Identification of SEC61G As a Novel Prognostic Marker for Predicting Survival… CLINICAL RESEARCH © Med Sci Monit, 2019; 25: 3624-3635

CLINICAL RESEARCH

e-ISSN 1643-3750 © Med Sci Monit, 2019; 25: 3624-3635 DOI: 10.12659/MSM.916648

Received: 2019.03.31 Accepted: 2019.04.24 Identification of SEC61G as a Novel Prognostic Published: 2019.05.16 Marker for Predicting Survival and Response to Therapies in Patients with Glioblastoma

Authors’ Contribution: CDEF 1 Bo Liu 1 Department of Neurosurgery, Xiangya Hospital, Central South University, Study Design A F 1 Jingping Liu Changsha, Hunan, P.R. China Data Collection B 2 Department of Neurosurgery, The Second People’s Hospital of Hunan Province, Statistical Analysis C BD 1 Yuxiang Liao The Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, Data Interpretation D F 1 Chen Jin P.R. China Manuscript Preparation E F 1 Zhiping Zhang 3 Department of Psychiatry, The Second People’s Hospital of Hunan Province, The Literature Search F Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, P.R. China Funds Collection G F 1 Jie Zhao 4 Department of Clinical Pharmacology, Xiangya Hospital, Central South University, E 2 Kun Liu Changsha, Hunan, P.R. China E 2 Hao Huang F 3 Hui Cao ACG 1,4 Quan Cheng

Corresponding Author: Quan Cheng, e-mail: [email protected] Source of support: This work was supported by the National Natural Science Foundation of China (No. 81703622); China Postdoctoral Science Foundation (No. 2018M633002); and Hunan Provincial Natural Science Foundation of China (No. 2018JJ3838)

Background: The survival and therapeutic outcome vary greatly among glioblastoma (GBM) patients. Treatment resistance, including resistance to temozolomide (TMZ) and radiotherapy, is a great obstacle for these therapies. In this study, we aimed to evaluate the predictive value of SEC61G on survival and therapeutic response in GBM patients. Material/Methods: Survival analyses were performed to assess the correlation between SEC61G expression and survival of GBM patients from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) datasets. Univariate and multivariate Cox proportional hazard regression analysis was introduced to determine prognostic factors with independent impact power. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to illustrate possible biological functions of SEC61G. Results: High expression of SEC61G was significantly correlated with poor prognosis in all GBM patients. High expression of SEC61G was also associated with poor outcome in those who received TMZ treatment or radiotherapy in TCGA GBM cohort. Univariate and multivariate Cox proportional hazards regression demonstrated that SEC61G was an independent prognostic factor affecting the prognosis and therapeutic outcome. The combination of age, SEC61G expression, and MGMT promoter methylation in survival analysis could provide better outcome assessment. Finally, a strong correlation between SEC61G expression and Notch pathway was observed in GSEA and GSVA, which suggested a possible mechanism that SEC61G affected survival and TMZ resistance. Conclusions: SEC61G expression may be a potential prognostic marker of poor survival, and a predictor of poor outcome to TMZ treatment and radiotherapy in GBM patients.

MeSH Keywords: Drug Resistance • Glioblastoma • Prognosis • Radiotherapy

Full-text PDF: https://www.medscimonit.com/abstract/index/idArt/916648

2707 4 5 28

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3624 [Chemical Abstracts/CAS] Liu B. et al.: Identification of SEC61G as a novel prognostic marker for predicting survival… CLINICAL RESEARCH © Med Sci Monit, 2019; 25: 3624-3635

Background CGGA database (http://www.cgga.org.cn) were obtained to assess the relationship between SEC61G expression and prog- Glioblastoma multiforme (GBM) accounts for about 80% of pri- nosis of GBM patients. Cases lacking gene expression or sur- mary malignant brain tumors. The median survival time of GBM vival data were excluded. Therapeutic information including is only about 15 months during the past decades [1]. Despite the TMZ chemotherapy status and radiotherapy status were only tremendous progress in post-operative therapies, GBM remains available in TCGA microarray dataset. Thus, we used this data- an incurable disease with the highest morbidity. Owing to the un- set to evaluate the response to TMZ and radiotherapy in GBM satisfactory prognosis of GBM, identification of novel prognostic patients. Expression data of SEC61G in different tumors and markers or molecular pathways in GBM is urgently needed [2,3]. normal controls were obtained from TCGA and the Genotype- Tissue Expression (GTEx) databases (https://gtexportal.org/ Postoperative radiotherapy and temozolomide (TMZ) chemo- home). Expression data of SEC61G in various tumor cell lines therapy, together with surgery, form the standard treatment were download from the Human Protein Atlas website (http:// for newly diagnosed GBM patients. However, radiotherapy re- www.proteinatlas.org). sistance and intrinsic or acquired TMZ resistance represents a major obstacle for these therapies [4–6]. In that sense, it is Gene functional analysis further important to identify the biomarkers not only related to GBM overall survival, but also related to the outcome of ra- Gene set enrichment analysis (GSEA) was used to identify the diotherapy and TMZ chemotherapy [7,8]. possible gene sets and correlated biological processes or pathways of statistical difference, |NES| >1, P-value <0.05, and FDR SEC61G, also known as Sec61 translocon gamma subunit, q value <0.25 were considered as statistically significant [15]. is one of the 3 subunits of the Sec61 complex. The Sec61 Additionally, we applied gene set variation analysis (GSVA) to complex is the central component of the protein translocation further verify significant differences of biological processes apparatus of the endoplasmic reticulum (ER) membrane [9], that were defined by gene sets [16]. Ontology gene sets files which is involved in protein folding, modification, transloca- were obtained from the Gene Ontology (GO) and the Kyoto tion and unfolded protein response especially under condi- Encyclopedia of Genes and Genomes (KEGG) databases. tions of hypoxia and nutrient deprivation in tumor microenvironment [10,11]. SEC61G was found to be overexpressed in Statistical analysis gastric cancer [12] and breast carcinomas [13]. SEC61G gene was also investigated to coamplify with epidermal growth fac- All statistical analyses were performed with bioconductor pack- tor receptor (EGFR) in 47% of GBMs and overexpressed in 77% ages in R software (v.3.5.1). Patients with survival time less of GBMs [14]. However, the correlation between SEC61G and than 30 days were excluded in survival analyses to avoid po- GBM prognosis has not been characterized. tential biases. Kaplan-Meier plots and log-rank test were used to estimate and compare the survival times. The median ex- In this study, we comprehensively evaluated the prognostic pression of SEC61G was used to dichotomize patients into ei- value of SEC61G expression in GBM patients, especially in ther a low-SEC61G or a high-SEC61G group in survival anal- those who received TMZ treatment or radiotherapy accord- yses. Univariate and multivariate Cox proportional hazards ing to the gene expression profile and corresponding clinical regression were used to assess the influence of the poten- information of GBM patients from the Cancer Genome Atlas tial prognostic factors on survival. Pearson correlation anal- (TCGA) and the Chinese Glioma Genome Atlas (CGGA) data- ysis was performed to evaluate the association between dif- bases. Bioinformatic methods: gene set enrichment analysis ferent genes based on their expression. A P-value <0.05 was (GSEA) and gene set variation analysis (GSVA) were performed regarded as statistically significant in all analyses. to get further insight into the biological role of SEC61G involved in GBM pathogenesis. We believe our study will contribute to the improvement of molecular diagnosis, prognosis prediction, Results and individualized therapy for GBM patients. The expression pattern of SEC61G in tumor tissues and tumor cell lines Material and Methods SEC61G expression was significantly higher in various tu- Data source mor tissues compared with their normal controls (Figure 1A), which indicated that SEC61G might participate in tumorigen- Microarray datasets of 523 GBM patients from TCGA database esis in various tumors. High expression of SEC61G was also (http://cancergenome.nih.gov) and 126 GBM patients from the observed in GBM cell lines, such as U-87MG, U-251MG, and

