Distinct expression and function of breast cancer metastasis suppressor 1 in mutant P53 glioblastoma

Abstract

Purpose

Glioblastoma (GBM) is the most malignant subtype of astrocytic tumors with the worst prognosis in all its progressive forms. Breast cancer metastasis suppressor 1 (BRMS1) is a metastasis suppressor gene that controls malignancy in multiple tumors. As yet, however, its clinical and functional significance in mutant P53 GBM remains inconclusive. Here, we attempted to study the importance of BRMS1 in mutant P53 GBM.

Methods

BRMS1 expression was evaluated in 74 human astrocytoma tissues by qRT-PCR, Western blotting and immunohistochemistry. BRMS1 expression in the astrocytoma tissues was correlated with clinicopathological parameters, the P53 mutation status and BRMS1 downstream targets, and compared with TCGA and NCI-60 datasets. siRNA-mediated knockdown of BRMS1 was performed in selected GBM cell lines to evaluate the functional role of BRMS1.

Results

Our study revealed an enhanced expression of BRMS1 in GBM which was associated with a poor patient survival, and this observation was corroborated by the TCGA dataset. We also found a positive correlation between BRMS1 expression and a mutant P53 status in GBM which was associated with a poor prognosis. In vitro BRMS1 silencing reduced the growth of mutant P53 GBM cells and repressed their colonization and migration/invasion by modulating EGFR-AKT/NF-κB signaling. Transcriptional profiling revealed a positive and negative correlation of uPA and ING4 expression with BRMS1 expression, respectively.

Conclusion

Our data indicate upregulation of BRMS1 in high grade astrocytomas which correlates positively with mutant P53 and a poor patient survival. Silencing of BRMS1 in mutant P53 GBM cell lines resulted in a reduced cellular growth and migration/invasion by suppressing the EGFR-AKT/NF-kB signaling pathway. BRMS1 may serve as a predictive biomarker and therapeutic target in mutant P53 GBM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13402-022-00729-x.

Introduction

Astrocytomas are frequently reported and constitute 75% of all primary brain tumors [1]. As per the WHO-2007 histopathological classification, astrocytomas are divided into four grades [2]. Grade I (GI-pilocytic astrocytomas) and Grade II (GII-diffuse astrocytoma) are low-grade astrocytomas with a 5–6 year median survival and can be managed with surgery and chemotherapy [1, 3]. Grade III (GIII-anaplastic astrocytoma) and Grade IV (GIV-glioblastoma-GBM) are high-grade astrocytomas. Compared to the low grade astrocytomas, the clinical outcomes for the high grade cases are poor with a median survival of about 16–18 months, especially for GBM [3, 4]. The updated WHO-2016 classification system incorporates GBM profiling based on a wild-type or mutation status of the isocitrate dehydrogenase (IDH) gene to facilitate the prediction of clinical outcomes. Notably, GBM patients with a mutant (mut) IDH status exhibit a better survival time (42 months) than wild (wt) type IDH cases whose survival time is about 14 months [5–7]. Given that wtIDH covers more than 90% of GBM cases, this accounts for ≤ 5.5% five-year post-diagnosis survival in spite of current advancements in diagnostic and therapeutic regimes [1, 6].

Breast cancer metastasis suppressor gene 1 (BRMS1) is a potential metastatic suppressor gene (MSG) that can control metastasis without affecting tumor growth [8]. BRMS1 was initially identified through differential display analysis [9]. Transfection of BRMS1 cDNA into breast cancer cell lines revealed metastasis suppression without affecting tumorigenicity [9]. Although it has been over a decade since the discovery of BRMS1 and its recognition as a potential tumor regulator gene, the expression pattern of BRMS1 and its clinical significance remain unclear [10–15]. Although Mei et al. recently reported that downregulation of BRMS1 in glioma may be associated with glioma pathogenesis [16], further investigation is required to understand the clinical significance of BRMS1 in GBM.

P53 is a well-studied tumor suppressor gene known to maintain genome stability [17]. Missense P53 mutations have frequently been reported in astrocytomas, and P53 mutations have been found to enhance its protein stability [17, 18]. P53 protein expression has been linked to astrocytoma progression, i.e., P53 expression increases with astrocytoma progression from benign (GII: 18–46%) to malignant (GIII: 29–57% and GIV: 49–70%), and the frequency of P53 mutations in primary GBM is low (< 10%) compared to that in secondary GBM (> 65%) [18, 19]. P53 is also known as a potential driver gene that can modulate the expression and/or function of other relevant genes in various cancers, including astrocytoma [17, 18, 20]. Interestingly, BRMS1 has been found to elicit its function by modulating the expression of target genes such as OPN, uPA and CXCR4 [21–23], genes that also serve as downstream targets of P53 [24–26]. This notion prompted us to study the impact of BRMS1 on wt/mut P53 GBM. Moreover, Hall et al. reported a role of BRMS1 in cellular migration and invasion in K-Ras expressing and P53 negative non-small lung cancer cell lines [27].

