Introduction
Currently, breast cancer (BRCA) is the most common malignant tumor among women [
1]. The main treatment for BRCA is surgery, supplemented with radiotherapy, chemotherapy, and endocrine therapy. In addition, targeted molecular drugs are also emerging; these include drugs targeting human epidermal growth factor receptor-2 (HER2), inhibitors of poly ADP ribose polymerase (PARP), and inhibitors of the phosphoinositide 3-kinase/AKT/mammalian target of rapamycin (PI3K/Akt/mTOR) pathway [
2‐
5]. The emergence of these drugs has significantly prolonged the survival of patients. However, as BRCA is phenotypically and functionally heterogeneous, further research is still needed to identify new prognostic markers and therapeutic targets [
6].
Growth regulation by estrogen in breast cancer 1 like (GREB1L), an estrogen-regulated gene, is a coactivator of the retinoic acid receptor (RAR) gene, the activation of which regulates the RAR pathway. RAR signaling has been confirmed to trigger Müllerian epithelial cell differentiation and establish the border between the uterus and vagina. Thus, GREB1L plays an important role in developing the embryonic metanephros and reproductive tract [
7]. Additionally, some studies have shown that the mutation of GREB1L also plays a vital role in nonsyndromic inner ear malformations and deafness, familial and sporadic hereditary urogenital a dysplasia, and Mayer–Rokitansky–Kuster–Hauser syndrome [
8,
9]. Recent bioinformatics research suggested that GREB1L was associated with immune regulation and methylation in gastric and lung adenocarcinoma. Moreover, GREB1L is a novel predictive and prognostic biomarker of gastric and lung adenocarcinoma [
10,
11]. These studies suggest that GREB1L has multifaceted underlying functions and mechanisms in different malignancies. However, the relationship between GREB1L and BRCA is still unclear.
In our study, we used bioinformatics approaches, such as differential expression analysis, Kaplan‒Meier survival analysis, multivariate Cox regression analysis, coexpression gene analysis, and gene set enrichment analysis (GSEA), to reveal that GREB1L is highly expressed in BRCA tissues compared with normal breast tissues and that its upregulation is associated with a good prognosis. Moreover, we verified that the expression of GREB1L in BRCA tissues was higher than that in paired adjacent tissues, and its expression in BRCA cells was higher than that in normal mammary epithelial cells. Transwell assays proved that knockdown of GREB1L expression promoted cell migration and invasion. Subcutaneous xenograft models showed that GREB1L can affect the expression of tumor metastasis-related genes. These findings suggest that GREB1L plays an important role in predicting the prognosis of BRCA and preliminarily clarified its function in BRCA.
Material and methods
Data source and online analysis tool
Gene expression data with clinical information (Workflow Type: HTSeq-TPM and HTSeq-FPKM) were acquired from The Cancer Genome Atlas (TCGA) database (
https://portal.gdc.cancer.gov/) [
12]. Samples without sufficient clinical information were excluded. A total of 8626 pancancer tissues and 713 corresponding paracancerous normal tissues from the TCGA-ALL dataset and 1083 tumor tissues and 111 paracancerous normal tissues from the TCGA-BRCA dataset, including 110 paired samples, were enrolled in this study. R (3.6.3 version) with the ggplot2 package (3.3.3 version), survminer package (0.4.9 version), survival package (3.2–10 version), pROC package (1.17.0.1 version), RMS package (6.2–10 version), and GSVA package (1.34.0 version) were used to process the original data and generate some figures and tables. Three online databases, including LinkedOmics (
http://www.linkedomics.org/login.php), TISIDB (
http://cis.hku.hk/TISIDB/index.php), and Kaplan‒Meier Plotter (
http://kmplot.com/analysis/), were applied in this study.
Differential expression analysis
We proceeded with logistic regression analysis of the correlation between GREB1L mRNA expression and clinical characteristics in BRCA. We compared GREB1L expression levels between tumors and corresponding paracancerous normal tissues in 23 human tumor types via the Wilcoxon rank sum test. In the BRCA cohort, we analyzed GREB1L expression levels between tumor tissues and paracancerous normal tissues. The expression levels in paired samples were also explored. The results are shown in scatter plots.
