Abstract
Introduction
Karyopherin alpha 2 (KPNA2) and karyopherin beta 1 (KPNB1) constitute nuclear transport protein complexes involved in nuclear import and are significant in tumor progression. Although high KPNA2 expression was associated with poor prognosis in solid tumors, the relationship between KPNA2 and KPNB1 expression and their prognostic role in gastric cancer (GC) remains unclear.
Methods
Immunohistochemistry was used to correlate the expression of KPNA2 and KPNB1 with various features, including clinicopathological characteristics in 130 patients with GC and survival in 94 patients with invasive lesions extending to the submucosa or deeper.
Results
High expression of KPNA2 and KPNB1 was found in 25% and 36% of the patients, respectively. Both were significantly related to tumor depth, lymph node metastasis, lymphatic invasion, venous invasion, and Ki-67 expression. KPNA2 expression was significantly related to that of KPNB1 (p < 0.001). Patients with high KPNB1 expression had poorer prognosis than those with low expression (p = 0.027), as was also observed in case of KPNA2 (p < 0.001). Patients with high expression of both KPNA2 and KPNB1 accounted for 18% and had a poorer prognosis than those with high expression of either and those with low expression of both (p = 0.001). Multivariate analysis revealed that high expression of both KPNA2 and KPNB1 was an independent prognostic factor in patients with GC (hazard ratio, 3.46; 95% confidence interval, 1.64–2.73, p = 0.001).
Conclusion
KPNA2 expression was correlated with KPNB1 expression, and high co-expression of KPNA2 and KPNB1 may represent a strong prognostic biomarker in GC.
Keywords: Karyopherin alpha, Karyopherin beta, Gastric cancer, Prognosis
Introduction
Gastric cancer (GC) is one of the most common malignancies and the leading cause of cancer-related deaths worldwide [1]. Although progress in endoscopic diagnosis has facilitated early detection of GC, the disease is often advanced by the time of diagnosis. Recently, systemic chemotherapy against advanced GC has improved the survival rates of patients, but its prognosis remains poor. The survival of patients with HER2-positive GC has improved with the use of trastuzumab/chemotherapy combination [2]. However, except in the case of HER2, there are no biomarkers for advanced GC. Therefore, a novel therapeutic target biomarker is required for advanced GC.
Nucleocytoplasmic transport mechanisms have been shown to be related to many cellular processes including gene expression, cell cycle, and signal transduction [3]. Nucleocytoplasmic transport occurs through cylindrical structures spanning the nuclear envelope, which are known as nuclear pore complexes (NPCs). Small proteins (<20 kDa) can pass through NPCs by diffusion, whereas the passage of macromolecules (>40 kDa) is restricted to those bearing appropriate signals such as the nuclear localization signal (NLS). Nucleocytoplasmic transport is mediated by soluble receptors that recognize the NLS in their cargo. Karyopherin proteins are soluble nuclear transport receptor proteins that shuttle cargo proteins between the cytoplasm and nucleus. Karyopherin alpha 2 (KPNA2) and karyopherin beta 1 (KPNB1) belong to the karyopherin family and form a nuclear transport protein complex involved in nuclear import [4], whereas Crm1 is a karyopherin protein involved in nuclear export [5]. KPNB1 can import proteins directly or through the complex of the KPNB1 and karyopherin alpha family. The concerted action of KPNB1 and KPNA2 is necessary for the nuclear import of proteins containing a classical NLS [6].
Van der Watt et al. [7] showed that KPNA2 and KPNB1 were more highly expressed in cervical tumors and cervical cancer cell lines than in normal cervical tissues and that their promoter activity was higher in cervical cancer cells because of activation by the cell cycle regulator, E2F. E2F activity is known to be deregulated in cervical cancer cells through the inhibition of retinoblastoma protein (Rb). In their study, deregulation of E2F/Rb activity caused KPNA2 and KPNB1 overexpression in cervical cancer cells. Inhibition of KPNA2 and KPNB1 protein expression in cancer cells led to apoptosis, whereas in noncancer cells, it had only a minor effect on cell viability. These results demonstrate the potential use of KPNA2 and KPNB1 as anticancer targets.
In the 2000s, the clinicopathological features and prognosis of high KPNA2 expression were investigated in several cancers. In breast cancer, KPNA2 expression was significantly associated with higher tumor stage, lymph node metastases, higher tumor grade, negative hormone receptor (estrogen and progesterone receptor), and a higher Ki-67 labeling index [8, 9]. Moreover, high KPNA2 expression in tumors was associated with poor survival and was an independent prognostic factor in breast cancer [10]. Similarly, in other cancers such as esophageal cancer, non-small cell lung cancer, prostate cancer, and ovarian cancer, high KPNA2 expression was associated with a significantly poorer prognosis compared to that with low KPNA2 expression [11, 12, 13]. In GC, the relationship between KPNA2 overexpression and prognosis has been investigated in Japan and China [14, 15]. Both previous studies revealed that high KPNA2 expression was related to poorer survival than low KPNA2 expression in patients with GC. However, the relationship between KPNB1 overexpression and prognosis in solid tumors remains unclear. The interaction between KPNB1 and KPNA2 is required for the nuclear import of proteins [6]; however, the relationship between KPNA2 and KPNB1 in cancers remains unclear. A previous study demonstrated that cervical cancer showed high expression of KPNA2 and KPNB1 [16]. However, the relation between survival and the co-expression of KPNA2 and KPNB1 was not evaluated.
