Open Access

Mammaglobin B may be a prognostic biomarker of uterine corpus endometrial cancer

  • Authors:
    • Jie Li
    • Wenwen Xu
    • Yuxi Zhu
  • View Affiliations

  • Published online on: September 18, 2020     https://doi.org/10.3892/ol.2020.12118
  • Article Number: 255
  • Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Mammaglobin B, also referred to as secretoglobin family 2A member 1 (SCGB2A1), has been reported to be highly expressed in uterine corpus endometrial cancer (UCEC) compared with in the normal endometrium. However, the prognostic value of SCGB2A1 in UCEC remains unclear. The Oncomine, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium databases were used to explore the differential expression of SCGB2A1. Furthermore, data of patients with UCEC were downloaded from TCGA, and logistic regression analysis, survival analysis, univariate and multivariate analyses, and nomogram construction were performed to identify its prognostic value in UCEC. Additionally, gene set enrichment analysis (GSEA) was utilized to estimate the mechanisms of SCGB2A1 in UCEC. Finally, immune infiltration of SCGB2A1 in UCEC was analyzed using the Tumor Immune Estimation Resource. Decreased mRNA and protein expression levels of SCGB2A1 were significantly associated with poor prognostic clinicopathological characteristics (all P<0.05). Additionally, low expression levels of SCGB2A1 were associated with decreased survival of patients with UCEC compared with high expression levels of SCGB2A1. Furthermore, the independent prognostic value of SCGB2A1 in UCEC was identified by univariate and multivariate analyses. A nomogram based on 6 variables, including SCGB2A1 expression, was developed for the estimation of the 1‑, 3‑, and 5‑year survival probability in UCEC. Additionally, GSEA suggested that the vascular endothelial growth factor, PTEN, platelet‑derived growth factor, DNA repair, KRAS signaling, and PI3K‑AKT‑mTOR signaling pathways were differentially enriched in the low SCGB2A1 expression phenotype. Finally, high infiltration levels of CD8+ T cells were associated with SCGB2A1 in UCEC and this was associated with prognosis. The present results indicated that SCGB2A1 may be a promising independent prognostic factor in UCEC. These signaling pathways may be crucial for the regulation of UCEC via SCGB2A1.

Introduction

Uterine corpus endometrial cancer (UCEC) is the second most prevalent type of malignancy among women in the United States of America (1). Despite the rapid development of the modern medical industry, the mortality of UCEC has been continuously increasing (2). Due to a lack of effective therapeutic strategies, the 5-year survival rate of patients with advanced-stage disease is only 16%. However, patients diagnosed at an early stage have a favorable prognosis (3,4). Recently, cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) have been utilized as serum biomarkers in UCEC; however, they only have modest effects due to relatively low predictive accuracy (57). Therefore, it is necessary to identify reliable molecular biomarkers to predict prognosis, guide treatments and monitor recurrence.

Mammaglobin B, also referred to as secretoglobin family 2A member 1 (SCGB2A1), is a member of the uteroglobin superfamily which is localized on chromosome 11q12.2 and includes nine human secretoglobins (8,9). SCGB2A1 was first isolated from the human endometrium, and it is highly homologous to mammaglobin A (secretoglobin family 2A member 2) (10). Although its biological function has not been clarified, the differential expression and specific significance of SCGB2A1 in various malignancies have been reported (11). SCGB2A1 has been identified as a candidate biomarker for the detection of lymph node micrometastases in breast cancer (12,13) and abdominal cancer types (14). In addition, SCGB2A1 has been considered as a promising diagnostic marker for occult tumor cells in effusions of several malignancies (15,16) and as a potential immunotherapeutic target in ovarian cancer (17). However, to the best of our knowledge, the prognostic value of SCGB2A1 in UCEC has not been reported, although Tassi et al (18) observed the overexpression of SCGB2A1 in endometrioid endometrial cancer.

The present study assessed the prognostic significance of SCGB2A1 in UCEC using bioinformatics. Additionally, gene set enrichment analysis (GSEA) was performed to further explore the function of SCGB2A1. A number of other databases were utilized to explore the significance of SCGB2A1 in transcriptomics, proteomics, and the immune microenvironment. In conclusion, the present study may provide further insights into potential therapeutic targets in UCEC.

Materials and methods

Oncomine database analysis

The Oncomine database (http://www.oncomine.com) (19) was utilized to compare the differential expression levels of SCGB2A1 between tumor and normal tissues in various tumor types. The threshold was set according to the following values: P<0.0001; fold change >2; and gene ranking of all.

Clinical Proteomic Tumor Analysis Consortium (CPTAC) database analysis

The CPTAC database enables large-scale proteome and genome analyses, in order to understand the molecular basis of cancer (20). UALCAN (http://ualcan.path.uab.edu) (21), a comprehensive web resource for analyzing cancer-omics data, includes CPTAC analysis for various tumor types. The analysis of protein expression levels of SCGB2A1 in UCEC was performed by UALCAN based on the CPTAC database. UALCAN performed the comparison of differential expression between each two groups by using t-tests (22), and similar results from the UALCAN using the same statistical methods have been published previously (2325). Differential protein expression of SCGB2A1 between UCEC and normal tissues, and the association between clinical characteristics and protein expression levels of SCGB2A1, were analyzed. Additionally, all P-values from the UALCAN were adjusted using Bonferroni's correction.

