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Introduction

Of the worldwide population of patients with gastric cancer (GC), ~42% of the men and 19% of the women are Chinese, and the number of patients suffering from GC has increased in recent years (1,2). Although significant progress has been made in the comprehensive treatment with surgery, the prognosis for postoperative patients with GC remains poor (3). A detailed understanding of GC occurrence and development, and the factors that influence its prognosis are required.

The barrier-to-autointegration factor 1 (BANF1) family of proteins has a variety of functions associated with the maintenance of the intact cellular genome (4). BANF1 is an ~10-kDa, highly conserved DNA-binding protein that forms homodimers. It was first identified during the combination of a retrovirus and host, promoting the fusion of retroviruses and target genes in vitro (5). As an important part of the lamina, BANF1 has essential interactions with numerous cellular proteins, including transcription factors and DNA damage repair proteins (6,7). It regulates gene expression, participates in the formation of karyotin structures and is associated with cell mitosis (8).

In recent years, with the advent of microarray technology, novel ideas and insights into the diagnosis and treatment of tumors have been proposed (9). To understand the effect of BANF1 on GC progression, its mRNA and protein expression levels in the gastric tumor tissue were compared with that in adjacent non-tumorous tissue. BANF1 expression was demonstrated to be upregulated in patients with GC. In addition, a high expression of BANF1 was identified as a poor prognostic factor. Furthermore, BANF1 expression correlated with patient age, tumor differentiation and infiltration depth. Therefore, BANF1 may be an oncogene. The results of the present study may aid in developing a reference in prognostic evaluation of patients with GC.

Materials and methods

Data source

The microarray profiles of GC were extracted from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database under the accession number of GSE54129. A total of 132 specimens, including 21 gene chips from adjacent non-tumorous tissue and 111 gene chips from tissues of GC patients, were available for the analysis by GEO2R. The GEO data was passed through quality control and homogeneous processing as previously described (9). A total of 876 cases of gastric cancer used for prognostic analysis were derived from the Kaplan-Meier Plotter database. Survival analysis was performed using the GC database with the Kaplan-Meier (K-M) estimator data. For comparison, the data group (n=876) was segmented into the high (n=604) and low (n=272) BANF1-expression groups with the median as the boundary.

Clinical GC specimen collection

Postoperative specimens were collected between June 2012 and January 2013 from The First Affiliated Hospital of Jinzhou Medical University (Jinzhou, China), which included 23 normal and 118 tumorous tissues. Specimens were placed in 10% neutral buffered formalin for 24 h at room temperature (RT) and then embedded in a paraffin wax block for storage. No patients received any other treatment prior to surgery. Table I lists the clinicopathological features of all patients. The 8th edition tumor node metastasis (TNM) staging system of the Union for International Cancer Control was the standard for tumor staging (10). The present study was approved by the Ethics Committees of the First Affiliated Hospital of Jinzhou Medical University. Written informed consent was obtained from all patients.

Table I. Association between BANF1 expression and clinicopathological features.

Table I.

Association between BANF1 expression and clinicopathological features.

		BANF1 expression
Clinicopathological features	No.	−	+	++	+++	PR (%)	P-value
Sex							0.260
Male	76	3	7	26	40	96.1
Female	42	0	1	16	25	100.0
Age, years							0.001a
≥60	60	2	6	29	23	96.7
<60	58	1	2	13	42	98.3
Tumor size, cm							0.648
≥5	51	1	2	21	27	98.0
<5	67	2	6	21	38	97.0
Differentiation							0.000a
Poor	57	0	0	3	54	100.0
Moderate	50	1	4	37	8	98.0
Well	7	2	4	1	0	71.4
Infiltration depth							0.011a
T1/T2	28	2	5	10	11	92.9
T3/T4	90	1	3	32	54	98.9
Lymph node metastasis							0.154
With	86	3	6	33	44	96.5
Without	32	0	2	9	21	100.0
Distant metastasis							0.214
M0	116	3	8	42	63	97.4
M1	2	0	0	0	2	100.0
TNM staging							0.207
I and II	37	2	6	10	19	94.6
III and IV	81	1	2	32	46	98.8

a P<0.05. PR, positive rate; BANF1, barrier-to-autointegration factor 1; TNM, tumor node metastasis.