Therefore, the aim of our study was to clarify the prognostic role of KPNA2 and KPNB1 in GC. First, we examined the expression of KPNA2 and KPNB1 in GC tissues by using immunohistochemical assays. We then examined the relationship between KPNA2 and KPNB1 expression and clinicopathological features. We also analyzed the survival of patients with GC expressing KPNA2 and KPNB1. Finally, we investigated the prognostic factors in patients with GC, including the expression of KPNA2 and KPNB1.
Materials and Methods
Tumor Specimens and Survival Data
Specimens were collected from 130 patients with GC who underwent surgical resection between January 2004 and December 2007 at KKR Sapporo Medical Center. Informed consent was obtained from all the patients. This study was approved by the Medical Ethics Committee of the KKR Sapporo Medical Center and Hokkaido University Hospital. Clinicopathological findings were retrospectively obtained from clinical and pathology records. The pathologic stage (pStage) was based on the Japanese Classification of Gastric Carcinoma, edited by the Japanese Gastric Cancer Association [17].
Among the 130 patients with GC, 82 were male and 48 were female, and their median age was 68 years (range, 31–90 years). Tumor differentiation was divided into two subtypes according to Lauren's classification: intestinal type (well and moderately differentiated) and diffuse type (poorly differentiated and signet ring cells). The specimens represented pStage I (n = 74), pStage II (n = 14), pStage III (n = 26), and pStage IV (n = 16). Survival data were analyzed for 94 patients with GC who had invasive lesions extending to the submucosa (SM) in their tumors. The median follow-up period was 55 months (range, 1–113 months).
Cell Lines and Western Blotting
Four human GC cell lines, MKN7, MKN45, MKN74, and KATO-III, were used in this study. All four cell lines were purchased from the Japanese Collection of Research Bioresources (Osaka, Japan). These GC cell lines were maintained in RPMI-1640 medium supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 U/mL streptomycin. All cell lines were cultured in 5% CO2 at 37°C.
Western blotting was performed to confirm the expression of KPNA2, KPNB1, and β-actin in GC cell lines. Lysates derived from each GC cell line were prepared by disrupting the cells in radioimmunoprecipitation assay buffer (150 mM NaCl, 1% Triton X-100, 1% deoxycholate, 0.1% sodium dodecyl sulfate, and 10 mM Tris, pH 7.4), supplemented with 100 μg/mL leupeptin, 100 μg/mL aprotinin, and 10 mM phenylmethylsulfonyl fluoride. The cell lysates were sonicated and then centrifuged to remove debris. The protein concentration in each lysate sample was determined using a Bio-Rad Protein Assay kit (Bio-Rad Laboratories, Hercules, CA, USA). Samples containing equal amounts of protein were loaded onto gels, and the proteins in each sample were separated on 12% or 15% sodium dodecyl sulfate gels, followed by transfer to nitrocellulose membranes (Amersham Biosciences, St. Albans, UK); the membranes were incubated with the following antibodies: goat anti-KPNA2 polyclonal antibody (C-20; Santa Cruz Biotechnology, 1:1,000), rabbit anti-KPNB1 monoclonal antibody (H-300; Santa Cruz Biotechnology, 1:1,000), and rabbit anti-actin antibodies (A-2066; Sigma-Aldrich Co., 1:1,000) diluted in TBST (Tris-buffered saline, with 0.1% Tween 20). Primary antibodies were reacted with horseradish peroxidase-conjugated anti-goat or anti-rabbit IgG (Jackson ImmunoResearch, West Grove, PA, USA) for immunodetection. The immune complexes were visualized by enhanced chemiluminescence (Amersham, Piscataway, NJ, USA) and analyzed using Image Gauge software (Fujifilm, Tokyo, Japan).
Immunohistochemistry
Staining was performed using the Leica Biosystems protocol. Formalin-fixed tissue sections or cell clot sections were deparaffinized in xylene and rehydrated in graded alcohol and distilled water. After rehydration, antigen retrieval was performed using a Leica Epitope Retrieval Solution (pH 9.0) at 98°C for 20 min; the sections were then passively cooled at room temperature (20–25°C). Endogenous peroxidase activity was blocked by Refine Detection Kit Peroxide Block, and the sections were washed several times. After blocking, the sections were incubated with goat anti-KPNA2 polyclonal antibody (1:100, 40 min), rabbit anti-KPNB1 polyclonal antibody (1:1,000, 30 min), or rabbit anti-Ki-67 (Clone SP6) monoclonal antibody (1:200, 30 min, Thermo Scientific). After washing, the sections were incubated with anti-goat IgG for 30 min or with anti-rabbit IgG for 8 min twice. Immunostaining was performed using a Refine Detection Kit. Formalin-fixed tissue sections were also stained with hematoxylin and eosin. Cell clot sections immunostained for KPNA2 and KPNB1 were used as positive controls, and normal gastric mucosa was used as a negative control in this study (Fig. 1a-c).