Prognostic value
According to the median value of GREB1L expression, patients were classified into low- and high-expression groups. The TISIDB website was used to create a bar plot to compare the association between GREB1L expression and overall survival (OS) across 30 human malignancies. Data were processed by the log-rank test. Kaplan‒Meier curves of OS and relapse-free survival (RFS) between the GREB1L low- and high-expression groups were generated based on gene chip data from the Kaplan‒Meier plotter database. The adopted statistical approach was also the log-rank test. A forest plot of the multivariate Cox regression analysis was processed to show the prognostic factors in BRCA. We calculated the p value, hazard ratio (HR), and 95% confidence interval (CI) of every potential predictor. Prognostic factors with HR > 1 and p < 0.05 were risk factors for BRCA prognosis. Moreover, those with HR < 1 and p < 0.05 were regarded as protective factors.
Coexpression networks and gene set enrichment analysis
To predict the potential biological mechanism of GREB1L in BRCA, we used the LinkFinder module in the LinkedOmics website (
http://www.linkedomics.org/login.php) to research the coexpression network of GREB1L in the TCGA-BRCA dataset. Then, in the LinkInterpreter module of the same portal, GSEA was utilized to identify terms significantly related (FDR < 0.05) to GREB1L coexpressed genes. The analysis contains four aspects, including Gene Ontology biological process (GO-BP), Gene Ontology cellular component (GO-CC), Gene Ontology molecular function (GO-MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We utilized Pearson correlation as a statistical approach, and a false discovery rate (FDR) < 0.05 was considered to indicate significant correlations or enrichment.
Specimens and cells
Breast tissue specimens were obtained from Qilu Hospital of Shandong University (Qingdao). The specimens were frozen in liquid nitrogen immediately after surgical resection. All breast tissues were collected according to the protocol approved by the Ethics Committee of Shandong University Qilu Hospital (Qingdao). MCF10A, MCF7, Hs578T, ZR-75-1, MDA-MB-231, MDA-MB-453, and SK-BR-3 cells were preserved by our laboratory. Hs578T cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (BI, C3113) containing 10 μg g/ml insulin. MCF10A cells were cultured in DMEM/F12 containing 5% horse serum, 20 ng/ml epidermal growth factor, 0.5 μg/ml hydrocortisone, 10 μg/ml insulin, and 1% nonessential amino acids (Procell, CM-0525). ZR-751 cells were cultured in Roswell Park Memorial Institute (RPMI)-1640 (Procell, PM150110) medium. MDA-MB-453 cells were cultured in Leibovitz’s L-15 (Procell, PM151010) medium. MDA-MB-231, MCF7, and SK-BR-3 cells were cultured in DMEM. All cell culture media included 10% fetal bovine serum (FBS) (BI, C04001) and 1% penicillin‒streptomycin solution (Procell, PB180120).
Lentiviral construction and transfection
Lentiviruses carrying short hairpin RNA (shRNA) against GREB1L were constructed by Genechem Company (Shanghai, China). The RNAi sequence targeting human GREB1L was 5′- GCGTTTGGTATCACTGTGTAT-3′. The negative control sequence was 5′-TTCTCCGAACGTGTCACGT-3′. Viruses were transfected with HitransG P according to the manufacturer’s instructions.
Transwell migration and invasion assays
Transwell migration and invasion assays were performed using 24-well insert transwell chambers (Corning, #3422). Approximately 5 × 104 MCF7 cells and 3.5 × 104 MDA-MB-231 cells were resuspended in 200 µl DMEM without FBS and seeded in the upper chamber. DMEM containing 20% FBS was added to the bottom wells to stimulate migration or invasion. For the migration assay, the seeded MCF7 cells were incubated for 24 h (MDA-MB-231 cells for 12 h). For the invasion assay, MCF7 cells were incubated for 24 h (MDA-MB-231 cells for 18 h). After incubation, the cells on the upper surface of the chamber were wiped off with a cotton swab and then rinsed with PBS, and the cells on the lower surface of the chamber were fixed with methanol and stained with 0.1% crystal violet. Then, the cells were counted at 100 × with a microscope. For the invasion assay, the top chamber was coated with Matrigel (ABW, 082704).