Cell culture

The SGC-7901 and MNK-45 GC cell lines (Hangzhou Baisi Biotechnology Co., Ltd., Hangzhou, China) were grown in RPMI-1640 (HyClone; GE Healthcare, Logan, UT, USA) supplemented with 10% fetal bovine serum (Gibco; Thermo Fisher Scientific, Inc., Waltham, MA, USA), 100 U/ml penicillin and 100 µg/ml streptomycin, at 37°C in 5% CO2.

Western blot analysis

Cells were lysed in RIPA lysis buffer (cat. no. WLA016a; Wanleibio Co., Ltd., Shenyang, China). Protein quantification was performed using the Coomassie Brilliant Blue method. Subsequently, 30 µg protein/lane were separated by 12% SDS-PAGE and transferred to a polyvinylidene fluoride membrane. The membrane was blocked with 5% skim milk in Tris-buffered saline with Tween 20 (TBST, 10 mM Tris-HCl, 150 mM NaCl, 0.1% Tween 20) for 1 h at RT, and then incubated with the rabbit monoclonal anti-BANF1 (cat. no. McAb129184; 1:500; Abcam, Cambridge, UK) and rabbit anti-GAPDH (cat. no. WL01114; 1:2,000; Wanleibio Co., Ltd) antibodies overnight at 4°C. The membranes were washed with TBST and incubated with the goat anti-rabbit IgG secondary antibody (cat. no. WLA023a; 1:5,000; Wanleibio Co., Ltd. Shenyang, China). The bands were visualized with a LAS4010 imager (GE Healthcare Life Sciences, Little Chalfont, UK) using ECL-Plus detection reagent (Santa Cruz Biotechnology, Inc., Dallas, TX, USA). The densitometric quantification of protein bands was performed with GAPDH as a control using ImageJ software version 1.8.0 (National Institutes of Health, Bethesda, MD, USA). To validate the specificity of anti-BANF1 antibodies, rabbit anti-BANF1-peptide-specific antibodies (cat. no. 4019P; 1:100; ProSci Inc., Poway, CA, USA) were used against the 15 amino acids near the carboxy-terminus of human BANF1. After the diluted anti-BANF1 antibodies were preincubated with (+) and without (−) 0.5 mM anti-BANF1-peptide-specific antibodies for 2 h at RT, they were used to determine BANF1 expression in SGC-7901 and MNK-45 GC cells (11). The aforementioned western blotting protocol was performed in the same way following this incubation.

Immunohistochemistry

Tissue specimens were cut into successive 4 µm-thick sections. Then, antigen retrieval was performed by heating the specimens using the pressure cooker antigen repairing method at 120°C, followed by washing with xylene and gradual rehydration with a descending ethanol series. Endogenous peroxidase activity in the specimens was neutralized via incubation with 3% hydrogen peroxide for 5 min at room temperature. Subsequently, the specimens were incubated in 5% bovine serum albumin (cat. no. A8020; Beijing Solarbio Science & Technology Co., Ltd., Beijing, China) for 30 min at RT to block nonspecific binding. Then, the tissues were incubated with rabbit monoclonal anti-BANF1 antibody (1:100) overnight at 4°C. Following incubation with the horseradish peroxidase-conjugated goat anti-rabbit IgG secondary antibodies (cat. no. WLA023a; 1:500; Wanleibio Co., Ltd.) for 1 h at RT, all sections were visualized using 3,3′-diaminobenzidine, and the slides were counterstained with haematoxylin (cat. no. WLA051a; Wanleibio Co., Ltd.) for 1 min at RT (1,2). The coverslips were then mounted with anti-fade mounting medium and observed under a microscope (magnification ×200; Olympus DP73; Japan).

Staining intensity score

The BANF1 expression-intensity scores were determined by two independent observers blinded to the clinicopathological data of patients. In order to calculate the percentage of BANF1-positive cells, 100 cells were randomly selected and counted in five representative fields of each section. The positive expression area was counted as follows: <5%, score of 0; <30%, score of 1; 30–70%, score of 2; and >70%, score of 3. The staining intensity score was graded as follows: Colourless, 0; weak, 1; intermediate, 2; and strong, 3. The positive expression area and staining intensity scores were multiplied (2).