Tumor Immune Estimation Resource (TIMER) analysis

TIMER (https://cistrome.shinyapps.io/timer/) (26) is a tool for the systematic analysis of tumor-infiltrating immune cells (TIICs) across diverse types of cancer in The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) (27). TIMER consists of several modules: The ‘DiffExp’ module provides the differential expression between tumor and adjacent normal tissues for genes in TCGA; the ‘Gene’ module provides visualization of the association between gene expression and tumor purity and immune infiltration levels in tumors; the ‘Survival’ module provides survival curves of TIICs at high and low levels and genes in specific tumors; and the ‘SCNA’ module provides the comparison of tumor infiltration levels among tumors with different somatic copy number alterations (SCNAs) for a given gene. Defined by Genomic Identification of Significant Targets in Cancer 2.0 (28,29), SCNAs include deep deletion (−2), arm-level deletion (−1), diploid/normal (0), arm-level gain (1) and high amplification (2). The infiltration level for each SCNA category in UCEC was compared with that in normal tissues using a Wilcoxon rank-sum test. SCGB2A1 was analyzed using the ‘DiffExp’, ‘Gene’, ‘Survival’, and ‘SCNA’ modules.

Downloaded data

RNA-sequencing (RNA-seq) expression data of UCEC and corresponding clinical data were downloaded from TCGA. The details of RNA-seq data were as follows: Project, TCGA-UCEC; data category, transcriptome profiling; data type, gene expression quantification; workflow type, HTSeq-FPKM. Furthermore, data of normal samples were excluded.

Statistical analysis and nomogram construction

Statistical analysis was performed using R software (v.3.6.2) (30). Expression differences for discrete variables were visualized using boxplots and the survival curve was drawn using the survival package (https://cran.r-project.org/web/views/Survival.html). The association between clinical characteristics and SCGB2A1 expression was determined by logistic regression analysis. Notably, the median value of SCGB2A1 expression was set as the cut-off value. Furthermore, univariate Cox analysis was used to estimate the prognostic value of certain clinicopathologic variables, including age, BMI, grade, stage, peritoneal cytology, pelvic lymph node status, para-aortic lymph node status, histological subtype, myometrial invasion, residual tumor and tumor status. Additionally, multivariate Cox analysis was performed to identify the independent prognostic value of SCGB2A1 with stage, peritoneal cytology, pelvic lymph node status, myometrial invasion, and tumor status.

Following integration of the results of univariate and multivariate Cox analysis, 6 variables (stage, tumor status, peritoneal cytology, pelvic lymph node status, myometrial invasion, and SCGB2A1 expression) were selected for nomogram construction. The rms package (https://cran.r-project.org/web/packages/rms/index.html) in R was used to construct the nomogram.

GSEA

The present study performed GSEA (31), which determines whether an a priori defined set of genes indicates statistically significant differences between 2 biological states, to identify the potential mechanism of SCGB2A1 in UCEC. In the present study, GSEA software v3.0 was used to analyze the ‘h.all.v6.2.symbols.gmt’ and ‘c2.cp.biocarta.v6.2.symbols.gmt’ gene sets from the Molecular Signatures Database (32). Based on the expression levels of SCGB2A1, ‘high’ and ‘low’ were applied as phenotype labels. For each analysis, 1,000 gene set permutations were run to obtain the normalized enrichment score (NES). False discovery rate <0.25 and normal P<0.05, were used as the cut-off to identify the significantly enriched gene sets.

Results

Pan-cancer analysis of SCGB2A1 mRNA expression in different databases

The Oncomine and TCGA databases were utilized to determine the mRNA expression levels of SCGB2A1 in tumor and normal tissues in different tumor types. According to the Oncomine database, SCGB2A1 was expressed at low levels in breast, colorectal, gastric, and kidney cancer, melanoma, ovarian and prostate cancer, and sarcoma, whereas overexpression of SCGB2A1 was identified in breast, esophageal, kidney, and ovarian cancer in some analyses (P<0.0001; Fig. 1A). Detailed information of SCGB2A1 expression in various cancer types based on the Oncomine database is shown in Table SI. In addition, all tumor and adjacent normal tissues in TCGA were analyzed to further comprehend the differential expression of SCGB2A1 (Fig. 1B). The results revealed that SCGB2A1 expression was markedly decreased in breast invasive carcinoma, colon adenocarcinoma, esophageal carcinoma, head and neck cancer, kidney renal clear cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, prostate, rectum, and stomach adenocarcinoma, and thyroid carcinoma compared with in adjacent normal tissues. However, SCGB2A1 expression was markedly increased in cholangiocarcinoma, liver hepatocellular carcinoma and UCEC tissues compared with in adjacent normal tissues.

Patient characteristics

Gene expression and clinical data of 545 primary tumors from the TCGA-UCEC project were downloaded in June 2019. After discarding unqualified samples with apparently abnormal data or gene expression data missing (Table SII), the data of 540 patients were retained for further analysis. Notably, a 64-year-old female patient was identified in the database with a weight of 93 kg, but her height was recorded as only 66 cm. As the accuracy of these data could not be verified, the data of this patient was excluded in a previous study (33). Therefore, this data was defined as apparently abnormal data in the present study. The clinicopathological characteristics of these patients, including age, BMI, grade, stage, peritoneal cytology status, lymph node status, histology, myometrial invasion, tumor status, residual tumor, and surgery approach, are shown in Table I. The median age of these patients was 64 years old, ranging between 31 and 90 years old, while the median BMI was 32.2, ranging between 17.4 and 81.6.

Table I.

Clinical characteristics of patients with uterine corpus endometrial cancer (n=540) downloaded from The Cancer Genome Atlas database.

Table I.

Clinical characteristics of patients with uterine corpus endometrial cancer (n=540) downloaded from The Cancer Genome Atlas database.

