the majority of evaluated index tests. This may undermine the reliability of summary estimates from the meta-analyses. At present there is no liquid biopsy that is ready to be used as a replacement test for endometrial biopsy. However, to the best of our knowledge this is the first study to report and meta-analyze the diagnostic accuracy of different classes of blood and urine biomarkers for detection of endometrial cancer. This review may thus provide a reference guide for those wishing to explore candidate biomarkers for further research.

Introduction
Endometrial cancer (EC) is the most common form of uterine cancer and arises from the lining of the uterus, known as the endometrium. In 2020, GLOBOCAN the World Health Organizations' International Agency for Research on Cancer recorded 417,367 new cases of uterine cancer and 97,370 deaths making this the fourth most common cancer of women and the third leading cause of cancer mortality in females after breast and lung cancer [1]. Incidence in the UK has increased by around 55% since the 1990s with 9700 women diagnosed per year in 2016-2018 [2]. The rise in cases of EC diagnosed annually is set to increase globally by 30% by the year 2040 [3]. This is likely due to the advancing age of the population and rising rates of obesity. Currently if diagnosed and treated at International Federation of Gynecology and Obstetrics [FIGO] Stage I or II, EC 5-year survival rates are around~92% and 75%, respectively, whereas advanced stage III and IV ECs have 5-year survival rates of 48% and 15%, respectively [4][5][6]. Strategies for early diagnosis are therefore critical.
Symptoms suspicious for EC are post-menopausal bleeding, unscheduled bleeding on hormone replacement therapy (HRT), persistent intermenstrual or irregular bleeding, hematuria or abnormal vaginal discharge. Although EC is less commonly diagnosed in pre-menopausal women, in the UK, 6.5% of women diagnosed with EC between 2015-2017 were less than 50 years old [7]. In post-menopausal women with bleeding, transvaginal ultrasound scan (TVS) is performed as a triaging test to assess endometrial thickness (ET). An ET < 4 mm is associated with an <1% risk of EC [8,9]. For those with an ET > 4 mm, an endometrial biopsy is recommended as the gold standard diagnosis of EC [8]. However, in symptomatic perimenopausal or pre-menopausal women with risk factors for EC, the National Institute of Clinical Excellence recommend hysteroscopy and targeted biopsy in the first instance as TVS has limited value in women who are still menstruating [10].
Whilst the gold standard of EC diagnosis, endometrial biopsy has an almost 99% accuracy, the current technique for triaging with ultrasound lacks specificity resulting in more than 50% of patients needing invasive biopsy [11]. Furthermore, failure to obtain a biopsy in the outpatient setting is common and occurs in around one third of women, often due to sampling failure or pain during the investigation [12]. In these patients, repeat investigations are needed, often under general anesthetic which is not without associated risks and costs [13]. A simple, easy to administer, non-invasive test that could triage women with EC for diagnostic biopsy whilst safely reassuring healthy women, would vastly improve patient care over the current model. The ideal detection tool would be simple to perform, non-invasive to obtain, accurate in reassuring women without disease and cost effective to allow implementation as a screening program within the primary care setting.
'Liquid biopsy' has the potential to offer this solution and refers to the sampling and analysis of non-solid biological tissue for its tumoral elements [14]. These elements might be circulating tumor cells (CTCs), cell-free tumor DNA (ctDNA), as well as extracellular vesicles (EVs), microRNAs (miRNAs), mRNA, long noncoding RNAs (lncRNAs), small RNA, circulating cell-free proteins, and tumor-educated platelets (TEPs) [14]. Many body fluids can be used for liquid biopsy, however the most non-invasive and widely investigated fluids are blood and urine. Multiple approaches have been employed in the search for diagnostic cancer biomarkers. Advances in areas such as proteomics, metabolomics and genomic sequencing have increased the scope for individual or panels of biomarkers to be discovered. However, biomarkers must overcome several hurdles before they are implemented into clinical practice; discovery, validation, and verification [14]. An ideal biomarker should be accurate and reproducible between laboratories. Its clinical validity should be reported in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). However, between given studies, it is difficult to compare the performance of one or multiple biomarkers unless they share a fixed false positive rate. For this reason, the area under the curve (AUC) value of a receiver operator characteristic (ROC) curve plotting Sensitivity over the False Positive Rate (1-Specificity) gives an objective comparison between test performance with one value and is therefore the best tool of comparison between studies analyzing the same marker [15,16].
The aim of this systematic review is to evaluate studies assessing blood and urinebased biomarkers as a replacement test for endometrial biopsy or as a triage test to inform the decision to perform endometrial biopsy. Specific objectives are to provide summary estimates of the diagnostic accuracy of blood or urinary biomarkers for the diagnosis of EC compared to endometrial biopsy and to assess the diagnostic utility of biomarkers that could differentiate between benign and malignant endometrium.

Materials and Methods
The study was carried out according to the Cochrane Collaboration recommendations as well as the Preferred Reporting Items for Systematic Review and Meta-analysis of Diagnostic Accuracy Studies (PRISMA-DTA) [17]. This study was registered with PROSPERO, registration number: CRD42020202191 [18].

Literature Search
Systematic literature searches were carried out in the NICE Healthcare Databases Search tool using CINAHL, EMBASE, Medline and Pubmed. The database was searched from 2000 to January 2022. The search was restricted to English language papers, full text articles and non-review articles. The search strategy included the following key words: (endometrial cancer [Title/Abstract]) AND (biomarker [Title/Abstract]. Cross-referencing of key texts and grey literature (Google Scholar) searches were also carried out.
Titles and abstracts were screened for the eligibility of the study and evaluated by two different operators (RK, SW). Relevant articles were retrieved in full-text and assessed against the inclusion criteria. Inclusion criteria were as follows; firstly, studies evaluating the diagnostic accuracy of either blood or urine biomarkers for EC detection; second, those studies diagnosing EC using endometrial histopathological assessment as the reference test; third, those studies reporting AUC as well as sensitivity and specificity and fourth those studies comparing a control group cohort without EC. Reviews, letters, conference reports, and duplicated publications were excluded to make sure only primary publications of original studies were included. Studies not reporting diagnostic accuracy with AUC and studies where biomarker performance was reported with only grouped markers were excluded from further analysis. Studies solely reporting HE4 or CA125 where multiple systematic reviews and meta-analyses have already been published were excluded from further review [19][20][21][22].

Data Extraction and Quality Assessment
Data was extracted using a standard form that included methods, basic study population characteristics, inclusion and exclusion criteria and accuracy of diagnostic tests used.
The risk of bias was evaluated by three independent authors (RK, SW, JC) in each study using the Diagnostic Precision Study Quality Assessment Tool (QUADAS-2) as recommended by the Cochrane Collaboration [23]. Any discrepancies between authors were discussed and the opinion of a third reviewer was sought. The assessment was carried out with use of the Review Manager Software (Version 5.4, The Cochrane Collaboration, London, UK, 2020).
Each study was deemed to be at 'low', 'high' or 'unclear' risk for each of four domains. Studies were deemed as having low methodological quality when they were at high or unclear risk of bias or when there was high concern regarding applicability in at least in one domain. The original signaling question, 'Was a case-control design avoided?' was amended to 'Was a two-gate design avoided?' in agreement with the Cochrane study of Nissenblat et al., 2016 [24]. Diagnostic accuracy studies are cross-sectional in nature, comparing an index test with the reference standard in the same group of participants. Study investigators measure the parameters at a single point in time and classify the groups by the outcome of the reference standard test. Therefore, unlike in epidemiological studies, Cancers 2022, 14, 4666 4 of 28 the terminology 'cohort' and 'case-control' is less informative so 'single-gate' and 'two-gate' design was subtitled. This question was included because a two-gate design has more potential to introduce selection bias and overestimate accuracy of a given biomarker [25].