Statistical analysis

All data were analysed with the SPSS 20.0 software program (IBM Corp., Armonk, NY, USA). Results were expressed as the mean ± standard deviation. Student's t-tests were performed to evaluate the differences in BANF1 mRNA expression between the gastric tumor and normal tissues. The Spearman's rank correlation coefficient test was used to analyze the correlation between BANF1 expression and clinicopathological features. The Mann-Whitney U-test was used to calculate the statistical significance. Survival curves were derived using the K-M estimator method and compared using the log-rank test. Cox's proportional hazards model was used to analyze the effect of clinicopathological parameters on patient survival. P<0.05 was considered to indicate a statistically significant difference.

Results

High expression of BANF1 mRNA in GC tissues

In the GSE54129 data sets, BANF1 expression in the gastric tumor tissues was identified to be significantly higher compared with in the adjacent non-tumorous tissues (Fig. 1), as demonstrated by statistical parameters for the BANF1 gene expression profiles in Table II (P=1.50×10−87).

Figure 1. mRNA expression of BANF1 in the GSE54129 data sets. The gastric cancer microarray profiles were extracted from the Gene Expression Omnibus database under the accession number of GSE54129. BANF1 expression at the mRNA level in tumor tissues (10.05825) was significantly higher compared with that in normal tissues (6.93204) (P=1.50×10−87). *P<0.05. BANF1, barrier-to-autointegration factor 1.

Table II. Statistical parameters of BANF1 gene expression profiles.

Table II.

Statistical parameters of BANF1 gene expression profiles.

Gene name	ID	Normal mean	Tumor mean	logFC	t	P-value	Adjust P-value
BANF1	210125_s_at	6.93204	10.05825	3.126	49.7	1.50×10-87a	8.20×10-83a

a P<0.05. BANF1, barrier-to-autointegration factor 1; FC, fold change.

Expression of BANF1 in GC cells

After the diluted anti-BANF1 antibodies were preincubated with the anti-BANF1-peptide-specific antibodies, the latter were used for western blotting. Examination of total extracts of SGC-7901 and MNK-45 GC cells by immunoblotting revealed that the antibodies specifically recognized a 10-kDa protein (Fig. 2, lanes 1 and 2), which was not present at the same positions in the lanes with anti-BANF1-peptide-specific antibody preincubation (Fig. 2, lanes 3 and 4). This indicates that BANF1 protein is expressed in SGC-7901 and MNK-45 GC cells and the anti-BANF1 antibody can specifically recognize the 10 kDa BANF1 protein.

Figure 2. Detection of BANF1 protein in SGC-7901 and MNK-45 gastric cancer cells by western blot analysis. Lanes 1, 3, 5 and 7 correspond to total cell extracts of SGC-7901 gastric cancer cells. Lanes 2, 4, 6 and 8 correspond to total cell extracts of MNK-45 gastric cancer cells. The diluted anti-BANF1 antibodies were preincubated with (+) and without (−) 0.5 mM anti-BANF1-peptide-specific antibodies. The positions of the BANF1 proteins are marked at 10 kDa in lanes 1 and 2, which were not present at the same position in lanes 3 and 4. BANF1, barrier-to-autointegration factor 1.

Difference in BANF1 protein expression between the normal and gastric tumor tissues

The intensity of BANF1 expression in the nucleus of GC cells varied (Fig. 3). The total of 118 GC tissues included three cases of negative BANF1 expression, eight weakly positive (+) samples, 42 moderately positive (++) samples and 65 strongly positive (+++) samples. By contrast, negative (−) and weakly positive (+) BANF1 staining was detected in 18 samples and five samples of 23 normal tissues, respectively, while no moderately positive or strongly positive staining was detected. The protein expression of BANF1 in GC specimens was significantly higher compared with that in normal tissues (P<0.001; Table III).

Figure 3. Immunohistochemistry staining of BANF1 in gastric cancer. (A) Normal gastric tissues. (B) Atypical hyperplasia tissues. (C) Gastrointestinal stromal tumor tissues. (D) Poorly differentiated adenocarcinoma. (E) Moderately differentiated adenocarcinoma. (F) Highly differentiated adenocarcinoma. (A) No BANF1 expression (−); (F) weakly stained; (E) moderately positive expression (++) (C) and (D) strongly positive expression (+++) Magnification, ×200. BANF1, barrier-to-autointegration factor 1.

Table III. BANF1 expression in gastric cancer.

Table III.

BANF1 expression in gastric cancer.