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3625 [Chemical Abstracts/CAS] Liu B. et al.: CLINICAL RESEARCH Identification of SEC61G as a novel prognostic marker for predicting survival… © Med Sci Monit, 2019; 25: 3624-3635

A *** *** *** *** *** *** *** *** *** *** ns *** *** *** *** *** *** *** * *** ns *** *** ns *** *** *** ns *** *** ***

TPM) 10 ession ( 0

SEC61G expr Tumor Normal –10

ACCBLCA BRCA CESC CHOL COAD DLBC ESCA GBM HNSC KICH KIRC KIRP LAML LGGLIHCLUADLUSC OV PAAD PCPG PRAD READ SARC SKCM STAD TGCT THCA THYM UCEC USC

Brain Skin B Myeloid Lymphoid Lung TPM Abdominal 1,200 Breast, female reproductive system Renal Sarcoma 1,000 Fibroblast TPM) Endothelial 800 Miscellaneous ession ( 600

400 SEC61G expr 200

0 4 3 1 4 1 V -1 -2 aT BJ T2 T+ T+ T- -3 0 -60 T8 8 RT HEL REH CEpi SiHa -56 2 PC-3 -47 d CO TIME T166 HeLa NB-4 AF22 AN-2 A549 HAP1 MCF7 -BR- T Daudi HL K T24-B ge TER TE RT TE RT HaC THP A-43 1 BEWO C-21 H U-93 7 U-69 8 RH-30 hT TER -HME1 U-2 OS HMC-1 HSkMC Hep G2 EFO-21 TER HHSteC CA U-2197 MOL ASC di AN3-CA T SK HDLM-2 HEK 29 3 -MEL SH-SY5Y NTERA-2 U-87 MG CAP LHCN-M 2 SCL TER U-266/70 U-266/84 WM=115 HBEC3-KT U-138 MG U-251 MG ASC BJ h SK RPMI-8226 Karpas-707 HBF T+RasG12 jTER RPTEC HUVEC fHDF/TER ge TEC/SV T+SV40 Lar h TER BJ h T+SV40 Lar TER BJ h

Figure 1. Profile of SEC61G gene expression and Kaplan-Meier plots of SEC61G in TCGA RNA-seq dataset. A( ) The expression of SEC61G in tumor tissues compared with corresponding normal controls. * P<0.05, *** P<0.001, ns: not significant. B( ) High expression of SEC61G was observed in various tumor cell lines including brain tumors. TCGA – The Cancer Genome Atlas.

SH-SY5Y, which indicated that SEC61G might be a significant subtypes and IDH-WT patients (Figure 2B). There was no sig- marker for GBM (Figure 1B). These results suggested SEC61G nificant difference between male and female patients. These as a potential prognostic gene marker. findings suggested that GBM patients with high SEC61G expression were prone to have a poorer outcome than those Association between SEC61G expression and with low expression. clinicopathologic factors in GBMs SEC61G had significant prognostic value in GBM patients We examined SEC61G expression in TCGA and CGGA GBM cohorts stratified by age (<65 years and³ 65 years), molecular We used TCGA and CGGA GBM cohorts to investigate the cor- type, isocitrate dehydrogenase (IDH) status, and MGMT pro- relation between SEC61G expression and prognosis of GBM moter status. We used 65 years as the cutoff age because patients. Kaplan-Meier plots demonstrated that TCGA GBM pa- the median age of diagnosis of GBM is about 65 years [17]. tients in the low-SEC61G group had significantly longer overall In TCGA GBM cohort, an increased expression of SEC61G was survival (OS) than those in the high-SEC61G group (P=0.00035). observed in MGMT promoter methylated, classical, and mesen- The same trend was also observed in the CGGA GBM cohort chymal subtypes, IDH wild-type (WT), and older age (age ³65 (P=0.011, Figure 2C). years) groups (Figure 2A). In the CGGA set, an increased expression of SEC61G was observed in classical and mesenchymal

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3626 [Chemical Abstracts/CAS] Liu B. et al.: Identification of SEC61G as a novel prognostic marker for predicting survival… CLINICAL RESEARCH © Med Sci Monit, 2019; 25: 3624-3635

A TCGA GBM cohort MGMT Molecular types IDH Age group * Mathylated **** Classical **** Mutant * Old (≥65) 14 Unmethylated 17.5 ns**** Mesenchymal 14 WT Young (<65) Neural **** Proneural 13 15.0 *** 12 **** 12 12.5 11 SEC61G 10 SEC61G SEC61G 10 SEC61G 10.0 9 7.5 Mathylated Unmethylated Classic Mesen- NeuralProneural Mutant WT Old Young chymal MGMT promoter status Molecular types IDH status Age group

CCGA GBM cohort ns ns B Molecular types **** IDH Age group **** Classical 6 Mutant Old (≥65) **** Mesenchymal WT Young (<65) 8 * Neural 13 ns * Proneural 4

