Small ubiquitin‐like modifier/sentrin‐specific peptidase 1 associates with chemotherapy and is a risk factor for poor prognosis of non‐small cell lung cancer

Abstract

Background

SUMO/sentrin‐specific peptidase 1 (SENP1) was associated with radioresistance of cancer cells and was upregulated in non‐small cell lung cancer (NSCLC). This study was to investigate the association of SENP1 with resistance of NSCLC tumor to chemoradiotherapy.

Methods

Sentrin‐specific peptidase 1 expression profile was detected using the immunohistochemistry and quantitative real‐time PCR (qRT‐PCR) analyses. The relative expression level of SENP1 mRNA was detected using qRT‐PCR. The response to chemoradiotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumors.

Results and Conclusion

When compared with adjacent non‐tumor tissues, the overexpression of SENP1 mRNA and protein in NSCLC tumor tissues was determined using qRT‐PCR and immunochemistry. Based on the chemoradiotherapy response rate, we found that NSCLC patients with higher SENP1 expression showed lower rates of complete response and higher partial and non‐response rate to chemoradiotherapy. In the overall survival analysis, we found patients with high SENP1 expression showed significant shorter survival time compared with those with low SENP1 expression. In the multivariate Cox regression model, we found SENP1 overexpression, TNM stage, and lymph metastasis were independent risk factors for poor prognosis of NSCLC. SENP1 overexpression contributed to chemoradiotherapy resistance of NSCLC. The overexpression of SENP1 could be used as a risk factor for the poor prognosis of NSCLC.

Abbreviations

ccRCC

clear cell renal cell carcinoma

CR

complete response

DAB

3, 3’‐diaminobenzidine

EMT

epithelial‐mesenchymal transition

HIF‐1α

hypoxia‐inducible factor‐1 alpha

NF‐кB

nuclear factor‐kappa B

NR

non‐response

NSCLC

non‐small cell lung cancer

PD

progressive disease

PR

partial response

qRT‐PCR

quantitative real‐time PCR

RECIST

Response Evaluation Criteria in Solid Tumors

SD

stable disease

SENP1

SUMO/sentrin specific peptidase 1

SUMO

small ubiquitin‐like modifier

VEGF

vascular endothelial growth factor

1. INTRODUCTION

Non‐small cell lung cancer (NSCLC) is a predominant lung cancer which accounts for a large part of cancer‐caused deaths worldwide.1 The high occurrence, mortality, and resistance to chemoradiotherapy make NSCLC a significant issue to deal with.

Small ubiquitin‐related modifier (SUMO)ylation is a crucial segment for the modulation of DNA replication/repair, DNA damage response, cell cycle, protein degradation and stabilization, signal transduction, and the hypoxic response.2, 3, 4 The deSUMOylation or SUMOylation‐dependent transcription is important for tumorigenesis.5, 6, 7, 8 SUMO/Sentrin‐specific peptidase 1 (SENP1) is a deSUMOylation enzyme which targets a number of key proteins for promoting the stabilization of hypoxia‐inducible factor‐1 alpha (HIF‐1α).9, 10 The overexpression of SENP1 contributes to the abnormity of the cell nucleus and mitochondria.11, 12 It has been reported that the expression of SENP1 was upregulated in NSCLC.13

Hypoxia‐inducible factor‐1 alpha activates downstream glycolytic enzyme‐encoding genes and promotes glycolysis and glucose metabolism, epithelial‐mesenchymal transition (EMT), and tumor metastasis.9, 14, 15, 16, 17 The overexpression of HIF‐1α is well known to be correlated with the poor overall survival of several cancers.18, 19 The stable activity of SENP1 under hypoxia‐stressed hepatocellular carcinoma could sustain cancer stem cell stemness by promoting deSUMOylation and stabilization of HIF‐1α.17, 20 SENP1 expression desensitizes cancer cells to chemoradiotherapy via the upregulation of HIF‐1α.21 Vascular endothelial growth factor (VEGF) is a target of HIF‐1α, and the activation of HIF‐1α/VEGF signaling pathway is essential for angiogenesis, tumor angiogenesis, atherosclerosis, and diabetes mellitus.22, 23, 24 In contrast, the suppression of SENP1, HIF‐1α, and VEGF was reported to suppress tumor development and aggressiveness of cancers, including NSCLC.13, 25 However, there was less information on the in vivo association of SENP1 expression with chemotherapy resistance of NSCLC.