In the current study the expression of BRMS1 was evaluated in different grades of astrocytomas, and linked to various pathological features and patient survival. The clinical observations were validated through in-silico analysis using an external TCGA cohort and a NCI-60 cell lines dataset. Our study reveals a correlation between BRMS1 and mut P53 expression in GBM and its association with a dismal prognosis. BRMS1 knockdown in mut P53 GBM cell lines underscored a distinct role of BRMS1 in the growth, migration or invasion of mut P53 GBM cells by suppressing EGFR-AKT/NF-kB signaling. To gain further insight into the role of of BRMS1 in GBM, we assessed the effect of BRMS1 on its downstream targets, including urokinase plasminogen activator (UPA) and inhibitor of growth family-4 (ING4) [28, 29] known to play an important role in GBM progression [30, 31].

Materials and methods

Tissue specimens: collection and processing

In the present study a total of 74 astrocytoma biopsies of different grades and 12 non-tumor (temporal epilepsy) brain tissues were collected from 2012 to 2017 at the Krishna Institute of Medical Science (KIMS), Secunderabad, India. Of the total biopsies received, 22 were GII and GIII while 30 were GIV, respectively. The grading was based on the WHO-2007 histopathological classification [2]. After surgical resection, the tissues were frozen in liquid nitrogen and stored at -80 °C until further analysis. All procedures involving human biopsies were performed under institutional guidelines and regulations. Written informed consent was obtained from all patients or their guardians, and the study was approved by the Institutional ethics committee (IEC), University of Hyderabad, Hyderabad (TS).

Cell culture

GBM cell lines LN18, U373 and U87 were obtained from the cell repository, National Centre for Cell Sciences, Pune, India. The T98 cell line was a kind gift from Dr. Ellora Sen (National Brain Research Center, Gurgaon, India). U373, LN18 and T98 cell lines are mut P53, while U87 is a wt P53 cell line [32]. All cell lines were grown in Dulbecco’s Modified Eagle’s medium (DMEM #AL007A), supplemented with 1X antibiotic-antimycotic solution (15,240,062) and 10% fetal bovine serum (FBS, RM9955), and maintained under normoxic conditions at 37 °C and 5% CO₂ in a Sanyo MCO-18AIC CO₂ incubator. All cell lines were tested for mycoplasma and bacterial contamination.

RNA isolation and quantitative PCR (qRT-PCR)

Total RNA was isolated from clinical tissues or cell lines using trizol (T9424-Sigma) reagent as per manual instructions. RNA was quantified using a NanoDrop, and 1–5 µg was used for cDNA synthesis using a BluePrint 1st Strand cDNA Synthesis Kit (#6115T). The reaction mixture for cDNA synthesis was incubated at 30 °C for 10 min and 42 °C for 60 min, after which the enzyme was inactivated by heating at 95 °C for 5 min. qPCR was performed using SYBR® Premix Ex Taq™ (RR420A) and an Applied Biosystems 7500 Fast Real-Time PCR System. Tenfold diluted cDNA was used in a 20 µl reaction in triplicate for each gene. The primer sequences used are listed in supplementary Table 1 (Table S1).

Western blotting

Protein extraction from astrocytoma biopsies and cell lines was performed as described before [33]. Total protein quantities were estimated using the Bradford method, and 50–75 µg protein was used to be separated by SDS-PAGE and transferred onto nitrocellulose membranes. The resulting membranes were blocked with 5% nonfat milk or 5% bovine serum albumin solution prepared in 0.05% Tween 20-tris buffered saline (TTBS) at room temperature for one hour. After blocking, the membranes were probed with primary antibodies overnight at 4 °C in a dilution of 1:2000 for BRMS1 (Abcam #ab134968), 1:5000 for β-actin (Abcam #ab8227) and 1:1000 for EGFR (Cell Signaling Technology-CST #4267), pAKT (CST #9271) AKT (CST #9272), P53 (CST #2524) and NF-κB (CST #9936), followed by four times 5 min washes with TTBS and one hour incubation with anti-rabbit IgG (CST #7074) or anti-mouse IgG (CST #7076) horseradish peroxidase (HRP) conjugated secondary antibody at room temperature. Immunoreactivity was visualized by ECL reagent and protein bands were captured using a ChemiDoc imaging system (Bio-Rad).

