Human Wings Apart-Like Protein as a Serum Diagnostic Biomarker in Cervical Cancer: An Integrative Bioinformatics Analysis with Serum Validation

Summary

This study evaluates human wings apart-like (hWAPL) protein as a serum diagnostic biomarker in cervical cancer through integrative bioinformatics analysis and serum validation and demonstrates that combining serum hWAPL with squamous cell carcinoma antigen improves diagnostic performance.

Abstract

Cervical cancer remains a major threat to women’s health worldwide, and reliable serum biomarkers for early detection and therapeutic stratification remain limited. Human wings-apart-like (hWAPL) protein has been implicated in cervical carcinogenesis, but its diagnostic and clinical value has not been fully elucidated. To address this gap, this study integrated public multi-omics datasets, including The Cancer Genome Atlas, GEPIA2, the Human Protein Atlas, and single-cell transcriptomic data, to characterize hWAPL expression, clinicopathological associations, immune infiltration, co-expression networks, post-translational modifications, and drug sensitivity predictions. These findings were evaluated in an independent single-center serum cohort comprising 89 patients with histologically confirmed cervical squamous cell carcinoma and 89 healthy female controls. Serum hWAPL and squamous cell carcinoma antigen (SCC) levels were measured, and diagnostic performance was assessed by receiver operating characteristic curve analysis. In silico, hWAPL was broadly upregulated across multiple malignancies, particularly cervical cancer, enriched in malignant epithelial cells and monocytes/macrophages, and associated with shorter progression-free interval, predicted reduced sensitivity to cisplatin, paclitaxel, and 5-fluorouracil, and predicted sensitivity to MCL-1 and Wee1 inhibitors. In the serum cohort, hWAPL levels were significantly higher in patients than controls and discriminated cervical cancer with an area under the curve of 0.961, exceeding SCC alone. Combining hWAPL with SCC further improved diagnostic performance (area under the curve, 0.974; sensitivity, 93.3%; specificity, 95.5%). These findings suggest that serum hWAPL is a potential novel diagnostic biomarker for cervical squamous cell carcinoma whose performance is enhanced by SCC, whereas the observed associations with chemoresistance and immune microenvironment remodeling are hypothesis-generating and require experimental confirmation.

Introduction

Cervical cancer ranks as the fourth most prevalent malignancy and the fourth leading cause of cancer-related mortality in women globally, with approximately 660,000 new diagnoses and 350,000 deaths recorded in 2022¹. This disease burden disproportionately affects low- and middle-income nations, where systematic screening programs remain insufficient². While HPV vaccination and Pap smear screening have successfully reduced incidence rates in numerous high-income settings, current diagnostic approaches exhibit notable limitations. HPV DNA testing demonstrates suboptimal specificity, and cytological interpretation is inherently subjective and variable. These shortcomings underscore the pressing requirement for innovative, objective, and minimally invasive serum biomarkers to augment existing diagnostic frameworks.

Squamous cell carcinoma antigen (SCC-Ag) serves as the predominant serological biomarker for cervical squamous cell carcinoma, a histological subtype comprising roughly 85–90% of all cervical cancer cases³. A systematic review and meta-analysis indicated that elevated pretreatment SCC-Ag levels in squamous cell carcinoma are associated with markedly poorer overall and progression-free survival⁴. In the context of recurrence monitoring, SCC-Ag demonstrates a sensitivity of 78.9% and a specificity of 91.2% for squamous cell carcinoma⁴. Nevertheless, the utility of SCC-Ag for early detection is limited, as increased levels are observed in only 28–88% of individuals diagnosed with squamous cell cervical cancer⁵. Furthermore, many early-stage tumors do not present with elevated SCC-Ag concentrations, and this biomarker is not relevant for non-squamous histological variants³. Consequently, discovering new serum biomarkers that offer improved diagnostic sensitivity, especially for early-stage cervical cancer, represents a significant and unresolved clinical priority.

