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. 2022 May 15;39(5):71.
doi: 10.1007/s12032-022-01665-4.

A bioinformatic analysis of WFDC2 (HE4) expression in high grade serous ovarian cancer reveals tumor-specific changes in metabolic and extracellular matrix gene expression

Nicole E James  1   2 , Megan Gura  3 , Morgan Woodman  1 , Richard N Freiman  3 , Jennifer R Ribeiro  4   5
Affiliations

A bioinformatic analysis of WFDC2 (HE4) expression in high grade serous ovarian cancer reveals tumor-specific changes in metabolic and extracellular matrix gene expression

Nicole E James et al. Med Oncol. .

Abstract

Human epididymis protein-4 (HE4/WFDC2) has been well-studied as an ovarian cancer clinical biomarker. To improve our understanding of its functional role in high grade serous ovarian cancer, we determined transcriptomic differences between ovarian tumors with high- versus low-WFDC2 mRNA levels in The Cancer Genome Atlas dataset. High-WFDC2 transcript levels were significantly associated with reduced survival in stage III/IV serous ovarian cancer patients. Differential expression and correlation analyses revealed secretory leukocyte peptidase inhibitor (SLPI/WFDC4) as the gene most positively correlated with WFDC2, while A kinase anchor protein-12 was most negatively correlated. WFDC2 and SLPI were strongly correlated across many cancers. Gene ontology analysis revealed enrichment of oxidative phosphorylation in differentially expressed genes associated with high-WFDC2 levels, while extracellular matrix organization was enriched among genes associated with low-WFDC2 levels. Immune cell subsets found to be positively correlated with WFDC2 levels were B cells and plasmacytoid dendritic cells, while neutrophils and endothelial cells were negatively correlated with WFDC2. Results were compared with DepMap cell culture gene expression data. Gene ontology analysis of k-means clustering revealed that genes associated with low-WFDC2 were also enriched in extracellular matrix and adhesion categories, while high-WFDC2 genes were enriched in epithelial cell proliferation and peptidase activity. These results support previous findings regarding the effect of HE4/WFDC2 on ovarian cancer pathogenesis in cell lines and mouse models, while adding another layer of complexity to its potential functions in ovarian tumor tissue. Further experimental explorations of these findings in the context of the tumor microenvironment are merited.