Immunohistochemistry (IHC)

Tissue sections of 5–10 μm were prepared using a cryotome (LEICA-1850) and stored at room temperature. Before staining, the tissue sections were briefly dewaxed by heating at 100 °C for 5 min, followed by three washes in xylene for 5 min each, and rehydrated by simultaneous 5 min washes in 100, 95, 75 and 50% ethanol and distilled water. Antigen retrieval was done by 18 min cooking in Tris/EDTA (pH 9.0) buffer, after which the sections were incubated with an anti-BRMS1 antibody (ab134968) in a dilution of 1:100 for one hour in a humid box at room temperature. Immunohistochemical staining was performed according to the DAKO LSAB + Kit manual and visualized using 3,3’-diaminobenzidine tetrahydrochloride and hematoxylin counterstaining as described previously [34, 35]. Quantification of positively stained and unstained cells was performed using ImageJ plugin IHC profiler [36].

Si-RNA transfection assay

For siRNA-mediated knockdown, siRNAs specific for BRMS1 (Sense seq.: 5’GAU-GGA-UGA-UGA-GGA-CUA-U3, Antisense seq.: 5’AUA-GUC-CUC-AUC-AUC-CAU-C 3’) were designed and synthesized by Eurogentec Belgium (SR-NP001-004-siRNA Duplex SePOP). Commercially available non-targeting (NT) siRNA was purchased from Qiagen (#1,022,076). Polyplus-jetPRIME transfection reagent (Illkirch Cedex- France) was used for BRMS1 and nontarget siRNA treatment following manual instructions. The maximum transfection efficiency was calculated at 15 nM siRNA concentration. BRMS1 silencing was confirmed at both mRNA and protein levels after 48 or 72 h of siRNA treatment.

MTT and clonogenic assays

Approximately 5 × 10³ cells/well were seeded in 96 wells plates and kept in a normoxic CO₂ incubator (Sanyo MCO-18AIC) for overnight growth. When the cells reached 50–60% confluency, BRMS1-siRNA and NT- siRNA together with transfection reagent (jet PRIME) were added to the cells. After 24 or 48 h of cellular growth, 20 µl MTT (5 mg/ml) was added to each well and incubated for 4 h in the normoxic CO₂ incubator. Next, 50 µl DMSO was added to each well, and absorbance was measured at 570 nm using a multiple plate reader (Tecan Infinite 200). For the colony formation assay, 500–1000 cells in single-cell suspension were seeded in each well of six-well plates. After 24 h of growth, BRMS1-siRNA and NT-siRNA were added to the cells. The medium was replaced every 48 h and the cells were grown until colonies were visible. These colonies were fixed in 4% paraformaldehyde (PFA) for 10 min at room temperature and stained with 1% crystal violet. Images were captured using an Olympus digital camera (Olympus, Japan). The data were processed and analyzed as described previously [37].

Cell migration and invasion assays

Cell migration and invasion assays were performed as reported before [35]. Briefly, U87 and LN18 cells (6 × 10⁵) were seeded in a six-well plate, and after the overnight growth, a vertical and a horizontal wound was created by scratching the wells with a pipette tip, followed by treatment with BRMS1-siRNA and NT-siRNA. Next, images were captured from the same position at 0, 6, 12 and 24 h intervals. As reported before, wound-healing percentages were calculated by the ratio of the healing area at each time point to the wound area at the zero hour time point [35]. For the invasion assay, BRMS1-siRNA and NT-siRNA transfected cells were seeded in gelatin-coated transwell inserts in serum-free medium and grown overnight in a CO2 chamber. Upon 12 h of growth, the inserts were carefully removed and washed with PBS. Cells that traversed the membrane were fixed in methanol and stained with 1% crystal violet. Finally, the results were analyzed by counting cells in a minimum of six microscopic fields.

TCGA data analysis through the UALCAN portal

To underscore our clinical observations, we performed an in-silico analysis of The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) confirmatory/discovery datasets through the UALCAN portal (http://ualcan.path.uab.edu/index.html). The UALCAN portal is a publicly available resource for assessing gene expression and survival data [38]. TCGA transcript expression data, CPTAC protein expression data and survival graphs were downloaded and processed from the UALCAN portal. NCI-60 cell line transcript data were downloaded from CellMiner (https://discover.nci.nih.gov/cellminer/) to evaluate the expression of BRMS1 and its downstream genes in 60 different human cancer cell lines with a P53 wild-type or mutation status.

Statistical analysis

Statistical analyses were performed using GraphPad Prism. One-way ANOVA was used to calculate differences between different data sets, and student t-test was used to determine differences between two variables. Survival statistics were analyzed using the Kaplan-Meier method, and clinicopathological associations with BRMS1 expressions were evaluated using Freeman–Halton, an extension of the Fisher exact probability test. The Pearson correlation coefficient was used to assess correlations between BRMS1 expression and that of its downstream genes in different astrocytoma grades. A correlation coefficient value r² = + 1 was considered a strong positive correlation, while r² = -1 indicated a negative correlation and r² = 0 indicated no correlation. All experiments were repeated three times unless indicated otherwise, and continuous data are presented as mean ± SEM. A p-value ≤ 0.05 was considered statistically significant, (*) indicates p ≤ 0.05, (**) indicates p ≤ 0.01 and (***) indicates p ≤ 0.001.