Beyond SCC-Ag, several other circulating markers have been evaluated in cervical cancer, including carcinoembryonic antigen, the cancer antigen CA-125, cytokeratin-19 fragment (CYFRA 21-1), and, more recently, circulating tumor DNA and HPV cell-free DNA. In practice, however, these markers are constrained by limited sensitivity for early-stage disease, poor specificity for the squamous histology that predominates in cervical cancer, or a requirement for specialized molecular platforms that are not yet broadly available. This landscape leaves considerable room for a protein marker that is both mechanistically linked to cervical carcinogenesis and measurable by a routine immunoassay.

The human wings apart-like (hWAPL) gene, also referred to as WAPL, encodes a cohesin release factor essential for the proper separation of sister chromatids during mitotic division⁶. Initially identified by Oikawa et al., hWAPL was characterized as a novel human gene whose expression is induced by the HPV oncoproteins E6 and E7. It shows strong expression in uterine cervical carcinomas, while only minimal expression is observed across other normal and neoplastic tissues⁷. Further investigations revealed that hWAPL mRNA levels are markedly elevated in cervical carcinoma relative to benign cervical epithelium, with expression increasing in correlation with the grade of cervical intraepithelial neoplasia (CIN)^8,9. From a mechanistic perspective, dysregulated overexpression of WAPL disrupts mitotic and cytokinetic processes, thereby fostering chromosomal instability and facilitating tumor advancement¹⁰. Additionally, WAPL contributes significantly to tumorigenesis and the regulation of cell cycle progression. Multiple alternatively spliced isoforms of WAPL have been identified in various cervical epithelial samples, underscoring its molecular diversity¹¹. In addition to its established involvement in cervical cancer development, growing evidence highlights the diagnostic utility of hWAPL in cervical lesions. Moreover, TAT-mediated knockdown of hWAPL reduced HPV E6 expression, suppressed tumor invasion and colony formation in cervical cancer stem cells, and significantly attenuated tumor growth in nude mouse models¹². These findings suggest that hWAPL serves as a marker for cervical cancer stem cell proliferation and represents a potentially viable therapeutic target¹².

While these results are encouraging, several important questions remain unanswered. First, the ability of serum hWAPL to differentiate cervical cancer patients from healthy individuals and its performance relative to, or in combination with, the established biomarker SCC has not been assessed in a rigorously controlled study population. Second, the relationship between serum hWAPL levels and HPV infection status, as well as key clinicopathological features such as clinical stage, histological grade, and lymph node metastasis, is still unclear. Third, it has not been investigated whether hWAPL and SCC offer complementary value for diagnosis.

Although hWAPL has been studied at the tissue and cellular levels, no prior work has established whether the protein is measurable in circulation or whether a serum-based measurement can distinguish cervical cancer from a control population; this is the specific knowledge gap that the present study addresses. Building on, rather than repeating, earlier tissue-based reports, we combined an integrative bioinformatics survey of hWAPL, using data from The Cancer Genome Atlas and other publicly available repositories, with the first serum-based evaluation of the protein in an independent, single-center cohort comprising 89 patients with cervical squamous cell carcinoma and 89 healthy female controls. The primary objective was to determine the diagnostic performance of serum hWAPL, quantified as the area under the receiver operating characteristic curve, for distinguishing cervical squamous cell carcinoma from healthy controls, both alone and in combination with SCC-Ag; the primary outcome measure was therefore the area under the curve, with sensitivity and specificity at the optimal Youden threshold as secondary measures. Secondary objectives were to characterize the association of serum hWAPL with HPV status and other clinicopathological features, and to generate mechanistic hypotheses regarding the biological context of hWAPL through the bioinformatic analyses.

Protocol

This study was approved by the Institutional Review Board of Shaanxi Provincial People’s Hospital (Approval No. 2024-R055) and was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from every participant before any blood was drawn. All human blood and serum specimens were collected, handled, and stored in compliance with the institutional guidelines of the Department of Laboratory Medicine.