Keywords: HE4; Ovarian cancer; SLPI; The Cancer Genome Atlas; Tumor microenvironment; WFDC2.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
HE4 (WFDC2) expression correlates with clinical survival outcomes A WFDC2 mRNA and HE4 protein levels were correlated by Spearman rank correlation analysis using all samples of the Firehose Legacy cohort with mRNA (RNA Seq V2 RSEM) and protein (mass spectrometry by CPTAC) levels available (n = 105), showing strong correlation between mRNA and protein. B Putative copy number alterations (CNAs) were plotted against WFDC2 mRNA (RNA Seq V2 RSEM) levels in the Firehose Legacy cohort. A majority of samples were diploid (n = 95) or had CNA gains (n = 169). Median mRNA levels generally corresponded to CNAs. C WFDC2 mRNA levels (RNA Seq V2 RSEM) were correlated to mutation counts using Spearman rank correlation (left panel). Firehose Legacy TCGA samples were split into low (8–43) and high (43–158) mutation count groups and median WFDC2 mRNA levels (RNA Seq V2 RSEM and U133 microarray) determined for each group (right panels). There was a small, non-significant inverse correlation between WFDC2 and mutation count, as well as a small, but significant decrease in WFDC2 mRNA levels in patients with fewer mutations. D Kaplan–Meier curves for overall survival and progression-free survival were determined for WFDC2 using all cohorts (GEO Series and TCGA) available for ovarian cancer at http://KMplot.com. Analysis was restricted to serous Stage III and IV, grade 2 and 3. Patients with higher WFDC2 levels had worse overall survival. E Kaplan–Meier curves for overall survival and progression-free survival were determined for WFDC2 using all cohorts (GEO Series and TCGA) available for ovarian cancer at http://KMplot.com. Analysis was restricted to serous Stage III and IV, grade 2 and 3, optimally debulked only. Patients with higher WFDC2 levels had worse overall survival and progression-free survival
Fig. 2
Fig. 2
Differential gene expression reveals a positive correlation of WFDC2 and SLPI across many cancers. A Principal component analysis (PCA) of all TCGA-OV samples. B Volcano plot analysis showing top five differentially expressed genes (DEGs) between high- and low-WFDC2 levels. Protein-coding genes significantly changed (p-adj. < 0.05) with log2 fold-change ≥ 0.5 in either direction are shown as red dots. C All DEGs were correlated with WFDC2 in cBioPortal. Log2 fold-change (log2 FC) and Spearman r-values are represented in a heat map side-by-side comparison for all genes that significantly correlated with WFDC2 ≥ 0.3 in either direction. Fold-change data versus correlation data show a high degree of similarity. D, E SLPI was determined to be the high-WFDC2 DEG that most strongly correlated with WFDC2 in the Firehose Legacy cohort, while AKAP12 was the low-WFDC2 DEG most negatively correlated with WFDC2. Average FPKM values for SLPI and AKAP12 were plotted for WFDC2-high versus WFDC2-low samples. ****p < 0.0001 F, G Spearman rank correlations are shown for SLPI and AKAP12. H Pan-cancer Spearman rank correlation analysis of WFDC2 and SLPI
Fig. 3
Fig. 3
Gene ontology analysis implicates metabolism and extracellular matrix correlations with WFDC2 mRNA expression. A Gene ontology analysis was performed for all DEGs associated with high-WFDC2, revealing enrichment in categories related to metabolism/oxidative phosphorylation. Number of genes in each category (“Count”) are indicated by circle size, while adjusted p-value (“p.adjust”) is indicated by color. B Gene ontology analysis was performed for all differentially expressed genes associated with low-WFDC2, revealing enrichment in categories related to extracellular matrix, vascular development, and proliferation. Number of genes in each category (“Count”) are indicated by circle size, while adjusted p-value (“p.adjust”) is indicated by color
Fig. 4
Fig. 4
Survival outcomes related to top correlated DEGs. Kaplan–Meier curves were generated at http://KMplot.com for all available datasets (TCGA and GEO Series). Top DEGs that were most associated with overall survival (OS) are shown in A–L (hazard ratio (HR) > 1.5; p < 0.01). M All NADH:Ubiquinone (NDU) genes were combined and analyzed by Kaplan–Meier. N Summary of hazard ratios
Fig. 5
Fig. 5
WFDC2 levels influence immune cell infiltration. TIMER 2.0 was used to determine the relationship between immune cell infiltration and WFDC2 transcripts per million (TPM) in TCGA-OV dataset. Purity correction was performed for all analyses (A). Significantly correlated immune subsets using the indicated algorithm (TIMER, XCELL, MCPCOUNTER, or EPIC) are shown in B–F
Fig. 6
Fig. 6
Comparison of TCGA data with DepMap ovarian cancer cell line data. A, B Pearson correlation of SLPI and PI3 (elafin) with WFDC2 TPMs in DepMap HGSOC cell lines. C Principal component analysis (PCA) of the top five low- and high-WFDC2 expressing HGSOC cell lines. D Two k-means clustering analysis of the top five low- and high-WFDC2 expressing cell lines, using the 500 most variable genes. E Gene ontology analysis was performed for Cluster B genes (genes associated with low-WFDC2 in DepMap HGSOC cell lines), revealing enrichment in categories related to extracellular matrix and adhesion. Number of genes in each category (“Count”) are indicated by circle size, while adjusted p-value (“p.adjust”) is indicated by color. F Gene ontology analysis was performed for k-means cluster A genes (genes associated with high-WFDC2 in DepMap HGSOC cell lines), revealing enrichment in categories related to epidermis development, proliferation, and peptidase activity. Number of genes in each category (“Count”) are indicated by circle size, while adjusted p-value (“p.adjust”) is indicated by color
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References

    1. Hellström I, et al. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res. 2003;63:3695–3700. - PubMed
    1. Moore RG, et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol Oncol. 2009;112:40–46. - PMC - PubMed
    1. James NE, et al. Beyond the biomarker: understanding the diverse roles of human epididymis protein 4 in the pathogenesis of epithelial ovarian cancer. Front Oncol. 2018;8:124. - PMC - PubMed
    1. Ribeiro JR, et al. HE4 promotes collateral resistance to cisplatin and paclitaxel in ovarian cancer cells. J Ovarian Res. 2016;9:28. - PMC - PubMed
    1. Ribeiro JR, et al. Human epididymis protein 4 promotes events associated with metastatic ovarian cancerviaregulation of the extracelluar matrix. Front Oncol. 2017;7:332. - PMC - PubMed