Results

BRMS1 is overexpressed in malignant astrocytomas

To study the clinical importance of BRMS1 in astrocytomas, BRMS1 mRNA expression in different grades of astrocytoma (n = 74) and control brain tissues (n = 12) was evaluated. Using qRT-PCR, we found statistically significantly high BRMS1 mRNA expression levels in GIV (**p ≤ 0.004) followed by GIII (*p ≤ 0.039) compared to control samples (Fig. 1A). The mRNA expression level of BRMS1 increased with increasing grade. Similarly, we observed an increase in BRMS1 mRNA expression in GII, but this increase was not statistically significant. Independent validation through the TCGA cohort also revealed a significant increase in BRMS1 transcript expression in GIV (*p ≤ 0.020) and GIII (*p ≤ 0.029) compared to control brain tissues (Fig. 1B). Using Western blotting (n = 60), we found that the BRMS1 protein level was remarkably upregulated in GIV compared to GIII, GII and control tissues (Fig. 1C). Densitometric analysis of the Western blot data confirmed a statistical significant increase in BRMS1 protein in GIV (***p ≤ 0.0005) and GIII (*p ≤ 0.043) compared to control tissues (Fig. 1D). We also detected a significantly upregulated BRMS1 protein level in GIV compared to that in GIII and GII (Fig. 1D). Independent validation of BRMS1 protein using the CPTAC proteomics dataset indeed revealed a significantly increased BRMS1 protein expression in GBM (n = 99) (*p ≤ 0.029) compared to control brain tissues (n = 10) (Fig. 1E). IHC staining results with an anti-BRMS1 antibody on randomly selected astrocytoma (n = 15) and control tissue (n = 3) sections were consistent with the qRT-PCR and Western blot data, showing significantly less negative staining of BRMS1 in GII, GIII and GIV compared to the control (Fig. 2A). In contrast, we observed a significantly high BRMS1 positivity in GIV (> 40%) compared to GII (< 3% stained cells), GIII (< 4% stained cells) and control (< 2% stained cells) (Fig. 2A), confirming an increased expression of BRMS1 in GBM.

Fig. 1.

BRMS1 overexpression in malignant astrocytoma. A Internal astrocytoma cohort (n = 74) showing significantly increased BRMS1 mRNA levels in GIII (*p ≤ 0.039) and GIV (**p ≤ 0.004) tissues compared to control tissues. B Independent TCGA validation cohort showing a significantly high BRMS1 transcript level in GIII (*p ≤ 0.029) and GIV (*p ≤ 0.020) tissues compared to control tissues. C Western blots showing increased BRMS1 protein levels in high-grade astrocytoma (GIII and GIV) compared to low grade and control tissues. D Densitometry analysis revealing significant BRMS1 upregulation in GIII (*p ≤ 0.043) and GIV (***p ≤ 0.0005) astrocytoma compared to control. E CPTAC dataset showing upregulated BRMS1 protein levels in GBM tissues compared to control. The column indicates the mean, the bar represents the SEM, and p ≤ 0.05 is considered statistically significant

Fig. 2.

Increased BRMS1 expression is associated with poor survival. A Immunohistochemistry images and quantified graph depicting significantly less negative staining of BRMS1 in GII, GIII and GIV tissues compared to the control tissue and considerably high BRMS1 positivity in GIV tissue (> 40% staining) compared to GIII (< 4% staining), GII (< 3% staining) and control (< 2% staining) tissues. B Kaplan-Meier survival graph indicating that high BRMS1 expression inflicts a poor prognosis; the Log-rank test reveals significant survival statistics (***p ≤ 0.0001)

Increased BRMS1 expression is associated with a poor prognosis in GBM

To understand the clinical significance of BRMS1 expression in astrocytoma malignancy and patient prognosis, BRMS1 expression in astrocytoma was divided into three groups: negative, low and high based on the degree of expression evident from Western blots and IHC. The five-year survival was evaluated in 66 of 74 cases through Kaplan-Meier survival statistics; follow-up data were not available for 8 subjects. The log-rank (Mantle-Cox) test revealed a significant survival distribution (*p ≤ 0.0001) between the patients with negative, low and high BRMS1 expression levels, and the median survival of high BRMS1 expressing astrocytoma patients was 16 months (Fig. 2B). The Kaplan-Meier survival curve and log-rank test (p ≤ 0.0001) indicated that astrocytoma patients with a high BRMS1 expression had a poor prognosis compared to the negative or low BRMS1 expressing patients (Fig. 2B), which was supported by TCGA survival data (Fig. S1A, B).