To investigate it, we evaluated the expression of SENP1 in NSCLC tumor and adjacent non‐tumor tissues. The chemotherapy response rate of NSCLC patients with high and low SENP1 expression was evaluated for the assessment of the association between SENP1 expression and chemotherapy resistance of NSCLC. Overall survival and Cox's regression model analyses were performed to evaluate the potential of using SENP1 as a risk factor for the poor prognosis of NSCLC and the independent risk factors for NSCLC. This study would provide us with more information on the association of SENP1 overexpression with NSCLC resistance to chemotherapy.

2. METHODS AND MATERIALS

2.1. Patients

A total of 97 inpatients with NSCLC were enrolled from the Department of thoracic surgery, The Affiliated Hospital of Hangzhou normal university, Hangzhou City, Zhejiang Province, China, between Jan 2007 and May 2011. All patients were treated with chemotherapies. The chemotherapy strategies were made by treating physicians. Tumor lung tissues and adjacent non‐tumor lung tissues were obtained from these 97 patients undergoing surgeries and stored in a tissue bank of this hospital. The baseline clinical features prior to and after treatment of patients were collected from this hospital. Written informed consents were obtained from patients before surgery. The last follow‐up occurred on October 15 2017, with a 3‐month interval. The experimental protocol was approved by the Ethics Committee of this Hospital. All patients received chemotherapy of vinorelbine plus cis‐platinum, or paclitaxel liposome plus carboplatin.

2.2. Analysis of SENP1 profile

Sentrin‐specific peptidase 1 expression was detected using the immunohistochemistry and quantitative real‐time PCR (qRT‐PCR) analysis. Tumor histological examination was conducted by 2 pathologists blinded to chemotherapy treatments and patient responses. Specific primary antibody anti‐SENP1 (1:200; Santa Cruz Biotechnology, Santa Cruz, CA, USA), anti‐β‐actin (1:500, Santa Cruz) and horseradish peroxidase (HRP)‐conjugated secondary antibody (1:10 000; Abcam Inc. Cambridge, MA, USA) were used for immunohistochemistry analysis with 3, 3′‐diaminobenzidine (DAB) staining (Boster Biological Engineering Company, Wuhan, China). Images were captured using a BX‐51 light microscopy (Olympus, magnification × 200). Histological scores (positive cell numbers and intensity), differentiation, and pathology types were evaluated in a double‐blind manner.

The relative expression level of SENP1 mRNA was detected using qRT‐PCR. Total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's protocol. The cDNA was generated and relative SENP1 mRNA expression level was detected using SYBR Premix Ex Taq kit (TaKaRa, Tokyo, Japan) according to the manufacturer's instructions. Data collection was performed on an ABI 7500 real‐time instrument (Applied Biosystems, Carlsbad, CA, USA) using standard conditions. Results were analyzed with normalization to the expression of β‐actin.

2.3. Response evaluation

The response to chemotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST).26 Response rate was defined as complete response (CR): the disappearance of all target lesions; partial response (PR): ≥30% decrease in the sum of the longest diameter of target lesions; progressive disease (PD): ≥20% increase in the sum of the longest diameter of target lesions); and stable disease (SD): neither sufficient shrinkage to threshold for PR nor sufficient increase to threshold for PD. PD and SD were defined as non‐responses (NRs) in this study.

2.4. Statistical analysis

Clinical variables (age, gender ratio, smoking history, TNM stage, and chemotherapy) were analyzed using Wilcoxon rank‐sum tests or chi‐squared (Fisher's exact) test. Kaplan‐Meier overall survival analysis (interval from first‐line treatment to the last visit or death) was performed. Difference between overall survival curves was assessed using Log‐rank test. Deaths out of NSCLC were the primary end and deaths out of other causes were censored. Cox proportional hazards regression model was used to analyze the independent risk factors for NSCLC prognosis. All statistical analyses were performed using SPSS 18.0 software (IBM, Armonk, NY, USA). P < .05 was considered as statistically significant.

3. RESULTS

3.1. Overexpression of SENP1 in NSCLC tissues

We found that the expression of SENP1 in NSCLC tissues was significantly higher than that in adjacent non‐tumor tissues (P < .05, Figure 1A,B). The expression level of SENP1 was gradually upregulated in patients with CR, PR, and NR tumors (Figure 1C,D). These suggested that the overexpression of SENP1 in NSCLCs might be related to chemotherapy resistance.

Figure 1.

