Cancer Biol Med 2020. doi: 10.20892/j.issn.2095-3941.2019.0164

ORIGINAL ARTICLE

Downregulation of SNRPG induces cell cycle arrest and sensitizes human glioblastoma cells to temozolomide by targeting Myc through a p53-dependent signaling pathway

Yulong Lan1,2*, Jiacheng Lou2*, Jiliang Hu1, Zhikuan Yu1, Wen Lyu1, Bo Zhang1,2 1Department of Neurosurgery, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518020, China;2 Department of Neurosurgery, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China

ABSTRACT Objective: Temozolomide (TMZ) is commonly used for glioblastoma multiforme (GBM) chemotherapy. However, drug resistance limits its therapeutic effect in GBM treatment. RNA-binding proteins (RBPs) have vital roles in posttranscriptional events. While disturbance of RBP-RNA network activity is potentially associated with cancer development, the precise mechanisms are not fully known. The SNRPG gene, encoding small nuclear ribonucleoprotein polypeptide G, was recently found to be related to cancer incidence, but its exact function has yet to be elucidated. Methods: SNRPG knockdown was achieved via short hairpin RNAs. Gene expression profiling and Western blot analyses were used to identify potential glioma cell growth signaling pathways affected by SNRPG. Xenograft tumors were examined to determine the carcinogenic effects of SNRPG on glioma tissues. Results: The SNRPG-mediated inhibitory effect on glioma cells might be due to the targeted prevention of Myc and p53. In addition, the effects of SNRPG loss on p53 levels and cell cycle progression were found to be Myc-dependent. Furthermore, SNRPG was increased in TMZ-resistant GBM cells, and downregulation of SNRPG potentially sensitized resistant cells to TMZ, suggesting that SNRPG deficiency decreases the chemoresistance of GBM cells to TMZ via the p53 signaling pathway. Our data confirmed that SNRPG suppression sensitizes GBM cells to TMZ by targeting Myc via the p53 signaling cascade. Conclusions: These results indicated that SNRPG is a probable molecular target of GBM and suggested that suppressing SNRPG in resistant GBM cells might be a substantially beneficial method for overcoming essential drug resistance. KEYWORDS SNRPG; glioblastoma; cell cycle; temozolomide; therapy