BRMS1 expression positively correlates with astrocytoma grade

Next, BRMS1 expression levels were correlated with various clinicopathological parameters, including age, gender and different grades of astrocytoma, as summarized in Table 1. In a total of 74 astrocytoma biopsies, we observed BRMS1 expression in 45 (60.81%) astrocytoma tissues. Out of the 45 BRMS1 expressing astrocytoma tissues, 26 (57.77%) were found to exhibit a high BRMS1 expression and 19 (42.22%) a low BRMS1 expression. Twenty-nine of the 74 biopsies (39.18%) were BRMS1 negative. BRMS1 overexpression was significantly correlated with astrocytoma grade progression (*p ≤ 0.004). No correlation was found between patient age (p ≤ 0.71) or gender (p ≤ 0.30) (Table 1).

Table 1.

The correlation between BRMS1 expression with clinicopathological features

Features	BRMS1 Expression Profile (N = 74)			Chi-square test
	Low (n = 19)	High (n = 26)	Negative (n = 29)
Gender
Male (n = 45)	12 (17.77%)	17 (33.33%)	16 (48.88%)	ns
Female (n = 29)	07 (20.68%)	09 (48.27%)	13 (31.03%)
Age (years)
≤45 (n = 53)	11 (35.18%)	20 (25.92%)	22 (38.88%)	ns
>45 (n = 21)	08 (23.80%)	06 (33.33%)	07 (38.09%)
Histopathological Grades (WHO-2007, Classification System)
GII (n = 22)	05	03	14	*p = 0.004
GIII (n = 22)	09	06	07
GIV (n = 30)	05	17	08

Concomitant upregulation of BRMS1 and mut P53 exacerbates astrocytoma malignancy and prognosis

Frequent P53 mutations are associated with astrocytoma progression and malignancy [17, 18], and a functional relationship between mut P53 and BRMS1 has recently been reported in lung and other cancers [27, 39, 40]. This prompted us to explore the link between P53 and BRMS1 in our astrocytoma tissue samples and, therefore, P53 transcript and protein levels in the astrocytoma tissues were analyzed. Using qRT-PCR (Fig. 3A) and Western blotting (Fig. 3B) we found that the P53 transcript and protein levels in BRMS1 expressing astrocytoma tissues, particularly in GIV tissue, were similar to the BRMS1 expression level (Fig. 3A, B). The correlation study illustrated the positive correlation between BRMS1 and P53 at both transcript (r² = 0.9701; *p < 0.0299) (Fig. 3C) and protein (r² = 0.9508; *p < 0.049) (Fig. 3D) levels, indicating a possible association between mut P53 and BRMS1 in astrocytoma (Table 2). The independent TCGA dataset also displayed a significant increase in BRMS1 transcription in mut P53 GBM compared to normal and wt P53 GBM (Fig. 3E). A dismal survival of patients with positive BRMS1 and P53 expression was inferred from the Kaplan-Meier curve (Fig. 3F). The log-rank (Mantle-Cox) test showed a significant survival distribution (*p ≤ 0.0008) between the mentioned groups with a hazard ratio of 5.6 (95% CI 2.062 to 15.71) (Fig. 3F).

Fig. 3.

Positive correlation between BRMS1 and mut P53 expression. A Transcript and B protein expression of P53 through qRT-PCR and Western blotting showing a positive correlation in BRMS1 expressing tissues. C, D Correlation analysis revealing a significant positive correlation between BRMS1 and mut P53 expression at both mRNA and protein levels. E TCGA data showing a high BRMS1 transcript level in mut P53 GBM compared to wt P53 GBM and normal control. F Survival analysis indicating that BRMS1 and P53 positive patients show a more dismal survivability than BRMS1 and P53 negative patients. The Log-rank test reveals a significant survival difference (***p ≤ 0.0008) with a hazard ratio of 5.6 (95% CI2 062 to 15.71)

Table 2.

Association between BRMS1 and P53 in astrocytoma (n = 48)

	BRMS1 expression profile		Fisher exact probability test
	(+)	(-)
P53 (+)	19 (39.58%)	14 (29.16%)	*p = 0.045
P53 (-)	13 (27.08%)	02 (04.16%)

The association between BRMS1 and P53 was analyzed in astrocytoma tissues (n = 48) by Fisher’s exact probability test. The positive or negative expression is depicted by (+)/ (-).