		BANF1 expression
Group	No.	−	+	++	+++	PR (%)	P-value
Normal	23	18	5	0	0	21.7	<0.001a
Cancer	118	3	8	42	65	97.5

a P<0.05. PR, positive rate; BANF1, barrier-to-autointegration factor 1.

BANF1 expression is associated with patient age, and the degree of GC differentiation and depth of invasion

The immunohistochemistry results are summarized in Table I. The rates of positive BANF1 expression in GC tissues with low, medium and high differentiation statuses were 100, 98 and 71.4%, respectively. The BANF1 expression in poorly differentiated GC tissues was significantly higher compared with that in moderately and highly differentiated tissues, and was associated with the differentiation degree of tumors (P<0.001). In addition, BANF1 expression correlated with the patient age (P=0.001) and tumor infiltration depth (P=0.011).

Effect of BANF1 expression on the overall survival (OS) of postoperative patients

Online survival analysis was performed using the GC database in K-M Plotter, which revealed that the survival times for patients with high BANF1 mRNA expression was significantly reduced compared with that for the low-expression patients [log-rank test, P<0.001; hazard ratio (HR), 2.37; 95% confidence interval (CI), 1.93–2.92; Fig. 4A]. Subsequently, the K-M estimator method was used to analyze the effect of BANF1 expression on the postoperative overall survival (OS) time of patients, the follow-up period was 150 months, and the mortality was considered as the end point during the follow-up period. The survival curve suggested that the OS of the patients with low BANF1 expression was significantly increased compared with the OS of high expression patients (log-rank test, P<0.001; Fig. 4B). Thus, the level of BANF1 expression may be used as a prognostic indicate for patients with GC. Next, the age, the TNM stage and other clinical features were incorporated into the Cox regression model. It was revealed that the tumor differentiation status and the TNM stage were independent factors affecting the OS of patients with GC (Table IV).

Figure 4. Survival curve for patients with GC patients. (A) The K-M curve of BANF1 mRNA expression from the K-M Plotter data (log-rank test, P<1×10−16). (B) The K-M curve of BANF1 protein expression (log-rank test, P<0.001). The overall survival of the patients in the low-expression group was significantly improved compared with that of the patients in the high-expression group. BANF1, barrier-to-autointegration factor 1; Cum, cumulative; K-M, Kaplan-Meier.

Table IV. Cox multivariate regression analysis of the association between the clinical factors and overall survival.

Table IV.

Cox multivariate regression analysis of the association between the clinical factors and overall survival.

Clinicopathological factors	Relative risk (95% CI)	P-value
BANF1	0.686 (0.305–1.542)	0.362
Negative (−)
Positive (+)
Age, years	0.749 (0.457–1.227)	0.25
≥60
<60
Sex	0.899 (0.550–1.470)	0.672
Male
Female
Tumor size, cm	1.578 (0.948–2.628)	0.079
≥5
<5
Differentiation
Poor
Moderate	7.495 (1.448–38.809)	0.016a
Poor	4.989 (1.129–22.052)	0.034a
Well
Infiltration depth	0.872 (0.402–1.889)	0.728
T1/T2
T3/T4
Lymph node metastasis	0.913 (0.479–1.742)	0.783
With
Without
TNM staging	0.230 (0.109–0.485)	<0.001a
Phase I and II
Phase III and IV

a P<0.05. CI, confidence interval; BANF1, barrier-to-autointegration factor 1; TNM, tumor node metastasis.

Discussion

The five-year survival rates of postoperative patients with GC vary between 15 and 60%, with GC diagnosis and detection lacking effective biomarkers (1,12). Despite the significant advances in the understanding of GC pathogenesis, there is no effective therapy available for GC treatment. Therefore, the identification of effective biomarkers is required to significantly improve the early diagnosis rate and prognosis for patients with GC.

BANF1 encodes a highly conserved BAF protein consisting of 89 amino acids and binding to double-stranded DNA with high-affinity. It was discovered due to its involvement in the integration of retroviral DNA (13). Regulation of BAF phosphorylation, mediated by cell or virus, regulates numerous cellular activities, including protein dimerization, its binding to DNA, and subcellular localization of the protein (14–16). Additionally, BAF participates in karyomitosis, karyon assembly, karyoplasm regulation and the DNA damage response (17). As the first protein that is recruited to the core area of karyolemma, the role of BAF in mitosis is important. Once BAF is located at the core of chromatin, other karyotheca anchoring and membrane-spanning proteins are recruited to complete the nuclear membrane reformation, which is a key step in cell mitosis (18,19). Previous studies have demonstrated that the absence of BAF or the blockage of its phosphorylation results in abnormal morphological abnormalities of the caryotheca (13). This causes aberrant nuclear architecture and disrupts normal cell mitosis process (20). Abnormal mitosis may cause chromosomal instability, which is considered a characteristic change in malignant tumor cells (21).

