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. 2022 Mar 14:12:840038.
doi: 10.3389/fonc.2022.840038. eCollection 2022.

WASF2 Serves as a Potential Biomarker and Therapeutic Target in Ovarian Cancer: A Pan-Cancer Analysis

Xiaofeng Yang  1 , Yuzhen Ding  1 , Lu Sun  1 , Meiting Shi  1 , Ping Zhang  1 , Andong He  1 , Xiaotan Zhang  2 , Zhengrui Huang  1 , Ruiman Li  1
Affiliations

WASF2 Serves as a Potential Biomarker and Therapeutic Target in Ovarian Cancer: A Pan-Cancer Analysis

Xiaofeng Yang et al. Front Oncol. .

Abstract

Background: Wiskott-Aldrich syndrome protein family member 2 (WASF2) has been shown to play an important role in many types of cancer. Therefore, it is worthwhile to further study expression profile of WASF2 in human cancer, which provides new molecular clues about the pathogenesis of ovarian cancer.

Methods: We used a series of bioinformatics methods to comprehensively analyze the relationship between WASF2 and prognosis, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and tried to find the potential biological processes of WASF2 in ovarian cancer. Biological behaviors of ovarian cancer cells were investigated through CCK8 assay, scratch test and transwell assay. We also compared WASF2 expression between epithelial ovarian cancer tissues and normal ovarian tissues by using immunohistochemical staining.

Results: In the present study, we found that WASF2 was abnormally expressed across the diverse cancer and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the WASF2 expression level also significantly related to the TME. Our results also showed that the expression of WASF2 was closely related to immune infiltration and immune-related genes. In addition, WASF2 expression was associated with TMB, MSI, and antitumor drugs sensitivity across various cancer types. Functional bioinformatics analysis demonstrated that the WASF2 might be involved in several signaling pathways and biological processes of ovarian cancer. A risk factor model was found to be predictive for OS in ovarian cancer based on the expression of WASF2. Moreover, in vitro experiments, it was demonstrated that the proliferative, migratory and invasive capacity of ovarian cancer cells was significantly inhibited due to WASF2 knockdown. Finally, the immunohistochemistry data confirmed that WASF2 were highly expressed in ovarian cancer.

Conclusions: Our study demonstrated that WASF2 expression was associated with a poor prognosis and may be involved in the development of ovarian cancer, which might be explored as a potential prognostic marker and new targeted treatments.