Clinical characteristicsValue%
Median age (range), years64 (31–90)
Median BMI (range)32.2 (17.4–81.6)
Grade, n
  19718.3
  212022.7
  331259.0
Stage, n
  I33762.4
  II519.4
  III12322.8
  IV295.4
Peritoneal cytology, n
  Negative34986.0
  Positive5714.0
Pelvic lymph nodes, n
  Negative36683.2
  Positive7416.8
Para-aortic lymph nodes, n
  Negative32789.6
  Positive3810.4
Histology, n
  Endometrioid40474.8
  Mixed serous and endometrioid224.1
  Serous11421.1
Myometrial invasion, n
  ≤50%31467.1
  >50%15432.9
Status, n
  With tumor7815.5
  Tumor-free42584.5
Residual tumor, n
  R037090.7
  R1225.4
  R2163.9
Surgical approach, n
  Minimally invasive20138.8
  Open31761.2

[i] BMI, body mass index.

mRNA expression levels of SCGB2A1 in UCEC according to TCGA

As shown in Fig. 2A and S1, the expression levels of SCGB2A1 in normal tissues were significantly decreased compared with those in UCEC, G3 cancer, stage III or IV, with tumors, and peritoneal cytology-positive tissues (P<0.05), and no significant differential expression was identified between normal tissues and serous endometrial adenocarcinoma and stage IV tissues. Furthermore, the association between SCGB2A1 expression and clinicopathological variables in UCEC was analyzed using boxplots. The results indicated that the decreased expression levels of SCGB2A1 were significantly associated with the grade (P<0.001), stage (P<0.001), tumor status (P<0.001), histological subtype (P<0.001) and peritoneal cytology status (P=0.005) (Fig. 2B-F). Additionally, the results of the logistic regression analysis revealed that decreased expression levels of SCGB2A1 were significantly associated with poor prognostic clinicopathological features, including grade [odds ratio (OR)=0.11 for grade 3 vs. grade 1 or 2; P<0.001], stage (OR=0.35 for stage III or IV vs. stage I or II; P<0.001), peritoneal cytology status (OR=0.37 for positive vs. negative; P=0.001), pelvic lymph node status (OR=0.26 for positive vs. negative; P<0.001), para-aortic lymph node status (OR=0.49 for positive vs. negative; P=0.045), histological subtype (OR=0.09 for serous vs. endometrioid; P<0.001), myometrial invasion (OR=0.47 for >50 vs. ≤50%; P<0.001), status (OR=0.31 for with tumor vs. tumor-free; P<0.001) and residual tumor (OR=0.49 for R1 or R2 vs. R0; P=0.044) (Table II).

Table II.

Logistic regression on the association between SCGB2A1 expression and clinical pathological characteristics.

Table II.

Logistic regression on the association between SCGB2A1 expression and clinical pathological characteristics.

Clinical characteristicsTotal (N)Odds ratio in SCGB2A1 expressionP-value
Age (continuous)5380.96 (0.94–0.98) <0.01a
BMI (continuous)5091.04 (1.02–1.07) <0.01a
Grade (3 vs. 1 or 2)5290.11 (0.08–0.17) <0.01a
Stage (III or IV vs. I or II)5400.35 (0.23–0.51) <0.01a
Peritoneal cytology (positive vs. negative)4060.37 (0.20–0.67)0.001a
Pelvic lymph nodes (positive vs. negative)4400.26 (0.14–0.45) <0.01a
Para-aortic lymph nodes (positive vs. negative)3650.49 (0.23–0.97)0.045a
Histology (serous vs. endometrioid)5180.09 (0.05–0.16) <0.01a
Myometrial invasion (>50vs. ≤50%)4680.47 (0.32–0.70) <0.01a
Status (with tumor vs. tumor free)5030.31 (0.18–0.53) <0.01a
Residual tumor (R1 or R2 vs. R0)4080.49 (0.24–0.97)0.044a
Surgical approach (open vs. minimally invasive)5180.95 (0.67–1.36)0.787

a P<0.05. SCGB2A1, secretoglobin family 2A member 1.

Protein expression levels of SCGB2A1 in UCEC according to CPTAC database

Analysis of the protein expression levels of SCGB2A1 in UCEC was performed by UALCAN based on the CPTAC database. As shown in Fig. 3A, the protein expression levels of SCGB2A1 in UCEC were significantly increased compared with those in normal tissues (P<0.05). Furthermore, the association between SCGB2A1 protein expression and clinicopathological variables in UCEC is shown in Fig. 3B-E. The results revealed that decreased protein expression levels of SCGB2A1 were associated with high grade (P<0.05). No significant association was identified between decreased protein expression levels of SCGB2A1 and serous histological subtype, advanced stage and advanced age.

Analysis of the prognostic value of SCGB2A1 mRNA expression and clinicopathological variables in UCEC

The survival curve suggested that low expression levels of SCGB2A1 were associated with poor prognosis in UCEC (Fig. 4A). Furthermore, the prognostic value of SCGB2A1 was estimated by univariate Cox analysis (Table III). It was revealed that low expression levels of SCGB2A1, advanced stage, positive peritoneal cytology status and pelvic lymph node status, deep myometrial invasion, ‘with tumor status’ and residual tumor were associated with poor prognosis in UCEC (Table III). As defined in TCGA, ‘with tumor status’ meant that new tumors occurred after operation during the follow-up, while ‘tumor-free status’ meant that no new tumors occurred until the follow-up finished. Finally, multivariate Cox analysis was performed to estimate the independent prognostic value of SCGB2A1. Considering that residual tumor was uncommon in clinical practice, this variable was not included in the multivariate analysis. The results revealed that, in addition to stage, peritoneal cytology, pelvic lymph node status, myometrial invasion, and tumor status, SCGB2A1 was independently associated with poor prognosis in UCEC (hazard ratio, 0.88; P=0.025; Table III).