All venous blood and serum specimens were treated as potentially infectious biological material. Sample processing was performed in a biosafety level 2 laboratory by trained personnel wearing appropriate personal protective equipment. Disposable plasticware, pipette tips, and used microplates that had come into contact with biological material were autoclaved or collected as biohazardous waste and disposed of through the institution’s licensed medical waste stream in accordance with local regulations.

Study design overview:
The workflow of this study is summarized in a study design schematic (Supplementary Figure S1) and is divided into two complementary arms. In the in silico arm, public transcriptomic, single-cell, protein, post-translational modification, immune, and drug-sensitivity datasets were analyzed to characterize the biological context of hWAPL and to generate mechanistic hypotheses. In the experimental arm, serum hWAPL and SCC-Ag were measured in an independent single-center cohort, and their diagnostic performance, alone and in combination, was assessed by receiver operating characteristic analysis and logistic regression. The two arms were integrated only at the level of interpretation, with the serum arm providing the sole experimental validation.

Pan-cancer and cervical squamous cell carcinoma gene expression profiling
RNA-sequencing data from The Cancer Genome Atlas (TCGA) were obtained via the UCSC Xena Browser. All public databases and web tools used in this study were accessed between September and October 2025, and the specific versions and access dates are listed in the Table of Materials. Expression levels of WAPL, quantified as fragments per kilobase of transcript per million mapped reads (FPKM), were subjected to a log₂ transformation with a pseudocount of 1 added. These transformed values were then used to perform comparative analyses between malignant tissues and matched adjacent normal samples across multiple cancer types. For the focused investigation on cervical malignancies, WAPL expression data specific to cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) were isolated. The expression profile in these tumor samples was subsequently visualized and compared against expression levels in normal cervical epithelial control tissues.

Analysis of WAPL transcript isoforms
The expression profiles of WAPL transcript isoforms were examined using the GEPIA2 database. Specific isoforms were delineated by their corresponding Ensembl transcript identifiers (ENST). Expression levels were calculated as the logarithm base 2 of transcripts per million plus one [log₂(TPM+1)] and are presented graphically as violin plots.

Single-cell RNA sequencing analysis
The scRNA-seq dataset GSE168652 was obtained from the Gene Expression Omnibus (GEO) repository. Data preprocessing, quality assessment, normalization, and clustering were performed with the scCancerExplorer platform. Cells expressing fewer than 200 genes and genes detected in less than three cells were filtered out. Normalization was carried out using the Log-Normalize approach with a scaling factor of 10,000.

The 2,000 most variable genes were identified using variance-stabilizing transformation and subsequently used for principal component analysis (PCA). Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction was applied based on the first 30 principal components. Graph-based clustering was implemented via the Louvain algorithm at a resolution of 0.8. Cell clusters were annotated according to established marker genes. Expression levels of WAPL were measured across different cell populations, and cell cycle stages were determined using the CellCycleScoring method.

Protein expression and posttranslational modification analysis
The expression and subcellular distribution of WAPL in CSCC were assessed using the Human Protein Atlas database. Post-translational modification (PTM) sites were identified using PhosphoSitePlus. Furthermore, potential associations between WAPL expression levels and genes implicated in protein modification processes, specifically AKT1, AKT2, KAT2B, EP300, USP7, and KAT5, within CSCC were investigated using the Xiantao Academic tool^{13,14,15,16,17}.

Co-expression network and functional enrichment analysis
The RNA sequencing data for TCGA-CSCC, processed using the STAR aligner and normalized to transcripts per million (TPM), were obtained from the Genomic Data Commons (GDC) portal. To identify genes co-expressed with WAPL, a screening threshold was applied based on an absolute Pearson correlation coefficient greater than 0.6 and a statistical significance level of P < 0.05.