Mut P53 GBM cells exhibit increased BRMS1 expression levels

BRMS1 expression in multiple GBM cell lines, including LN18, U373, T98 (mut P53) and U87 (wt P53), was assessed to reveal the pattern of BRMS1 expression. In vitro expression analysis suggested a differential expression pattern of BRMS1 and P53 at both the mRNA and protein levels with respect to cell lines and P53 mutation status (Fig. 4A, B). We observed an increased BRMS1 and P53 expression in mut P53 GBM cells (U373, LN18 and T98) compared to wt P53 GBM cells (U87) (Fig. 4A, B). U87 is a wt P53 GBM cell line that shows a low BRMS1 level and absence of P53 protein expression, possibly because of a short half-life of the P53 protein [32]. In addition, we evaluated BRMS1 mRNA expression in 60 different human cancer cell lines with known wt P53 or mut P53 genetic statuses using the NCI-60 cell line transcript dataset [41]. Interestingly, we found an increased expression of BRMS1 in mut P53 cell lines compared to wt P53 cell lines (Fig. 4C).

Fig. 4.

High BRMS1 expression in mut P53 GBM cell lines; BRMS1 knockdown reduces cell growth and colonization. A mRNA expression pattern of BRMS1 and P53 in GBM cell lines. B Western blots showing a high BRMS1 expression in mut P53GBM cell lines. C NCI-60 transcript analysis indicating an increased mRNA level of BRMS1 in mut P53 cell lines compared to wt P53 cell lines. D-F Significant inhibition of BRMS1 confirmed at mRNA and protein levels upon BRMS1 siRNA transfection in U87, LN18 and U373 cells. G MTT result showing significant inhibition of cell proliferation upon BRMS1 silencing in mut P53 LN18 and U373 cell lines. H Clonogenic assay depicting reduced colony formation by LN18 and U373 cells after BRMS1 knockdown. H Quantified graph indicating a significant reduction in colony formation by LN18 and U373 cell lines. The column indicates the mean, the bar represents SEM, and a p value ≤ 0.05 was considered statistically significant

BRMS1 knockdown significantly reduces the growth of mut P53 GBM cells

BRMS1 siRNA-based knockdown in the GBM cell lines was performed to validate the functional involvement of BRMS1 in cell proliferation and cell growth. BRMS1 knockdown was confirmed at both the transcript and protein levels in U87 (wt P53), LN18 and U373 (both mut P53) cell lines (Fig. 4D-F). Using a MTT cell proliferation assay we found that BRMS1 silencing inhibits the proliferation of U87, LN18 and U373 cells after 48 h of BRMS1 siRNA treatment compared to NT siRNA and untreated controls (Fig. 4G). A significant inhibition in cell proliferation was observed only in mut P53 LN18 (**p ≤ 0.007) and U373 (***p ≤ 0.001) cells (Fig. 4G). A subsequent clonogenic assay revealed a reduced colony-forming capacity of U87, LN18 and U373 cells upon BRMS1 siRNA treatment compared to NT siRNA controls (Fig. 4H). A statistically significant reduction in colony formation was observed in mut P53 LN18 (*p ≤ 0.039) and U373 (*p ≤ 0.047) cells (Fig. 4H).

BRMS1 knockdown attenuates cell migration and invasion by selectively modulating EGFR-AKT-NF-κB signaling

To assess the effect of BRMS1 knockdown on cell migration or invasion, a scratch wound healing and an invasion assay were performed. The scratch wound healing assay revealed a reduced cell migration (Fig. 5A, B) in U87 and LN18 cells upon BRMS1 silencing compared to the NT-siRNA control. A quantified graph indicated a significant reduction in cell migration in only LN18 cells (Fig. 5C). Similarly, we observed a reduced invasion of U87 and LN18 cells upon BRMS1 silencing (Fig. 5D). Quantified invasion indicated a significant reduction only in LN18 cells upon BRMS1 silencing (Fig. 5E, F). To provide a molecular basis for these observations, we next set out to study EGFR modulated PI3K-AKT signaling, known to play a crucial role in cell survival and apoptosis via the NF-κB pathway in GBM [35, 42, 43]. Using Western blotting upon BRMS1 siRNA treatment, we observed an increased EGFR and pAKT expression in U87 (wt P53) cells, while LN18 (mut P53) cells showed a decreased EGFR, pAKT, NF-κB-p65 and p-IKBα expression compared to the NT-siRNA and untreated controls (Fig. 5G). Densitometric quantification revealed a significantly increased expression of EGFR and pAKT in U87 cells upon BRMS1 knockdown (Fig. 5H-J). The LN18 cell line showed a significantly reduced EGFR, pAKT and NF-κB-p65 expression upon BRMS1 knockdown (Fig. 5H-J), in agreement with previous studies suggesting cell-specific signaling crosstalk of BRMS1 [44, 45].

Fig. 5.