Sentrin‐specific peptidase 1 (SENP1) is upregulated in non‐small cell lung cancer (NSCLC) tissues. A, the relative SENP1 mRNA expression level in 97 patients with NSCLC by qRT‐PCR. B, the represented images of immunochemistry for SENP1 protein in the NSCLC tissues and adjacent non‐tumor tissues. C and D, mRNA and protein intensity of SENP1 in patients with CR, PR, and NR NSCLC tumors. CR and PR represent complete response and partial response, respectively. We defined stable diseases and progressive diseases as non‐response (NR). ***P < .001 vs non‐tumor

3.2. Clinical characteristics

According to the histological scores, we defined NSCLC with scores ≥3 as SENP1 overexpression (n = 61, 62.89%) and those with scores <3 as low expression (n = 36, 37.11%), respectively. The clinical characteristics between the 2 groups were analyzed (Table 1). We found there were significant differences in tumor differentiation and T, N, and TNM classifications between the 2 groups. Tumors with SENP1 overexpression showed lower differentiation (P < .05) and higher classifications (P < .01) than those with low SENP1 expression. These data showed SENP1 overexpression was associated with moderate and low differentiation of NSCLC tumors.

Table 1.

Clinical characteristics of patients with high and low expressions of SENP1

Variables	Group (based on SENP1 expression)		P value
	Overexpression	Low expression
Number	61	36	—
Male (Male/ratio)	35 (57.4%)	19 (52.8%)	.660
Age at diagnosis	52.7 ± 3.4	56.75 ± 7.6	.121
Smoking history	23 (37.7%)	15 (41.6%)	.699
Pathology type
Squamous carcinoma	32 (52.5%)	21 (58.3%)	.662
Adenocarcinoma	28 (45.9%)	15 (41.7%)
Other	1 (1.6%)	0
Differentiation
High	3 (4.9%)	4 (11.1%)	.019
Mild	27 (44.3%)	24 (66.7%)
Low	31 (50.8%)	8 (22.2%)
T classification (%)
T1	5 (8.2%)	12 (33.3%)	.0041
T2	46 (75.4%)	22 (61.1%)
T3	10 (16.4%)	2 (5.6%)
N classification (%)
N0	10 (16.4%)	19 (52.8%)	.0009
N1	24 (39.3%)	11 (30.6%)
N2	22 (36.1%)	6 (16.7%)
N3	5 (8.2%)	0
TNM classification (%)
Stage I	9 (14.8%)	19 (52.8%)	.0003
Stage II	37 (60.7%)	12 (33.3%)
Stage IIII	15 (24.6%)	5 (13.9%)

3.3. Death, chemoradiotherapy response, metastasis, and overall survival

At the end of 6‐year follow‐up, a total of 45 deaths were informed and there was higher death rate (57.4%) in patients with SENP1 overexpression than that (33.3%) in patients with low SENP1 expression (P < .05, Table 2). All patients were available for the response evaluation of chemotherapy, and 37 (38.14%), 21 (21.65%), and 39 patients (40.21%) were defined as CRs, PRs, and NRs (including 17 PDs and 21 SDs), respectively. The percentages of PR and NR of tumors with SENP1 overexpression were obviously higher than those with low expression of SENP1 (P < .05), while the percentage of CR of tumors with SENP1 overexpression (21.3%) was lower than that with low SENP1 expression (50%, P < .05). We also found that the recurrence and metastasis rates in patients with SENP1 overexpression (42.6%) was higher than that in patients with low expression of SENP1 (13.9%, P < .01, Table 2). These data might reveal that the higher expression of SENP1 was related to chemotherapy resistance and metastasis of NSCLC.

Table 2.

The chemoradiotherapy response and outcome of non‐small cell lung cancer treatment between patients with high and low SENP1 expression

Variables	Group (based on SENP1 expression)		P value
	Overexpression	Low expression
Number at diagnosis	61	36	—
Death	35 (57.4%)	12 (33.3%)	.0348
Response rate (%)
CR	13 (21.3%)	18 (50%)	.0134
PR	33 (54.1%)	13 (36.1%)
NR	15 (24.6%)	5 (13.9%)
Metastasis and recurrence (%)	26 (42.6%)	5 (13.9%)	.0035

CR, complete response, the disappearance of all target lesions; PR, partial response, at least a 30% decrease in the sum of the longest diameter of target lesions, taking as reference the baseline sum longest diameter. NR, non‐response, including stable diseases (neither sufficient shrinkage to qualify for partial response nor sufficient increase to qualify for progressive disease, taking as reference the smallest sum longest diameter since the treatment started) and progressive diseases (at least a 20% increase in the sum of the longest diameter of target lesions, taking as reference the smallest sum longest diameter).

