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. 2024 Sep 27;15:498. doi: 10.1007/s12672-024-01234-3

Diagnostic value of serum human epididymal secretory protein 4 for endometrial cancer: a systematic review and meta analysis

Jie Yuan 1,#, Chen Xu 1,#, Chunyong Liu 2,✉, Ruiling Yan 1,✉
PMCID: PMC11436588  PMID: 39331203

Abstract

Objective

The diagnostic value of serum human epididymal secretory protein 4 (HE4) for endometrial cancer (EC) was assessed via evidence-based medicine (EBM) and systematic review (SR) methodologies.

Methods

The Cochrane Library, PubMed, Web of Science, Embase, CBM, CNKI, and Wan Fang databases were searched up to April 1st, 2024, to identify relevant literature. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was utilized to evaluate the quality of the selected studies. The meta-analysis was conducted via RevMan 5.3, STATA 16.1, and Meta-disc software.

Results

A total of 22 studies comprising 9036 cases (3776 cases in the case group and 5260 cases in the control group) were included. The results revealed that HE4 exhibited a pooled sensitivity of 0.59 [95% CI (0.53, 0.64)], specificity of 0.93 [95% CI (0.88, 0.96)], positive likelihood ratio (PLR) of 6.87 [95% CI (4.57, 10.33)], negative likelihood ratio (NLR) of 0.46 [95% CI (0.39, 0.54)], diagnostic odds ratio (DOR) of 14.36 [95% CI (9.37, 21.17)], and area under the receiver operating characteristic curve (AUC) of 0.78 [95% CI (0.75, 0.82)].

Conclusions

Serum HE4 demonstrates high specificity and moderate sensitivity for diagnosing EC, thus serving as a valuable biomarker for clinicians either alone or in conjunction with other tumour markers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01234-3.

Introduction

Endometrial cancer (EC) is a type of epithelial malignant tumour that occurs in the endometrium and ranks as one of the top three malignant tumours of the female genital tract, along with ovarian cancer (OC) and cervical cancer. According to 2022 cancer statistics and estimates, EC is the most common gynaecologic malignant tumour, and its mortality rate is second only to that of OC. In China, EC ranks second in terms of morbidity and third in terms of mortality among gynaecological tumours [1]. Recent lifestyle changes, including improvements in living standards, shifts in reproductive concepts, adjustments in diet structure, disruptions in daily routines, and an ageing population, have led to a significant increase in both the incidence and mortality rates of EC, and the age at onset has shown a decreasing trend [2, 3].

The primary objective of enhancing overall survival and prognosis is to increase the rate of early diagnosis. Serum human epididymal secretory protein 4 (HE4) is a serum tumour marker that is characterized by its small acid-secreting protein nature. Serum HE4 is notably overexpressed in malignant tumour tissues and offers the benefits of easy detection, minimal trauma, and high patient acceptance, among others. Previous studies have indicated that serum HE4 has excellent diagnostic ability for EC, OC, and lung cancer. Consequently, serum HE4 is promising as a novel screening tool for diagnosing EC [4, 5].

Numerous small-sample studies both domestically and internationally have outlined the potential for serum HE4 to aid in the diagnosis, prognosis, monitoring of recurrence, and assessment of therapeutic efficacy for treating EC [5–7]. However, the clinical diagnostic value of serum HE4 for EC remains unclear. In this study, we employed evidence-based medicine (EBM) and meta-analysis to assess the clinical diagnostic value of serum HE4 for EC.

Materials and methods

Literature sources and retrieval strategies

The Cochrane Library, PubMed, Web of Science, Embase, CBM, CNKI and Wan Fang databases were searched up to April 1, 2024, to identify relevant publications. Furthermore, the reference lists of high-quality studies were manually searched to identify additional studies. Different search strategies were developed for each database. For example, PubMed uses “AND”, “OR”, and “NOT” to connect search terms based on Bourg’s logic algorithm, as shown in Table 1.

Table 1.

PubMed database retrieval strategy

Serial number Search expression
1 WAP four-disulfide core domain protein 2
2 Epididymis-Specific protein E4, human
3 EDDM4 protein, human
4 HE4 protein, human
5 ESP-H4 protein, human
6 WAP5 protein, human
7 Epididymal secretory protein E4, human
8 WAP four-disulfide core domain 2 protein, human
9 #1 OR #2 OR #3 OR #4 OR #5 OR #6 OR #7 OR #8
10 Endometrial neoplasms
11 Endometrial carcinoma
12 Endometrial cancer
13 Uterine cancer
14 Uterine carcinoma
15 #10 OR #11 OR #12 OR #13 OR #14
16 #9 AND #15
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Inclusion criteria and exclusion criteria

The inclusion criteria were as follows: I) patients with EC by histology composed the case group; II) patients with benign uterine disorders that could be mistaken for EC or healthy individuals composed the control group; III) direct extraction or computation could be used to obtain the entire 2 × 2 contingency table from which HE4 detected EC; and IV) BLOOD samples were taken prior to the start of antitumour medication.