Fig. 1.
Immunohistochemical staining for KPNA2 (a) and KPNB1 (b) in normal gastric tissues. c Western blot analysis of KPNA2 (52 kDa) and KPNB1 (97 kDa) and immunohistochemical staining for KPNA2 and KPNB1 in GC cell lines (KATO-III. MKN74, MKN45, and MKN7).
Evaluation of Immunostaining
Immunohistochemical staining for KPNA2 and KPNB1 was evaluated by two experienced researchers (Suzuki A and Ohhara Y). The percentage of stained nuclear cells was assessed in the invasive front by examining at least 500 cancer cells in four representative fields (0–100%). In a previous study, nuclear KPNA2 staining percentages were scored as follows: 0%, no staining; 1+, 1–10%; 2+, 11–50%; 3+, 51–100% [14]. The intensity of stained nuclear cells was divided into four classes as follows: 0, no staining; 1+, weak staining; 2+, moderate staining; and 3+, strong staining. We adopted the previous study's scoring system, which defined the high or low expression of KPNA2 as the percentage score multiplied by the intensity score (0, 1+, 2+, 3+, 4+, 6+, and 9+). The cut-off point was defined as high expression (4+, 6+, and 9+) or low expression (0, 1+, 2+, and 3+). However, KPNB1 expression in GC tissues was not evaluated. Therefore, we used the same scoring system as KPNA2 for evaluating KPNB1 expression. The Ki-67 index was defined as high (>20%) and low (≤20%) expression, as previously described [14].
Statistical Analyses
Relationships between KPNA2 or KPNB1 expression and clinicopathological factors were analyzed using χ2 tests or Fisher's exact tests. Overall survival (OS) was defined as the interval between the day of surgical operation and death or the date of last follow-up. Survival data were estimated using the Kaplan-Meier method. Survival rates were compared using log-rank tests. Univariate and multivariate analyses of various factors and OS were performed using the Cox proportional hazards regression model. Statistical significance was set at p < 0.05. All statistical analyses were performed using SPSS software, version 18 (SPSS Inc., Chicago, IL, USA).
Results
KPNA2 and KPNB1 Expression in Normal Gastric Tissues and GC Cell Lines
In normal gastric tissues, mucosal cells showed no expression of KPNA2 and KPNB1, serving as internal negative controls (Fig. 1a, b). The results of immunohistochemical assays and Western blot analysis of KPNA2 and KPNB1 expression in GC cell lines, KATO-III, MKN74, MKN45, and MKN7, are shown inFigure 1c. Immunohistochemical analyses for KPNA2 and KPNB1 expression showed moderate to low staining in all GC cell lines. Western blotting confirmed that all GC cell lines expressed KPNA2 and KPNB1. These results indicate that immunochemical analyses using anti-KPNA2 and anti-KPNB1 antibodies could detect KPNA2 and KPNB1 expression and could, therefore, be used for their evaluation.
Clinicopathological Significance of KPNA2 and KPNB1 Expression in GC
We evaluated the immunohistostaining results based on the criteria described in the Materials and Methods and our findings are shown in Figure 2. The relationships between KPNA2 or KPNB1 expression and clinicopathological features of patients with GC were examined. For KPNA2 staining, high expression was observed in 32 cases (24.6%) and low expression was found in 98 cases (75.4%). High expression of KPNA2 was strongly associated with tumor depth (p = 0.006), lymph node metastasis (p = 0.008), lymphatic invasion (p = 0.024), and venous invasion (p = 0.016) (Table 1). KPNB1 expression was similar to KPNA2 expression in GC tissues. There were 47 cases (36.2%) with high expression of KPNB1 and 83 cases (63.8%) with low expression. High KPNB1 expression was significantly associated with tumor depth (p = 0.005), lymph node metastasis (p < 0.001), lymphatic invasion (p = 0.02), and venous invasion (p = 0.006) (Table 1). The relationship between KPNA2 and KPNB1 was statistically significant (p < 0.001). Based on the results, tumor depth, lymph node metastasis, lymphatic invasion, and venous invasion showed a strong relationship with high expression of KPNA2 and KPNB1 in resected GC tissues.
Fig. 2.
Levels of immunostaining intensity for KPNA2 (a) and KPNB1 (b) in GC tissues (objective, ×20). 0 = no staining, 1+ = weak staining, 2+ = moderate staining, and 3+ = strong staining.
Table 1.