Introduction new therapeutic approaches are urgently needed. The SNRPG protein is a core element of U1, U2, U4, and U5 small nuclear Glioblastoma multiforme (GBM) is the most commonly ribonucleoprotein (snRNP) complexes, which are the building occurring malignant form of brain cancer in adults. The blocks (or originators) of the spliceosome, and might also be median overall survival of GBM patients remains between a component of the U7 snRNP complex, which is involved in 6–15 months, with a 5 year survival rate from the date of processing of the 3′ ends of histone transcripts. Numerous diagnosis of less than 5%1,2. Without treatment, the majority transcript alternates encoding various isoforms of SNRPG of patients with GBM survive for only a few months3, and have been identified (http://www.genecards.org/), and the molecular mechanisms underlying the roles of SNRPG in GBM need to be clarified. *These authors contributed equally to this work. Myc can mediate a transcriptional program encompassing Correspondence to: Wen Lyu and Bo Zhang E-mail: [email protected] and [email protected] cell growth, metabolism, the cell cycle, and survival in cancer 4,5 ORCID ID: https://orcid.org/0000-0002-8130-6022 and https://orcid. cells . Substantial effort has been devoted to targeting Myc org/0000-0001-6635-6577 for cancer therapy, and Myc inhibition appears to be of Received May 17, 2019; accepted August 29, 2019. significant therapeutic value for cancers expressing high Available at www.cancerbiomed.org 6 ©2020 Cancer Biology & Medicine. Creative Commons levels of Myc . Moreover, Myc expression correlates with the Attribution-NonCommercial 4.0 International License glioma grade7, and approximately 60%–80% of GBMs exhibit Cancer Biol Med Vol 17, No 1 February 2020 113 increased Myc levels8. Importantly, preclinical studies have Materials and methods validated Myc inhibition as an effective therapeutic strategy for human gliomas9, and the identification of new protein- All experiments were performed in accordance with regular coding genes and the development of novel compounds to institutional biosecurity and institutional safety processes. pharmacologically target Myc-driven cancers are key research Each experimental workplace included a biosafety level 1 goals. The p53 (also known as TP53) protein is a well-known (BSL-1) facility, and all individuals involved were committed cancer suppressor with pleiotropic roles, as it regulates to using BSL-1 techniques. transcription by binding to exact DNA sequences10-12 and to other cellular proteins, such as Mdm2, TBP, and Gadd4513-15. Cancer patient samples The p53 also participates in DNA replication16 and restoration procedures17. Interestingly, in numerous mouse models of Experiments on human subjects followed the Helsinki Myc-driven tumors, tumor deterioration via Myc repression Declaration32 and were approved by the Ethics Committee of is hindered by simultaneous suppression of the TP53 protein, Dalian Medical University (reference number: KY2019-01). highlighting the relevance of an intact p53 pathway for treating When donor material/samples were obtained from deceased cancer by targeting Myc18-20. patients, written informed consent was obtained from the Temozolomide (TMZ) chemotherapy shows remarkable patients’ family members. Materials were harvested from therapeutic enhancement by extending tumor control as 15 fresh tissue samples obtained from GBM patients who well as patient survival in newly detected GBM21. However, were admitted to the Second Affiliated Hospital of Dalian the effective rate of TMZ is only 35%22, and overcoming Medical University from September 2014 to December chemoresistance is thus essential for enhancing the 2016 (Supplementary Table S1). Normal brain tissues were survival rate of GBM patients23. Intriguingly, p53 has been acquired from the Institute of Biochemistry and Cell Biology substantially associated with the efficacy of TMZ treatment (Shanghai, China). for GBM, and contradictory results regarding the clinically significant influence of the p53 status on TMZ resistance have been reported24. Various studies have shown either Cell culture an enhanced ability of TMZ to prevent cell viability when p53wt is functionally repressed25,26 or a sensitization of The authenticities of the human U87 MG, U251, LN229, and cells to medications when p53wt is efficient27,28. Nuclear A172 cells were verified based on their genomic short tandem overexpression of p53 generally reflects a marker of repeat (STR) profiles by GeneChem Biotechnology Co., Ltd. mutation, and numerous studies have indicated that (Shanghai, China), while the U118 cell line was verified based expression of p53 is 90% associated with its mutation29. In on its genomic STR profiles by Shanghai Zhong Qiao Xin Zhou cells containing mutant p53, TMZ causes temporary cell Biotechnology Co., Ltd. (Shanghai, China). The cell lines were cycle arrest in addition to cell death through apoptosis or determined to be free of mycoplasma using the Mycoplasma mitotic catastrophe30 along with attenuated DNA repair31. Detection Kit-Quick Test (Biotool Biotechnology, Shanghai, Thus, the activation of p53 may contribute to the efficacy of China). The human U87MG (ATCC® HTB-14), LN229 TMZ for treating GBM. (ATCC® CRL-2611), A172 (ATCC® CRL-1620), and U118 Because SNRPG is a favorable candidate based on gene (ATCC® HTB-15) cell lines were acquired from the American screening, we examined its roles in GBM occurrence and Type Culture Collection (ATCC, Old Town, Manassas, VA, tumor development as well as TMZ resistance. Interestingly, USA). The U251 cell line was obtained from the Type Culture the association between SNRPG and Myc- and p53-mediated Collection of the Chinese Academy of Sciences (Shanghai, cell cycle signaling required further elucidation. In this study, China). The human astrocytes were obtained from ScienceCell we performed experiments both in vivo and in vitro to detect (San Diego, CA, USA). Normal brain tissues were acquired the exact mechanisms by which SNRPG promotes GBM. from the Institute of Biochemistry and Cell Biology (Shanghai, Targeting the SNRPG-driven p53 signaling axis is a potential China). Cells were preserved in Dulbecco’s Modified Eagle’s therapeutic strategy for effective GBM treatment and for Medium supplemented with 10% fetal bovine serum. All cell overcoming chemoresistance. cultures were maintained at 37 °C in a humidified environment 114 Lan et al. SNRPG in glioblastoma