4 2 11 SEC61G SEC61G 0 SEC61G 0 9 –2 Classic Mesen- NeuralProneural Mutant WT Old Young chymal Molecular types IDH status Age group C TCGA GBM cohort CCGA GBM cohort 1.00 1.00 p=0.00035 p=0.011

y 0.75 y 0.75

obabilit 0.50 obabilit 0.50 vival pr vival pr

Sur 0.25 Sur 0.25 Strata High SEC61G 0.00 Low SEC61G 0.00 0 1000 2000 3000 4000 0 500 1000 2000 3000 4000 Time Time D TCGA GBM cohort CCGA GBM cohort 1.00 1.00

0.75 0.75 y y

0.50 AUC=0.719 0.50 AUC=0.714 Sensitivit Sensitivit

0.25 0.25

0.00 0.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 1-Speci city 1-Speci city

Figure 2. The expression pattern of SEC61G in different subgroups and survival analyses of patients with different expression level of SEC61G (median as the cutoff point). A( ) SEC61G expression in TCGA GBM cohorts stratified by MGMT promoter status, molecular subtypes, IDH status and ages (<65 years or ³65 years). (B) SEC61G expression in CGGA GBM cohorts stratified by ages (<65 or ³65), molecular subtypes and IDH status. * P<0.05, *** P<0.001, **** P<0.0001, ns – not significant. C( ) Kaplan- Meier plots of patients in TCGA and CGGA GBM cohorts. (D) Receiver operating characteristic (ROC) curve was plotted to assess the predictive accuracy of SEC61G expression in 3-year survival of GBM patients. TCGA – The Cancer Genome Atlas; GBM – glioblastoma; IDH – isocitrate dehydrogenase; CGGC – Chinese Glioma Genome Atlas.

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3627 [Chemical Abstracts/CAS] Liu B. et al.: CLINICAL RESEARCH Identification of SEC61G as a novel prognostic marker for predicting survival… © Med Sci Monit, 2019; 25: 3624-3635

Table 1. Univariate and multivariate Cox regression analyses of SEC61G expression for GBM patients’ survival in TCGA dataset.

Univariate Multivariate

HR 95% CI P value HR 95% CI P value

SEC61G (low vs. high) 1.492 1.140–1.952 0.004 1.344 1.008–1.792 0.044

Age (<65 vs. ³65) 2.195 1.641–2.936 <0.001 2.061 1.536–2.765 <0.001

Gender (Male vs. Female) 0.655 0.500–0.858 0.002 0.614 0.466–0.810 0.001

Molecular types (CL+ME vs. NE+PN)* 1.410 1.078–1.845 0.012 1.266 0.953–1.681 0.103

MGMT (unmethylated vs. methylated) 0.793 0.609–1.032 0.085 0.855 0.655–1.115 0.248

* CL – classical; ME – mesenchymal; NE – neural; PN – proneural.

Table 2. Univariate and multivariate Cox regression analyses of SEC61G expression for GBM patients’ survival in CGGA dataset.

Univariate Multivariate HR 95% CI P value HR 95% CI P value

SEC61G (low vs. high) 1.656 1.116–2.456 0.012 1.598 1.031–2.477 0.036

Age (<65 vs. ³65) 1.350 0.494–3.687 0.170 1.580 0.563–4.434 0.385 Gender (Male vs. Female) 0.865 0.581–1.286 0.473 0.895 0.597–1.343 0.593 Molecular types (CL+ME vs. NE+PN)* 1.401 0.893–2.198 0.142 1.155 0.701–1.901 0.572

* CL – classical; ME – mesenchymal; NE – neural; PN – proneural.

Receiver operating characteristic (ROC) curves were plotted These results indicated that SEC61G might be a predictor for to assess the predictive accuracy. The area under the curve the TMZ response in GBM patients. (AUC) was 0.719 and 0.714 in TCGA and CGGA GBM datasets, respectively (Figure 2D). SEC61G expression also exhibited stronger predicting power than MGMT promoter status in TCGA GBM patients who re- Univariate and multivariate Cox proportional hazards regres- ceived radiotherapy. SEC61G expression showed more signif- sion analyses of relevant clinicopathologic features such as age, icant impact (P=0.00014) on survival compared with MGMT gender, molecular types, and MGMT promoter status together promoter status (P=0.038) (Figure 3C). In patients without with SEC61G expression further confirmed that SEC61G was radiotherapy, neither SEC61G nor MGMT promoter status an independent factor affecting the OS in TCGA GBM cohort showed significant association with survival, but similarly to (hazards ratio [HR]=1.344, P=0.044) and in the CGGA GBM co- the last paragraph, SEC61G expression showed stronger as- hort (HR=1.598, P=0.036) (Tables 1, 2). sociation (P=0.13) with survival than MGMT promoter status (P=0.46) (Figure 3D). These results indicated that SEC61G SEC61G had significant prognostic value in GBM patients might be a predictor for the response to radiotherapy in GBM who received TMZ treatment or radiotherapy patients. Additionally, by conducting univariate and multivariate Cox hazards regression analyses of relevant clinicopath- We then inspected the correlation between SEC61G expres- ologic features such as age, gender and MGMT promoter sta- sion and survival in TCGA GBM patients who received TMZ tus together with SEC61G expression, we confirmed SEC61G treatment to evaluate the response to TMZ treatment. MGMT expression was an independent prognostic factor affecting the promoter status was included as a comparison. Kaplan-Meier response to TMZ (HR=1.436, P=0.033), which showed more plots demonstrated SEC61G expression was significantly as- significance than MGMT promoter status (HR=0.743,P =0.077) sociated with survival (P=0.004), which was more significant (Table 3). Similarly, SEC61G expression was also an indepen- than MGMT promoter status (P=0.036) (Figure 3A). In GBM pa- dent prognostic factor affecting the response to radiotherapy tients without TMZ treatment, neither SEC61G expression nor (HR=1.567, P=0.004), while MGMT promoter status was not MGMT promoter status showed significant impact on survival, (HR=0.803, P=0.146) (Table 4). but SEC61G expression showed stronger association (P=0.085) with survival than MGMT promoter status (P=0.42) (Figure 3B).