Relationship between KPNA2 and KPNB1 expression and clinicopathological features in GC
| KPNA2 expression |
KPNB1 expression |
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| low |
High |
p value | low |
High |
p value | |||||
| N = 98 | N = 32 | N = 83 | N = 47 | |||||||
| Age, median (range) | 68 | (34–86) | 70 | (31–90) | 0.111 | 68 | (34–90) | 71 | (31–86) | 0.356 |
| Sex | ||||||||||
| Male | 59 | (72%) | 23 | (28%) | 0.235 | 56 | (68%) | 26 | (32%) | 0.168 |
| Female | 39 | (81%) | 9 | (19%) | 27 | (56%) | 21 | (44%) | ||
| Histology | ||||||||||
| Intestinal | 43 | (73%) | 16 | (27%) | 0.546 | 37 | (63%) | 22 | (37%) | 0.806 |
| Diffuse | 55 | (77%) | 16 | (23%) | 46 | (65%) | 25 | (35%) | ||
| Tumor depth | ||||||||||
| T1 (M, SM) | 52 | (87%) | 8 | (13%) | 0.006 | 46 | (77%) | 14 | (23%) | 0.005 |
| T2–4 (MP, SS, SE) | 46 | (66%) | 24 | (34%) | 37 | (53%) | 33 | (47%) | ||
| Lymph node metastasis | ||||||||||
| Absent | 60 | (85%) | 11 | (15%) | 0.008 | 55 | (77%) | 16 | (23%) | <0.001 |
| Present | 38 | (64%) | 21 | (36%) | 28 | (47%) | 31 | (53%) | ||
| Lymphatic invasion | ||||||||||
| Absent | 50 | (85%) | 9 | (15%) | 0.024 | 44 | (75%) | 15 | (25%) | 0.02 |
| Present | 48 | (68%) | 23 | (32%) | 39 | (55%) | 32 | (45%) | ||
| Venous invasion | ||||||||||
| Absent | 74 | (81%) | 17 | (19%) | 0.016 | 65 | (71%) | 26 | (29%) | 0.006 |
| Present | 24 | (62%) | 15 | (38%) | 18 | (46%) | 21 | (54%) | ||
| KPNB1 expression | ||||||||||
| low | 71 | (86%) | 12 | (14%) | <0.001 | |||||
| High | 27 | (57%) | 20 | (43%) | ||||||
Relationships between Survival and KPNA2 and KPNB1 Expression in GC
Among the 130 patients with GC, 94 had SM or a deeper tumor depth. We analyzed the survival data of these 94 patients according to the expression of KPNA2 and KPNB1. Among them, 25 (26.6%) had high expression of KPNA2, and 40 patients (42.6%) had high expression of KPNB1. According to the KPNA2 expression, OS in the high expression group (n = 25) was significantly shorter than that in the low expression group (n = 69, p = 0.007) (Fig. 3a). The 5-year survival rate was 40.8% with high expression and 78.3% with low expression. According to the KPNB1 expression, OS in the high expression group (n = 40) was also significantly shorter than that in the low expression group (n = 54, p = 0.027) (Fig. 3b). The 5-year survival rate was 54.6% with high expression and 79.2% with low expression.
Fig. 3.
Survival curves according to KPNA2 (a) and KPNB1 (b) expression in 94 patients with GC. c shows the survival curves according to three cohorts; cohort 1, high expression of both KPNA2 and KPNB1; cohort 2, high expression of KPNA2 or KPNB1; cohort 3, low expression of KPNA2 and KPNB1.
High expression levels of both KPNA2 and KPNB1 were observed in 17 of 94 cases (18%) (Fig. 4). We thus divided the patients into three cohorts as follows: cohort 1, with high expression of both KPNA2 and KPNB1 (N = 17, 18%); cohort 2, with high expression of either KPNA2 or KPNB1 (N = 30, 32%); and cohort 3, with low expression of both KPNA2 and KPNB1 (N = 47, 50%). High expression of both KPNA2 and KPNB1 (cohort 1) was correlated with a poorer survival compared to that with high expression of either (cohort 2) or low expression of KPNA2 and KPNB1 (cohort 3) (p = 0.001); the 5-year survival rates were 29.9%, 70.9%, and 81.5% in cohort 1, cohort 2, and cohort 3, respectively (Fig. 3c). Clinicopathological features between cohort 1, cohort 2, and cohort 3 were shown in online supplementary Table 1 (see www.karger.com/doi/10.1159/000526807 for all online suppl. material). The patients in cohort 1 were significantly associated with tumor depth compared with those in cohorts 2 and 3 (p = 0.04).
Fig. 4.
Representative case with high expression of both KPNA2 and KPNB1 in GC tissue (scale bar, 50 μm).
Prognostic Value of KPNA2 and KPNB1 in GC
Univariate and multivariate analyses were performed to determine the prognostic factors for survival in patients with GC. Based on our results, patients with GC showing high expression of both KPNA2 and KPNB1 had the poorest survival; thus, we added the high expression of both KPNA2 and KPNB1 to the prognostic factor analysis. High expression of both KPNA2 and KPNB1 (hazard ratio, 3.458; 95% confidence interval, 1.64–7.29, p = 0.001) and lymph node metastasis (hazard ratio, 2.36; 95% confidence interval, 1.01–5.50, p = 0.046) were found to be prognostic factors in univariate analysis and remained significant in multivariate analysis (Table 2).
Table 2.