Statistical Analysis
For each primary study, a diagnostic accuracy of different biomarkers was assessed by sensitivity, specificity, likelihood ratios and area under ROC curve with their 95% confidence intervals. It is common that primary studies report sensitivity and specificity for a studyspecific cut off. AUC cannot be generated where source data is not available and thus it was necessary for inclusion criteria for this to be reported. Where the AUC 95% confidence interval (CI) was not reported in the text, study authors were contacted individually for missing data. Where no response was received the decision to calculate the standard error of the mean using non-parametric Wilcoxon's statistics was assessed individually [15,26]. If study numbers of cases and control patients reached less than 60 and no 95% CI for AUC was reported, the reliability of this method was deemed suboptimal and hence the paper was excluded from meta-analysis. A weighted summary AUC was calculated with the assumption of non-homogeneity and non-normality of empirical sensitivity and specificity as reported by Zhou et al. [15]. Forest plots of summary statistics were constructed using the data from the random-effect models. The heterogeneity of the studies was established by using Cochran's heterogeneity statistics Q, which was calculated as the weighted sum of squared differences between the individual study effects and pooled effect across the studies [27]. I 2 , assesses the percentage of variation across individual studies that is due to heterogeneity rather than chance and does not depend on the number of studies included in the meta-analysis [27]. Values (I 2 ) of 0-40% might be insignificant, 30-60% considered as moderate, 50-90% substantial and 75-100% considerable. Publication bias was evaluated by the Egger regression and Beggs' correlation tests with funnel plots [27]. Analyses were carried out with MedCalc software (MedCalc Software 20.112, Ostend, Belgium).

Results
The literature search of MEDLINE, EMBASE and PUBMED yielded 1951 citations. A total of 56 studies were included in the systematic review. Publications were then grouped by biomarker category into publications relating to exosomal biomarkers, proteomic biomarkers, metabolomic biomarkers and circulating tumor materials (CTMs). Proteomic biomarkers were further sub-categorized. Those biomarkers that were reported in studies on two or more occasions were included for meta-analysis. Those biomarkers reported by one study only were included in the systematic review and tabulated in order of test performance statistics. A flow diagram of the literature review is shown in Figure 1 in accordance with PRISMA guidance [28].

Assessment of Quality and Heterogeneity of Studies
The methodological quality of studies was assessed using the QUADAS-2 for exosomal, proteomic, metabolomic and studies discussing circulating tumor material. Data was inputted and tabulated graphically as seen in  The key area of concern for multiple studies was the patient selection domain where a two-gate study design was chosen.

Assessment of Quality and Heterogeneity of Studies
The methodological quality of studies was assessed using the QUADAS-2 for exosomal, proteomic, metabolomic and studies discussing circulating tumor material. Data was inputted and tabulated graphically as seen in  The key area of concern for multiple studies was the patient selection domain where a two-gate study design was chosen.

Meta-Analysis of Exosomal Biomarkers
15 studies reported 29 different exosomal biomarkers and studied a total of 5527 patients of which 2530 had a diagnosis of EC and 2456 were non-cancerous control group patients. All biomarkers were obtained by blood sample. The control groups consisted of healthy women with normal endometrium as well as those with benign endometrial lesions such as polyps and fibroids. Only studies of micro-RNA 21, 27a, and 223 were suitable for meta-analysis after meeting the inclusion criteria [29][30][31][32][33][34][35]. None of the included studies reported positive predictive values (PPV) or negative predictive values (NPV) ( Table 1).

Meta-Analysis of Exosomal Biomarkers
15 studies reported 29 different exosomal biomarkers and studied a total o tients of which 2530 had a diagnosis of EC and 2456 were non-cancerous con patients. All biomarkers were obtained by blood sample. The control groups co healthy women with normal endometrium as well as those with benign endo sions such as polyps and fibroids. Only studies of micro-RNA 21, 27a, and 223 able for meta-analysis after meeting the inclusion criteria [29][30][31][32][33][34][35]. None of the studies reported positive predictive values (PPV) or negative predictive values ( ble 1).

Micro RNA-21
Three studies examined the diagnostic accuracy of MiRNA 21 as a biomarker for the diagnosis of EC ( Figure 5, Table 2). The study by Gao et al., 2016 examined test performance against two control cohorts, one with healthy endometrium and one cohort with benign endometrial changes [29]. Sensitivity and specificity varied between the studies (0.640-0.850 and 0.760-0.920, respectively). The summary weighted AUC was deemed excellent at 0.825 (95%CI 0.735-0.915, p < 0.001). There was considerable heterogeneity between studies as seen by a Q-test score of 13.4 and I 2 of 77.6%.  (Table 3) [32,33]. The study by Wang examined test performance against a control cohort with both normal and benign endometrial changes. The study by Ghazala included a control cohort with normal endometrium only. The summary weighted AUC was outstanding and calculated at 0.925 (95%CI 0.801-1.000, p < 0.001) ( Figure 5). There was considerable heterogeneity between studies as seen by both the Q test and I 2 test (10.3 and 90.3%, respectively).

Protein Based Biomarkers
There were 35 studies reporting 47 different proteomic biomarkers (after the exclusion of CA-125 and HE-4 as previously described) and a total of 3526 patients of which 1483 had a diagnosis of EC and 2043 were non-cancerous control group patients . A total of 34 studies were assessing blood-based biomarkers and one study a urine derived biomarker [64]. The control groups comprised of healthy women with normal endometrium as well as those with benign endometrial lesions such as polyps and fibroids. The summary of the diagnostic accuracy and performance of those biomarkers is outlined in Table 5. For the purpose of meta-analysis, proteomic markers were classed as cancer antigens (CA-15.3, CA-19.9, CA-72.4, CEA), hormones (leptin, visfatin, prolactin) and other proteomic markers (G-CSF, YKL-40 and DJ-1).    (Table 6) [48,55,70]. In the study of Unuvar et al., 2020, the diagnostic accuracy of CA 15-3 was assessed against healthy controls with normal endometrium as well as those with benign endometrial changes [48]. The summary weighted AUC showed good performance reporting 0.608 (95%CI 0.536-0.681 p < 0.001) ( Figure 6). Tests for heterogeneity between studies showed insignificant heterogeneity (Q = 1.271, I 2 = 0.00%; 95%CI: 0.00-69.53).

Metabolomic Biomarkers
There were 4 studies reporting the diagnostic accuracy of 23 different metabolomic biomarkers in a total of 669 patients of which 190 had EC and the remainder were noncancerous control group patients. All biomarkers were obtained by blood sample. The control groups comprised of healthy women with normal endometrium as well as those with benign endometrial lesions such as polyps and fibroids. Two out of four studies reported findings on endometroid adenocarcinoma only. One study did not specify the histological subtype of EC. The summary of the diagnostic accuracy and performance of those biomarkers is outlined in Table 16.   1 Tested by electrospray ionisation tandem mass spectrometry (ESI-MS/MS). 2 Tested by mass spectrometry. 3 Tested by gas-chromatography mass-spectrometry. 4 Tested by the ultra-performance liquid chromatography coupled with triple-quadruple tandem mass spectrometry (UPLC-TQ/MS). AUC: Area Under the Curve; CI: Confidence Interval; PPV: positive predictive value; NPV: negative predictive value.