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3628 [Chemical Abstracts/CAS] Liu B. et al.: Identification of SEC61G as a novel prognostic marker for predicting survival… CLINICAL RESEARCH © Med Sci Monit, 2019; 25: 3624-3635

A GBM patients with TMZ GBM patients with TMZ 1.00 Strata 1.00 Strata High SEC61G MGMT Methylated Low SEC61G MGMT Unmethylated

y 0.75 y 0.75

obabilit 0.50 obabilit 0.50 vival pr vival pr

Sur 0.25 Sur 0.25

0.00 0.00 0 1000 2000 3000 0 1000 2000 3000 Time Time B GBM patients without TMZ GBM patients without TMZ 1.00 Strata 1.00 Strata High SEC61G MGMT Methylated Low SEC61G MGMT Unmethylated

y 0.75 y 0.75

obabilit 0.50 obabilit 0.50 vival pr vival pr

Sur 0.25 Sur 0.25

0.00 0.00 0 1000 2000 3000 4000 0 1000 2000 3000 4000 Time Time C GBM patients with radiotherapy GBM patients with radiotherapy 1.00 Strata 1.00 Strata High SEC61G MGMT Methylated Low SEC61G MGMT Unmethylated

y 0.75 y 0.75

obabilit p=0.00014 obabilit p=0.038 0.50 0.50 vival pr vival pr

Sur 0.25 Sur 0.25

0.00 0.00 0 1000 2000 3000 4000 0 1000 2000 3000 4000 Time Time D GBM patients without radiotherapy GBM patients without radiotherapy 1.00 Strata 1.00 Strata High SEC61G MGMT Methylated Low SEC61G MGMT Unmethylated

y 0.75 y 0.75

obabilit p=0.13 obabilit p=0.046 0.50 0.50 vival pr vival pr

Sur 0.25 Sur 0.25

0.00 0.00 0 500 1000 1500 2000 0 200 400 600 800 Time Time

Figure 3. Comparisons between SEC61G expression and MGMT promoter status in predicting the therapeutic outcomes of TMZ and radiotherapies in TCGA GBM cohort. (A) Kaplan-Meier plots of patients who received TMZ treatment. (B) Kaplan-Meier plots of patients who did not receive TMZ treatment. (C) Kaplan-Meier plots of patients who received radiotherapy. (D) Kaplan-Meier plots of patients who did not receive radiotherapy. TMZ – temozolomide; TCGA – The Cancer Genome Atlas; GBM – glioblastoma.

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3629 [Chemical Abstracts/CAS] Liu B. et al.: CLINICAL RESEARCH Identification of SEC61G as a novel prognostic marker for predicting survival… © Med Sci Monit, 2019; 25: 3624-3635

Table 3. Univariate and multivariate Cox regression analyses of SEC61G expression for survival of patients who received TMZ treatment in TCGA dataset.

Univariate Multivariate HR 95% CI P value HR 95% CI P value

SEC61G (low vs. high) 1.409 1.017–1.953 0.040 1.436 1.029–2.003 0.033

Age (<65 vs. ³65) 1.640 1.121–2.399 0.011 1.579 1.075–2.317 0.020 Gender (Male vs. Female) 0.654 0.469–0.912 0.012 0.637 0.454–0.894 0.009 MGMT (unmethylated vs. methylated) 0.710 0.515–0.980 0.038 0.743 0.537–1.028 0.077

* CL – classical; ME – mesenchymal; NE – neural; PN – proneural.

Table 4. Univariate and multivariate Cox regression analyses of SEC61G expression for survival of patients who received radiotherapy in TCGA dataset.

Univariate Multivariate HR 95% CI P value HR 95% CI P value

SEC61G (low vs. high) 1.539 1.143–2.071 0.004 1.567 1.156–2.123 0.004

Age (<65 vs. ³65) 1.662 1.175–2.352 0.004 1.566 1.102–2.225 0.012 Gender (Male vs. Female) 0.627 0.465–0.847 0.002 0.602 0.442–0.819 0.001 MGMT (unmethylated vs. methylated) 0.736 0.550–0.985 0.039 0.803 0.598–1.080 0.146

* CL – classical; ME – mesenchymal; NE – neural; PN – proneural.

Prognosis stratification of GBM patients based on age and as cofactors into the survival analysis and found that patients SEC61G expression with methylated MGMT, low SEG61G expression, and age <65 years had the best OS (median survival: 715 days) (Figure 4C), TCGA GBM patients in the age group <65 years had significantly which was significantly better than the other groups. The flow- better OS than those in the age group ³65 years (median sur- chart of our stratification is shown in Figure 4D. This finding vival: 504 days versus 291 days) (P<0.0001). We then added might enable us to predict the response to TMZ in GBM pa- SEC61G expression as a cofactor into the survival analysis in tients with more accuracy. order to obtain more distinct survival prediction. Patients in the age group <65 years with low SEC61G expression had the SEC61G might be associated with Notch pathway best OS (median survival: 557 days), which was significantly better than the other age group (Figure 4A). SEC61G expres- To illustrate the possible biological functions and pathways of sion was significant correlated with survival among patients SEC61G in GBMs, we performed GSEA using TCGA and CGGA age < 65 years, but not among patients age ³65 years. A pos- GBM datasets. Several tumor-related biological processes and sible explanation could be the impact of age on survival was pathways, such as macroautophagy, apoptosis, NIK/NF-kappaB too strong, and it covered up the impact of SEC61G expression signaling, Notch receptor processing, endoplasmic reticulum in patients older than 65 years. The flowchart of the stratifi- unfolded protein response, and p53 signaling pathway were cation was shown in Figure 4B. This finding might enable us significantly enriched in TCGA GBM dataset. While regulation of to estimate the prognosis of GBM patients more accurately. NIK/NF-kappaB signaling, endoplasmic reticulum unfolded protein response, Notch receptor processing, p53 signaling path- Prognosis stratification of GBM patients who received TMZ way, JAK-STAT signaling pathway, PI3K-Akt signaling pathway treatment based on MGMT promoter status and SEC61G were significantly enriched in CGGA GBM dataset (Figure 5A). expression and age GSVA in TCGA and CGGA GBM datasets generated heatmaps As for GBM patients who received TMZ treatment, patients with that enrichment score of the related GO terms was depicted methylated MGMT have significant better OS than those with along with clinicopathologic factors such as IDH mutation, unmethylated MGMT (median survival: 585 days versus 454 age, gender, MGMT promoter status, and SEC61G expression days) (P=0.036). We then added SEC61G expression and age level. GSVA further confirmed GO terms such as Notch receptor