BAF is able to positively or negatively regulate cell activities, although the specific mechanisms remain unclear. At the early stages of Caenorhabditis elegans embryogenesis, BAF regulates seam cell fusion by suppressing the expression of epithelial fusion failure-1-fused protein and the development of germ cells (22). It has been reported that BAF inhibits transcription in vaccinia virus deficiency in the viral B1 kinase (23,24). Activation of BANF1 may inhibit skin inflammation in psoriatic lesions by inactivation of S100A9 and c-Jun, but it may also promote keratinocyte proliferation (5). A coding mutation of the BANF1 alanine-12 to threonine was reported to be associated with BANF1-protein instability and abnormalities of the caryotheca, which has been confirmed as the hereditary fundamental of Néstor-Guillermo Progeria syndrome (NGPS) (25). The disruption of the interaction between Prelamin A and BAF may be the cause of NGPS (26). In addition, certain studies have reported that BANF1 expression is increased in breast and oesophageal cancer, and is associated with the poor prognosis of patients (27,28). Furthermore, its role is essential in regulating the S-phase process, stem cell self-renewal, and differentiation and proliferation of keratinocytes (5,29).

Targeted therapy and individualized tumor treatment are directed against specific gene mutations and proteins. Based on the notion of tumor therapy treatment, it is essential to identify suitable oncogenes to develop small molecules or monoclonal antibodies to control the disease. BANF1 has been demonstrated to be a target for the treatment of tumors with a specialized inhibitor aimed at BAF phosphorylation mediated by vaccinia related kinase 1, which blocks cell cycle progression in tumor cells by interfering with the caryomitotic phase (30).

In the present study, the expression level of BANF1 was demonstrated to be significantly increased in GC tissues, and was inversely correlated with the patient age, tumor differentiation status and infiltration depth. Furthermore, the survival curve analysis revealed that the prognosis for patients with high BANF1 expression was poor at the mRNA and protein levels. To further investigate the association between the clinical characteristics and prognosis, Cox regression analysis was performed. The results suggested that tumor differentiation status and TNM stage were independent risk factors affecting the OS of patients with GC. The HR of BANF1 was 0.686 (95% CI, 0.305–1.542; P=0.362) in the Cox regression analysis, but BANF1 significantly impacts poor survival of GC in the K-M analysis (log-rank P<0.001). This indicates that BANF1 has an impact on prognosis together with other factors, but is not an independent prognostic factor by itself. A limitation of the present study was that the BANF1 protein levels in the tissues from patients with GC were only detected by immunohistochemistry, and that western blot analysis is required to support the immunohistochemistry results.

In summary, BANF1 expression was upregulated in clinical GC tissues, and was associated with the tumor age, infiltration depth and differentiation. Additionally, the results of the present study confirmed that BANF1 closely correlates with adverse outcomes in GC patients undergoing surgery. Taken together, the present study is the first to propose that high BANF1 expression may be used as a novel indicator of poor prognosis or as a therapeutic target for patients with GC.

Acknowledgements

Not applicable.

Funding

The present study was supported by the Liaoning Province Science and Technology Project (grant no. 2010010280-401).

Availability of data and materials

The datasets generated and/or analyzed during the present study are available in the GEO repository (http://www.ncbi.nlm.nih.gov/geo/) and the Kaplan-Meier Plotter database (http://kmplot.com/analysis/index.php?p=service&cancer=gastric).

Authors' contributions

JL, BH, LF, YG, SS and HH performed the experiments and analyzed the data. JL, LF, XL and CY designed the study and co-wrote the manuscript. JL, YG, LF and HH were involved in revising the manuscript. All the authors have read and approved the final version of the manuscript.

Ethics approval and consent to participate

The present study was approved by the Ethics Committees of the First Affiliated Hospital of Jinzhou Medical University. Written informed consent was obtained from all patients.

Patient consent for publication

Written informed consent was obtained from all patients regarding the publication of the data and associated images.

Competing interests

The authors declare that they have no competing interests.

References