containing 5% CO2. The TMZ-resistant GBM cell lines Tween 20, 0.1% RNase in PBS]. The stained cells were were established as described below. U87 cells (1 × 105/mL) examined using an Accuri C6 fluorescence-activated cell sorter were first incubated for 24 h and then treated with an initial (FACS) (Genetimes Technology Inc., Shanghai, China). concentration of TMZ (5 μM), and the medium containing TMZ was changed every 2–3 days. After application of the Apoptosis analysis initial dosage for 2 weeks, the dosage was doubled, and each dosage was continued for 14 days; the ultimate dose reached Apoptosis was measured by FACS using an annexin 400 μM. The U87 drug-resistant cell line was labeled U87/TMZ. V-fluorescein isothiocyanate (FITC) apoptosis detection kit The technique used to induce the U251 drug-resistant cell line (Nanjing KeyGEN Biotech. Co. Ltd., China). In brief, the cells was similar to that used for the U87/TMZ line. were plated in 6-well plates, collected, washed once with cold PBS, and immediately stained with FITC-labeled annexin V SNRPG knockdown by shRNA and PI. These stained cells were examined using the FACS Accuri C6 instrument (Genetimes Technology Inc., Shanghai, Cells were plated in a 6-well plate (~5 × 104 cells/well) China). and then incubated at 37 °C with 5% CO2 until reaching a confluence of ~30%, after which the cells were transfected. High resolution O6-methylguanine-DNA Lentiviral vectors were purchased from Shanghai GeneChem methyltransferase gene (MGMT) methylation Company Ltd. (Shanghai, China). A nonsilencing shRNA analysis (5′-GCCTAACTGTGTCAGAAGGAA-3′) was used as the negative control (shCtrl). The shRNA sequence targeting the Bisulfite samples were analyzed using high sensitive SYBR® SNRPG gene was 5′-TGGACAACAGAACAATATT-3′. Green (KapaBiosystems, Wilmington, MA, USA) at the Center for Genomics and Oncological Research, University Cell viability assay of Granada (Granada, Spain). The reaction was performed on an Eco Real-Time PCR System (Illumina, San Diego, CA, Cell viability was measured by the [3-(4,5-dimethylthiazol-2- USA), and data were evaluated by Eco Real-Time PCR System, yl)-2,5-diphenyltetrazolium bromide] (MTT) assay (Roche version 4.0 software (Illumina). Methylated EpiTect Control Diagnostics, Santa Clara, CA, USA). Cell lines were plated at DNA, both methylated and unmethylated (Qiagen, Madrid, 6 × 103 cells/well in 96-well plates and allowed to adhere for Spain), was used to construct the methylation curve at more than 5 days after transfection. The cell growth was then methylated-unmethylated ratios of 0, 0.25, 0.5, 0.75, and 1. A evaluated using optical density values. pair of primers capable of amplifying a precise region was used to analyze both the samples and the methylation curve. Celigo assay Western blot analysis U87MG or U251 cells in the logarithmic growth phase were processed with trypsin, resuspended in standard medium, Cells were lysed, and the proteins were separated on 7.5%–12% and then plated in 96-well plates (2,000 cells/well). The sodium dodecyl sulfate-polyacrylamide gel electrophoresis amount of green fluorescent protein-positive cells was mini gels and then transferred to a polyvinylidene fluoride calculated on five consecutive days using a Cellomics membrane. The blots were incubated with appropriate Array Scan High-Content Screening Reader (Olympus antibodies, and the protein bands were identified using Corporation, Tokyo, Japan). enhanced chemiluminescence. Analogous experiments were performed at least 3 times. The total protein concentration Cell cycle analysis was identified using a bicinchoninic acid protein assay kit. Primary antibodies against glyceraldehyde 3-phosphate First, the cells were trypsinized into single cells, collected, dehydrogenase (GAPDH) were acquired from Santa Cruz washed with phosphate-buffered saline (PBS) and suspended Biotechnology (Dallas, TX, USA), and an anti-CCNB1 in a staining buffer [10 μg/mL propidium iodide (PI), 0.5% antibody was purchased from Proteintech (Hubei, China). Cancer Biol Med Vol 17, No 1 February 2020 115