The exclusion criteria were as follows: (I) duplicate articles, case studies, reviews, abstracts, speeches, or partial content missing; (II) individuals suffering from various benign and malignant conditions that could impact the expression level of serum HE4; and (III) studies that received a low score on the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) [8].

Data extraction and quality assessment

The following data were extracted from each study: first author, year of publication, population, cut-off value, test method, sample size, true positive (TP), false-positive (FP), true negative (TN), false-negative (FN) and reconstructed complete 2 × 2 contingency table. If necessary, the author was contacted via e-mail to prevent missing information. The QUADAS-2 was used to evaluate the quality of the included studies, including patient selection, index tests, reference standards, and their administration (flow and timing).

All of the above steps were performed independently by two researchers, and a third researcher was consulted in cases of disagreement.

Statistical analysis

The meta-analysis was performed with RevMan 5.3, STATA 16.1 and Meta-disc software. The pooled specificity (Spe), sensitivity (Sen), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated and used to construct the SROC curve. Then, the area under the curve (AUC) was calculated. Deeks' funnel plots and bivariate box plots were used to assess publication bias and heterogeneity. The results are presented with 95% confidence intervals (CIs) [9].

Results

Characteristics of the retrieved studies

A total of 1160 articles were initially retrieved from the databases. A total of 968 articles were excluded due to being case reports, reviews, abstracts, talks or obviously irrelevant to the current study. Then, 192 articles were subjected to title and abstract screening. Using Endnote, 110 duplicate studies were excluded. After screening the full texts of the remaining studies, 60 articles with incomplete or contradictory data and the inability to construct a 2 × 2 contingency table were excluded. The 22 remaining studies [10–31] were classified as high-quality studies based on the QUADAS-2; therefore, these 22 studies met the criteria for inclusion in this meta-analysis. There were 15 studies written in Chinese and 7 studies written in English. There were 3776 patients in the case group and 5260 patients in the control group. The total sample size was 9036 cases. The screening process and general characteristics of the included studies are shown in Fig. 1 and Table 2, respectively.

Fig. 1.

Fig. 1

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Flow chart of literature screening

Table 2.

The Basic characteristics of included studies [10–31]

First author Year Population Cut-off (pmol/L) Test method TP NP TN FN Control group Case group
Shuangge Zhang 2013 Asia 51.896 ECLIA 139 129 73 558 687 212
Yingying Lin 2014 Asia 69.45 CLIA 53 1 47 129 130 100
Zinv Pan 2014 Asia 86 ELISA 118 15 112 155 170 230
Jianan Lv 2015 Asia 96.1165 ELISA 68 4 62 42 46 130
Hao Yan 2015 Asia 91.25 ECLIA 77 9 59 193 202 136
Xingqin Wang 2015 Asia 150 ELISA 61 1 44 106 107 105
Zhi Zhang 2016 Asia 150 ELISA 86 1 32 81 82 118
Yanfang Ren 2018 Asia 150 ELISA 72 26 54 169 195 126
Jia Tan 2019 Asia 140 ECLIA 157 31 139 619 650 296
Li Xia 2020 Asia NR ELISA 85 6 35 44 50 120
Jianmei Wang 2020 Asia NR ELISA 78 37 23 171 208 101
Guangquan Liu 2020 Asia NR CLIA 83 20 72 128 148 155
Li Zhou 2020 Asia

a: 72.6

b: 104

 ELISA 67 1 33 199 200 100
Weiqiang You 2022 Asia 140 NR 193 36 188 616 652 381
Chunfang Cai 2022 Asia 140 NR 47 17 221 609 626 268
Moore 2008 America NR ELISA 94 16 77 140 156 171
Bignotti 2011 Europe NR EIA 92 8 46 68 76 138
Angioli 2012 Europe 70 EIA 59 1 42 102 103 101
Jing Bian 2017 Asia 140 ECLIA 61 41 44 46 87 105
Chunhua Dong 2017 Asia 86 ELISA 85 10 65 190 200 150
Jing Liu 2021 Asia 52.4 mmol/L ELISA 78 30 58 97 127 136
Barr 2022 Europe 77 CLEIA 315 168 82 190 358 397
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NR not reported, a premenopausal, b postmenopausal, ELISA enzyme-linked immunosorbent assay, EIA enzyme immunoassay, CLEIA chemiluminescence enzyme immunoassay, CLIA chemiluminescence immunoassay, ECLIA electrochemiluminescence immunoassay

Quality assessment

Using the QUADAS-2, two researchers independently assessed the quality of the included studies. The evaluation criteria and procedures were based on the QUADAS-2 entry, and the results of the quality assessment are summarized in Fig. 2a and b.