To investigate the biological functions associated with WAPL, differential expression analysis was first performed after stratifying patients into high- and low-WAPL expression groups using the median expression value as the cutoff. Differentially expressed genes were identified with the Xiantao Academic platform and subsequently subjected to Gene Ontology enrichment analysis, including biological process, cellular component, and molecular function categories, together with Kyoto Encyclopedia of Genes and Genomes pathway analysis. Independently, protein interaction relationships involving WAPL were explored using STRING, and the resulting interaction network was reconstructed and examined in Cytoscape.

Gene Set Enrichment Analysis (GSEA) was performed using the Reactome, WikiPathways, and KEGG databases, with screening criteria set at an absolute normalized enrichment score (|NES|) > 1 and a false discovery rate (FDR) < 0.25.

Immune infiltration analysis
The TIMER 2.0 and xCell computational tools were employed to analyze transcriptomic data from the TCGA-CESC cohort, enabling the quantitative assessment of infiltration status for 24 distinct immune cell populations.

Drug sensitivity prediction
Drug sensitivity predictions were derived from the GDSC2 database using the oncoPredict R package. The half-maximal inhibitory concentration (IC₅₀) was calculated for a panel of 86 chemotherapeutic compounds.

Survival and diagnostic analysis
Overall survival (OS) and progression-free interval (PFI) curves were constructed using the Xiantao analytical platform, with patient cohorts dichotomized at the median WAPL expression level. The prognostic significance of variables was appraised through both univariate and multivariate Cox proportional hazards regression models. To evaluate the diagnostic capability of WAPL and SCC (SERPINB3) in discriminating CSCC from adjacent normal tissue, receiver operating characteristic (ROC) curve analysis was applied to transcriptomic data, expressed as transcripts per million (TPM), obtained from The Cancer Genome Atlas.

Serum sample collection and ELISA
This study received approval from the Institutional Review Board of Shaanxi Provincial People's Hospital (Approval No. 2024-R055) and was performed in compliance with the Declaration of Helsinki. A total of 89 serum specimens were obtained from patients with histopathologically confirmed CSCC, alongside 89 age-matched healthy female controls. The median age was 54 years, range 31–77 years, in the CSCC cohort and 41 years, range 30–52 years, in the control cohort. All cervical cancer cases were newly diagnosed and treatment-naïve at the time of blood collection; specimens were obtained before any surgery, chemotherapy, or radiotherapy was initiated. We note that although the two groups were drawn from a comparable clinical setting, the median ages differed by 13 years. This difference is examined directly in the Results section and in a dedicated age-adjusted sensitivity analysis, and it is acknowledged as a limitation in the Discussion section.

Venous blood was collected into anticoagulant-free tubes, left to clot at ambient temperature for 10–20 min, and subsequently centrifuged at 2,000–3,000 × g for 20 min. Serum was separated within 1 h of collection, aliquoted immediately, and stored at −20 °C; the interval between venipuncture and freezing did not exceed 2 h for any sample. Aliquots underwent a single freeze–thaw cycle before analysis, and all measurements were completed within three months of storage. The resulting serum was divided into aliquots and preserved at −20 °C. Any hemolyzed or lipemic samples were excluded from analysis.

Quantification of serum hWAPL protein levels was performed using a commercially available enzyme-linked immunosorbent assay (ELISA) kit based on a double-antibody sandwich principle. In brief, both calibration standards and test samples were dispensed in duplicate into microplate wells pre-coated with a capture antibody specific for hWAPL, followed by a 30 min incubation at 37 °C. After a series of wash steps to remove unbound material, a horseradish peroxidase (HRP)-labeled detection antibody directed against hWAPL was introduced and incubated under identical conditions.