Antibodies against Myc, p53, CDK2, P-gp, and GST-π were Lentivirus construction, production, and purchased from Abcam, Cambridge, UK. Primary antibodies infection against SNRPG and β-actin were acquired from Sigma- Aldrich (Shanghai, China) and Santa Cruz Biotechnology, The SNRPG lentivirus plasmid was constructed, produced, respectively. and used to infect glioma cells as described in a previous publication33. At 48 h after infection, the virus-infected cells Quantitative real-time reverse transcription were cultured in medium containing 2.5 μg/mL puromycin polymerase chain reaction (RT-qPCR) (Sigma-Aldrich, St. Louis, MO, USA) for selection. The surviving cells were used for subsequent experiments. The Total RNA was extracted from glioma and adjacent normal SNRPG-overexpressing cells were named Lenti-SNRPG; the tissues using TRIzol reagent according to the manufacturer’s corresponding control cells were named Lenti-Vector. instructions (TaKaRa Bio, Beijing, China). The cDNA was reverse transcribed using the PrimeScript RT Reagent Kit Confocal immunofluorescence (TaKaRa Bio) according to the manufacturer’s protocol. The qPCR was performed according to the kit instructions Immunofluorescence staining was performed in cells cultured (TaKaRa Bio) using the Mx3005P Real-Time PCR System on chamber slides. The cells were washed with PBS and (Agilent, Santa Clara, CA, USA). GAPDH RNA levels were then fixed with 4% paraformaldehyde for 10 min at room used to normalize the relative mRNA expression. The data temperature. The samples were permeabilized with 0.2% were evaluated by the 2−ΔΔCT method. Triton X-100 for 5 min and then blocked with 10% bovine serum albumin in PBS for 30 min. Antibodies against Myc Microarray gene expression analysis and p53 in 1% blocking solution were added to the sample and incubated overnight (4 °C). After washing the samples U87MG cells were transfected with shSNRPG; total RNA was with PBS 3 times, fluorescein isothiocyanate- and rhodamine- extracted, and 50–500 ng was used to create biotin-modified conjugated secondary antibodies were added to a 1% blocking amplified RNA (aRNA) using a GeneChip ′3 IVT Express Kit solution, and the mixtures were incubated for 1 h. The (Affymetrix, Santa Clara, CA, USA). Reverse transcription stained samples were mounted using 4′,6-diamidino-2- was performed using a T7 oligo (dT) primer. To generate phenylindole-containing Vectashield solution (Vector numerous biotin-modified aRNA copies, a First-Strand IVT Laboratories Inc., Burlingame, CA, USA) to counterstain the Labeling Master Mix (Thermo Fisher Scientific, Waltham, nuclei. After 5 additional 5 min washes, the samples were MA, USA) was used, and the aRNA was then purified examined using a Leica DM 14000B confocal microscope and quantified. Following fragmentation, the aRNA was (Leica, Wetzlar, Germany). hybridized to the GeneChip PrimeView Human Expression Array (Affymetrix). Following hybridization, the chips were Protein-ligand docking stained with phycoerythrin and washed using the Genechip Fluidics Station 450 (Thermo Fisher Scientific). The Genechip The molecular docking simulations were conducted using Array Scanner 3000 7G (Thermo Fisher Scientific) was used to Molecular Operating Environment (MOE) software, version scan and analyze the microarray signals. 2018.01 (Chemical Computing Group Inc., Montreal, Canada). Gene signatures were used to compare shCtrl to shSNRPG. The two-dimensional structure of the small molecule, TMZ, To recognized pathways that were generally deregulated in was drawn using ChemBioDraw 2014 (PerkinElmer, Waltham, U87MG-shSNRPG cells, and enhancement of differentially MA, USA) and converted into three-dimensional (3D) expressed gene signatures in human pathways were assessed conformations using energy minimization. The 3D structure using gene set enrichment analysis (GSEA); the pathways of the SNRPG protein was downloaded from the Research were collected in the H and C2 curated gene sets using 1,000 Collaboratory for Structural Bioinformatics Protein Data Bank gene label permutations (gene sets). Significantly augmented (PDB, code 5XJL; https://www.rcsb.org/) and prepared using gene sets, well-defined by P < 0.05, were compared among the the QuickPrep module (MOE; Chemical Computing