Table III.

Univariate and multivariate analyses of the association between SCGB2A1 expression with overall survival among patients with uterine corpus endometrial cancer.

Table III.

Univariate and multivariate analyses of the association between SCGB2A1 expression with overall survival among patients with uterine corpus endometrial cancer.

Univariate analysisMultivariate analysis


ParametersHR (95% CI)P-valueHR (95% CI)P-value
Age (continuous)1.03 (0.99–1.08)0.156
BMI (continuous)1.03 (0.97–1.09)0.284
Grade (3 vs. 1 or 2)1.91 (0.76–4.81)0.167
Stage (III or IV vs. I or II)5.62 (2.29–13.79)0.000a1.27 (0.50–3.22)0.615
Peritoneal cytology (positive vs. negative)4.21 (1.62–10.97)0.003a2.75 (1.27–5.97)   0.010a
Pelvic lymph nodes (positive vs. negative)1.58 (1.26–1.98)0.000a1.82 (0.77–4.33)0.174
Para-aortic lymph nodes (positive vs. negative)1.57 (0.36–6.78)0.546
Histology (serous vs. endometrioid)2.35 (0.90–6.14)0.081
Myometrial invasion (>50 vs. ≤50%)2.62 (1.09–6.31)0.032a1.51 (0.71–3.21)0.290
Status (with tumor vs. tumor-free)6.00 (2.49–14.43)0.000a3.93 (1.97–7.87) <0.01a
Residual tumor (R1 or R2 vs. R0)3.19 (1.16–8.77)0.025a
SCGB2A1 expression (continuous)0.82 (0.72–0.93)0.003a0.88 (0.79–0.98)0.025a

a P<0.05. SCGB2A1, secretoglobin family 2A member 1; HR, hazard ratio.

Construction of the nomogram

A nomogram was constructed for the prediction of 1-, 3-, and 5-year survival probabilities of patients with UCEC based on 6 variables, including stage, tumor status, myometrial invasion, peritoneal cytology, pelvic lymph node status, and SCGB2A1 expression (Fig. 4B). According to this nomogram, the variables corresponded to the respective points, and the sum of the six variable points was defined as the total points. Additionally, the estimated 1-, 3-, and 5-year survival probability could be obtained based on the total points.

GSEA

Based on the value of the NES, the most significantly enriched signaling pathways were selected. As demonstrated in Fig. 5, the vascular endothelial growth factor (VEGF) pathway, PTEN pathway, platelet-derived growth factor (PDGF) pathway, DNA repair, coactivator associated arginine methyltransferase (CARM) and estrogen receptor (ER) pathway, KRAS signaling pathway, PI3K-AKT-mTOR signaling pathway, ataxia-telangiectasia and Rad3-related (ATR) and BRCA pathway, and G2M checkpoint were significantly enriched in the SCGB2A1 low-expression phenotype. The details are shown in Table IV.

Table IV.

Gene sets enriched in phenotype low.

Table IV.

Gene sets enriched in phenotype low.

MSigDB collectionGene set nameNESNOM P-valueFDR q-value
c2.cp.biocarta.v6.2.symbols.gmt BIOCARTA_VEGF_PATHWAY−1.6810.0270.072
BIOCARTA_PTEN_PATHWAY−1.7030.0250.070
BIOCARTA_PDGF_PATHWAY−1.8960.0000.042
BIOCARTA_ATRBRCA_PATHWAY−1.6900.0250.069
BIOCARTA_CARM_ER_PATHWAY−1.6980.0190.069
h.all.v6.2.symbols.gmt HALLMARK_DNA_REPAIR−1.7220.0440.069
HALLMARK_KRAS_SIGNALING_DN−1.6750.0090.073
HALLMARK_PI3K_AKT_MTOR_SIGNALING−1.7770.0080.057
HALLMARK_G2M_CHECKPOINT−2.2780.0000.005

[i] MSigDB, Molecular Signatures Database; VEGF, vascular endothelial growth factor; PDGF, platelet-derived growth factor; ATR, ataxia-telangiectasia and Rad3-related; CARM, coactivator associated arginine methyltransferase; ER, estrogen receptor; FDR, false discovery rate; NES, normalized enrichment score; NOM, nominal.

Systematic analysis of immune infiltrates associated with SCGB2A1 mRNA expression in UCEC

TIMER was used to further investigate the association between SCGB2A1 and immune infiltration in UCEC. SCGB2A1 exhibited a significant positive association with the infiltration level of CD8+ T cells (P<0.05) and macrophages (P<0.05), and a negative association with neutrophils (P<0.05) (Fig. 6A). Furthermore, high infiltration levels of B cells and CD8+ T cells were statistically significant in UCEC according to the cumulative survival analysis (P<0.05; Fig. 6B). Finally, the distribution of tumor infiltration levels in UCEC with different SCNAs for SCGB2A1 is shown in Fig. 6C. Compared with those in normal tissues, the infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells for high amplification in UCEC were significantly different (P<0.05). In addition, the infiltration levels of CD8+ T cells and dendritic cells for arm-level gain in UCEC were statistically different from those of the normal tissues (P<0.05).