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3630 [Chemical Abstracts/CAS] Liu B. et al.: Identification of SEC61G as a novel prognostic marker for predicting survival… CLINICAL RESEARCH © Med Sci Monit, 2019; 25: 3624-3635

processing, regulation of phospholipase A2 activity, and reg- Discussion ulation of epithelial to mesenchymal transition were significantly associated with the expression of SEC61G (Figure 5B). This study first investigated a genomic marker related to prognosis and therapeutic response based on different GBM co- Notch was observed in both GSEA and GSVA. So, we further per- horts. We identified SEC61G gene as a potential prognostic formed Pearson’s correlation analysis between SEC61G and hub marker. High SEC61G expression was significantly correlated genes of Notch pathway. SEC61G was significantly correlated to poor outcome of GBM patients. The most impressive find- with Notch hub genes, such as JAG1, PSENEN, and NOTCH4 ing was that SEC61G expression, even more significantly than in TCGA GBM dataset (Figure 5C), and NOTCH3, PSEN2, and MGMT promoter status, could predict the outcome of TMZ NOTCH4 in the CGGA GBM dataset (Figure 5D). These results treatment. Interestingly, SEC61G also showed significance in suggested that the elevated expression of SEC61G might be predicting the outcome of radiotherapy. We then confirmed associated with upregulated Notch pathway in GBMs. SEC61G as an independent impact factor on survival and response to TMZ and radiotherapy. Moreover, as we combined age, MGMT promoter status, and SEC61G expression into the survival analysis, GBM patients with TMZ treatment could be

A TCGA GBM cohort TCGA GBM cohort 1.00 Age≥65, MS=291 days 1.00 Age<65, SEC61 G=low, MS=557 days Age<65, MS=504 days Age<65, SEC61 G=high, MS=452 days *** Age≥65, SEC61 G=low, MS=298 days ***

y 0.75 y 0.75 Age≥65, SEC61 G=high, MS=291 days ns

obabilit p<0.0001 obabilit 0.50 0.50 ival pr ival pr rv rv

Su 0.25 Su 0.25

0.00 0.00 0 1000 2000 3000 4000 0 1000 2000 3000 4000 Time Time

B Age<65, SEC61G=low, n=178 Median survival=557 days Age<65, n=337 Median survival=504 days Age<65, SEC61G=high, n=159 TCGA GBM cohort, n=500 Median survival=452 days Median survival=382 days Age≥65, SEC61G=low, n=72 Median survival=298 days Age≥65, n=163 Median survival=291 days Age≥65, SEC61G=low, n=91 Median survival=291 days C TCGA GBM patients with TMZ TCGA GBM patients with TMZ 1.00 1.00 MGMT=mathylated, Age<65, SEC61 G=low, MS=715 days MGMT Methylated MGMT=mathylated, Age<65, SEC61 G=high, MS=543 days MGMT Unmethylated MGMT=unmathylated, Age<65, SEC61 G=low, MS=537 days MGMT=unmathylated, Age≥65, SEC61 G=low, MS=482 days

y 0.75 y 0.75 MGMT=mathylated, Age≥65, SEC61 G=high, MS=476 days MGMT=unmathylated, Age<65, SEC61 G=high, MS=442 days

obabilit p=0.036 obabilit MGMT=mathylated, Age≥65, SEC61 G=low, MS=404 days 0.50 0.50 MGMT=unmathylated, Age≥65, SEC61 G=high, MS=388 days ival pr ival pr rv rv

Su 0.25 Su 0.25

0.00 0.00 0 1000 2000 3000 0 1000 2000 3000 Time Time

MGMT=mathylated, Age<65, SEC61G=low n=99, Median survival=715 days

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- MGMT=mathylated, Age<65, SEC61G=high [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3631 n=16, Median survival=543 days [Chemical Abstracts/CAS] MGMT=mathylated, n=99 Median survival=585 days MGMT=mathylated, Age≥65, SEC61G=high n=16, Median survival=476 days

MGMT=mathylated, Age≥65, SEC61G=low n=12, Median survival=404 days TCGA GBM patients with TZM, n=300 Median survival=437 days MGMT=mathylated, Age<65, SEC61G=low n=43, Median survival=537 days

MGMT=mathylated, Age≥65, SEC61G=high n=11, Median survival=482 days MGMT=unmathylated, n=112 Median survival=454 days MGMT=unmathylated, Age<65, SEC61G=high n=41, Median survival=442 days

MGMT=unmathylated, Age≥65, SEC61G=high n=17, Median survival=388 days TCGA GBM cohort TCGA GBM cohort 1.00 Age≥65, MS=291 days 1.00 Age<65, SEC61 G=low, MS=557 days Age<65, MS=504 days Age<65, SEC61 G=high, MS=452 days *** Age≥65, SEC61 G=low, MS=298 days ***

y 0.75 y 0.75 Age≥65, SEC61 G=high, MS=291 days ns

obabilit p<0.0001 obabilit 0.50 0.50 ival pr ival pr rv rv

Su 0.25 Su 0.25

0.00 0.00 0 1000 2000 3000 4000 0 1000 2000 3000 4000 Time Time

Age<65, SEC61G=low, n=178 Median survival=557 days Age<65, n=337 Median survival=504 days Age<65, SEC61G=high, n=159 TCGA GBM cohort, n=500 Median survival=452 days Median survival=382 days Age≥65, SEC61G=low, n=72 Median survival=298 days Age≥65, n=163 Median survival=291 days Age≥65, SEC61G=low, n=91 Median survival=291 days

TCGA GBM patients with TMZ TCGA GBM patients with TMZ 1.00 1.00 MGMT=mathylated, Age<65, SEC61 G=low, MS=715 days MGMT Methylated MGMT=mathylated, Age<65, SEC61 G=high, MS=543 days MGMT Unmethylated MGMT=unmathylated, Age<65, SEC61 G=low, MS=537 days MGMT=unmathylated, Age≥65, SEC61 G=low, MS=482 days

y 0.75 y 0.75 MGMT=mathylated, Age≥65, SEC61 G=high, MS=476 days MGMT=unmathylated, Age<65, SEC61 G=high, MS=442 days

obabilit p=0.036 obabilit MGMT=mathylated, Age≥65, SEC61 G=low, MS=404 days 0.50 0.50 MGMT=unmathylated, Age≥65, SEC61 G=high, MS=388Liu days B. et al.: ival pr ival pr Identification of SEC61G as a novel prognostic marker for predicting survival… CLINICALrv RESEARCH rv

0.00 0.00 0 1000 2000 3000 0 1000 2000 3000 Time Time

D MGMT=mathylated, Age<65, SEC61G=low n=99, Median survival=715 days

MGMT=mathylated, Age<65, SEC61G=high n=16, Median survival=543 days MGMT=mathylated, n=99 Median survival=585 days MGMT=mathylated, Age≥65, SEC61G=high n=16, Median survival=476 days

MGMT=mathylated, Age≥65, SEC61G=low n=12, Median survival=404 days TCGA GBM patients with TZM, n=300 Median survival=437 days MGMT=mathylated, Age<65, SEC61G=low n=43, Median survival=537 days