Group shCtrl and shSNRPG gene signatures. Inc.). The protonation states and orientations of the hydrogens 116 Lan et al. SNRPG in glioblastoma were optimized by LigX (MOE; Chemical Computing Group with isoflurane (2%) in oxygen (1 dm3/min). Beginning 5 min Inc.) at a pH of 7 and a temperature of 300 K. The docking after luciferin injection, the images were acquired at various process was performed under the AMBER10/EHT force field exposure times (1, 5, 30, and 60 s). Data were quantified with (MOE; Chemical Computing Group Inc.) with an internal Living Imaging software (PerkinElmer) using absolute photon dielectric constant of 1, an external dielectric constant of 80, counts (photons/s) in a region of interest that was manually and an implicit solvation model of the reaction field. The drawn to outline the bioluminescence image signals. Triangle Matcher (MOE; Chemical Computing Group Inc.) On day 30 after tumor cell inoculation, all experimental algorithm was used for the initial placement of 1,000 returned mice were sacrificed with ether anesthesia, and the total weight conformations, and the top 100 conformations ranked by the of the tumors in each mouse was measured. The tumor tissues London dG (MOE; Chemical Computing Group Inc.) scoring were harvested, freshly fixed with 10% neutral formalin, function were further refined through energy minimization desiccated and embedded in paraffin. Four µm sections were and then rescored using the Generalized Born/Volume Integral stained with hematoxylin and eosin (H&E) and rabbit anti- (MOE; Chemical Computing Group Inc.) solvation energy, proliferating cell nuclear antigen (PCNA; 1:100) and examined which was a more accurate implicit solvent model scoring using a light microscope and a DM 4000B fluorescence function. The induced-fit protocol was applied for energy microscope equipped with a digital camera (Leica). minimization in which the ligand was fully flexible during All animals were purchased from the Experimental Animal the conformation sampling process, and the side chains of the Center, Dalian Medical University [certificate of conformity: receptor were also allowed to move. Finally, the 20 top-ranked no. SYXK (Liao) 2018-0007 and SCXK (Liao) 2018-0003]. This poses were retained, and the most representative pose was study was conducted in accordance with recommendations of retrieved by visual inspection for further analysis. the National Institutes of Health Guide for Care and Use of Laboratory Animals (Publication No. 85-23, revised 1985). All Animal studies animal maintenance and procedures were performed in strict accordance with the recommendations established by the To evaluate the effect of SNRPG knockdown in the human guidelines in the U.S. National Institutes of Health Guide for U87MG glioblastoma mouse model (female nu/nu mice the Care and Use of Laboratory Animals. aged 4–6 weeks), U87MG cells (2 × 106 in 100 mL of PBS) were subcutaneously injected using a 27-gauge needle near Statistical analysis the axillary fossa. These mice were randomly divided into 2 groups with 10 mice each. Tumors were measured daily with Statistical analyses were performed with GraphPad Prism and calipers, and the tumor volumes were calculated according to GraphPad InStat software (GraphPad Software, La Jolla, CA, the formula V = 1/2 (width2 × length). The body weights were USA). Student’s t-test was used to compare the values for the also recorded. test and control samples in vitro and in vivo. SPSS statistical The cellular bioluminescence of each group was assayed by software for Windows, version 17.0 (SPSS, Chicago, IL, USA) adding 5 μL of Pierce D-luciferin (Thermo Fisher Scientific, was used for all statistical analyses. P < 0.05 was considered Rockford, IL, USA) to the cell culture medium and incubating a statistically significant difference, *P < 0.05; **P < 0.01; the mixture for 20 min. In total, 100 μL of culture medium was ***P < 0.001. mixed with 100 μL of 1% agarose in a cylindrical glass tube. To monitor tumor growth, bioluminescence imaging Results with the IVIS Spectrum system (Caliper, Xenogen, Alameda, CA, USA) was initiated approximately 28 days after tumor SNRPG expression is upregulated in human implantation. The tumor-bearing mice were anesthetized GBM tissues