Discussion

The present study revealed that decreased expression levels of SCGB2A1 were associated with poor prognostic clinicopathological characteristics and short survival time in UCEC. In addition, the significance of SCGB2A1 in transcriptomics, proteomics and the immune microenvironment was explored using Oncomine, CPTAC and TIMER. However, in certain cancer types, SCGB2A1 expression is controversial. In breast, kidney, and ovarian cancer, SCGB2A1 was identified to be highly expressed in some analyses, while in other analyses, it was identified to be expressed at low levels (Fig. 1A). Based on the detailed information in Table SI, it was proposed that different cancer subtypes and the number of samples may affect SCGB2A1 expression. Additionally, a nomogram based on 6 variables, including SCGB2A1 expression, was developed for the estimation of the 1-, 3-, and 5-year survival probability in UCEC. GSEA was utilized to further understand the function of SCGB2A1, which revealed that the VEGF, PTEN, and PDGF pathways, DNA repair, CARM and ER, KRAS, and PI3K-AKT-mTOR signaling pathways, and the ATR and BRCA pathway were differentially enriched in the low SCGB2A1 expression phenotype. These results suggested that SCGB2A1 may be considered as a candidate prognostic marker and a novel therapeutic target in UCEC.

As a member of the uteroglobin gene family, SCGB2A1 was first isolated from the human endometrium (10); however, it has rarely been investigated in UCEC. Tassi et al (18) reported that SCGB2A1 was upregulated in endometrioid endometrial cancer tissues compared with normal tissues; however, the aforementioned study presented some limitations due to a lack of prognostic analysis and subgroup analysis in UCEC. The present study revealed the differential expression of SCGB2A1 in UCEC, and that the mRNA and protein expression levels of SCGB2A1 in serous carcinoma were decreased compared with those in endometrioid carcinoma, which suggested that SCGB2A1 may be involved in the carcinogenesis of UCEC cells. Although no significant differential expression of SCGB2A1 was identified between normal tissues and serous carcinoma and stage IV cancer tissues, the expression levels of SCGB2A1 in normal tissues were significantly decreased compared with those in G3 cancer, stage III or IV, with tumor and peritoneal cytology-positive tissues (P<0.05). The specific mechanism requires further exploration. In UCEC, genetic alternations of KRAS and PTEN are common (34,35). PTEN is an essential tumor suppressor gene in UCEC (36), and changes in PTEN could result in disorders of the cell cycle, and abnormal proliferation and differentiation in carcinogenesis (37). As an oncogene, KRAS has a synergistic effect with PTEN in tumorigenesis and upregulates the expression levels of ER (38,39). Furthermore, the activation of the PI3K-AKT-mTOR signaling pathway via the ER signaling pathway results in cell proliferation (40). The present results revealed that SCGB2A1 was associated with the PTEN, KRAS, and PI3K-AKT-mTOR signaling pathways. Therefore, SCGB2A1 may be involved in the carcinogenesis of UCEC by mediating cell proliferation via these signaling pathways. Although these pathways have not been reported to be associated with SCGB2A1, further exploration is required.

In the past, the prognostic value of SCGB2A1 expression has been analyzed in some specific tumors. Higher expression levels of SCGB2A1 may decrease the risk of recurrence of epithelial ovarian cancer (9). However, upregulation of SCGB2A1 in colorectal cancer decreases the sensitivity to 5-fluorouracil and oxaliplatin, and promotes chemoresistance and radio-resistance, which results in poor prognosis (16). To the best of our knowledge, the prognostic value of SCGB2A1 in UCEC remains unclear. The results of the present study revealed that decreased SCGB2A1 expression was associated with short survival time in UCEC. Furthermore, a nomogram was constructed to predict the prognosis of patients with UCEC more accurately. Notably, SCGB2A1 expression levels decreased as age, stage, grade, and level of myometrial invasion increased, suggesting that SCGB2A1 may be associated with the progression of UCEC. Furthermore, SCGB2A1 was downregulated in samples with positive peritoneal cytology, and positive pelvic lymph node and para-aortic lymph node statuses, and upregulated in the samples with negative statuses of these indicators. It has been acknowledged that angiogenesis is a common process in the development of tumors, including UCEC (41,42). VEGF acts as a key mediator of tumor angiogenesis, and it is upregulated by the induction of several growth factors and hypoxia (43,44). In addition, overexpression of VEGF in UCEC has been reported to be associated with deep myometrial invasion and lymph node metastasis (45). Therefore, SCGB2A1 may be involved in the progression of UCEC by mediating angiogenesis via the VEGF signaling pathway. Additionally, serum biomarkers are critical during the management of patients with cancer in clinical practice, while advances in UCEC are limited. CA125 and HE4 have been identified as promising serum biomarkers in guiding the management of UCEC, but some limitations remain (46). Further analysis of serum levels of SCGB2A1 may prompt it to become a potential marker for monitoring the development of UCEC and predicting prognosis (47).

The present study performed immune infiltration analysis of SCGB2A1 in UCEC, and the levels of B cells, CD8+ T cells, macrophages and neutrophils were identified to be statistically significant. To the best of our knowledge, no studies have been reported regarding the association between SCGB2A1 and TIICs in UCEC, but there are some analyses regarding the effect of TIICs on UCEC (4850). A previous study revealed that high levels of CD8+ T lymphocytes are an independent favorable prognostic predictor in UCEC (51), which is consistent with the results of the present study. A high density of macrophages is associated with type 2 endometrial cancer (52), and tumor-associated macrophages have been reported to promote the invasion of UCEC cells (53). However, the present results indicated that the infiltration levels of macrophages were positively associated with SCGB2A1. As the immune infiltration analysis by TIMER was limited to the general scope of macrophages, further specific analysis is required.