Univariate and multivariate analyses of clinicopathological factors affecting the OS in GC
| Univariate analysis |
Multivariate analysis |
|||||
|---|---|---|---|---|---|---|
| hazard ratio | 95% CI | p value | hazard ratio | 95% CI | p value | |
| Age; <65 versus ≥65 years | 1.33 | 0.55–3.25 | 0.529 | |||
| Sex; male versus female | 0.57 | 0.26–1.24 | 0.156 | |||
| Histology; intestinal versus diffuse | 1.16 | 0.57–2.36 | 0.683 | |||
| T1 versus T2–4 | 2.17 | 0.83–5.66 | 0.113 | |||
| Lymph node metastasis | 2.44 | 1.05–5.67 | 0.038 | 2.36 | 1.01–5.50 | 0.046 |
| Lymphatic invasion | 2.00 | 0.77–5.21 | 0.157 | |||
| Venous invasion | 1.79 | 0.88–3.62 | 0.108 | |||
| KPNA2 and KPNB1 high expression | 3.55 | 1.70–7.47 | 0.001 | 3.458 | 1.64–7.29 | 0.001 |
Relationships between KPNA2 or KPNB1 Expression and Ki-67 in GC
The relationship between KPNA2 or KPNB1 expression and Ki-67 was examined in 94 GC tissues that had invasive lesions into the SM or deeper. The incidence of high expression of KPNA2 or KPNB1 has been described above. We found that GC tissues with high expression of KPNA2 had higher Ki-67 expression compared to that in tissues with low expression of KPNA2 (88% [22/25] vs. 12% [3/25], p = 0.005). Similarly, GC tissues with high expression of KPNB1 had higher Ki-67 expression compared to those with low expression of KPNB1 (78% [31/40] vs. 23% [9/40], p = 0.028) (Table 3).
Table 3.
Relationship between KPNA2/KPNB1 and Ki-67 expression in GC
| Ki-67 |
p value | ||||
|---|---|---|---|---|---|
| low | High | ||||
| KPNA2 | |||||
| low | 30 | (43%) | 39 | (57%) | 0.005 |
| High | 3 | (12%) | 22 | (88%) | |
| KPNB1 | |||||
| low | 24 | (44%) | 30 | (56%) | 0.028 |
| High | 9 | (23%) | 31 | (78%) | |
Discussion
In this study, we demonstrated that high expression of KPNA2 and KPNB1 in GC tissues was associated with poor prognosis. KPNA2 expression was significantly associated with KPNB1 expression in GC. Moreover, expression of both KPNA2 and KPNB1 was found to be a strong prognostic factor in patients with GC. Although several studies have reported that KPNA2 expression has potential as a prognostic factor in solid tumors, the prognostic role of KPNB1 overexpression has not been investigated [18]. To the best of our knowledge, this is the first study to investigate the co-expression of KPNA2 and KPNB1 in GC.
Zhou et al. [19] reported a meta-analysis of 24 published studies that evaluated whether the expression of KPNA2 was associated with prognosis in patients with solid tumors. Their study included 15 types of tumors, including GC, and they concluded that KPNA2 expression was an independent predictor for the prognosis of solid tumors in the meta-analysis. However, the relationship between KPNB1 overexpression and prognosis in solid tumors was unclear. For GC, only one study reported an association between KPNB1 expression and poor prognosis [20]. In this study, we used 150 GC tissues to evaluate KPNB1 expression, which was found to be associated with tumor grade, infiltration depth, and Ki-67 expression. KPNB1 expression was an independent poor prognostic factor in the univariate and multivariate analyses. Our results corroborated these observations, indicating the prognostic value of KPNB1 expression in GC.
Our study revealed that KPNA2 expression was related to KPNB1 expression in GC and that co-expression of KPNA2 and KPNB1 played the role of an independent prognostic factor more significantly compared with the expression of KPNA2 or KPNB1 alone. KPNA2 and KPNB1 form a complex for import proteins into the nucleus [6]. High expression of both KPNA2 and KPNB1 in the tumor nucleus is suggested to be related to the complex of KPNA2 and KPNB1, which function as nuclear transporters.
The roles of KPNA2 and KPNB1 in GC remain unclear. However, KPNA2 has recently been suggested to act as a transporter for several tumor suppressors [21]. A previous report demonstrated that the cell cycle regulator Chk2 is one of the most prominent cargo proteins of KPNA2 [22]. KPNA2 interacts with the NLS of Chk2, which is indispensable for its import, and overexpression of KPNA2 correlates with increased nuclear import. KPNA2 has also been suggested to be involved in importing the cancer progression-related gene product, NBS1, into the nucleus [23]. It is speculated that KPNA2 determines the role of NBS1 in regulating its cellular localization. In breast cancer, KPNA2 expression was associated with the cytoplasmic localization of several key DNA repair proteins including BRCA1, RAD51, BARD1, PIAS1, and Chk1 [24]. These results indicated the potential of KPNA2 as a therapeutic biomarker. KPNB1 has been reported to regulate several cellular functions including inflammation, migration, apoptosis, morphology, and the circadian clock [25, 26, 27]. KPNB1 has been studied as a target for anticancer treatment. Several studies have shown that the inhibition of KPNB1 resulted in anticancer effects through various mechanisms, including interference with E2F1 activity [28], disruption of proteostasis [29], alteration of MET proto-oncogene expression, and downregulation of epithelial-mesenchymal transition [30]. Based on these studies, KPNB1 is also a novel therapeutic biomarker.