Circulating Tumor Related Material Biomarkers
There were three studies reporting on circulating tumor related materials. Two studies reported the diagnostic accuracy of circulating cell-free DNA (cCFDNA) and one study reported on Survivin expressing circulating tumor cells (CTC). All biomarkers were obtained by blood sample. No studies were eligible for meta-analysis. The performance of these biomarkers is reported in Table 17.

Discussion
Endometrial cancer is one of the most common malignant tumors in females, and the primary symptom is abnormal vaginal bleeding or discharge. The frequency with which this symptom occurs and the invasive nature of endometrial biopsy, means that at present the triage of women with suspected EC is suboptimal for women and clinicians alike. The optimum biomarker for EC would have a high sensitivity and specificity for detecting EC compared to benign and healthy controls. It would be utilizable for both pre-menopausal and post-menopausal women. Those women at low risk of disease could be reassured without the need for secondary care interventions such as imaging and biopsy. The ideal receiver-operating characteristic area under curve (AUC) would be close to 1, with a minimum of 0.7 to indicate clinical utility as a biomarker. An accurate diagnostic biomarker utilized in primary care could reduce the number of women referred for painful and costly investigations and indeed might also be used for consideration of screening of high-risk groups such as women with Lynch Syndrome or those with multiple risk factors. Hence, the aim of this study was to determine which biomarkers have been assessed for their diagnostic accuracy to date.
At present, the biomarkers most assessed for their diagnostic accuracy of EC are serum CA125 and HE4 which have been reviewed by multiple studies and meta-analyses. The performance of CA 125 is poor in terms of sensitivity and specificity making it unsuitable for use [20,21]. HE4 may have utility as a diagnostic tool, however it has been assessed by several meta-analyses and each of these point to considerable heterogeneity within the data [21,86,87]. Similarities in the data seem to suggest that HE4 may have high specificity but a lower sensitivity than would be needed as a primary care screening tool.
There have been promising results yielded from studies assessing biomarker panels and specifically those using spectroscopic techniques [88,89]. The study by Paraskevaidi et al. assessed the diagnostic accuracy of infrared spectroscopy as a method of detecting EC with an overall diagnostic accuracy of 0.83 [88]. Spectroscopic techniques do not allow for analysis of a single biomarker because peaks may be formed by multiple biological entities. As such, these studies were not eligible for inclusion into this review; however, they are simple techniques yielding promising results.
Obesity is accompanied by changes in expression of adipose factors that act both locally and systemically. With the known link between obesity, insulin resistance and EC, adipokines and hormones such as leptin have become the focus of intense investigation. In EC, leptin activates STAT3 proteins, which increase their activity in the process of oncogenesis by stimulating proliferation, promoting angiogenesis and avoiding the control of the immune system. There is a positive correlation between leptin levels and body mass index (BMI). Adiponectin is predominantly secreted by visceral adipose tissue and is the most abundant adipokine, with circulating concentrations inversely correlated with adiposity. This review included three studies of leptin and one study of adiponectin [50,51,53,61]. The diagnostic accuracy of leptin is moderate with a summary ROC of 0.757. Adiponectin assessed in one study in tandem with Visfatin showed that adiponectin was inversely associated with EC risk [61]. The AUC of this adiponectin alone was 0.814 and the adiponectin to Visfatin ratio 0.838 suggesting some potential as diagnostic biomarkers. However, similarly to other hormones such as FSH, LH, estradiol and prolactin which are also raised markedly in EC patients, these may be markers of the major risk factors, obesity and polycystic ovarian syndrome rather than EC itself, thus showing poorer specificity than required of a screening biomarker. A meta-analysis of the effect of adipokines in obesity driven cancers using mendelian randomization supports this conclusion as they failed to find any causal role between adipocytokines and EC and other obesity driven cancers [90].
Exosomes represent a wide group of membrane-bound lipid particles that originate from the plasma membrane or the endosomal system and are secreted from cells. Exosomes released from both healthy and cancer cells, are abundant in body fluids and mediate cell-to-cell communication by shuttling DNA, RNA, lipids, metabolites, and proteins. In this way, exosomes are implicated in numerous physiological processes but also participate in the formation of the tumor microenvironment and cancer progression. Of all the biomolecules contained in exosomes, miRNAs seem to have the most clinical utility in EC diagnosis. MiRNAs are small non-coding single stranded molecules that regulate all hallmarks of cancer as defined by Hanahan and Weinberg, including proliferation, invasion, angiogenesis, as well as influencing cancer cells chemosensitivity [91].
With regard to the review of exosomal biomarkers, 15 studies were eligible for inclusion, and these reported on 29 different exosomal biomarkers and a total of 5527 patients. Amongst this group 2530 had a diagnosis of EC and 2456 were non-cancerous control-group patients [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. Only Mi-RNA 21, 27a, and 223 were suitable for meta-analysis as they met the inclusion criteria and were reported by two or more studies. The summary ROC score was 0.825 (95% CI 0.735-0.915), 0.925 (95%CI 0.801-1.00), and 0.813 (95%CI 0.735-0.890) for MiRNA-21, MiRNA 27a and MiRNA-223, respectively. In comparison to HE4, the relatively high performance of MiRNAs is promising. However, it must be noted that MiRNA 27a included in a study by Ghazala et al. reported an AUC of 1.00 making it the perfect test in terms of sensitivity and specificity, positive and negative likelihood ratio [33]. This has not been replicated and has likely overestimated the performance of this biomarker on meta-analysis. The only other study by Wang et al., reported much lower AUC of 0.813 [32]. This may potentially be explained by the fact that Ghazala et al. used a healthy control cohort whereas Wang included a mixed cohort of those women with PMB who had both benign disease and normal endometrium. Both cohorts were relatively small in size with under 75 patients in each study and thus results must be interpreted with caution.
To the best of our knowledge this is the first study to report and meta-analyze the diagnostic accuracy of different classes sole biomarkers obtained by non-invasive biopsy for detection of EC and thus it may provide a reference guide for those wishing to explore candidate biomarkers for research or those wishing to assess the current evidence for implementation into current practice.
However, there are numerous limitations to this review and the evidence summarized within it. The main limitation is that there were a low number of small, heterogeneous studies for the majority of the evaluated index tests. It was not apparent from many studies whether a 'test-phase' had been conducted prior to the biomarker validation phase which is the point at which the diagnostic accuracy should be assessed. This may undermine the reliability of the summary estimates from the meta-analyses and is likely to have contributed to the marked variability in AUC and sensitivity and specificity seen for most index tests. For the vast majority of biomarkers, no meta-analysis was possible. The decision to meta-analyze using two studies or more was conducted based on numbers of study participants in each study and not solely on the number of studies conducted. However, it is evident that there was significant heterogeneity amongst the biomarkers analyzed. Formal assessments of heterogeneity, such as Eggers's test were deemed likely too unreliable given the low number of studies in most evaluations. A significant limitation of this review was also that the studies varied with respect to the control group used, the type, stage and grade of endometrial cancer, the age of the cohorts assessed and the cut-off thresholds for index tests. Additionally, most of the included studies evaluated the diagnostic cut-off thresholds using a ROC analysis without any subsequent validation in an independent cohort. Lack of validation of the diagnostic data in conjunction with the low number of studies for the majority of the presented tests contributed to the low quality of evidence presented in this review. A standardized methodology for fluid biospecimen collection, processing and storage was published in 2014 and would likely improve the quality of studies if adopted for use by future studies.
The variation in the selection of the case and control groups with inclusion of participants that may not be reflective of the EC population is also a limitation in this systematic review. The recent change towards molecular classification has shifted clinicians away from describing EC cell types as just serous, endometrioid or other histological cell types however some studies made no attempt to sub-classify their EC population other than to report grade and stage. Subgroup analysis of data was considered but due to the size of the studies and relative paucity of studies discussing the same biomarker this would have yielded no more reliable information.
Most studies do not report whether they attempted to reduce selection bias by consecutively enrolling participants. More than two-thirds of the included studies (38/56, 68%) had a two-gate design and included a wide group of participants who underwent surgery for various indications. Inclusion of healthy asymptomatic individuals or participants with other pathological conditions represents a potential selection bias with regard to the control group, which could have biased the test outcomes. The majority of studies involved a normal healthy control group which was either age-matched or in some cases non-age matched. This is likely to overestimate diagnostic accuracy of a biomarker more than in studies where a one-gate design is used and patients with a presenting symptom such as PMB go on to be part of the case or control group.
We suggest cautious interpretation of the presented results. Although studies demonstrated diagnostic potential for a number of tests, the level of heterogeneity, wide confidence intervals and risk of bias in many studies included in this review undermine reliability of the presented results, and hence these data are insufficient to confidently inform clinical practice at this stage. Additional biomarkers, reported in individual studies, displayed diagnostic estimates that qualified for either replacement or triage tests; however, there were not enough data for a meaningful recommendation on the use of any of these tests. As with all biomarkers and index tests, the next phase of validation will be to assess diagnostic accuracy amongst a non-symptomatic, low risk, low prevalence cohort in order to assess its performance as a true screening test.