One of the limitations of the present study was that it was primarily based on in silico analysis, while in vitro and in vivo experiments were lacking. The present study developed a multi-omics analysis and prognostic module, and several databases were utilized to validate the results. However, it remains necessary to conduct further assessments using in vitro and in vivo analyses. Furthermore, the validation of the feasibility of serum SCGB2A1 levels is also essential for clinical practice value.

In conclusion, low expression levels of SCGB2A1 in UCEC may predict poor prognosis, and these signaling pathways may be crucial for the regulatory effect of SCGB2A1 in UCEC. As the present results were primarily based on bioinformatics analysis, further studies are required to validate the role of SCGB2A1 in UCEC and to improve the understanding of the underlying mechanisms.

Supplementary Material

Supporting Data

Acknowledgements

The results shown here are based on the data generated by The Cancer Genome Atlas Research Network (http://cancergenome.nih.gov/).

Funding

No funding was received.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request

Authors' contributions

JL was responsible for the conception and design of the study, drafting the manuscript, and the acquisition, analysis, and interpretation of data. WX collected, analyzed and interpreted the data. YZ made substantial contributions to conception and design, and he contributed to revising this manuscript critically for important intellectual content and overall supervision. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM, Jemal A, Kramer JL and Siegel RL: Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin. 69:363–385. 2019. View Article : Google Scholar : PubMed/NCBI

2 

Siegel RL, Miller KD and Jemal A: Cancer statistics, 2019. CA Cancer J Clin. 69:7–34. 2019. View Article : Google Scholar : PubMed/NCBI

3 

Brooks RA, Fleming GF, Lastra RR, Lee NK, Moroney JW, Son CH, Tatebe K and Veneris JL: Current recommendations and recent progress in endometrial cancer. CA Cancer J Clin. 69:258–279. 2019.PubMed/NCBI

4 

Colombo N, Creutzberg C, Amant F, Bosse T, González-Martín A, Ledermann J, Marth C, Nout R, Querleu D, Mirza MR, et al: ESMO-ESGO-ESTRO consensus conference on endometrial cancer: Diagnosis, treatment and follow-up. Ann Oncol. 27:16–41. 2016. View Article : Google Scholar : PubMed/NCBI

5 

Reijnen C, IntHout J, Massuger LFAG, Strobbe F, Küsters-Vandevelde HVN, Haldorsen IS, Snijders MPLM and Pijnenborg JMA: Diagnostic accuracy of clinical biomarkers for preoperative prediction of lymph node metastasis in endometrial carcinoma: A systematic review and meta-analysis. Oncologist. 24:e880–e890. 2019. View Article : Google Scholar : PubMed/NCBI

6 

Lee YC, Lheureux S and Oza AM: Treatment strategies for endometrial cancer: Current practice and perspective. Curr Opin Obstet Gynecol. 29:47–58. 2017. View Article : Google Scholar : PubMed/NCBI

7 

Tewari KS, Burger RA, Enserro D, Norquist BM, Swisher EM, Brady MF, Bookman MA, Fleming GF, Huang H, Homesley HD, et al: Final overall survival of a randomized trial of bevacizumab for primary treatment of ovarian cancer. J Clin Oncol. 37:2317–2328. 2019. View Article : Google Scholar : PubMed/NCBI

8 

Ni J, Kalff-Suske M, Gentz R, Schageman J, Beato M and Klug J: All human genes of the uteroglobin family are localized on chromosome 11q12.2 and form a dense cluster. Ann N Y Acad Sci. 923:25–42. 2000. View Article : Google Scholar : PubMed/NCBI

9 

Tassi RA, Calza S, Ravaggi A, Bignotti E, Odicino FE, Tognon G, Donzelli C, Falchetti M, Rossi E, Todeschini P, et al: Mammaglobin B is an independent prognostic marker in epithelial ovarian cancer and its expression is associated with reduced risk of disease recurrence. BMC Cancer. 9:2532009. View Article : Google Scholar : PubMed/NCBI

10 

Becker RM, Darrow C, Zimonjic DB, Popescu NC, Watson MA and Fleming TP: Identification of mammaglobin B, a novel member of the uteroglobin gene family. Genomics. 54:70–78. 1998. View Article : Google Scholar : PubMed/NCBI

11 

Wong RL, Wang Q, Treviño LS, Bosland MC, Chen J, Medvedovic M, Prins GS, Kannan K, Ho SM and Walker CL: Identification of secretaglobin Scgb2a1 as a target for developmental reprogramming by BPA in the rat prostate. Epigenetics. 10:127–134. 2015. View Article : Google Scholar : PubMed/NCBI

12 

Ouellette RJ, Richard D and Maicas E: RT-PCR for mammaglobin genes, MGB1 and MGB2, identifies breast cancer micrometastases in sentinel lymph nodes. Am J Clin Pathol. 121:637–643. 2004. View Article : Google Scholar : PubMed/NCBI

13 

Hassan EM, Willmore WG, McKay BC and DeRosa MC: In vitro selections of mammaglobin A and mammaglobin B aptamers for the recognition of circulating breast tumor cells. Sci Rep. 7:144872017. View Article : Google Scholar : PubMed/NCBI

14 

Aihara T, Fujiwara Y, Miyake Y, Okami J, Okada Y, Iwao K, Sugita Y, Tomita N, Sakon M, Shiozaki H and Monden M: Mammaglobin B gene as a novel marker for lymph node micrometastasis in patients with abdominal cancers. Cancer Lett. 150:79–84. 2000. View Article : Google Scholar : PubMed/NCBI