In our study, the patients in KPNB1 high expression showed relatively higher percentages of diffuse type histology, T2-4 tumor, lymph node metastasis, lymphatic invasion, and venous invasion than those in KPNA2 high expression. KPNB1 facilitates nuclear import not only by interacting with karyopherin alpha subunits including KPNA2 which bind to the classical NLS of cargo proteins but also by directly interacting with cargo proteins via nonclassical NLS [31]. Interestingly, KPNB1 has been shown to promote nuclear import of Creb and AP-1 transcription factors involved in cell survival and proliferation, independent of karyopherin alpha [32]. The differences in the cargo proteins between KPNA2 and KPNB1 might cause a trend of more aggressive clinicopathological features in the KPNB1 high group compared with the KPNA2 high group.
Recently, the small-molecule compound, inhibition of nuclear import-43 (INI-43), demonstrated a nuclear import inhibitory effect on KPNB1 cargoes and reduced cervical and esophageal tumor growth in xenograft mouse models [27]. Further, pretreatment of cervical cancer cells with INI-43 significantly enhanced cisplatin sensitivity [33]. This study thus suggests the possible synergistic use of INI-43 and cisplatin for treating cervical cancer. Moreover, selinexor (KPT-330), an inhibitor of nucleocytoplasmic transporter proteins, has been reported to exert an antitumor effect in advanced solid tumors [34]. Selinexor (KPT-330) is a novel oral small-molecule inhibitor of exportin 1 (XPO1/CRM1). In the first-in-human phase I study, 189 patients with solid tumors received selinexor treatment. Among 157 patients evaluable for response, 7 patients had a complete or partial response (4%) and 27 patients (17%) had stable disease for 4 months or more. Based on this trial, selinexor has been approved by the Food and Drug Administration (FDA) for treating multiple myeloma and diffuse large B-cell lymphoma [35, 36]. Moreover, selinexor demonstrated efficacy in patients with differentiated liposarcoma in a phase II/III SEAL study [33]. Nuclear transporter proteins have the potential for targeted therapy in several cancers. Thus, the development of targeted therapies against KPNA2 or KPNB1 is also warranted in human cancer.
In conclusion, the expression of KPNB1 as well as KPNA2 was found to be correlated with tumor depth, lymph node metastasis, lymphatic invasion, venous invasion, and Ki-67 expression in GC. The limitations of this study were a small sample size and an older cohort. Recently, adjuvant chemotherapy against GC was improved. Therefore, we need to analyze recent and large samples of GC. KPNA2 expression was strongly correlated with KPNB1 expression, and high expression of both KPNA2 and KPNB1 was a strong independent prognostic factor in patients with progressive GC. In our future work, one of our goals is to analyze the high expression of both KPNA2 and KPNB1 as potential therapeutic targets.
Statement of Ethics
This study protocol was reviewed and approved by the Medical Ethics Committee of the KKR Sapporo Medical Center and Hokkaido University Hospital, approval number 012-0304. All patients provided a signed informed consent form at the time of surgical resection of gastric cancer. The study was conducted according to the tenets of the Declaration of Helsinki.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
The authors declare that they received no funding support for this study.
Author Contributions
Conception and design of the study: Yoshihito Ohhara, Ichiro Kinoshita, and Akira Suzuki. Assessment of IHC and clinical data: Yoshihito Ohhara, Akira Suzuki, and Makoto Imagawa. Analysis and interpretation of data and drafting the manuscript: Yoshihito Ohhara and Ichiro Kinoshita. Revising the manuscript: Akira Suzuki, Makoto Imagawa, Jun Taguchi, Takuro Noguchi, Satoshi Takeuchi, Yasushi Shimizu, Hideyuki Seki, Junichi Suzuki, and Hirotoshi Dosaka-Akita.
Data Availability Statement
The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from Ichiro Kinoshita upon reasonable request.
Acknowledgments
We thank the staff of the Department of Pathology, KKR Sapporo Medical Center, for their excellent technical assistance. We would like to thank Editage (www.editage.com) for English language editing.
Funding Statement
The authors declare that they received no funding support for this study.