BRMS1 knockdown attenuates cell migration and invasion by affecting EGFR-AKT-NF-κB signaling in LN18 cells. A-C Scratch wound healing and D-F invasion assays depicting significantly reduced migration and invasion by LN18 cells upon BRMS1 siRNA transfection compared to NT-siRNA. G Western blots and H-J densitometry graphs representing selective induction or inhibition of EGFR, pAKT, NF-κB-p65 and p-IkBα expression levels in U87 and LN18 cell lines, respectively, upon BRMS1 knockdown. At least three independent experiments were performed in triplicates; the column indicates the mean, the bar represents SEM, and a p value ≤ 0.05 was considered statistically significant

BRMS1 expression positively correlates with uPA expression and negatively with ING4 expression

Earlier investigations have shown that BRMS1 may inhibit uPA and enhance ING4 expression, thereby suppressing tumor malignancy [28, 29]. Here, we selected BRMS1 expressing astrocytoma tissues to examine the expression of uPA and ING4 and correlated the results with BRMS1 expression. We found that the BRMS1 expressing samples showed a high mRNA expression of uPA and a low mRNA expression of ING4 (Fig. 6A, B). Correlation analysis revealed a positive correlation between BRMS1 and uPA expression (r² = 1.0; p ≤ 0.083) (Fig. 6A) and a negative correlation between BRMS1 and ING4 expression (r² = -0.040; p ≤ 0.75) (Fig. 6B). Subsequent BRMS1 knockdown significantly inhibited uPA expression in both cell lines (Fig. 6C) and, unexpectedly, inhibited ING4 expression (Fig. 6C). NCI-60 transcript analysis revealed a positive expression correlation of uPA with BRMS1 in mut P53 cell lines, whereas a negative ING4 expression correlation with BRMS1 was noted irrespective of P53 status (Fig. 6D). Additional qRT-PCR analysis revealed a significantly reduced expression of the BRMS1 downstream targets uPA and ING4 upon BRMS1 silencing in both cell lines (Fig. 6E, F).

Fig. 6.

BRMS1 expression correlation with its downstream targets in astrocytoma tissues and mut P53 GBM cells. A, B Correlation analysis showing a positive and a negative mRNA expression correlation between uPA (r² = 1.0; p ≤ 0.083) and ING4 (r² = -0.040; p ≤ 0.75) with BRMS1, respectively, in astrocytoma. C Agarose gel images showing a significant decrease in transcript levels of uPA and ING4 upon BRMS1 knockdown in two cell lines. D NCI-60 data revealing a positive mRNA expression correlation of uPA with BRMS1, while ING4 shows a negative mRNA expression correlation with BRMS1 in mut P53 cell lines. E, F qRT-PCR graphs affirming significant transcript inhibition of uPA and ING4 upon BRMS1 knockdown. A p value ≤ 0.05 was considered statistically significant

Discussion

Metastasis is the utmost threat to cancer patients as it leads to disease progression and mortality. In the last couple of years, various studies on different cancer types have indicated BRMS1 as a potential metastasis suppressor gene [9, 15]. Our current study illustrates the clinical and functional significance of BRMS1 in astrocytomas. Both mRNA and protein expression of BRMS1 in our internal cohort and externally validated TCGA/CPTAC dataset signify an enhanced expression pattern of BRMS1 in high-grade (GIII + GIV) astrocytomas compared to low-grade (GII) and control brain tissues. We found that BRMS1 upregulation is associated with a poor prognosis in GBM and is significantly correlated with grade progression but not with patient age or sex. These observations are inconsistent with the earlier studies [13, 16, 46]. However, the TCGA dataset and emerging reports suggest that increased BRMS1 expression in certain cancers inflicts cancer malignancy and a poor prognosis in accordance with our study [10, 47, 48]. Although the molecular mechanisms underlying aberrant BRMS1 expression in cancer are still unclear, some studies suggest epigenetic regulation modulating BRMS1 downregulation in breast cancer and non-small cell lung cancer [11, 48–50].

Previous reports have suggested that BRMS1 may prevent tumor malignancy by regulating critical steps of cancer metastasis such as cell migration, invasion, angiogenesis, apoptosis and cytoskeletal rearrangement [11, 29, 51]. It has also been reported that BRMS1 can exert its function by regulating metastatic-associated microRNAs [52]. A recent report has shown BRMS1 inhibition in cells harboring oncogenic driver mutations such as P53 loss and K-Ras mutation may induce migration and invasion [27]. P53 mutations are frequently reported in several cancers and are known to act as driver mutations that can modulate various signaling pathways, including its shared downstream targets with BRMS1, such as OPN, uPA and CXCR4 that are involved in GBM progression and/or malignancy [40, 53]. In agreement with the above notions, our data indicated high mRNA and protein levels of P53 in BRMS1 expressing astrocytomas compared to control tissues. In addition, by using the NCI 60 cell line database we found an increased BRMS1 expression in mut P53 cell lines over wt P53 cell lines. Correlation analysis indicated a positive relationship between BRMS1 and P53 expression in astrocytomas that may confer a dismal prognosis in mut P53 GBM patients (Table 2), suggesting a possible association between BRMS1 and P53 in astrocytoma malignancy as encountered in other cancers [27, 39].