Subsequent to further washing, 50 µL each of chromogenic substrate solutions A and B were combined in the wells and allowed to develop in the dark at 37 °C for 10 min. Stop solution (50 µL) was added to terminate the enzymatic reaction. Optical density was measured at a primary wavelength of 450 nm, with a reference reading at 630 nm, within 15 min of stopping the reaction. A standard curve was constructed using serially diluted calibrators spanning a concentration range of 10–450 pg/mL. The hWAPL concentration for each unknown sample was derived by interpolation from this standard curve and adjusted for any preanalytical dilution. The assay was performed strictly according to the manufacturer’s instructions. Per the manufacturer’s specifications, the kit's working detection range is 10–450 pg/mL. Samples were assayed at a final 5-fold dilution, and specimens whose optical density exceeded that of the highest calibrator were further diluted and re-assayed with the total dilution factor applied during back-calculation. Analytical performance was verified in the serum matrix before the study samples were run: a serially diluted high-concentration serum pool showed acceptable linearity across the calibration range, and spike recovery of the recombinant calibrator in three serum pools fell within an acceptable window, consistent with the kit's double-antibody sandwich design. The lower limit of quantification corresponded to the lowest calibrator (10 pg/mL). We emphasize that this kit is labeled by the manufacturer for research use only and is not a clinically validated in vitro diagnostic device; the absolute concentrations and the derived cutoff should therefore be interpreted as platform-specific and provisional, as discussed below.

Concurrently, serum SCC-Ag levels were quantified using the Abbott Alinity i platform via a chemiluminescent microparticle immunoassay (CMIA), with an upper reference limit of 1.5 ng/mL. All analytical procedures were conducted in duplicate by laboratory personnel who were blinded to disease status and to all clinical variables, including clinical stage, histological grade, and HPV status. Both the inter-assay and intra-assay coefficients of variation for the methodologies employed were maintained below 10%.

Statistical analysis
Because serum hWAPL concentrations did not satisfy the assumption of normality according to the Shapiro–Wilk test (all P < 0.05), subsequent analyses were performed using non-parametric methods. Continuous variables are therefore presented as medians with interquartile ranges (IQRs). Differences between two independent groups were assessed using the Mann–Whitney U test, whereas comparisons across three or more groups were conducted with the Kruskal–Wallis H test. Correlations between variables were evaluated using Spearman's rank correlation analysis for continuous or ordinal data and the point-biserial correlation coefficient for binary variables, such as HPV infection status. Survival outcomes were compared using the log-rank test, and hazard ratios with 95% confidence intervals were estimated from Cox proportional hazards regression models.

Diagnostic performance was assessed through ROC curve analysis, with the optimal cutoff for each biomarker determined by maximizing the Youden index. Differences in area under the curve (AUC) were tested using the DeLong method. To investigate the combined diagnostic utility, a binary logistic regression model was constructed with disease status (CSCC = 1; control = 0) as the dependent variable and serum hWAPL and SCC antigen as independent predictors. The resulting predicted probabilities were saved as a composite score for subsequent ROC analysis.

Differential drug sensitivity between WAPL-high and WAPL-low groups was evaluated with the limma package, applying thresholds of adjusted P < 0.05 and |log₂ fold change| > 0.3. Statistical significance was defined as a two-tailed P-value < 0.05. All analyses were conducted using SPSS, R, or the specified online tools. R packages included survival, survminer, pROC, ggplot2, oncoPredict, and limma. For all high-dimensional analyses in which many hypotheses were tested simultaneously, namely the pan-cancer comparisons, the co-expression screen, the differential-expression and functional-enrichment analyses, and the drug-sensitivity analysis, P-values were adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate, and adjusted P-values are reported where applicable; the pairwise correlations between hWAPL and individual genes or immune-cell fractions are reported with nominal P-values, and these correlational results are regarded as hypothesis-generating rather than confirmatory. Because a small number of clinicopathological fields in the public cohort were incompletely populated, cases with missing data were excluded from the corresponding analysis on a pairwise basis rather than imputed, and the number of evaluable cases is given for each variable in Table 1. To address the age difference between the serum cohorts, a sensitivity analysis using age-adjusted binary logistic regression was performed, and the Spearman correlation between serum hWAPL and age was computed within each group.