Fig. 2.

Fig. 2

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a Summary of methodological quality evaluation using QUADAS-2. b The evaluation results of the studies in QUADAS-2

Meta-analysis results

Deeks' funnel plot was utilized to assess publication bias in the 22 included articles. The results, depicted in Fig. 3a, indicated a symmetrical distribution of studies around the line with no significant difference (P = 0.12 > 0.05), suggesting the absence of publication bias.

Fig. 3.

Fig. 3

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a Deek’s funnel plot of HE4. b Bivariate box plot for heterogeneity analysis

Furthermore, the bivariate box plot in Fig. 3b revealed weak heterogeneity among the articles, with only 2 out of 22 falling outside the grey box. To address heterogeneity during meta-analysis, the I2 quantitative test results were considered, leading to the selection of either the Mantel–Haenszel method (fixed effects model) or the Der Simonian–Laird method (random effects model).

The results of the meta-analysis revealed that the pooled Sen, Spe, PLR, NLR, DOR, and AUC of HE4 for the diagnosis of EC were 0.59 [95% CI (0.53, 0.64)], 0.93 [95% CI (0.88, 0.96)], 6.87 [95% CI (4.57, 10.33)], 0.46 [95% CI (0.39, 0.54)], 14.36 [95% CI (9.37, 21.17)], and 0.78 [95% CI (0.75, 0.82)], respectively. The results are shown in Figs. 4, 5, 6, 7.

Fig. 4.

Fig. 4

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a Forest plot of Sen of HE4. b Forest plot of Spe of HE4

Fig. 5.

Fig. 5

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a Forest plot of PLR of HE4. b Forest plot of NLR of HE4

Fig. 6.

Fig. 6

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Forest plot of DOR of HE4

Fig. 7.

Fig. 7

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The AUC of HE4

Discussion

EC is a common gynaecological malignancy, and its risk factors include hormonal imbalances (e.g., abnormal ovarian ovulation, functional ovarian tumours), unprotected oestrogen replacement therapy (e.g., tamoxifen), metabolic disorders (e.g., obesity, hypertension, diabetes), early menarche, infertility, and delayed menopause [3]. Recent statistical data from China show a noticeable increase in both the incidence and mortality rates of EC, with a trend towards younger individuals being affected. In 2016, there were approximately 71,100 new cases of uterine corpus carcinoma in China, with an incidence rate of approximately 10.54/100,000, thus indicating an increase from the previous year [32, 33]. Approximately 90% of EC patients experience irregular vaginal bleeding after menopause, with approximately 70% being diagnosed at an early stage and having a favourable prognosis. However, 30% of patients are diagnosed at an advanced stage, leading to a poorer prognosis. Improving early detection rates can significantly increase the five-year survival rate and overall quality of life for EC patients [3]. Recurrence of EC is associated with various risk factors, such as age, histological type, tumour grade and stage, depth of myometrial invasion, and lymphovascular space invasion. The latest retrospective study by Enrico Vizza et al. [34] indicated that the rate of positive immunohistochemistry for L1 cell adhesion molecule (L1CAM) in patients with recurrent EC is as high as 84.2%. The L1CAM has high clinical value in predicting the recurrence of EC, especially distant recurrence and metastasis. Through a systematic review and meta-analysis, Andrea Giannini et al. [35] presented scientific evidence that the expression level of L1CAM affects the survival outcomes of patients with stage I EC. The aim of this study was to identify a serological marker that is both sensitive and specific and may be used in combination with L1CAM for screening in the future to improve the diagnostic rate or reduce the recurrence rate of EC.