Conclusions
In conclusion this systematic review and meta-analysis has sought to summarize the existing literature on the performance of non-invasive biomarkers for suspected EC triage and diagnosis. There is a clear need for a biomarker with a low false positive rate that can be used in primary care to reduce the number of unwarranted, invasive investigations. It has become clear from review that many biomarkers are still at discovery phase, rather than validation phase and are thus not at a stage where diagnostic accuracy should be assessed. There is also a high degree of heterogeneity between studies, this is likely due to studies reporting on different types of EC. Given that the dualistic Bohkman classification of type-1 and type-2 EC fails to adequately differentiate between EC or provide useful estimates of prognosis, it is likely studies reporting biomarker performance stratified by EC cancer subtype will demonstrate different biomarkers detecting different subtypes of EC. At the very least with more literature available, subgroup meta-analysis will become possible. Furthermore, it is clear from the review of current literature that two-gate study design may inflate the diagnostic accuracy of the biomarkers studied. Healthy control group patients are not reflective of the patient population undergoing evaluation of suspected EC. Until there is a diagnostic biomarker identified that is likely to be capable of high-performance triage amongst the symptomatic population, studies at the biomarker discovery or validation phase should consider inclusion of both healthy and symptomatic control cohorts.
There is wide recognition that an accurate non-invasive test for EC triage is likely to confer several advantages over the current standard of ultrasound scan and endometrial biopsy. These potential advantages include a reduction in cost (both in direct medical costs and in time off work), reduced discomfort, shorter recovery times and a reduction in the rate of serious complications associated with surgery. Another benefit of an accurate, non-invasive diagnostic test for EC is the prospect of early diagnosis and timely therapeutic interventions to minimize disease progression. Whilst this review highlights several methodological issues with the current body of evidence, there are some promising findings; exosomal compounds, in particular MiRNAs have shown moderate to good performance in the limited available data, but perhaps more reassuringly less heterogeneity between studies. Similarly, amongst proteomic compounds serum YKL-40 and DJ-1 have good to excellent performance and a next step might be to consider larger scale prospective evaluation of these biomarkers in order to determine their utility.


Generation of gene-corrected isogenic control cell lines from a DYT1 dystonia patient iPSC line carrying a heterozygous GAG mutation in TOR1A gene

Childhood-onset torsin dystonia (DYT1) is a rare hereditary movement disorder and usually caused by a heterozygous GAG deletion (c.907–909) in the TOR1A gene (ΔE, p.Glu303del). The neuronal functions of torsin proteins and the pathogenesis of ΔE mutation are not clear. Previously, we have generated a hiPSC line from DYT1 patient fibroblast cells. In this study, we genetically corrected GAG deletion and obtained two isogenic control lines. These hiPSC lines contain the wild-type TOR1A sequence, showed the normal stem cell morphology and karyotype, expressed pluripotency markers, and differentiated into three germ layers, providing a valuable resource in DYT1 research.

muscle contractions . It is often initiated or worsened by voluntary action and associated with overflow muscle activation. The specific mechanism by which the TOR1A mutation leads to the development of dystonic phenotype is still unknown.
Even though animal models provide insights into disease mechanisms, significant speciesdependent differences exist because animals with identical heterozygous mutation (ΔE) fail to show the pathology seen in human patients (Goodchild et al., 2005). In addition, the limited access to patient neurons greatly impedes the progress of research in dystonia. Excitingly, the generation of patient-specific neurons via human induced pluripotent stem cells (hiPSCs) provides an unprecedented approach for dystonia research Akter et al., 2022). Previously, we generated a hiPSC line (CSUi002-A) from a DYT1 patient fibroblast cell line that is carrying a heterozygous mutation in the TOR1A gene Wu et al., 2021). In this study, we genetically corrected the GAG deletion in this line with CRISPR/Cas9 method and obtained two hiPSC lines (LSUHSi003-A-2 and LSUHSi003-A-3), in which the GAG deletion in the TOR1A gene was restored. These isogenic controls together with the DYT1 patient hiPSC line provide a valuable resource in DYT1 dystonia research.
Both mutation-corrected hiPSC lines showed a typical pluripotent stem cell morphology with a high nucleus/cytoplasm ratio (Fig. 1A). DNA sequencing confirmed that the GAG deletion was restored in both newly generated hiPSC lines (LSUHSi003-A-2 and LSUHSi003-A-3) (Supp. Fig.S1A and B). Sanger DNA sequencing of polymerase chain reaction (PCR) products further verified that the DYT1 iPSC line (CSUi002-A) contains the heterozygous GAG deletion (c.907-909) in the TOR1A gene (one copy is wild-type and the sequence of the other copy is shifted due to GAG deletion), and this deletion was corrected in both newly generated lines (Fig. 1B). GTW banding method was used to analyze the chromosomes from both corrected hiPSC lines and demonstrated that they are normal male karyotype, 46, XY (Fig. 1C). Short tandem repeat (STR) analysis at 15 loci identified a 100% allele match between DYT1 and DYT1 correction clones (Supp. Fig.S1C). Immunostaining indicated that these iPSCs highly expressed pluripotency markers of OCT4, NANOG, SOX2, and SSEA4 (Fig. 1D). Quantitative RT-PCR analysis demonstrated that the pluripotency markers of OCT4, SOX2, NANOG, and KLF4 in DYT1 correction lines were expressed at similar levels as the parental line (CSUi002-A) (Fig. 1E). Following spontaneous differentiation, embryoid bodies (EBs) (Fig. 1F) derived from both corrected cell lines displayed dramatic upregulation of markers of the ectoderm (PAX6, OTX1), mesoderm (DCN, IGF2, GATA2), and endoderm (SOX7, SOX17) lineages. The expression levels of these trilineage markers were consistent with the parental line (CSUi002-A) and much higher than undifferentiated iPSCs (Fig. 1G). PCR screening demonstrated that both DYT1 correction hiPSC lines (LSUHSi003-A-2 and LSUHSi003-A-3) were negative for mycoplasma (Supp. Fig.S1 D).