15 

Fiegl M, Haun M, Massoner A, Krugmann J, Müller-Holzner E, Hack R, Hilbe W, Marth C, Duba HC, Gastl G and Grünewald K: Combination of cytology, fluorescence in situ hybridization for aneuploidy, and reverse-transcriptase polymerase chain reaction for human mammaglobin/mammaglobin B expression improves diagnosis of malignant effusions. J Clin Oncol. 22:474–483. 2004. View Article : Google Scholar : PubMed/NCBI

16 

Munakata K, Uemura M, Takemasa I, Ozaki M, Konno M, Nishimura J, Hata T, Mizushima T, Haraguchi N, Noura S, et al: SCGB2A1 is a novel prognostic marker for colorectal cancer associated with chemoresistance and radioresistance. Int J Oncol. 44:1521–1528. 2014. View Article : Google Scholar : PubMed/NCBI

17 

Bellone S, Tassi R, Betti M, English D, Cocco E, Gasparrini S, Bortolomai I, Black JD, Todeschini P, Romani C, et al: Mammaglobin B (SCGB2A1) is a novel tumour antigen highly differentially expressed in all major histological types of ovarian cancer: Implications for ovarian cancer immunotherapy. Br J Cancer. 109:462–471. 2013. View Article : Google Scholar : PubMed/NCBI

18 

Tassi RA, Bignotti E, Falchetti M, Calza S, Ravaggi A, Rossi E, Martinelli F, Bandiera E, Pecorelli S, Santin AD, et al: Mammaglobin B expression in human endometrial cancer. Int J Gynecol Cancer. 18:1090–1096. 2008. View Article : Google Scholar : PubMed/NCBI

19 

Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, et al: Oncomine 3.0: Genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 9:166–180. 2007. View Article : Google Scholar : PubMed/NCBI

20 

Boja E, Tezak Z, Zhang B, Wang P, Johanson E, Hinton D and Rodriguez H: Right data for right patient-a precisionFDA NCI-CPTAC Multi-omics mislabeling challenge. Nat Med. 24:1301–1302. 2018. View Article : Google Scholar : PubMed/NCBI

21 

Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI

22 

Chen F, Chandrashekar DS, Varambally S and Creighton CJ: Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers. Nat Commun. 10:56792019. View Article : Google Scholar : PubMed/NCBI

23 

Huo Q, Li Z, Cheng L, Yang F and Xie N: SIRT7 is a prognostic biomarker associated with immune infiltration in luminal breast cancer. Front Oncol. 10:6212020. View Article : Google Scholar : PubMed/NCBI

24 

Yang H, Gao S, Chen J and Lou W: UBE2I promotes metastasis and correlates with poor prognosis in hepatocellular carcinoma. Cancer Cell Int. 20:2342020. View Article : Google Scholar : PubMed/NCBI

25 

Chen W, Dai X, Chen Y, Tian F, Zhang Y, Zhang Q and Lu J: Significance of STAT3 in immune infiltration and drug response in cancer. Biomolecules. 10:8342020. View Article : Google Scholar

26 

Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B and Liu XS: TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77:e108–e110. 2017. View Article : Google Scholar : PubMed/NCBI

27 

Tomczak K, Czerwinska P and Wiznerowicz M: The cancer genome atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol (Pozn). 19A:A68–A77. 2015.

28 

Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina J, Boehm JS, Dobson J, Urashima M, et al: The landscape of somatic copy-number alteration across human cancers. Nature. 463:899–905. 2010. View Article : Google Scholar : PubMed/NCBI

29 

Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R and Getz G: GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12:R412011. View Article : Google Scholar : PubMed/NCBI

30 

Sepulveda JL: Using R and Bioconductor in clinical genomics and transcriptomics. J Mol Diagn. 22:3–20. 2020. View Article : Google Scholar : PubMed/NCBI

31 

Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, et al: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 34:267–273. 2003. View Article : Google Scholar : PubMed/NCBI

32 

Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P and Mesirov JP: Molecular signatures database (MSigDB) 3.0. Bioinformatics. 27:1739–1740. 2011. View Article : Google Scholar : PubMed/NCBI

33 

Dellinger TH, Smith DD, Ouyang C, Warden CD, Williams JC and Han ES: L1CAM is an independent predictor of poor survival in endometrial cancer-An analysis of the cancer genome atlas (TCGA). Gynecol Oncol. 141:336–340. 2016. View Article : Google Scholar : PubMed/NCBI

34 

Minaguchi T, Yoshikawa H, Oda K, Ishino T, Yasugi T, Onda T, Nakagawa S, Matsumoto K, Kawana K and Taketani Y: PTEN mutation located only outside exons 5, 6, and 7 is an independent predictor of favorable survival in endometrial carcinomas. Clin Cancer Res. 7:2636–2642. 2001.PubMed/NCBI

35 

Gibson WJ, Hoivik EA, Halle MK, Taylor-Weiner A, Cherniack AD, Berg A, Holst F, Zack TI, Werner HM, Staby KM, et al: The genomic landscape and evolution of endometrial carcinoma progression and abdominopelvic metastasis. Nat Genet. 48:848–855. 2016. View Article : Google Scholar : PubMed/NCBI

36 

Song MS, Salmena L and Pandolfi PP: The functions and regulation of the PTEN tumour suppressor. Nat Rev Mol Cell Biol. 13:283–296. 2012. View Article : Google Scholar : PubMed/NCBI

37 

Witek L, Janikowski T, Bodzek P, Olejek A and Mazurek U: Expression of tumor suppressor genes related to the cell cycle in endometrial cancer patients. Adv Med Sci. 61:317–324. 2016. View Article : Google Scholar : PubMed/NCBI