There are currently no suggested screening techniques for EC. The gold standard for diagnosing EC is still diagnostic curettage or endometrial biopsy with hysteroscopy; however, patients cannot routinely accept or promote such invasive procedures. Thus, the primary focus of clinical research is to identify a single or combination screening technique that possesses both sensitivity and specificity. Owing to its high specific expression level in malignant tumour tissues, ease of collection, low degree of trauma, and high patient acceptability, the serum tumour marker serum HE4 has emerged as a research hotspot and can be used as a novel, noninvasive biomarker. Moreover, HE4 has clinical importance in disease diagnosis, prognosis assessment, recurrence monitoring, and therapy impact evaluation. Additionally, HE4 is utilized as a routine biomarker for screening malignant tumours.

Kirchhoff et al. [36] initially discovered and extracted the HE4 protein, which was thought to be a secreted protein specific to the human epididymis and connected to sperm maturation, from the distal epithelial cells of the organ. Further investigation revealed that the HE4 protein is a tiny, acidic, secreted protein that is high in cysteine and homologous to protease inhibitors. Using DNA, tissue, and protein chips, Galgano et al. [37] examined the expression level of the HE4 gene and its protein in a wide range of normal and malignant tumour tissues. According to previous studies, normal epithelial cells in the digestive, reproductive, and urinary tracts express the HE4 protein. These findings confirmed that, compared with normal tissues, malignant tumour tissues overexpress the HE4 protein. In OC, EC, lung cancer, breast cancer, pancreatic cancer, stomach cancer, and other disorders, the HE4 protein has good diagnostic performance [38]. Serum HE4 has been a part of clinical practice for almost a decade, and it holds great promise for both clinical and research applications, particularly in the routine screening of EC.

The Sen and Spe values were 0.59 and 0.93, respectively, according to the meta-analysis results of this study, indicating that the serum HE4 level has high specificity and moderate sensitivity in the diagnosis of EC. A more reliable metric that is unaffected by prevalence is the likelihood ratio (LR), which includes both the PLR and NLR. The probability of successfully identifying EC with serum HE4 was 6.87 times greater than the probability of incorrectly diagnosing EC, as indicated by the PLR of serum HE4 in diagnosing EC. This explains why the misdiagnosis rate of this test was low. The NLR of serum HE4 in diagnosing EC was 0.46, meaning that there was a 0.46-fold greater chance of misdiagnosing EC with serum HE4 than of successfully diagnosing EC. This finding explains why there was a significant rate of missed diagnoses with this test. An assessment index of comprehensive Sen, Spe, and LR is called DOR. The diagnostic value and accuracy increase with the DOR value. For the diagnosis of EC, the DOR of serum HE4 is 14.36. Currently, the AUC is a widely used metric to assess how well diagnostic tests function. The better the diagnostic impact is, the closer it is to 1. The AUC of serum HE4 in identifying EC was 0.78, which is typically regarded as having good diagnostic value. The studies included in this study demonstrated good homogeneity via heterogeneity analysis, indicating high stability and dependability. Deeks’ funnel plot of the symmetrical distribution demonstrated that this study was free of publication bias.

The shortcomings of this study are as follows: (I) Twenty-two studies were included, 18 of which were Asian. The language format included only Chinese and English, which indicated that the group structure and language were single. (II) The study's control group had a variety of illnesses that were not all the same. Twelve studies included both benign gynaecological lesions and normal, healthy women as controls, whereas six studies used benign gynaecological lesions as controls. (III) The measurement of the threshold effect was impacted in several studies by inaccurately stated serum HE4 cut-off values.

In conclusion, this study revealed that serum HE4 has specific diagnostic value in patients with EC, with high specificity and sensitivity. Preliminary evidence supports the use of serum HE4 as a crucial technique for EC diagnosis, effectiveness assessment, prognosis evaluation, and recurrence monitoring. When used in conjunction with other screening techniques, serum HE4 can increase sensitivity and lower the percentage of missed diagnoses.

Supplementary Information

Supplementary Material 1 (12.5KB, xlsx)

Author contributions

J Y. and C X. were responsible for data collection and manuscript writing. J Y. C X. and C Y L. was responsible for data analysis. R L Y. and C Y L. were responsible for project development and judge of article disputes.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability

All data generated or analysed during this study are included in this published article and it is provided within the supplementary information files under the name “Data Extraction Table”.

Declarations

Competing interests

We declare that the authors have no competing interests as defined by Discover, or other interests that might be perceived to influence the results and/or discussion reported in this paper. The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jie Yuan and Chen Xu are co-first authors.

Contributor Information

Chunyong Liu, Email: liuchunyong5837@qq.com.

Ruiling Yan, Email: yanruilingjnu@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (12.5KB, xlsx)

Data Availability Statement

All data generated or analysed during this study are included in this published article and it is provided within the supplementary information files under the name “Data Extraction Table”.

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