Cell counting kit-8 (CCK-8) cell proliferation assays
Approximately 3 × 103 MDA-MB-231 cells per well or 4 × 103 MCF7 cells per well were seeded in 96-well plates, and 10 µl CCK-8 (Dojindo, CK04) reagent was added to each well before measurement. After incubation for 2 h in a 37 ℃ incubator, the absorbance at 450 nm (OD450) was measured. The cells were assessed once every 24 h.
Approximately 1 × 103 cells were seeded in six-well plates. After 2–3 weeks of treatment with puromycin, the cells were washed three times with PBS, fixed with methanol and stained with 0.1% crystal violet.
Subcutaneous xenograft models in vivo
Approximately 3 × 106 MDA-MB-231 cells were subcutaneously injected into the flanks of 6-week-old female BALB/c nude mice (n = 6 per group, Charles River, Beijing, China). The tumor diameter in the nude mice was measured every 5 days. The mice were sacrificed at 6 weeks, and the tumor weights and volumes were measured.
Real‑time quantitative PCR
Total RNAs were extracted from cells using TRIzol reagent (TIANGEN Biotech, Beijing, China) and reverse-transcribed into cDNA using All-In-One 5X RT MasterMix (abm, Vancouver, Canada). Then, qPCR was performed using BlasTaqTM 2X qPCR MasterMix (Abm, Vancouver, Canada). The primers were synthesized by Sangon Biotech (Shanghai, China). The following gene-specific primers were used: GAPDH, forward 5′-GGAGCGAGATCCCTCCAAAAT-3′, reverse 5′-GGCTGTTGTCATACTTCTCATGG-3′; GREB1L, forward 5′-GCTCTAGCAATGAGGTTCACTGG-3′, reverse 5′-GTCTCGTCACATCTCAGAAGTGG-3′.
Western blotting
RIPA lysis buffer (Solarbio, #R0010) was used to extract total cell protein. PMSF (Solarbio, P0100) was added to the lysis buffer. Lysates were separated into 6% and 10% acrylamide gels. Then, the proteins were transferred from the gel to a PVDF membrane (Immobilon-P, IPVH00010). A blocking buffer (Boster, AR0041) was used to block the blots. Anti-GREB1L (1:300, ATLAS ANTIBODIES, HPA041647), anti-β-Tubulin (1:1000, absin, abs830032), E-cadherin (1:1000, Cell Signaling Technology, 14472S), N-cadherin (1:1000, Cell Signaling Technology, 13116S), and Vimentin (1:1000, Cell Signaling Technology, 5471S) were used as the primary antibodies. Anti-β-Tubulin was used as an internal control. HRP-goat anti-mouse IgG (1:5000, Earthox, E030110) and HRP-goat anti-rabbit IgG (1:5000, Earthox, E030120) were used as the secondary antibodies.
Immunohistochemistry
Immunohistochemistry (IHC) was carried out as described previously [
13] with anti-GREB1L (1:300, ATLAS ANTIBODIES, HPA041647) in paraffin-embedded breast cancer tissue sections.
Statistical analysis
All experiments were repeated three or more times. All quantitative data are presented as the means ± SDs. We used a standard two-tailed unpaired t test for the statistical analysis of the two groups. p < 0.05 was considered to indicate statistical significance. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns represents no significance.
Discussion
BRCA is the most common female malignant tumor and seriously threatens women's health. The selection of BRCA prognostic markers is of great significance for predicting the biological behavior of BRCA and guiding comprehensive treatment. In recent years, research on molecular biomarkers related to BRCA prognosis has made continuous progress, providing a basis for effectively predicting the prognosis of BRCA. For example, ER and PR have been used in clinical practice; in addition to guiding clinical classification, studies have shown that the expression of ER and PR is significantly related to the prognosis of BRCA patients. ER-positive and PR-positive patients have lower recurrence rates, higher survival rates, and better prognoses than ER-negative and PR-negative patients [
21]. HER2 is an important regulator of cell growth, differentiation, and survival, and its high expression can promote the proliferation and metastasis of BRCA cells. The expression rate of HER2 was increased to 20–40% in BRCA, and this increase in expression indicated a high recurrence rate and a poor prognosis in BRCA [
22,
23]. Leukemia inhibitory factor receptor (LIFR) is a BRCA metastasis suppressor that is downstream of the microRNA miR-9 and upstream of Hippo signaling. Loss of LIFR correlated with poor clinical outcomes in BRCA [
24]. The expression of retinoid-inducible nuclear factor (RINF) is increased in breast tumors compared to normal tissues. Its overexpression is associated with a poor prognosis in locally advanced BRCA [
25].