References
- 1.Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A., Global cancer statistics 2018 GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68((6)):394–424. doi: 10.3322/caac.21492. [DOI] [PubMed] [Google Scholar]
- 2.Bang YJ, Van Cutsem E, Feyereislova A, Chung HC, Shen L, Sawaki A, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA) A phase 3, open-label, randomised controlled trial. Lancet. 2010;376((9742)):687–697. doi: 10.1016/S0140-6736(10)61121-X. [DOI] [PubMed] [Google Scholar]
- 3.Chook YM, Blobel G. Karyopherins and nuclear import. Curr Opin Struct Biol. 2001;11((6)):703–715. doi: 10.1016/s0959-440x(01)00264-0. [DOI] [PubMed] [Google Scholar]
- 4.Stewart M. Molecular mechanism of the nuclear protein import cycle. Nat Rev Mol Cell Biol. 2007;8((3)):195–208. doi: 10.1038/nrm2114. [DOI] [PubMed] [Google Scholar]
- 5.Fukuda M, Asano S, Nakamura T, Adachi M, Yoshida M, Yanagida M, et al. CRM1 is responsible for intracellular transport mediated by the nuclear export signal. Nature. 1997;390((6657)):308–311. doi: 10.1038/36894. [DOI] [PubMed] [Google Scholar]
- 6.Ström AC, Weis K. Importin-beta-like nuclear transport receptors. Genome Biol. 2001;2((6)):3008. doi: 10.1186/gb-2001-2-6-reviews3008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.van der Watt PJ, Ngarande E, Leaner VD. Overexpression of Kpnβ1 and Kpnα2 importin proteins in cancer derives from deregulated E2F activity. PLoS One. 2011;6((11)):e27723. doi: 10.1371/journal.pone.0027723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Dankof A, Fritzsche FR, Dahl E, Pahl S, Wild P, Dietel M, et al. KPNA2 protein expression in invasive breast carcinoma and matched peritumoral ductal carcinoma in situ. Virchows Arch. 2007;451((5)):877–881. doi: 10.1007/s00428-007-0513-5. [DOI] [PubMed] [Google Scholar]
- 9.Gluz O, Wild P, Meiler R, Diallo-Danebrock R, Ting E, Mohrmann S, et al. Nuclear karyopherin α2 expression predicts poor survival in patients with advanced breast cancer irrespective of treatment intensity. Int J Cancer. 2008;123((6)):1433–1438. doi: 10.1002/ijc.23628. [DOI] [PubMed] [Google Scholar]
- 10.Dahl E, Kristiansen G, Gottlob K, Klaman I, Ebner E, Hinzmann B, et al. Molecular profiling of laser-microdissected matched tumor and normal breast tissue identifies karyopherin α2 as a potential novel prognostic marker in breast cancer. Clin Cancer Res. 2006;12((13)):3950–3960. doi: 10.1158/1078-0432.CCR-05-2090. [DOI] [PubMed] [Google Scholar]
- 11.Li XL, Jia LL, Shi MM, Li X, Li ZH, Li HF, et al. Downregulation of KPNA2 in non-small-cell lung cancer is associated with Oct4 expression. J Transl Med. 2013;11((1)):232. doi: 10.1186/1479-5876-11-232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Mortezavi A, Hermanns T, Seifert HH, Baumgartner MK, Provenzano M, Sulser T, et al. KPNA2 expression is an independent adverse predictor of biochemical recurrence after radical prostatectomy. Clin Cancer Res. 2011;17((5)):1111–1121. doi: 10.1158/1078-0432.CCR-10-0081. [DOI] [PubMed] [Google Scholar]
- 13.Zheng M, Tang L, Huang L, Ding H, Liao WT, Zeng MS, et al. Overexpression of karyopherin-2 in epithelial ovarian cancer and correlation with poor prognosis. Obstet Gynecol. 2010;116((4)):884–891. doi: 10.1097/AOG.0b013e3181f104ce. [DOI] [PubMed] [Google Scholar]
- 14.Altan B, Yokobori T, Mochiki E, Ohno T, Ogata K, Ogawa A, et al. Nuclear karyopherin-α2 expression in primary lesions and metastatic lymph nodes was associated with poor prognosis and progression in gastric cancer. Carcinogenesis. 2013;34((10)):2314–2321. doi: 10.1093/carcin/bgt214. [DOI] [PubMed] [Google Scholar]
- 15.Li C, Ji L, Ding ZY, Zhang QD, Huang GR. Overexpression of KPNA2 correlates with poor prognosis in patients with gastric adenocarcinoma. Tumor Biol. 2013;34((2)):1021–1026. doi: 10.1007/s13277-012-0641-7. [DOI] [PubMed] [Google Scholar]
- 16.van der Watt PJ, Maske CP, Hendricks DT, Parker MI, Denny L, Govender D, et al. The Karyopherin proteins Crm1 and Karyopherin β1 are overexpressed in cervical cancer and are critical for cancer cell survival and proliferation. Int J Cancer. 2009;124((8)):1829–1840. doi: 10.1002/ijc.24146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Japanese Gastric., Cancer Association. Japanese classification of gastric carcinoma 2nd English edition. Gastric Cancer. 1998;1((1)):10–24. doi: 10.1007/s101209800016. [DOI] [PubMed] [Google Scholar]
- 18.Yang L, Hu B, Zhang Y, Qiang S, Cai J, Huang W, et al. Suppression of the nuclear transporter-KPNβ1 expression inhibits tumor proliferation in hepatocellular carcinoma. Med Oncol. 2015;32((4)):128. doi: 10.1007/s12032-015-0559-1. [DOI] [PubMed] [Google Scholar]