MGMT=mathylated, Age≥65, SEC61G=high n=11, Median survival=482 days MGMT=unmathylated, n=112 Median survival=454 days MGMT=unmathylated, Age<65, SEC61G=high n=41, Median survival=442 days

MGMT=unmathylated, Age≥65, SEC61G=high n=17, Median survival=388 days

Figure 4. The combination of multiple factors in survival analyses of TCGA GBM cohort can provide more accurate estimates of outcome. (A) Kaplan-Meier plots of patients in 2 age groups (<65 years versus ³65 years), and 4 subgroups based on the combination of ages and SEC61G expression. *** P<0.001, MS – median survival. (B) A flowchart to represent the application of our stratification in TCGA GBM cohorts. Patients with age <65 years, low SEC61G expression were found to have significant longer survival than the others P( <0.05). (C) Kaplan-Meier plots of patients who received TMZ treatment, patients were divided into 2 age groups (<65 years versus ³65 years), and 8 subgroups based on the combination of MGMT promoter status, ages and SEC61G expression. MS represents median survival. (D) A flowchart to represent the application of our stratification in TCGA GBM patients who received TMZ treatment. Patients with age <65 years, low SEC61G expression and methylated MGMT promoter were found to have significant longer survival than the others P( <0.05). TCGA – The Cancer Genome Atlas; GBM – glioblastoma; TMZ – temozolomide.

further separated into more distinct survival groups, which to evaluate the predicting power of SEC61G on TMZ treat- may enable us to better predict the response of TMZ treatment response. In this study, we confirmed MGMT promoter ment and thus develop better individualized therapeutic plans. status as a prognostic factor in GBM patients who received The functional study revealed SEC61G was significantly asso- TMZ treatment, but not as significant as SEC61G expression, ciated with the Notch pathway, which provided us with new which further confirmed that SEC61G expression was a po- insights into SEC61G’s role in the regulation network of GBM tential marker for TMZ treatment sensitivity. Beyond that, an unexpected finding was that both MGMT promoter status and High expression of SEC61G was associated with advanced SEC61G expression could predict the response to radiotherapy. clinicopathologic features like high World Health Organization (WHO) grade, classical and mesenchymal subtypes, older age As we know, the response to TMZ is not just depend on a sin- (³65 years), IDH-WT, and unmethylated MGMT, which sug- gle gene signature, but a combination of clinicopathologic gested that SEC61G might be a marker of poor prognosis. features and molecular events [19]. A prognostic assessment However, the correlations between SEC61G and such factors model base on multiple factors has become increasingly com- have yet to be explored. mon, and in this regard, although MGMT promoter methylation is already a well-known biomarker for predicting TMZ treat- The MGMT gene encodes DNA-repair proteins, and a meth- ment response, combining potential prognostic factors such ylated MGMT gene promoter could inhibit the expression of as age and SEC61G expression along with MGMT status could MGMT, and thus facilitate the effect of TMZ on cytotoxicity and provide further prognostic information for GBM patients than apoptosis. MGMT promoter methylation has been well recog- MGMT status alone. nized as a favorable prognostic marker in GBMs, especially for those patients who receive alkylating agents such as TMZ [18]. SEC61G encodes a membrane protein, which is 1 of the 3 sub- Therefore, we used MGMT promoter status as a comparison units of the Sec61 complex. The Sec61 complex is the central

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3632 [Chemical Abstracts/CAS] Liu B. et al.: Identification of SEC61G as a novel prognostic marker for predicting survival… CLINICAL RESEARCH © Med Sci Monit, 2019; 25: 3624-3635

component of the protein translocation apparatus of the en- under conditions of hypoxia and nutrient deprivation in tumor doplasmic reticulum (ER) membrane [9]. Together with other microenvironment [22]. Lu et al. found that SEC61G was over components such as ERj1, SEC62, and SEC63, the Sec61 com- expressed in GBM specimens and cell lines, and that knock- plex is involved in protein folding, modification, and transloca- down of SEC61G could inhibit glioma cell proliferation and even tion [14]. In addition, the Sec61 complex might also participate result in cell apoptosis. However, their study failed to reveal in unfolded protein response, which represents a set of cyto- a correlation between SEC61G and survival of GBM [14]. In our protective activities that enhances anti-apoptosis and the pro- study, significant association between SEC61G expression and cessing capability of misfolded proteins in ER [20,21], especially GBM prognosis was verified in different GBM cohorts, which

A GO terms in TCGA dataset KEEG pathways in TCGA dataset GO terms in CGGA dataset KEEG pathways in CGGA dataset t t t 0.8 t 0.6 0.4 0.4 0.3 0.6 e 0.4 e 0.2 e 0.4 e 0.2 scor 0.2 scor 0.1 scor 0.2 scor 0.0 0.0 0.0 0.0 Running enrichmen Running enrichmen –0.0 Running enrichmen Running enrichmen

1.0 1.0 1.0 1.0

ed list 0.5 ed list 0.5 ed list 0.5 ed list 0.5 metric 0.0 metric 0.0 metric 0.0 metric 0.0 Rank Rank Rank –0.5 Rank –0.5 2500 5000 7500 10000 2500 5000 7500 10000 4000 8000 12000 16000 4000 8000 12000 16000 Rank in ordered dataset Rank in ordered dataset Rank in ordered dataset Rank in ordered dataset Endoplasmic reticulum unfolded protein response Apoptosis Activation of NF-kappaB-inducing kinase activity JAK-STAT signaling pathway NK/NF-kappaB signaling MicroRNAs in cancer Endoplasmic reticulum unfolded protein response NF-kappaB signaling pathway Non-canonical Wnt signaling pathway Notch signaling pathway Negative regulation of NK/NF-kappaB signaling p53 signaling parthway Notch receptor procesing p53 signaling pathway Negative regulation of stem cell diﬀerentiation PI3K-Akt signaling pathway Positive regulation of NK/KF-kappaB signaling Notch receptor processing Protein processing in endoplasmic reticulum

GSVA in TCGA GBM dataset B SEC61G Event SEC61G 14 0 Radiotherapy 1 Chemotherapy 2 MGMT 8 OS IDH 1 Subtype 3500 Event 0 Radiotherapy OS No NO 500 Age –1 YES Yes Gender Age GO: Regulation of pathway restricted smad protein phosphorylation –2 Chemotherapy 3500 NO No GO: Negative regulation of epithelial to mesenchymal transistion YES Yes 500 GO: Negative regulation of developmental growth MGMT Gender GO: Regulation of phospholipase A2 activity Methylated Female GO: Perk mediated unfolded protein response Unmethylated Male GO: Protein mannosylation IDH Mutant GO: Pyrimidine containing compound metabolism process WT GO: Notch receptor processing Subtype Classical GO: Regulation of translation initiation in response to stress Mesenchymal Neural GO: Positive regulation of translational Proneural