Correction and culture of DYT1 iPSCs
The GAG mutation in the TOR1A gene in DYT1 hiPSC was genetically corrected at the Genome Engineering and iPSC Center (GEiC) at Washington University in St. Louis.
Briefly, approximately 1 × 10 6 single cells were resuspended in P3 primary buffer (Lonza) with gRNA/Cas9 ribonuclease protein (RNP) complex (200 pmol synthetic gRNA and 80 pmol HiFi Cas9 protein) and TOR1A correction ssODN (Table 2). A silent mutation (C to T) was introduced in the donor oligo sequence (ssODN) to avoid the re-cutting of the edited sequence by CRISPR/Cas9. Subsequently, cells were electroporated with a 4DNucleofector (Lonza) using the CA-137 program. Following nucleofection, the editing efficiency was confirmed by targeted deep sequencing using primer sets specific to target regions and then the pool was single-cell sorted. Single cell clones were screened with targeted deep sequencing analysis. All iPSCs were cultured with mTeSR Plus (STEMCELL Technology) on Matrigel-coated plates at 37 °C in a humidified, 5% CO2 incubator and passage at a 1:6 ratio using gentle cell dissociation reagent (Versene, Gibco).

Embryoid bodies (EB) formation
As our previous report , cultured hiPSCs were dissociated with Versene and transferred to low attachment 10-cm petri dishes in KOSR medium (DMEM/F12 medium containing 20% KnockOut Serum Replacement, 1% GlutaMax, 1% non-essential amino acids, 50 μM β-mercaptoethanol and 1% penicillin-streptomycin) in the presence of 10 μM Y-27632 (STEMCELL Technologies). Changed the medium every other day and EBs gradually formed (Fig. 1F). After 7 days of suspension culture, EBs were digested with 0.25% Trypsin and cultured on gelatin coated plates with KOSR medium for another 7 days. The total RNAs were extracted for RT-PCR analysis of trilineage markers.

Immunostaining and confocal microscopy
Cultured iPSCs were fixed with 4% paraformaldehyde in PBS for 15 min at room temperature and incubated in blocking buffer (3% bovine serum albumin in PBS) with (for nuclear markers) or without (for a cell surface marker SSEA4) 0.2% Triton X-100 for 1 h. Cells were then sequentially incubated with primary and secondary antibodies (Table 2) as previously described . Hoechst 33,342 (Invitrogen) was used to stain nuclei. Images were obtained with a Leica SP5 confocal microscope.

Quantitative PCR analysis
As previously report , cultured iPSCs and EBs were collected and lysed in TRIzol (Invitrogen). Total RNAs were extracted using the phenol/chloroform method, and then reverse-transcripted into cDNAs using the SuperScript™ III Reverse Transcriptase (Invitrogen). Quantitative PCR analysis was performed using SYBR Green PCR Master Mix (Applied Biosystems) and run on a StepOne qPCR machine (Applied Biosystems). The gene expression data were analyzed using the ΔΔC T method and the values were normalized to the expression of the housekeeping gene GAPDH (Fig. 1E and G). Primers used in this study were listed in Table 2.

Karyotyping
Chromosomes from iPSC clones were analyzed using the GTW banding method at GEiC at Washington University in St. Louis.

STR analysis
Short tandem repeat (STR) analysis of 15 loci (Fig. S1C) were performed at GEiC at Washington University in St. Louis.

Mycoplasma test
Mycoplasma test was performed by MycoAlert PLUS kit (Lonza) at GEiC at Washington University in St. Louis (Fig. S1D).

Supplementary Material
Refer to Web version on PubMed Central for supplementary material.   Table 2 Reagents details.  1.

Resource Table
Unique stem cell lines identifier


RNA-seq and ChIP-seq Identification of Unique and Overlapping Targets of GLI Transcription Factors in Melanoma Cell Lines

Simple Summary The majority of melanomas show hyperactivation of the MAPK signaling pathway, most often through mutations in BRAF and NRAS. Despite significant progress in therapy, targeting this signaling pathway solely has not been the solution for stopping the progression of this disease. Recently, researchers recognized the involvement of the Hedgehog-GLI (HH-GLI) signaling pathway in melanoma and its crosstalk with the MAPK pathway. In order to identify new HH-GLI-regulated targets that could be involved in the crosstalk with the MAPK pathway, we performed RNA sequencing and ChIP sequencing on three melanoma cell lines. By combining RNA-seq and ChIP-seq results, we successfully validated 15 novel targets of GLI proteins in melanoma cell lines. These findings will contribute to a better understanding of the GLI code and its role in melanoma. Abstract Background: Despite significant progress in therapy, melanoma still has a rising incidence worldwide, and novel treatment strategies are needed. Recently, researchers have recognized the involvement of the Hedgehog-GLI (HH-GLI) signaling pathway in melanoma and its consistent crosstalk with the MAPK pathway. In order to further investigate the link between the two pathways and to find new target genes that could be considered for combination therapy, we set out to find transcriptional targets of all three GLI proteins in melanoma. Methods: We performed RNA sequencing on three melanoma cell lines (CHL-1, A375, and MEL224) with overexpressed GLI1, GLI2, and GLI3 and combined them with the results of ChIP-sequencing on endogenous GLI1, GLI2, and GLI3 proteins. After combining these results, 21 targets were selected for validation by qPCR. Results: RNA-seq revealed a total of 808 differentially expressed genes (DEGs) for GLI1, 941 DEGs for GLI2, and 58 DEGs for GLI3. ChIP-seq identified 527 genes that contained GLI1 binding sites in their promoters, 1103 for GLI2 and 553 for GLI3. A total of 15 of these targets were validated in the tested cell lines, 6 of which were detected by both RNA-seq and ChIP-seq. Conclusions: Our study provides insight into the unique and overlapping transcriptional output of the GLI proteins in melanoma. We suggest that our findings could provide new potential targets to consider while designing melanoma-targeted therapy.

Introduction
Melanoma is known as the most aggressive and deadliest of all skin cancers. The most often dysregulated signaling pathway in melanoma is RAS/RAF/MEK1/2-ERK1/2 (MAPK pathway). Almost 50% of all melanomas have a mutation in the BRAF gene, while 15-20% have a mutation in the NRAS gene [1], which leads to constitutive pathway

Plasmids and Cell Transfection
For the GLI transfection experiments, cells were plated at density of 3 × 10 5 cells/well in 6-well plates, left to attach for 24 h, and then transfected with 5 µg of GLI expression plasmids: GLI1 (pcDNA4NLSMTGLI1, kindly gifted by Fritz Aberger, PhD), GLI2 and GLI3 (p4TO6MTGLI2, pcDNA4/TO/GLI3richtig, both a kind gift from Milena Stevanović, PhD) using the X-fect reagent (Clontech, Mountain View, CA, USA) following the manufacturer's instructions.

RNA-Sequencing
Human melanoma cell lines CHL-1 (wild-type for both NRAS and BRAF), MEL224 (NRAS Q61R ), and A375 (BRAF V600E ) were transfected with expression plasmids for GLI1, GLI2 or GLI3 in two independent experiments. Non-transfected lines were used as controls. Briefly, 200,000 cells were seeded in a Ø10 cm cell culture dish and transfected the next day with 5 µg of plasmid DNA, using the X-fect reagent (Clontech, Mountain View, CA, USA) and following the manufacturer's instructions. Forty-eight hours post-transfection, total RNA was isolated with Absolutely RNA miRNA Kit (Agilent Technologies, Santa Clara, CA, USA). The quality of the isolated RNA was checked on agarose gel, and RNA concentrations and purity were measured on NanoPhotometer N60 instrument (Implen, Munich, Germany). Sequencing libraries were generated and sequenced by DNA Link Company (Seoul, South Korea). The integrity of RNA samples was checked on Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The instrument provided data on RNA concentration, electropherogram, rRNA subunit ratios, and RNA integrity number (RIN). All samples had RIN ≥ 8.0 and a 28S:18S ratio ≥ 1.4. Total amount of 1 µg of RNA was used. cDNA libraries were prepared using TruSeq mRNA library Kit, and sequencing was performed on Novaseq 6000 instrument (Illumina, San Diego, CA, USA).