(isoflurane/O2 in an induction chamber; isoflurane from Baxter International Inc., Deerfield, IL, USA), and a solution SNRPG expression in GBM tissues was compared with that of D-luciferin (120 mg/kg in PBS in a total volume of 80 in healthy tissues according to The Cancer Genome Atlas mL; Biosynthesis, Naperville, IL, USA) was administered (TCGA) and found to be significantly upregulated by using the subcutaneously to the neck region. Anesthesia was maintained R programming language (Figure 1A). We also re-evaluated Cancer Biol Med Vol 17, No 1 February 2020 117

ABSNRPG expression in TCGA brain 14 *** 9.0 8.0 7.0 12 6.0 5.0 4.0 10 3.0 2.0 P-value: 1.04E-5 Relative expression of mRNA 8 1.0

Log2 median-centered internsity 0.0 Normal Cancer Brain (10) Brain glioblastoma (542)

SNRPG-TCGA-GBM CDStrata Group = high Group = low 1.00 Low 0.75 8 *** 0.50

0.25 High

6 Survival probability 0.00 P < 0.0001 0 500 1000 1500 2000 2500 Time (day) 4 Number at risk

Group = high 152 49 27 15 60

2 Strata Group = low 153 102 71 38 13 0

0 500 1000 1500 2000 2500 Relative SNRPG expression Time (day) 0 Number of censoring 2 Normal Glioblastoma 1 n.censor 0 0 500 1000 1500 2000 2500 EFTime (day) Color key

35

) 30 –2 0 2 Column Z-score 25 20 15 10 –log (P-value 5 Threshold 0 t e e Cancer Cell cycl e Cellular growth DNA replicatio n Cell morphology Cellular assembly Organismal injury Cellular movement Organismal survival Cellular developmen Cell death and survival KD-1 KD-2 KD-3 Gastrointestinal diseas NC-1 NC-2 NC-3 Endocrine system diseas e Reproductive system diseas Cardiovascular system function

Figure 1 The involvement of SNRPG in human glioblastoma multiforme (GBM). (A) The correlation of SNRPG RNA expression levels with the incidence of GBM was examined via the t-test using the R programming language. The selection criteria were a P < 0.05 and |logFC| ≥ 1 (with 2 as the base). FC = 2.46, P = 3.86E-16. (B) Using Oncomine (https://www.oncomine.org/resource/login.html), the SNRPG expression in GBM tissues (n = 542) was compared with that in normal tissues (n = 10) in the “TCGA brain” of the TCGA dataset, revealing that SNRPG was significantly upregulated in GBM tissue compared with that in normal tissue. (C)SNRPG expression was examined in 15 GBM tissues and nontumor tissues by RT-qPCR, revealing significantly increased expression in GBM tissues compared with that in the normal tissue samples. Error bars, standard error. ***P < 0.001. (D) Kaplan-Meier survival analyses of GBM with different levels of SNRPG using the TCGA dataset and the R programming language. (E-F) Gene expression profile analysis revealed the downstream targets affected by SNRPG. (E) The heat map shows the differentially expressed genes in all samples. (F) The histogram of disease and function shows the enrichment of differential genes in disease and their functional classifications. 118 Lan et al. SNRPG in glioblastoma

