WW domain-containing ubiquitin E3 ligase 1 regulates immune infiltration in tumor microenvironment of ovarian cancer

doi:10.12122/j.issn.1673-4254.2025.05.20

Abstract

Abstract:

Objective To explore the association of the expression of WW domain-containing ubiquitin E3 ligase 1 (WWP1) with immune infiltration in tumor microenvironment (TME) of ovarian cancer. Methods Ovarian cancer patient data from The Cancer Genome Atlas (TCGA) were used to analyze the association of WWP1 expression with patient prognosis. TISCH2 was utilized to analyze the changes in immune cell subtypes in TME of metastatic tumor and after chemotherapy. The impact of WWP1 on immune cell infiltration, somatic copy number alterations of WWP1 and evolution of immune cell subtypes was evaluated using TIMER and TIGER pseudo-time analysis. A deep learning model was used to analyze TCGA pathological images to investigate the effect of WWP1 on TME of ovarian cancer. RNA-seq analysis was conducted to identify the differentially expressed genes in WWP1-overexpressing SKOV3 cells and validate immune infiltration. Multicolor immunofluorescence assay was used to analyze the immune markers in SKOV3 and SKOV3/DDP cell xenografts in nude mice. Results The patients with high WWP1 expression levels had significantly lower overall survival rate (P=0.0012). High WWP1 expression levels and Stage IV disease were both associated with a poor prognosis (P<0.05). In metastatic ovarian cancer or after chemotherapy, the percentages of malignant tumor cells and tumor-associated fibroblasts increased in the TME, accompanied by elevated WWP1 levels. WWP1 expression level was positively correlated with pro-tumorigenic immunosuppressive cells (r=0.1323-0.3955, P<0.05) and negatively with tumor-inhibiting immune cells (r=-0.1949- -0.1333, P<0.05). Specific copy number alterations of WWP1 also influenced CD8⁺ T cell percentage and neutrophil infiltration levels in the TME. RNA-seq analysis of WWP1-overexpressing SKOV3 cells and immunofluorescence assay of the tumor-bearing mice yielded findings consistent with those of bioinformatics analysis. Conclusion WWP1 may serve as a prognostic biomarker and a potential target for immune regulation in the TME of ovarian cancer.

Key words: ovarian cancer, WW domain-containing ubiquitin E3 ligase 1, immune infiltration, tumor microenvironment

Xiaojuan GUO, Ruijuan DU, Liping CHEN, Kelei GUO, Biao ZHOU, Hua BIAN, Li HAN. WW domain-containing ubiquitin E3 ligase 1 regulates immune infiltration in tumor microenvironment of ovarian cancer[J]. Journal of Southern Medical University, 2025, 45(5): 1063-1073.

Figures/Tables 10

Tab.1 Clinical characteristics of ovarian cancer patients with low and high WWP1 expression levels

Characteristics	Low expression of WWP1	High expression of WWP1	P
Case (n)	185	187
Figo Clinical stage [n (%)]			0.0012
Stage III	152 (40.9%)	139 (37.4%)
Stage IV	20 (5.4%)	37 (9.9%)
Stage II	11 (2.9%)	12 (3.2%)
Stage I	1 (0.2%)	-
Primary therapy outcome [n (%)]			<0.0001
PD	13 (3.5%)	11 (3.0%)
SD	11 (3.0%)	11 (3.0%)
PR	18 (4.8%)	25 (6.7%)
CR	109 (29.3%)	101 (27.2%)
Race [n (%)]			0.846
Asian	7 (1.6%)	4 (1.6%)
Black or african american	14 (3.8%)	11 (3%)
White	155 (41.7%)	168 (45.2%)
Age [median (IQR)]	61 (51, 71)	58 (51, 65)	0.039

Fig.1 Survival probability of ovarian cancer patients with low and high WWP1 expression levels.

Tab.2 Univariate and multivariate analyses of the risk factors for poor prognosis of ovarian cancer patients

Characteristics	Case (n)	Univariate analysis		Multivariate analysis
Characteristics	Case (n)	Hazard ratio (95% CI)	P	Hazard ratio (95% CI)	P
WWP1	372
Low	185	Reference		Reference
High	187	1.499 (1.156-1.942)	0.002		0.024
Clinical stage	372
I & II	24	Reference
III	291	2.058 (0.911-4.649)	0.083
IV	57	2.556 (1.085-6.025)	0.032
Tumor status	337
Tumor free	72	Reference		Reference
With tumor	265	9.598 (4.487-20.532)	< 0.001	15.691 (3.811-64.606)	<0.001
Primary therapy outcome	299		< 0.001
PD	24	Reference		Reference
SD	22	0.441 (0.217-0.896)	0.024	0.397 (0.187-0.845)	0.016
PR	43	0.652 (0.384-1.108)	0.114	0.659 (0.374-1.160)	0.148
CR	210	0.154 (0.095-0.250)	<0.001	0.179 (0.106-0.302)	<0.001
Tumor residual	336
No	68	Reference
Yes	268	2.223 (1.441-3.430)	<0.001
Age (year)	372
≤60	207	Reference
>60	165	1.352 (1.045-1.749)	0.022

Fig.2 Analysis of WWP1-related cell type distribution in primary tumor, primary plus metastatic tumor, and primary tumor plus chemotherapy using scRNA seq database. A, B, D, E, G, H: Cell types and their distribution. C, F, I: Distribution of WWP1 in different cells in OV_GSE115007, OV_GSE130000 and OV_GSE158722 datasets.

Fig.3 Correlation between immune infiltration and WWP1 expression in ovarian cancer. XCELL, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, EPIC, MCPCOUNTER and TIMER are different immune infiltration algorithms.

Fig.4 Correlation between immune infiltration and WWP1 expression in ovarian cancer. XCELL,CIBERSORT, CIBERSORT-ABS, QUANTISEQ, EPIC, MCPCOUNTER and TIMER are different immune infiltration algorithms. *P <0.05, **P<0.01.

Fig.5 Pseudo-time analysis of the effect of WWP1 expression on dynamic changes of infiltrating immune cells in ovarian cancer. A, C, E, G: Developmental trajectories of the pooled infiltrating immune cells, CD4+ T cells, CD8+ T cells, NK cells, myeloid cells and B cells (The inferred direction to differentiation and maturation was from the left to the right). B, D, F, H: Dynamic expressions of WWP1 related to the differentiation and maturation along the pseudo-time axis.

Fig.6 HE staining images of ovarian cancer analyzed by HD-Staining model (Original magnification: ×400).

Fig.7 Effect of WWP1 overexpression on immune infiltration in SKOV3 cells. A: Comparison of immune microenvironment. B: Comparison of immune infiltration. C: Volcano map for Top 20 difference genes. D: Comparison of immune pathways of GSEA enrichment.

Fig.8 Comparison of immune markers in SKOV3 and SKOV3/DDP cell xenografts in nude mice (×200).

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