Chromatin Immunoprecipitation Sequencing (ChIP-seq)
Chromatin immunoprecipitation was performed using SimpleChIP Plus Enzymatic Chromatin IP kit (Cat. no. #9005, Cell Signaling Technology, Danvers, MA, USA) following the manufacturer's protocol. Three cell lines with the strongest basal expression of GLI proteins were selected for the experiment (CHL-1, A375, and MEL224). Cells were cultured until 80-90% confluence in 150 cm 2 flasks. To preserve protein-DNA interactions, cells were fixed twice, first using 2 mM Di(N-succinimidyl) glutarate (DSG) (Merck KGaA, Darmstadt, Germany) followed by 1% formaldehyde. Samples were incubated with specific ChIP-grade antibodies against GLI1 10 µg/sample (AF3324, R&D Systems, Minneapolis, MN, USA) [24], GLI2 4 µg/sample (AF3526, R&D Systems) [24], and GLI3 10 µg/sample (AF3690, R&D Systems) overnight. Library preparation for next-generation sequencing was performed using SimpleChip ChIP-seq DNA Library Prep Kit for Illumina (Cat. no. #56795, Cell Signaling Technology) and SimpleChIP ChIP-seq Multiplex Oligos for Illumina (Dual Index Primers) (Cat. no. #47538, Cell Signaling Technology) according to manufacturer's protocol. Libraries were prepared for 24 samples which included selected three cell lines for each GLI transcription factor with INPUT control in biological duplicates. A total of 5 ng of purified chromatin was used for each library preparation, followed by adaptor ligation. Samples were then purified from unbound adaptors using magnetic AMPure XP beads (Beckman Coulter, Brea, CA, USA), eluted, and amplified with a unique combination of dual index primers. Amplified chromatin fragments were additionally purified using magnetic beads. Quality control was performed on Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA) and Qubit V1 (Invitrogen Thermo Fisher Scientific, Waltham, MA, USA). Next-generation sequencing was completed on NextSeq 500 (Illumina, San Diego, CA, USA).

Bioinformatic Analysis of ChIP-seq Data
ChIP-seq libraries were first trimmed with trimgalore from FastQC (v.0.11.5) and mapped with BWA v0.7.9a to human genome hg38. After removing duplicates with picardtools (v. 1.138), peaks were called with MACS (v.2.0.10) with parameters -f BAMPE -g -B -q 0.01. Further analysis was performed in R (v.4.0.1) and packages ChIPpeakAnno and ChIPseeker using custom scripts available at https://github.com/NenBarto/GLI (accessed on 10 August 2022). Motif enrichment was performed with MEME Suite v.5.3.0 [25]. The lists of differentially expressed genes (DEGs) identified by RNA-seq (FDR < 0.01, logFC > 1) were compared with the ChIP-seq identified lists of targets for each of the GLI proteins using the Venny 2.1 tool [26] and identified overlapping targets were selected for qPCR validation. DEGs for each of the GLI proteins were analyzed using the GeneAnalytics platform [27] to examine their involvement in different pathways and diseases. The targets that showed high fold change values, association with a large number of cancers, and involvement in the MAPK signaling pathway were selected for qPCR validation.

Quantitative PCR Analysis
Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's instructions. cDNA was generated from 1 µg of RNA using the High-Capacity cDNA synthesis kit (Thermo Fisher Scientific, Waltham, MA, USA) and qRT-PCR performed on CFX-96 instrument (Bio-Rad Laboratories, Hercules, CA, USA) using SsoAdvanced SYBR Green Supermix (Bio-Rad Laboratories, Hercules, CA, USA) with genespecific primers. Fold change was calculated relative to the RPLP0 and TBP housekeeping genes. Primer sequences used for qPCR are listed in Supplementary Table S1.

Statistical Analysis
D'Agostino-Pearson test was used for testing normality of data distribution. Nonnormal data were log-transformed. An independent samples T-test or One-way ANOVA with Dunnett's post hoc test was used for inferring the differences in gene expression. Statistical analyses were performed with MedCalc v19.2.1 (MedCalc Software bv, Ostend, Belgium). Two-tail p-values < 0.05 were considered statistically significant.

RNA Sequencing Reveals Unique and Overlapping Targets of GLI Transcription Factors
As the first step in identifying novel GLI transcriptional targets that are in crosstalk with MAPK or other signaling pathways dysregulated in melanoma and that could be considered for combination therapy, we performed RNA sequencing on melanoma cell lines with overexpressed GLI1, GLI2, and GLI3 (Figure 2A and Supplementary Figure S3 show MDS plots). To our knowledge, there is no extensive data on the targets of all three GLI proteins in melanoma and their overlap. One study showed GLI1 and GLI2 transcriptional targets in primary neoplastic chondrocytes, detected by ChIP-seq and microarray methods, but in this study, only three targets were validated by qPCR [24]. Thus far, no one has taken into consideration transcriptional targets of all three GLI proteins. Across all cell lines, we found 1642 targets that were overlapping for GLI1 and GLI2, 23 overlapping targets of GLI2 and GLI3, and only 9 overlapping targets of GLI1 and GLI3. In total, we found 150 GLI1, GLI2, and GLI3 overlapping targets ( Figure 2B). There were 607 unique targets of GLI1, 1080 unique targets of GLI2, and 37 unique targets of GLI3. After filtering according to FDR < 0.01, we identified a total of 808 DEGs (631 upregulated, 183 downregulated) for GLI1. For GLI2, we found 941 DEGs (711 upregulated, 230 downregulated). For GLI3, there were only 58 DEGs (35 upregulated and 23 downregulated) found using this method (Supplementary Tables S2 and S3). Top scoring DEGs across all three cell lines are shown in Figure 2C. To identify pathways that are significantly represented in our list of differentially expressed genes, we performed pathway enrichment analysis. Figure 2D shows that some of the most enriched pathways in the case of GLI1 and GLI2 overexpression are Wnt signaling pathway, MAPK signaling pathway, and Ras signaling pathway. About 20-30% of DEGs are involved in these signaling pathways. An even bigger percentage of genes (30-40%) show involvement in Neuroactive ligand−receptor interactions. There is also significant involvement of DEGs in different cancer types ( Figure 2D, Supplementary Figure S4). Our findings confirm the crosstalk of HH-GLI with other signaling pathways, and to our knowledge, this is the first study that considered transcriptional targets of all three GLI proteins in melanoma.