SNRPG expression in GBM and healthy tissues in other indicating that functional loss of SNRPG inhibited GBM cell publicly available data on the Oncomine website (https:// proliferation. Flow cytometry was performed to determine www.oncomine.org/), and found that SNRPG expression whether SNRPG affected the proliferation of GBM cells by was also augmented in GBM according to the Oncomine altering cell cycle progression. The transfection of GBM cells dataset (Figure 1B). To confirm this possibility, RT-qPCR with shSNRPG blocked cell cycle development via inducing was performed to determine SNRPG expression in 15 pairs of G2/M cell cycle arrest (**P < 0.01) (Figure 2F). Moreover, to GBM tissues as well as in normal brain tissues (Figure 1C). define the physiological part of SNRPG in apoptosis, U87MG, The results suggested increased expression of SNRPG in GBM A172, and U118 cells were transfected with shSNRPG, and tissues compared with that in normal tissue samples (P < 0.001). apoptosis was investigated by flow cytometry after 48 h, to In addition, the prognostic signature of the SNRPG gene was show that transfection with shSNRPG significantly promoted also confirmed (Figure 1D). Overall, the results suggested that apoptosis (**P < 0.01) (Figure 2G). The experimental results GBM progression was always accompanied by upregulation of obtained in another GBM cell line, U251, were consistent SNRPG. with those obtained using U87MG, A172, and U118 cells To further elucidate the roles of SNRPG in tumors, we (Supplementary Figure S1). In addition, experiments compared the gene expression in U87MG-shCtrl and using GBM neurospheres with serum-free medium also U87MG-shSNRPG cells by microarray analysis. Differentially showed that SNRPG inhibition suppressed the proliferation expressed genes with at least a 1.5-fold variation were and invasiveness of neurospheres from GBM patients recognized. First, a heat map was used to illustrate the (Supplementary Figure S2). Furthermore, the expression aggregation of all samples and genes with differential levels of SNRPG in samples from primary and recurrent expression (Figure 1E). A histogram of disease and function GBM patients and the possible correlation between SNRPG helped to illustrate the enrichment of differentially expressed expression and the MGMT methylation status were also genes in disease and to classify their functions (Figure 1F), determined (Supplementary Figure S2). The role of SNRPG which showed that the level of SNRPG expression could in the context of mismatch repair was also analyzed, showing be associated with the cell cycle, cell growth, and more that SNRPG inhibition increased the protein expression of importantly, the incidence of cancer. the mismatch repair (MMR) protein MLH1, whose decreased expression could be a well-established mechanism of TMZ Functional loss of SNRPG inhibits GBM cell resistance34 (Supplementary Figure S2). Together, these proliferation in vitro results indicated that functional SNRPG damage inhibited GBM cell propagation, blocked cell cycle development, and First, using Western blot (Figure 2A, left panel) and RT-qPCR promoted apoptosis in vitro. (Figure 2A, right panel), we assayed the expression of SNRPG in normal brain tissues and in the human U251, A172, U87, Functional loss of SNRPG inhibits GBM LN229, and U118 cell lines. To investigate the effects of SNRPG tumorigenesis in vivo in GBM cells, we used the U87 and U251 cell lines because they exhibited the highest relative expression of SNRPG To confirm whether SNRPG had any impact on tumorigenesis, (Figure 2A). SNRPG knockdown was achieved by shRNA shCtrl/shSNRPG-transfected U87MG cells were injected (Figure 2B), and the Celigo assay was performed to identify into nude mice, and all animals developed xenograft tumors cell viability. Figure 2C shows that the survival rate of U87MG at the inoculation site. Figure 3A and 3B show that cancer cells transfected with shCtrl was higher than that of U87MG development in the shSNRPG group was sluggish compared to cells transfected with shSNRPG (**P < 0.01; ***P < 0.001; ****P that in the control group (upper panel), while the body weights < 0.0001) (Figure 2C). Western blot assays confirmed that of mice in the 2 groups were barely altered (lower panel) SNRPG protein expression was downregulated in SNRPG- (***P < 0.001; ****P < 0.0001). Furthermore, in vivo fluorescence silenced U87MG cells compared to that in shCtrl-transduced imaging was performed, showing that the shCtrl group mice cells (Figure 2D). The MTT assay also revealed that silencing had more extended cancer growth interruptions than the SNRPG expression increased U87MG cell production mice in the shSNRPG group (Figure 3C). Up to 30 days after compared to that in control cells (****P < 0.0001) (Figure 2E), injection, the typical tumor mass in the shSNRPG group was Cancer Biol Med Vol 17, No 1 February 2020 119

A B ** 2.5 1.2

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LN229 ) Astr U251 U87 A172 U118 2 1.0 SNRPG l 0.8 1.5 0.6 β-actin l

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lative pr A172 U87 (SNRPG ocyte U251 U118 LN229 ocyte LN229 U251 A172 U118 Re Astr Astr shCtrl **** C Day 1 Day 2 Day 3 Day 4 Day 5 2,000 1,600 shSNRPG **** 1,200 *** 800 shCtrl Cell count 400 0 12345

d 4 shCtrl **** shSNRPG 3 shSNRPG **** 2 *** ns 1

Cell count/fol 0 1 2345

DESNRPG GAPDH shCtrl shCtrl 1 shSNRPG 4 shSNRPG **** 1.2 ** 3.5 0.8 **** 1.0 **** 3 **** **** 0.6 2.5 0.8 **** **** OD490 2 **** 0.6 0.4 1.5 ns

ns OD490/fold 0.4 0.2 1 0.5 0.2 0 0 0.0 12345 12345 NC KD Time (day) Time (day)