Keywords: WASF2; biomarker; immune; pan-cancer; therapeutic.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The WASF2 expression level in human pan-cancer analyses. (A) The mRNA level of WASF2 in TCGA. The color refers to the tumor (yellow) or normal (blue), respectively. (B) The WASF2 expression level in 33 types from the GTEx database and TCGA database. (C) WASF2 expression in 30 tumor cells from CCLE database. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 2
Figure 2
The box plot shows the association of WASF2 expression with pathological stages in (A) adrenocortical carcinoma (ACC), (B) bladder urothelial carcinoma (BLCA), (C) breast invasive carcinoma (BRCA), (D) cholangiocarcinoma (CHOL), (E) colon adenocarcinoma (COAD), (F) esophageal carcinoma (ESCA), (G) head and neck squamous cell carcinoma (HNSC), (H) kidney chromophobe (KICH), (I) kidney renal clear cell carcinoma (KIRC), (J) kidney renal papillary cell carcinoma (KIRP), (K) liver hepatocellular carcinoma (LIHC), (L) lung adenocarcinoma (LUAD), (M) lung squamous cell carcinoma (LUSC), (N) mesothelioma (MESO), (O) pancreatic adenocarcinoma (PAAD), (P) rectum adenocarcinoma (READ), (Q) skin cutaneous melanoma (SKCM), (R) stomach adenocarcinoma (STAD), (S) testicular germ cell tumors (TGCT), and (T) uveal melanoma (UVM). Kruskal-Wallis test was used to assess the significance of differences between groups, followed by pair wise comparisons using Dunn’s multiple comparisons test used to evaluate differences among groups.
Figure 3
Figure 3
Association of WASF2 expression with patient overall survival (OS) in pan- cancer. (A) Forest plot of HR for the relationship between WASF2 expression and patient OS. (B–J) Kaplan-Meier analyses show the association between WASF2 expression and OS. Statistical significance was assessed using the log-rank test.
Figure 4
Figure 4
Association of WASF2 expression with patient progression-free interval (PFI) in pan- cancer. (A) Forest plot of HR for the relationship between WASF2 expression and patient PFI. (B–K) Kaplan-Meier analyses show the association between WASF2 expression and PFI. Statistical significance was assessed using the log-rank test.
Figure 5
Figure 5
WASF2 expression is correlated with the TME. (A) Correlation between the expression of WASF2 and 15 TME processes. Red denotes a correlation coefficient > 0, whereas blue denotes a correlation coefficient < 0. (B) The statistical chart after using the CIBERSORT method shows the proportion difference of TME between WASF2 high and low expression groups in ovarian cancer. Red represents the high WASF2 expression group, yellow represents the low WASF2 expression group. *P < 0.05; **P < 0.01; ***P < 0.001. ns, no significant.
Figure 6
Figure 6
WASF2 expression is correlated with cancer immunity. (A) Correlation between the expression of WASF2 and infiltration by 22 types of immune cells in pan-cancer analysis. Red denotes a correlation coefficient > 0, whereas blue denotes a correlation coefficient < 0. (B) The statistical chart after using the CIBERSORT method shows the proportion difference of immune cell between WASF2 high and low expression groups in ovarian cancer. Red represents the high WASF2 expression group, yellow represents the low WASF2 expression group. *P < 0.05; **P < 0.01; ***P < 0.001. ns, no significant.
Figure 7
Figure 7
WASF2 expression is correlated with immune-related genes. (A) The correlation between WASF2 and chemokine gene. (B) The correlation between WASF2 and immune checkpoint gene. (C) The correlation between WASF2 and immunoinhibitor gene. (D) The correlation between WASF2 and immunostimulator gene. (E) The correlation between WASF2 and MHC gene. (F) The correlation between WASF2 and receptor gene. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 8
Figure 8
WASF2 expression is correlated with TMB, MSI and drug sensitivity. (A) Correlation analysis between WASF2 expression in pan-cancer and TMB described using Spearman’s rank correlation coefficient. (B) Correlation analysis between WASF2 expression in pan-cancer and MSI described using Spearman’s rank correlation coefficient. (C) Analysis of drug sensitivity associated with WASF2. The positive correlation means that the gene’s high expression is resistant to the drug, while the negative is the opposite. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 9
Figure 9
Function and pathway enrichment analysis of ovarian cancer. (A) Correlation analysis results of GSVA and WASF2 in ovarian cancer. (B) KEGG results of WASF2 GSEA in ovarian cancer.
Figure 10
Figure 10
Association of WASF2 expression with WGCNA in ovarian cancer. (A) Module trait relationship (p-value) for detected modules (y-axis) in relation with traits (x-axis) for ovarian cancer. The relationships were colored based on the correlation between the identified module and traits. The color scale on the right demonstrate module-trait relationship from −1 (blue) to one (red), where blue represents strong negative correlation and red represents a strong positive correlation. (B) Functional annotations of MEsalmon module. Gene ontology (GO) and corresponding P-values are shown. (C) PPI network of GO enrichment analysis results.
Figure 11
Figure 11
Establishment and validation of the prognostic nomogram. (A) Nomogram based on the WASF2 signature and clinical information for prediction of the 3- and 5-year OS in patients with ovarian cancer in the TCGA dataset. (B) The calibration curves is used to verify the consistency of predicted and actual 3-, 5-year outcomes.
Figure 12
Figure 12
Knockdown of WASF2 inhibits the proliferation, migration and invasion capacities of ovarian cancer cells in vitro. (A) Western blot analysis of transfection efficiency of the siRNA. (B) Cell proliferation was detected by using the CCK8 proliferation reagent. (C) WASF2 knockdown suppressed filopodia formation. (D, E) Effect of WASF2 knockdown in cell migration was determined by wound healing assay and the percentage of scratch-width closure measured by quantifying the images the scratch assay at 0, 6 and 12 h after incubation. (F, G) Invasiveness of ovarian cancer cells analyzed by transwell invasion assay (magnification, ×200) and bar graph showing quantitative results of the transwell assay. (H, I) Representative immunohistochemical staining of ovarian cancer tissues using anti-WASF2 antibody showing high expression of WASF2. *P < 0.05; **P < 0.01; ***P < 0.001. Scale bars = 50μm in H.
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