Chromatin Immunoprecipitation (ChIP) Sequencing Reveals Novel Binding Targets of GLI Proteins
ChIP sequencing was used to identify GLI1, GLI2, and GLI3 binding regions in human melanoma cell lines and to further confirm RNA-seq results. For this purpose, cell lines with the highest endogenous GLI protein expression levels (CHL-1, A375, and MEL224) were selected. ChIP-seq datasets were merged across cell lines to observe overall GLI binding sites and increase the signal-to-noise ratio. More than 80% of ChIP-seq peaks were identified in the intergenic regions ( Figure 3A,B), which corresponds to their previously observed enhancer binding properties [29]. Overall, the three TFs shared most of the sites, with GLI2 containing the largest number of unique binding loci ( Figure 3C). We identified 2183 genes that contained GLI TFs binding sites in their promoters: 527 for GLI1 (24%), 1103 for GLI2 (50%), and 553 for GLI3 (25%) ( Figure 3C). As expected, only a small proportion of genes had all three GLI TFs in their promoter regions (157 out of 2183, 7.2%), with a much larger proportion of GLI3-specific promoter binding (35.8%) (Supplementary Table S4). The binding sites for GLI TFs were centered around the gene transcription start sites ( Figure 3D) and were enriched for previously established motifs for GLI1 (MA1990.1), GLI2 (MA0734.1), and GLI3 (MA1491.1), with p-values of 4.71 × 10 −61 , 8.4 × 10 −7 and 1.64 × 10 −75 , respectively. PTCH1, as a known target, showed two peaks in the transcriptional start site (TSS), which was previously reported for GLI2 [30], and its position corresponds to the H3K4me3 region. One of the newly identified target genes, EBI3, shows a broad peak in the promoter region of the gene ( Figure 3E).  B   GLI1  GLI2  GLI3   HRH3  GLI1  S100A9  KRT16  KCNT1  PRB2  PRB3  KRT17  DBH  FAM30A  LILRA2  PDCD1  FOXS1  PRB1  IL3  PRSS22  GALNT9  APOA4  LGR6  TPSAB1  GLI3  ERF  IL1RL1  CXCL10  OAS2  IFI44L  OASL  ANGPTL4  OAS1  FLG  MMP13  RSAD2  XAF1  IGF2  LCK  SLC4A1  PADI3  IL17B   Color denotes significance, with gray and blue circles denoting non-significant enrichments and red denotes significant enrichment.

qPCR Validation of RNA Sequencing and ChIP Sequencing Data Confirms 15 Novel GLI Target Genes
To validate the biological reproducibility of the results of DEG analysis, we performed qPCR experiments on seven melanoma cell lines. Cell lines were chosen to represent the different mutational backgrounds: A375 (BRAF V600E homozygous), SKMEL24 (BRAF V600E heterozygous), MEL224 (NRAS Q61R homozygous), SKMEL2 (NRAS Q61R heterozygous),

MEL505 (KRAS G12V heterozygous), CHL-1 and MEWO (wild-type for BRAF and NRAS).
Selected cell lines were transfected with GLI1, GLI2, or GLI3 expression plasmids, and gene expression of a total of 23 genes (21 novel targets, plus PTCH1 and GLI1 as known targets) was determined. To narrow down a list of targets for qPCR validation, two approaches were used ( Figure 4A). The first approach identified potential targets under direct transcriptional control of GLI proteins by comparing the list of DEGs obtained by RNA-seq with the list of genes identified by ChIP-seq analysis for all three cell lines. For GLI1, 808 DEGs were compared with 231 identified ChIP-seq GLI1 targets, identifying three common targets, namely: FLG, SAMMSON, and SPRY2. For GLI2, 941 DEGs were compared with 470 ChIP-seq GLI2 targets, identifying 11 common targets: HES1, EBI3, CACNA2D2, LAPTM5, LY6D, FLG, GLI1, PTCH1, RDH10, STK32C, and RAB34. Among the identified targets, there were two known HH-GLI pathway targets (PTCH1 and GLI1), confirming the validity of the selection process. A comparison of GLI3 RNA-seq and ChIP-seq data showed no common targets. The chIP-seq analysis identified GLI1 or GLI2 binding motifs in all 11 identified targets, suggesting they are direct transcriptional targets of GLI1 and/or GLI2 proteins. The second approach was to analyze the DEGs from RNA-seq independently of the ChIP-seq data to identify potential indirect targets. After filtering by FDR and logFC values, the GeneAnalytics tool of the GeneCards database (genecards.org, RRID:SCR_002773) was used to identify the role of DEGs in signaling pathways and diseases. Several categories of pathways and diseases with a high relevance score were considered when selecting genes of interest: "Pathways in cancer", "PI3K-AKT signaling pathway", "MAPK signaling pathway", "WNT/Hedgehog/NOTCH", "neoplasm", "melanoma" and "abnormalities of the skin", as well as logFC values of these genes in RNA-seq analysis. Additional screening was performed based on their expression in melanoma from the GEPIA database [31] (SKCM dataset vs TCGA normal and GTEx data, N(T) = 461, N(N) = 558) and The Human Protein Atlas (proteinatlas.org) [32] (RNA expression and staining of melanoma) as well as survival data from GEPIA database. With this approach, we were able to identify ten targets: KRT16, KRT17, S100A7, S100A9, GH1, SOX9, BIRC7, MRAS, RET, and IL1R2. Finally, 21 targets were selected for qPCR validation: 10 identified by RNA-seq only (KRT16, KRT17, S100A7, S100A9, GH1, SOX9, BIRC7, MRAS, RET and IL1R2) and 11 targets identified with both ChIP-seq and RNA-seq (HES1, FLG, RAB34, SAMMSON, SPRY2, CACNA2D2, LAPTM5, LY6D, RDH10, STK32C and EBI3) ( Figure 4B,C). A summary of known functions of these targets and their role in cancer is shown in Table 1. Identified targets, including KRT16, KRT17, S100A7, MRAS, BIRC7, IL1R2, as well as several direct GLI targets (confirmed by both RNA-seq and ChIP-seq)-RAB34, LAPTM5, RDH10, and STK32C-exhibited a consistent and uniform expression pattern. In addition, the genes EBI3, GH1, SOX9, RET, and SPRY2 are also good candidates for HH-GLI targets but exhibited a less uniform expression pattern in these melanoma cell lines. Overall, by combining RNA-seq and ChIP-seq results and elaborate filtering of these genes, we successfully validated 15 novel targets of GLI proteins in melanoma cell lines.  DEGs and two known pathway targets PTCH1 and GLI1 as controls. The relative expression level of each gene was determined using the 2 −∆∆Ct method with RPLP0 as the internal reference gene. Heatmap shows log2 fold change values, ND stating that expression levels could not be detected after 37th cycle.