In this study, we discovered a novel molecule, GREB1L, which was not only associated with BRCA process but could also predict the prognosis of BRCA. Our bioinformatics analysis showed that the expression of GREB1L was higher in BRCA tissues than in adjacent normal tissues. GREB1L overexpression played a protectives role in BRCA development. Moreover, we found that, consistent with the bioinformatics results, the expression of GREB1L in BRCA tissues was higher than that in paired adjacent tissues. The protein expression level of GREB1L in BRCA cells was higher than that in mammary epithelial cells. Transwell assays showed that the knockdown of GREB1L promoted the migration and invasion of MCF7 and MDA-MB-231 cells. However, GREB1L had no significant effect on the proliferation and colony formation abilities of MCF7 and MDA-MB-231 cells. Similarly, GREB1L had no effect on the tumorigenicity of MDA-MB-231 cells in vivo, but we found by detecting the expression of EMT-related genes in nude mouse tumors that the downregulation of GREB1L promoted the EMT process of tumors, and we speculated that GREB1L can also affect the metastasis ability of BRCA cells in vivo. Thus, we believe that GREB1L is likely a protective factor for BRCA.
GREB1L is a protein-coding gene. An important paralog of this gene is growth regulating estrogen receptor binding 1 (GREB1) [
26]. The function of GREB1L in BRCA has not yet been reported. Our functional enrichment analysis revealed that GREB1L is mainly associated with cell motility and energy metabolism, which suggests that GREB1L may regulate the migration ability and energy metabolism process of BRCA cells. Interestingly, the pathway enrichment analysis revealed that the HH signaling pathway was the only positively enriched pathway.
HH signaling is essential in embryonic development, tissue regeneration, and stem cell renewal [
27‐
30]. HH pathway signaling is mediated by three ligands [sonic HH (SHH), Indian HH (IHH), and desert HH (DHH)], two receptors [patched 1 (PTCH1) and smoothened (SMO)], and three transcription factors [glioma-associated oncogene homolog (GLI)-1, GLI2, and GLI3] [
31]. When there is no ligand signal, PTCH1, a transmembrane receptor on the target cell membrane, binds to SMO and inhibits SMO activity, preventing transduction. However, in the presence of an HH ligand, the HH ligand bound to PTCH1 and changed the spatial conformation of PTCH1, relieving SMO inhibition and activating the transcription factor GLI. GLI entered the nucleus, leading to the transcription of target genes and then regulating cell growth, proliferation, and differentiation [
32]. Studies have shown that abnormal activation of the HH pathway is associated with the development of skin, brain, digestive tract, lung, and prostate cancer [
33‐
37]. Abnormal reactivation of HH signaling was also reported in BRCA [
38,
39]. Some scholars have found that the genes related to the HH signaling pathway play an important role in guiding the prognosis of breast cancer. This study analyzed the RFS of 3951 patients and OS of 1402 patients in the online database. They found that without considering the BRCA subgroup, high expression of SHH, HHAT, GLI1, GLI2, GLI3, and PTCH1 is associated with better RFS. High expression of HHAT is associated with better OS [
40]. Our study found that GREB1L is also a gene related to the HH signaling pathway. Its high expression indicates better OS and RFS in BRCA patients, which provides a new predictive index for breast cancer prognosis.
Finally, although our study has given us an initial understanding of GREB1L in BRCA, some work remains to be completed. First, in clinical practice, we aim to determine whether the high expression of GREB1L is related to a better prognosis in BRCA patients. This aim requires long-term follow-up of our patients. Second, we need to verify whether GREB1L regulates the HH signaling pathway and regulatory mechanism in BRCA. These aims have not yet been accomplished, so we need to further explore them in the future.
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