- 19.Zhou LN, Tan Y, Li P, Zeng P, Chen MB, Tian Y, et al. Prognostic value of increased KPNA2 expression in some solid tumors a systematic review and meta-analysis. Oncotarget. 2017;8((1)):303–314. doi: 10.18632/oncotarget.13863. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Zhu J, Wang Y, Huang H, Yang Q, Cai J, Wang Q, et al. Upregulation of KPNβ1 in gastric cancer cell promotes tumor cell proliferation and predicts poor prognosis. Tumor Biol. 2016;37((1)):661–672. doi: 10.1007/s13277-015-3839-7. [DOI] [PubMed] [Google Scholar]
- 21.Tseng SF, Chang CY, Wu KJ, Teng SC. Importin KPNA2 is required for proper nuclear localization and multiple functions of NBS1. J Biol Chem. 2005;280((47)):39594–600. doi: 10.1074/jbc.M508425200. [DOI] [PubMed] [Google Scholar]
- 22.Zannini L, Lecis D, Lisanti S, Benetti R, Buscemi G, Schneider C, et al. Karyopherin-α2 protein interacts with Chk2 and contributes to its nuclear import. J Biol Chem. 2003;278((43)):42346–51. doi: 10.1074/jbc.M303304200. [DOI] [PubMed] [Google Scholar]
- 23.Teng SC, Wu KJ, Tseng SF, Wong CW, Kao L. Importin KPNA2 DNA repair and tumorigenesis. J Mol Histol. 2006;37((5–7)):293–299. doi: 10.1007/s10735-006-9032-y. [DOI] [PubMed] [Google Scholar]
- 24.Alshareeda AT, Negm OH, Green AR, Nolan CC, Tighe P, Albarakati N. KPNA2 is a nuclear export protein that contributes to aberrant localisation of key proteins and poor prognosis of breast cancer. Br J Cancer. 2015;112((12)):1929–1937. doi: 10.1038/bjc.2015.165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Stelma T, Chi A, van der Watt PJ, Verrico A, Lavia P, Leaner VD. Targeting nuclear transporters in cancer diagnostic, prognostic and therapeutic potential. IUBMB Life. 2016;68((4)):268–280. doi: 10.1002/iub.1484. [DOI] [PubMed] [Google Scholar]
- 26.Carden S, van der Watt P, Chi A, Ajayi-Smith A, Hadley K, Leaner VD. A tight balance of karyopherin β1 expression is required in cervical cancer cells. BMC Cancer. 2018;18((1)):1123. doi: 10.1186/s12885-018-5044-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.van der Watt PJ, Chi A, Stelma T, Stowell C, Strydom E, Carden S, et al. Targeting the nuclear import receptor Kpnβ1 as an anticancer therapeutic. Mol Cancer Ther. 2016;15((4)):560–573. doi: 10.1158/1535-7163.MCT-15-0052. [DOI] [PubMed] [Google Scholar]
- 28.Wang T, Huang Z, Huang N, Peng Y, Gao M, Wang X, et al. Inhibition of KPNB1 inhibits proliferation and promotes apoptosis of chronic myeloid leukemia cells through regulation of E2F1. Onco Targets Ther. 2019;12:10455–10467. doi: 10.2147/OTT.S210048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Zhu ZC, Liu JW, Li K, Zheng J, Xiong ZQ. KPNB1 inhibition disrupts proteostasis and triggers unfolded protein response-mediated apoptosis in glioblastoma cells. Oncogene. 2018;37((22)):2936–2952. doi: 10.1038/s41388-018-0180-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Zhang Y, Li KF. Karyopherin β1 deletion suppresses tumor growth and metastasis in colorectal cancer (CRC) by reducing MET expression. Biomed Pharmacother. 2019;120:109127. doi: 10.1016/j.biopha.2019.109127. [DOI] [PubMed] [Google Scholar]
- 31.Chook YM, Süel KE. Nuclear import by karyopherin-βs recognition and inhibition. Biochim Biophys Acta. 2011;1813((9)):1593–606. doi: 10.1016/j.bbamcr.2010.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Forwood JK, Lam MH, Jans DA. Nuclear import of Creb and AP-1 transcription factors requires importin-β1 and ran but is independent of importin-α. Biochemistry. 2001;40((17)):5208–5217. doi: 10.1021/bi002732+. [DOI] [PubMed] [Google Scholar]
- 33.Chi RA, van der Watt P, Wei W, Birrer MJ, Leaner VD. Inhibition of Kpnβ1 mediated nuclear import enhances cisplatin chemosensitivity in cervical cancer. BMC Cancer. 2021;21((1)):106. doi: 10.1186/s12885-021-07819-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Abdul Razak AR, Mau-Soerensen M, Gabrail NY, Gerecitano JF, Shields AF, Unger TJ, et al. First-in-class first-in-human phase I study of selinexor a selective inhibitor of nuclear export in patients with advanced solid tumors. J Clin Oncol. 2016;34((34)):4142–4150. doi: 10.1200/JCO.2015.65.3949. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Kalakonda N, Maerevoet M, Cavallo F, Follows G, Goy A, Vermaat JSP, et al. Selinexor in patients with relapsed or refractory diffuse large B-cell lymphoma (SADAL) a single-arm, multinational, multicentre, open-label, phase 2 trial. Lancet Haematol. 2020;7((7)):e511–e522. doi: 10.1016/S2352-3026(20)30120-4. [DOI] [PubMed] [Google Scholar]
- 36.Chari A, Vogl DT, Gavriatopoulou M, Nooka AK, Yee AJ, Huff CA, et al. Oral selinexor-dexamethasone for triple-class refractory multiple myeloma. N Engl J Med. 2019;381((8)):727–738. doi: 10.1056/NEJMoa1903455. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from Ichiro Kinoshita upon reasonable request.