Discussion
BRAF inhibitors have improved patient survival compared with standard chemotherapy, but these benefits are not persistent, as most patients develop resistance to therapy which leads to disease progression [103]. There are different mechanisms that can activate a variety of signaling pathways, thereby bypassing the effect of BRAF inhibition. It is already known that the HH-GLI signaling pathway is active in melanoma [11,12,28,104]. Here, we confirm HH-GLI pathway activity in 14 melanoma cell lines with different genetic backgrounds. Several studies also show that inhibition of the HH-GLI pathway can decrease melanoma cell proliferation [11,12,[105][106][107]. The HH-GLI pathway inhibitors affect signal transduction at different levels. Cyclopamine inhibits the SMO protein on the cell membrane [108]. In contrast, lithium chloride increases the phosphorylation of Ser9 residue on GSK3β kinase, which regulates GLI protein activity at the post-translational level. GSK3β phosphorylates GLI3 and thereby promotes its processing into GLI3R, which downregulates the HH-GLI pathway [109]. ATO and GANT61, in turn, affect the activity of GLI proteins [110,111]. One of these studies pointed out that primary melanoma cell lines with BRAF mutation are more sensitive to SMO inhibitor, sonidegib, than BRAF wild-type cells [11]. We also noticed that BRAF V600E mutated cell lines seem to be more sensitive to inhibitor GANT61 than cell lines that are wild-type for these genes, but in our case, this difference is not statistically significant. Thus far, studies have demonstrated that in colon cancer, neuroblastoma, and pancreatic cancer, GANT61 is the most effective inhibitor of cell growth among all tested HH-GLI inhibitors [112][113][114]. On the other hand, one study in melanoma shows comparable effects of GANT61 and cyclopamine [115]. Our MTT-assay results show that cyclopamine seems to have no or very little effect on the viability of melanoma cell lines, while the most effective inhibitor in melanoma cell lines was, indeed, GANT61. This result also supports the assumption that, in the case of melanoma, non-canonical pathway activation is likely more important than canonical [3,19,104,107].
Because the exact interplay between the HH-GLI pathway and MAPK signaling pathway is not yet understood, we decided to investigate the transcriptional targets of all three GLI proteins in melanoma with different genetic backgrounds, either harboring a BRAF mutation, an NRAS mutation, or no mutation in these two genes. We applied RNA sequencing and combined it with ChIP sequencing to identify direct but also unique and overlapping targets of GLI1, GLI2, and GLI3 in three melanoma cell lines. By doing so, we identified a total of 808 DEGs for GLI1, 941 DEGs for GLI2, and 58 DEGs for GLI3. KEGG analysis confirmed that many of the identified DEGs are involved in various signaling pathways, including MAPK, Ras, Hippo, and Wnt pathways [3,116], as well as in many types of cancer [8,[117][118][119]. Our next step of carefully screening and filtering targets led us to select 21 targets for qPCR validation. We successfully validated 15 novel targets of GLI proteins that were not identified in any previous study. To our knowledge, this is the first comprehensive study of transcriptional targets of all three GLI proteins in melanoma. Identified targets, such as KRT16, KRT17, S100A7, MRAS, BIRC7, IL1R2, as well as several direct GLI targets (confirmed by both RNA-seq and ChIP-seq)-RAB34, LAPTM5, RDH10, and STK32C-have a consistent and uniform expression pattern. Expression levels of these genes are increased with GLI1 or GLI2 overexpression, regardless of the mutational status of the cell lines. We were also able to validate genes EBI3, GH1, SOX9, RET, and SPRY2 as HH-GLI targets. Their expression was consistent in the majority of the cell lines, with few exceptions. Six targets-S100A9, FLG, SAMMSON, LY6D, CACNA2D2, and HES1-were found to have variable expression in different melanoma cell lines, so they could not be validated as GLI targets in melanoma. Table 1 represents a summary of protein functions and already published roles in cancer for 21 discovered GLI targets. Thus far, 8 out of 21 GLI targets we chose to validate-KRT16, S100A9, SOX9, BIRC7, EBI3, FLG, SAMMSON and SPRY2-were already implicated in melanoma pathogenesis [34,46,47,51,52,64,79,81,82,87,101,120]. KRT16, a regulator of innate immunity in the skin, seemed to be significantly downregulated in metastatic melanoma and was also found to be the highest discriminator between prognostic and metastatic melanoma [34]. Our RNA-seq and qPCR results show that out of all targets, KRT16 is by far the most upregulated overlapping target of GLI1 and GLI2, with a logFC value of 12.5 obtained by RNA-seq and log2FC value that goes up to 16 in qPCR experiments, depending on the cell line. LogFC values and expression patterns of KRT17 closely follow those of KRT16 in all seven melanoma cell lines ( Figure 4C). S100A9 is suggested to have a role in acquired resistance to BRAF inhibitors [47] and in melanoma metastasis [46]. Our results show that S100A9 is expressed only in one tested melanoma cell line, A375 (BRAF V600E mut). In the majority of our melanoma cell lines, S100A9 expression could not be detected. By contrast, S100A7 shows a much wider expression pattern than S100A9, but despite its ability to promote cell proliferation, migration, invasion, and tumor metastasis in cervical, breast, and ovarian cancer [42][43][44][45], S100A7 has not previously been implicated in melanoma pathogenesis. From other validated targets in this study that have not yet been investigated in melanoma, we would like to point out MRAS, IL1R2, RAB34, LAPTM5, RDH10, and STK32C. Similar to S100A7, we considered them important because of their involvement in processes of tumor cell proliferation, migration, and invasion, or their interactions with members of the MAPK cascade. For example, it is shown that cells overexpressing MRAS have higher migratory potential and that MRAS/SHOC2/SCRIB complex coordinates ERK pathway dynamics [56]. It has been proposed that increased IL1R2 levels are important during the initiation and progression of human gastric cancer [70]. One study demonstrates the existence of a novel mechanism of tyrosine phosphorylation of RAB34 in regulating cell migration, invasion, and adhesion through modulating the endocytosis, stability, and recycling of integrin β3 [84]. It has been shown that inhibition of LAPTM5 blocks bladder cancer cell proliferation and cell cycle via deactivation of ERK1/2 and p38 [121] and that silencing of STK32C inhibited tumor cell proliferation, migration, and invasion in human bladder cancer cells [99]. Finally, RDH10 overexpression has an antiproliferative effect on hepatocellular carcinoma cell lines [98].
Although we identified FLG as the overlapping target of GLI1, GLI2, and GLI3 and detected it with both ChIP-seq and RNA-seq, its expression levels detected by qPCR experiments are not consistent between the cell lines. A375 cell line shows increased expression levels of FLG in all GLI overexpressed samples, while SKMEL24 shows a decrease in FLG expression levels. In the other cell lines, FLG expression could not be detected with qPCR. A previous study has noted the important role of the long noncoding RNA (lncRNA) SAMMSON in melanoma [86]. More recently, SAMMSON has been shown to be important for human melanoma cell growth and survival while also highlighting the role of a SAMMSON in modulating the adaptive resistance of mutant BRAF melanoma to RAF inhibitors [87]. Similar to SAMMSON, SPRY2 has also been implicated in resistance to BRAF inhibitors [101,120]. Our RNA-seq results reveal that both SAMMSON and SPRY2 are downregulated targets of GLI1, and ChIP-seq confirms that SAMMSON has a GLI1 binding motif, while SPRY2 contains GLI1 and GLI2 binding motifs. Because GLI1 is a transcriptional activator, it is not clear how it downregulates the expression of these two targets. It is likely that some other factors, apart from GLI1, play a role in their regulation. qPCR results show that SAMMSON expression levels vary among the cell lines. For example, A375 and SKMEL2 (with GLI1 or GLI2 overexpression) exhibit decreased expression levels of SAMMSON, while other cell lines, such as SKMEL24, MEL224, and MEL505, generally show increased SAMMSON expression levels. qPCR results show that SPRY2 is detected in all melanoma cell lines, especially those with GLI3 overexpression.

Conclusions
Our studies confirm that in melanoma, the HH-GLI signaling pathway is in crosstalk with other signaling pathways and that its activation is more likely non-canonical than canonical. Out of 21 selected targets, we validated 15 as novel targets of GLI proteins, considering their expression in melanoma cell lines and possession of GLI binding motifs. Our study provides insight into the unique and overlapping transcriptional output of the GLI proteins in melanoma, which will contribute to a better understanding of the GLI code and its role in tumorigenesis. Other potential targets can also be functionally validated using this data in the future, especially by researchers in the HH-GLI field that are interested in other aspects of HH-GLI signaling. Our findings provide new potential targets to consider while designing melanoma-targeted therapy, especially in the case of recurrent disease due to therapy resistance.  Figure S1. HH-GLI pathway activity in melanoma cell lines; Figure S2. GANT61 IC50 values; Figure S3. MDS plot; Figure S4. KEGG pathway analysis. File S1. Full pictures of the Western blots.