38 

Chen J, Zhao KN, Li R, Shao R and Chen C: Activation of PI3K/Akt/mTOR pathway and dual inhibitors of PI3K and mTOR in endometrial cancer. Curr Med Chem. 21:3070–3080. 2014. View Article : Google Scholar : PubMed/NCBI

39 

Tu Z, Gui L, Wang J, Li X, Sun P and Wei L: Tumorigenesis of K-ras mutation in human endometrial carcinoma via upregulation of estrogen receptor. Gynecol Oncol. 101:274–279. 2006. View Article : Google Scholar : PubMed/NCBI

40 

McDonald ME and Bender DP: Endometrial cancer: Obesity, genetics, and targeted agents. Obstet Gynecol Clin North Am. 46:89–105. 2019. View Article : Google Scholar : PubMed/NCBI

41 

Lee II, Maniar K, Lydon JP and Kim JJ: Akt regulates progesterone receptor B-dependent transcription and angiogenesis in endometrial cancer cells. Oncogene. 35:5191–5201. 2016. View Article : Google Scholar : PubMed/NCBI

42 

Vassileva V, Millar A, Briollais L, Chapman W and Bapat B: Genes involved in DNA repair are mutational targets in endometrial cancers with microsatellite instability. Cancer Res. 62:4095–4099. 2002.PubMed/NCBI

43 

Shweiki D, Itin A, Soffer D and Keshet E: Vascular endothelial growth factor induced by hypoxia may mediate hypoxia-initiated angiogenesis. Nature. 359:843–845. 1992. View Article : Google Scholar : PubMed/NCBI

44 

Carmeliet P: VEGF as a key mediator of angiogenesis in cancer. Oncology. 69 Suppl 3:S4–S10. 2005. View Article : Google Scholar

45 

Hirai M, Nakagawara A, Oosaki T, Hayashi Y, Hirono M and Yoshihara T: Expression of vascular endothelial growth factors (VEGF-A/VEGF-1 and VEGF-C/VEGF-2) in postmenopausal uterine endometrial carcinoma. Gynecol Oncol. 80:181–188. 2001. View Article : Google Scholar : PubMed/NCBI

46 

Di Cello A, Di Sanzo M, Perrone FM, Santamaria G, Rania E, Angotti E, Venturella R, Mancuso S, Zullo F, Cuda G and Costanzo F: DJ-1 is a reliable serum biomarker for discriminating high-risk endometrial cancer. Tumour Biol. 39:10104283177057462017. View Article : Google Scholar : PubMed/NCBI

47 

Bernstein JL, Godbold JH, Raptis G, Watson MA, Levinson B, Aaronson SA and Fleming TP: Identification of mammaglobin as a novel serum marker for breast cancer. Clin Cancer Res. 11:6528–6535. 2005. View Article : Google Scholar : PubMed/NCBI

48 

Holub K and Biete A: New pre-treatment eosinophil-related ratios as prognostic biomarkers for survival outcomes in endometrial cancer. BMC Cancer. 18:12802018. View Article : Google Scholar : PubMed/NCBI

49 

Ning C, Xie B, Zhang L, Li C, Shan W, Yang B, Luo X, Gu C, He Q, Jin H, et al: Infiltrating macrophages induce ERα expression through an IL17A-mediated epigenetic mechanism to sensitize endometrial cancer cells to estrogen. Cancer Res. 76:1354–1366. 2016. View Article : Google Scholar : PubMed/NCBI

50 

Chang WC, Li CH, Huang SC, Chang DY, Chou LY and Sheu BC: Clinical significance of regulatory T cells and CD8+ effector populations in patients with human endometrial carcinoma. Cancer. 116:5777–5788. 2010. View Article : Google Scholar : PubMed/NCBI

51 

Kondratiev S, Sabo E, Yakirevich E, Lavie O and Resnick MB: Intratumoral CD8+ T lymphocytes as a prognostic factor of survival in endometrial carcinoma. Clin Cancer Res. 10:4450–4456. 2004. View Article : Google Scholar : PubMed/NCBI

52 

Kelly MG, Francisco AM, Cimic A, Wofford A, Fitzgerald NC, Yu J and Taylor RN: Type 2 endometrial cancer is associated with a high density of tumor-associated macrophages in the stromal compartment. Reprod Sci. 22:948–953. 2015. View Article : Google Scholar : PubMed/NCBI

53 

Jing X, Peng J, Dou Y, Sun J, Ma C, Wang Q, Zhang L, Luo X, Kong B, Zhang Y, et al: Macrophage ERα promoted invasion of endometrial cancer cell by mTOR/KIF5B-mediated epithelial to mesenchymal transition. Immunol Cell Biol. 97:563–576. 2019. View Article : Google Scholar : PubMed/NCBI

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Volume 20 Issue 5

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Spandidos Publications style
Li J, Xu W and Zhu Y: Mammaglobin&nbsp;B may be a prognostic biomarker of uterine corpus endometrial cancer. Oncol Lett 20: 255, 2020
APA
Li, J., Xu, W., & Zhu, Y. (2020). Mammaglobin&nbsp;B may be a prognostic biomarker of uterine corpus endometrial cancer. Oncology Letters, 20, 255. https://doi.org/10.3892/ol.2020.12118
MLA
Li, J., Xu, W., Zhu, Y."Mammaglobin&nbsp;B may be a prognostic biomarker of uterine corpus endometrial cancer". Oncology Letters 20.5 (2020): 255.
Chicago
Li, J., Xu, W., Zhu, Y."Mammaglobin&nbsp;B may be a prognostic biomarker of uterine corpus endometrial cancer". Oncology Letters 20, no. 5 (2020): 255. https://doi.org/10.3892/ol.2020.12118