In the overall survival analysis, we found the overall survival in patients with SENP1 overexpression (median 34.1 months) to be significantly shorter than patients with low SENP1 expression (66.7 months, P < .05, Figure 2). Using the multivariate Cox's regression model, we determined TNM stage at diagnosis, lymphatic metastasis, and SENP1 expression was an independent risk factor for NSCLC prognosis (Table 3).

Figure 2.

Overall survival curves of non‐small cell lung cancer (NSCLC) patients with high and low sentrin‐specific peptidase 1 (SENP1) expression. Red and green lines indicate the overall survival of patients with high and low SENP1 expression, respectively. Log‐rank test was used for difference analysis. Deaths out of NSCLC were the primary end points and deaths out of other causes were censored

Table 3.

Multivariate Cox's regression model analysis of the independent risk factors based on 6‐year survival rate of non‐small cell lung cancer

Variables	95% confidence interval (CI)	P value
N classification		.024
N0 (Reference)
N1	4.214 (1.327‐10.369)	.008
N2	0.699 (0.301‐6.377)	.731
N3	1.706 (0.704‐22.370)	.664
TNM classification		.003
Stage I (Reference)
Stage II	0.223 (0.081‐0.738)	.017
Stage IIII	2.197 (0.364‐12.875)	.359
SENP1 overexpression	2.792 (1.579‐5.671)	.001
Lymphatic metastasis	1.276 (1.379‐4.573)	.018

4. DISCUSSION

The overexpression of SENP1 had been reported in several cancers. Results in this present study showed that SENP1 was a risk factor for poor NSCLC prognosis. We also demonstrated that the overexpression of SENP1 in NSCLC was related to chemotherapy resistance.

It has been reported that the depletion of SENP1 could increase radiosensitivity in A549 cells.13 Wang et al13 showed that increased SENP1 level in A549 cells promoted cell proliferation, while the inhibition of SENP1 induced G1 cell cycle arrest and radiosensitivity. Dong et al9 also reported that the knockdown of SENPs in clear cell renal cell carcinoma (ccRCC) cells inhibited cell proliferation. They revealed that the expression of SENP1 was positively correlated with pathological grades of ccRCC tumor, and the high expression of SENP1 was an indicator of poor overall survival and advanced ccRCC tumors.9 The results in our present study were consistent with those from Wang and Dong et al,9, 13 showing that SENP1 was a risk factor for the poor prognosis of patients with NSCLC.

It has been reported that the overexpression and stabilization of HIF‐1α acts as an oncogenic actor by activating its downstream glycolytic enzymes and promoting glycolysis and glucose metabolism in tumor cells.9, 14, 15, 16 The prolonged expression of HIF‐1α in cancer cells under hypoxic conditions promoted cell proliferation and metastasis, and enhanced the radioresistance of cancer cells.9, 14, 15, 21 SENP1‐mediated deSUMOylation of ubiquitin E3 is essential for the deSUMOylation and stabilization of HIF‐1α, whose overexpression is positively correlated with the poor overall survival of cancers.9, 18, 19

There was evidence of the expression of SENP1 in desensitized cancer cells to chemoradiotherapy via the upregulation of HIF‐1α.21 This was consistent with our result that SENP1 expression was correlated with poor overall survival of NSCLC, and also consistent with the fact that NSCLC patients with higher SENP1 expression showed lower rate of complete response to chemotherapy when compared with patients with lower expression of SENP1. These suggested that high SENP1 expression in NSCLC patients might be associated with resistance to chemotherapy.

The suppression of SENP1, however, had anti‐angiogenic activity.27, 28, 29 The inhibition of SENP1 expression could induce cell apoptosis and cell cycle arrest by inactivating nuclear factor‐kappa B (NF‐кB) signaling pathway in multiple myeloma cells30 and could inhibit cell growth, migration, and EMT of hepatocellular carcinoma tumor cells by inhibiting N‐cadherin and increasing E‐cadherin.31 These reports might reveal the therapeutic potential of using SENP1 suppression for NSCLC treatment.

In summary, we concluded that the SENP1 overexpression in NSCLC patients was correlated with resistance to chemoradiotherapy. The survival analysis showed that NSCLC patients with relatively higher SENP1 expression showed short survival time. Moreover, SENP1 might be used as a risk factor for the poor prognosis of NSCLC.

Liu K, Zhang J, Wang H. Small ubiquitin‐like modifier/sentrin‐specific peptidase 1 associates with chemotherapy and is a risk factor for poor prognosis of non‐small cell lung cancer. J Clin Lab Anal. 2018;32:e22611 10.1002/jcla.22611