GSVA in CGGA GBM dataset SEC61G Age SEC61G 5 70 IDH 2 Subtype –1 20 Event 1 OS Gender 0 IDH Age Female Gender WT –1 Mutant Male GO: Regulation of pathway restricted smad protein phosphorylation –2

GO: Negative regulation of epithelial to mesenchymal transistion Subtype Classical GO: Negative regulation of developmental growth Mesenchymal Neural Proneural GO: Regulation of phospholipase A2 activity

GO: Protein mannosylation Event 0 GO: Notch receptor processing 1 OS GO: Pyrimidine containing compound metabolism process 3500

GO: Perk mediated unfolded protein response 500 GO: Regulation of translation initiation in response to stress

GO: Positive regulation of translational

TCGA GBM dataset TCGA GBM dataset TCGA GBM dataset Pearson’s r=0.323; p<0.001 Pearson’s r=0.313; p<0.001 Pearson’s r=0.293; p<0.001 Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3633 [Chemical Abstracts/CAS] 14 14 14

12 12 12

SEC61G 10 SEC61G 10 SEC61G 10

8 8 8 6810 46810 46578 JAG1 PSENEN NOTCH4 CGGA GBM dataset CGGA GBM dataset CGGA GBM dataset Pearson’s r=0.338; p<0.001 Pearson’s r=0.316; p<0.001 Pearson’s r=0.292; p<0.001

6 6 6 4 4 4 2 2 2 SEC61G SEC61G SEC61G 0 0 0 –2 –2 –2 –1 0 12 –1 0 1 –1 0 1 NOTCH3 PSEN2 NOTCH4 GO terms in TCGA dataset KEEG pathways in TCGA dataset GO terms in CGGA dataset KEEG pathways in CGGA dataset t t t 0.8 t 0.6 0.4 0.4 0.3 0.6 e 0.4 e 0.2 e 0.4 e 0.2 scor 0.2 scor 0.1 scor 0.2 scor 0.0 0.0 0.0 0.0 Running enrichmen Running enrichmen –0.0 Running enrichmen Running enrichmen

1.0 1.0 1.0 1.0

GSVA in TCGA GBM dataset SEC61G Event SEC61G 14 0 Radiotherapy 1 Chemotherapy 2 MGMT 8 OS IDH 1 Subtype 3500 Event 0 Radiotherapy OS No NO 500 Age –1 YES Yes Gender Age GO: Regulation of pathway restricted smad protein phosphorylation –2 Chemotherapy 3500 NO No GO: Negative regulation of epithelial to mesenchymal transistion YES Yes 500 GO: Negative regulation of developmental growth MGMT Gender GO: Regulation of phospholipase A2 activity Methylated Female GO: Perk mediated unfolded protein response Unmethylated Male GO: Protein mannosylation IDH Mutant GO: Pyrimidine containing compound metabolism process WT GO: Notch receptor processing Subtype Classical GO: Regulation of translation initiation in response to stress Mesenchymal Neural GO: Positive regulation of translational Proneural

GO: Protein mannosylation Event 0 GO: Notch receptor processing 1 OS Liu B. et al.: CLINICAL RESEARCH GO: Pyrimidine containing compound metabolism process Identification of SEC61G3500 as a novel prognostic marker for predicting survival… © Med Sci Monit, 2019; 25: 3624-3635 GO: Perk mediated unfolded protein response 500 GO: Regulation of translation initiation in response to stress

GO: Positive regulation of translational

C TCGA GBM dataset TCGA GBM dataset TCGA GBM dataset Pearson’s r=0.323; p<0.001 Pearson’s r=0.313; p<0.001 Pearson’s r=0.293; p<0.001

14 14 14

12 12 12

SEC61G 10 SEC61G 10 SEC61G 10

8 8 8 6810 46810 46578 JAG1 PSENEN NOTCH4 CGGA GBM dataset CGGA GBM dataset CGGA GBM dataset Pearson’s r=0.338; p<0.001 Pearson’s r=0.316; p<0.001 Pearson’s r=0.292; p<0.001

6 6 6 4 4 4 2 2 2 SEC61G SEC61G SEC61G 0 0 0 –2 –2 –2 –1 0 12 –1 0 1 –1 0 1 NOTCH3 PSEN2 NOTCH4

Figure 5. Functional investigation of SEC61G in GBM. (A) GSEA showed tumor-related biological processes and signaling pathways that were significantly enriched according to the expression of SEC61G in TCGA and CGGA dataset, respectively. Notch was significantly enriched in both TCGA and CGGA dataset. B( ) Ten tumor-related GO terms significantly enriched according to SEC61G expression in GSVA based on TCGA and CGGA GBM datasets, respectively. Notch receptor processing was significantly correlated with high expression of SEC61G. (C) The correlations between SEC61G and hub genes of Notch pathway based on TCGA and CGGA GBM datasets, respectively. The top 3 related hub genes were given in individual figures. GBM – glioblastoma; GSEA – gene set enrichment analysis; TCGA – The Cancer Genome Atlas; CGGC – Chinese Glioma Genome Atlas; GO – Gene Ontology; GSVA – gene set variation analysis.

at least partly confirmed the results of a previous study that at least partly caused by the activation of the Notch path- SEC61G exists as a GBM-specific proto-oncogene. way, which suggested another possible mechanism, that high SEC61G expression could result in a poor prognosis the TMZ Another important finding was that SEC61G was closely related resistance in GBM patients. to the Notch pathway. Notch receptors (Notch 1–4) play an es- sential role in preventing neuronal differentiation by driving neural stem cell maintenance [23]. As glioma cells share nu- Conclusions merous characteristics in common with neural stem cells, Notch is thus often implicated in the development of glioma [24]. SEC61G is a potential prognostic marker for GBM patients, Experimental data demonstrated that reduced Notch1 expres- and an indicator of TMZ and radiotherapy resistance. SEC61G sion in glioma cell lines led to an increased apoptosis and de- might exert its functions by regulating the Notch pathway in creased proliferation [25]. On the other hand, Notch was dem- GBM. Further investigations should focus on specific clinical onstrated to contribute to TMZ resistance in GBM patients; events and biological behaviors associated with SEC61G and pharmacological antagonism of the Notch pathway could en- the definite mechanism of SEC61G in the regulation network hance the therapeutic effect of TMZ [26,27]. Notch inhibitors of GBM, which may allow us to better understand the patho- g-secretase inhibitors (GSI), could even prolong survival time genesis and treatment resistance in GBM. in GBM patients who received TMZ treatment [28]. Our study demonstrated that SEC61G expression was significantly corre- Conﬂict of interest lated with the prognosis and TMZ treatment response in GBM patients. So, we speculated these effects of SEC61G might be None.

Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System] This work is licensed under Creative Common Attribution- [ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica] NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) 3634 [Chemical Abstracts/CAS] Liu B. et al.: Identification of SEC61G as a novel prognostic marker for predicting survival… CLINICAL RESEARCH © Med Sci Monit, 2019; 25: 3624-3635

References:

1. Alifieris C, Trafalis DT: Glioblastoma multiforme: Pathogenesis and treat- 15. Subramanian A, Tamayo P, Mootha VK et al: Gene set enrichment analy- ment. Pharmacol Ther, 2015; 152: 63–82 sis: A knowledge-based approach for interpreting genome-wide expression 2. Wang Y, Su Y, Ji Z, Lv Z: High expression of PTPN3 predicts progression and profiles. Proc Natl Acad Sci USA, 2005; 102(43): 15545–50 unfavorable prognosis of glioblastoma. Med Sci Monit, 2018; 24: 7556–62 16. Hanzelmann S, Castelo R, Guinney J: GSVA: Gene set variation analysis for 3. Cheng P, Ma Y, Gao Z, Duan L: High mobility group box 1 (HMGB1) predicts microarray and RNA-seq data. BMC Bioinformatics, 2013; 14: 7 invasion and poor prognosis of glioblastoma multiforme via activating AKT 17. Thakkar JP, Dolecek TA, Horbinski C et al: Epidemiologic and molecular prog- signaling in an autocrine pathway. Med Sci Monit, 2018; 24: 8916–24 nostic review of glioblastoma. Cancer Epidemiol Biomarkers Prev, 2014; 4. Yan Y, Xu Z, Dai S et al: Targeting autophagy to sensitive glioma to temo- 23(10): 1985–96 zolomide treatment. J Exp Clin Cancer Res, 2016; 35: 23 18. Hegi ME, Diserens AC, Gorlia T et al: MGMT gene silencing and benefit from 5. Zanders ED, Svensson F, Bailey DS: Therapy for glioblastoma: Is it working? temozolomide in glioblastoma. N Engl J Med, 2005; 352(10): 997–1003 Drug Discov Today, 2019 [Epub ahead of print] 19. Messaoudi K, Clavreul A, Lagarce F: Toward an effective strategy in glio- 6. Tomiyama A, Ichimura K: Signal transduction pathways and resistance to blastoma treatment. Part I: Resistance mechanisms and strategies to over- targeted therapies in glioma. Semin Cancer Biol, 2019 [Epub ahead of print] come resistance of glioblastoma to temozolomide. Drug Discov Today, 2015; 20(7): 899–905 7. Wu H, Liu Q, Cai T et al: MiR-136 modulates glioma cell sensitivity to temozolomide by targeting astrocyte elevated gene-1. Diagn Pathol, 2014; 20. Cubillos-Ruiz JR, Bettigole SE, Glimcher GH: Tumorigenic and immuno- 9: 173 suppressive effects of endoplasmic reticulum stress in cancer. Cell, 2017; 168(4): 692–706 8. She X, Yu Z, Cui Y et al: MiR-128 and miR-149 enhance the chemosensitiv- ity of temozolomide by Rap1B-mediated cytoskeletal remodeling in glio- 21. Yan MM, Ni JD, Song D et al: Interplay between unfolded protein response blastoma. Oncol Rep, 2014; 32(3): 957–64 and autophagy promotes tumor drug resistance. Oncol Lett, 2015; 10(4): 1959–69 9. Greenfield JJ, High S: The Sec61 complex is located in both the ER and the ER-Golgi intermediate compartment. J Cell Sci, 1999; 1120(Pt 10): 1477–86 22. Penaranda Fajardo NM, Meijer C, Kruyt FA: The endoplasmic reticulum stress/ unfolded protein response in gliomagenesis, tumor progression and as a 10. Oakes SA, Papa FR: The role of endoplasmic reticulum stress in human pa- therapeutic target in glioblastoma. Biochem Pharmacol, 2016; 118: 1–8 thology. Annu Rev Pathol, 2015; 10: 173–94 23. Jiang J, Xiao K, Chen P: NOTCH signaling in lung diseases. Exp Lung Res, 11. Liu Y, Ji W, Shergalis A et al: Activation of the unfolded protein response via 2017; 43(4–5): 217–28 inhibition of protein disulfide isomerase decreases the capacity for DNA repair to sensitize glioblastoma to radiotherapy. Cancer Res, 2019 [Epub 24. Teodorczyk M, Schmidt MHH: Notching on cancer’s door: Notch signaling ahead of print]. in brain tumors. Front Oncol, 2014; 4: 341 12. Tsukamoto Y, Uchida T, Karna S et al: Genome-wide analysis of DNA copy 25. Purow BW, Haque RM, Noel MW et al: Expression of Notch-1 and its ligands, number alterations and gene expression in gastric cancer. J Pathol, 2008; Delta-like-1 and Jagged-1, is critical for glioma cell survival and prolifera- 216(4): 471–82 tion. Cancer Res, 2005; 65(6): 2353–63 13. Reis-Filho JS, Pinheiro C, Lambros MB et al: EGFR amplification and lack 26. Ulasov IV, Nandi S, Dey M et al: Inhibition of Sonic hedgehog and Notch of activating mutations in metaplastic breast carcinomas. J Pathol, 2006; pathways enhances sensitivity of CD133(+) glioma stem cells to temozolo- 209(4): 445–53 mide therapy. Mol Med, 2011; 17(1–2): 103–12 14. Lu Z, Zhou L, Kilela P et al: Glioblastoma proto-oncogene SEC61gamma is 27. Hiddingh L, Tannous BA, Teng J et al: EFEMP1 induces gamma-secretase/ required for tumor cell survival and response to endoplasmic reticulum Notch-mediated temozolomide resistance in glioblastoma. Oncotarget, stress. Cancer Res, 2009; 69(23): 9105–11 2014; 5(2): 363–74 28. Yahyanejad S, King H, Iglesias VS et al: NOTCH blockade combined with radiation therapy and temozolomide prolongs survival of orthotopic glioblastoma. Oncotarget, 2016; 7(27): 41251–64