Our in vitro study revealed differential expression levels of BRMS1 in GBM cell lines. Interestingly, the mut P53 cell lines (U373, LN18 and T98) exhibited increased BRMS1 transcript and protein expression levels compared to the wt P53 cell line (U87), validating clinical data and previous reports explaining a cell-specific expression and activity of BRMS1 [44, 54]. BRMS1 knockdown reduced cell growth, migration and invasion by suppressing the EGFR-AKT-NF-κB axis [42, 43]. Notably, in the current study, we found that BRMS1 silencing in GBM cell lines with different P53 genotypes (mut P53 and wt P53) showed similar unlike effects in support of previous reports [44, 45, 54].

Furthermore, downstream targets of BRMS1 such as uPA, OPN and ING4 have been reported to be instrumental in suppressing BRMS1 regulated metastases [21, 22, 28, 29], and anomalous expression levels of uPA and ING4 have been reported to serve as prognostic and metastasis biomarkers in astrocytoma [30, 31]. Here, we observed a positive mRNA expression correlation of uPA and a negative mRNA expression correlation of ING4 with that of BRMS1 in GBM tissues. In vitro qRT-PCR data showed a significant inhibition of uPA mRNA expression upon BRMS1 knockdown. Surprisingly, we also found a significantly reduced mRNA expression of ING4 upon BRMS1 silencing. Besides, we observed a negative correlation of ING4 expression with that of BRMS1 in astrocytoma biopsies. Earlier studies have reported that variability in gene expression in tissues versus cell lines may be due to the cellular complexity or heterogeneity of tissues, which is absent in the cell lines [55]. However, we feel that this contrasting result demands a more in-depth study with an appropriate model to better understand the molecular association of BRMS1 and ING4 in GBM. The NCI-60 cell lines dataset showed a similar expression pattern of BRMS1 and its downstream targets uPA and ING4, particularly in mut P53 cell lines, suggesting a different molecular mechanism of BRMS1 in wt and mut P53 GBM. Our data, however, showed a clinical and molecular significance of BRMS1 in astrocytoma and its concomitant signaling in wt and mut P53 GBM. In vivo studies may further verify the involvement of BRMS1 in mut P53 GBM progression and malignancy.

In summary, our current study highlights an association between enhanced BRMS1 expression and an increased GBM malignancy and concomitant poor prognosis. Interestingly, we also found that increased BRMS1 expression in mut P53 GBM aggravates patient prognosis. In vitro BRMS1 knockdown in mut P53 cells resulted in a significantly attenuated cellular growth and migration/invasion by suppressing the EGFR-AKT-NF-κB signaling pathway, suggesting that BRMS1 may be a potential predictive and therapeutic candidate in mut P53 GBM.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

GBM

Glioblastoma

 BRMS1

Breast cancer metastasis suppressor 1

wt/mut P53

wild/mutant P53

MSG

Metastasis suppressor gene

UPA

Urokinase plasminogen activator

ING4

Inhibitor of growth family-4

EGFR

Epidermal growth factor receptor

siRNA

Small interfering RNA

NT-siRNA

Non-targeting-small interfering RNA

qPCR

Quantitative polymerase chain reaction

IHC

Immunohistochemistry

TCGA

The Cancer Genome Atlas

CPTAC

Clinical Proteomic Tumor Analysis Consortium

Author contributions

DB and PPB designed the study. DB and CR performed the experiments. DB and MP collected the tumor samples and the related clinical information. DB, CR, MP and PPB analyzed the data. DB drafted the manuscript. DB and PPB finalized the manuscript. All authors read and approved the final manuscript.

Funding

We acknowledge the financial assistance to the Lab from the Ministry of Science and Technology, Department of Science and Technology, Govt. of India, DST- SERB Core grant, file No. SR/CSRI/196/2016, and CRG/2020/005021, Department of Biotechnology, Govt. of India, BT/PR18168/MED/29/1064/2016, BT/PR17686/MED/30/1664/2016, and financial support to the University of Hyderabad-IoE by the Ministry of Education, Govt. of India F11/9/2019-U3 (A), and DST-FIST, and UGC-SAP for the department. D.B. acknowledges the Department of Biotechnology (DBT) India for the student fellowship (Award no: DBT/2013/UOH/79).

Data availability

All data generated or analyzed during this study are included in the manuscript or its supplementary files, and if more is needed, it shall be made available by the authors upon reasonable request.

Declarations

Ethical approval and consent to participate

All procedures performed in this study involving human participants were approved by the Institutional Ethics Committee (IEC), University of Hyderabad, Hyderabad (TS), India, with IEC reference number UH/IEC/2016/180. Written informed consent was obtained from all participants included in the study.

Consent for publication

Not applicable.

Conflict of interest/Competing interests

None.