Results

Broad hWAPL upregulation across human malignancies, especially cervical cancer tissues
Pan-cancer analysis of TCGA datasets revealed that WAPL exhibits significantly elevated expression in the majority of solid tumors compared to their corresponding normal tissues (Figure 1A). Notably, hWAPL was significantly overexpressed in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) among 21 other cancer types, including breast in...

Discussion

Our pan-cancer analysis of TCGA data revealed that WAPL expression is markedly elevated in most solid tumors relative to normal tissues, with the most pronounced upregulation observed in CESC. This observation aligns with prior findings that hWAPL mRNA is predominantly expressed in cervical cancer, with minimal expression in other normal and malignant tissues⁷. Oikawa et al. initially characterized hWAPL as a novel gene implicated in cervical cancer development, demonstr...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Abbott GmbH, Wiesbaden, Germany	Not provided	Used for SCC-Ag detection
Rayto Life and Analytical Sciences Co., Ltd., Shenzhen, China	Model RT-3100	Used for plate washing
Shanghai Anting Scientific Instrument Factory, Shanghai, China	Model TLXY-IIC low-speed centrifuge	Used for serum sample centrifugation
Cytoscape Consortium	Version 3.9.1	Used for network visualization; network visualization
National Cancer Institute	https://portal.gdc.cancer.gov; accessed October 2025	Used for TCGA-CSCC STAR-aligned TPM data retrieval; RRID:SCR_014514
Online platform/database	Not applicable	Used for drug sensitivity prediction; drug-sensitivity reference data
National Center for Biotechnology Information	GPL24676 Illumina NovaSeq 6000 (Homo sapiens)	Used for single-cell RNA sequencing dataset retrieval; single-cell RNA-seq dataset
Online platform	Not applicable	Used for transcript isoform and gene expression analysis; transcript isoform and expression analysis
R package	Version 3.4.4	Used for data visualization
Online platform/database	Not applicable	Used for protein expression and subcellular localization analysis; protein expression and localization
Shanghai Fusheng Biotechnology Co., Ltd.	A977261-96T	A977261-96T; double-antibody sandwich ELISA; detection range 10-450 pg/mL; research use only (www.affandi-e.com)
R package	Version 3.54.0	Used for differential analysis
Rayto Life and Analytical Sciences Co., Ltd., Shenzhen, China	Model RT-6100	Used for optical density measurement
R package	Version 0.2	Used for drug-sensitivity (IC50) prediction; RRID:SCR_023220
Online platform/database	Not applicable	Used for post-translational modification analysis; post-translational modification site analysis
R package	Version 1.18.0	Used for ROC curve analysis
R Foundation for Statistical Computing	Version 4.2.1	Used for statistical analysis and bioinformatics analysis; statistical and bioinformatic analysis
Abbott GmbH, Wiesbaden, Germany	REF 09P3374	Used for serum SCC-Ag quantification
Online platform	Not applicable	Used for single-cell RNA sequencing analysis
Sanli Medical Technology Development Co., Ltd., Liuyang, Hunan, China	Not provided	Used for venous blood collection
IBM Corp.	Version 26.0	Used for statistical analysis; statistical analysis
STRING Consortium	Version 11.5	Used for protein-protein interaction network analysis; protein-protein interaction networks
R package	Version 3.4	Used for survival analysis
R package	Version 0.4.9	Used for survival curve visualization
National Cancer Institute	https://www.cancer.gov/tcga; accessed October 2025	RRID:SCR_003193; cancer transcriptomic data
Online platform	http://timer.cistrome.org; accessed October 2025	RRID:SCR_018737; immune infiltration analysis
UCSC Xena Browser	https://xenabrowser.net; accessed October 2025	Used for TCGA data retrieval; TCGA data retrieval
Online platform/tool	Version 1.0	Used for immune cell infiltration estimation
Online platform	https://www.xiantaoxueshu.com; accessed October 2025	Used for differential expression, correlation, survival, and diagnostic analyses