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100 shCtrl shCtrl 100 shCtrl ** ) 100 ) 15 ** ** 80 shSNRPG 80 shSNRPG 80 shSNRPG shCtrl ** ** ** 10 60 60 60 shSNRPG 40 ** 40 ** 40 ** 5 20 ** 20 ** 20 ** Percentage (% 0 0 0 Percentage (% 0 S S S U251 U87 A172

G0/G1 G2/M G0/G1 G2/M G0/G1 G2/M 120 Lan et al. SNRPG in glioblastoma

Figure 2 Effects of SNRPG knockdown on cell proliferation. (A) Expression of SNRPG in normal astrocyte and glioma cell lines detected by Western blot and RT-qPCR. The RT-qPCR results indicated that all glioblastoma cell lines exhibited high expression of SNRPG (mean ± SD, n = 3). The U87MG and U251 cell lines were selected for further study to investigate the biological role of SNRPG in GBM cells. (B) SNRPG was knocked down using shRNA (mean ± SD, n =3, **P < 0.01). (C) The Celigo assay was performed to detect cell viability over 5 days after transfection. The colony numbers of U87MG cells transfected with shCtrl were larger than those of U87MG cells transfected with shSNRPG (mean ± SD, n = 3, ns, not significant; **P < 0.01; ***P < 0.001; ****P < 0.0001). Bar = 50 μm. (D) Simple Western lane view with results automatically generated by system software. The downregulated expression of SNRPG by shSNRPG in U87MG cells was further confirmed via a simple Western size assay (**P < 0.01). (E) The MTT assay showed that SNRPG knockdown significantly decreased the proliferation of U87MG cells compared with that of the control cells. The optical density values were measured on the indicated days (mean ± SD, n = 3, ns, not significant; ****P < 0.0001). (F) Flow cytometry analysis was performed to further examine the effect of SNRPG on the proliferation of glioblastoma multiforme cells by altering cell cycle progression. Error bars, standard error. **P < 0.01. (G) To further determine the physiological role of SNRPG in cell growth, U87MG, A172, and U118 cells were transfected with shSNRPG. After 48 h, cell apoptosis was examined by flow cytometry analysis. Graphs show the mean percentages of three biological replicates. Error bars, standard error. **P < 0.01.

less than that in the shCtrl group (***P < 0.001) (Figure 3D). (Figure 4C), demonstrating that downregulation of SNRPG Overall, these results showed that functional SNRPG damage might participate in GBM drug resistance. slowed down GBM tumorigenesis. The cancer cells in the We further investigated whether downregulating SNRPG shCtrl group were uneven and had ample cytoplasms, large expression repressed TMZ resistance in TMZ-resistant cells. and deformed nuclei, and high nucleocytoplasmic ratios The SNRPG deficiency suggested a decreased IC50 compared (Figure 3E). In addition, nuclear pleomorphism and nucleoli to that in the control group (*P < 0.05; **P < 0.01) (Figure 4D). were prominent. Cells with amphinucleoli and mitotic cells Furthermore, a colony assay was performed to assess the role were also observed. In contrast, in the shSNRPG group, of SNRPG in the proliferation of TMZ-resistant cells, which amphinucleoli and mitotic cells were rarely observed, and the showed that SNRPG suppression suppressed cell proliferation size of the nucleolus was reduced. We have also demonstrated (*P < 0.05; **P < 0.01) (Figure 4E). In a similar manner, that the tumors arising from U87MG-shSNRPG cells inhibition of SNRPG expression made the cells more sensitive exhibited inferior PCNA staining than tumors arising from to TMZ, as determined by reduced numbers of colonies shCtrl-transfected U87MG cells (**P < 0.01), as distinguished compared to that of cells transfected with the scrambled by immunohistochemistry (Figure 3E). control (Figure 4E). To further confirm the direct connection between SNRPG SNRPG suppression sensitizes human GBM and TMZ, docking simulations were conducted to predict cells to temozolomide the binding mode and binding affinity of the ligand TMZ with the SNRPG receptor. In the docking process, the top 20 TMZ-resistant U251 and U87 cells (U87/TMZ and U251/ scoring poses were retained for further analysis. Combining TMZ) were generated by treatment with increasing TMZ visual inspection and empirical analysis, we selected the most concentrations. The cells were incubated with various representative pose for the ligand TMZ, whose 3D and 2D concentrations of TMZ to determine the IC50 value, and binding modes are depicted in Figure 4G and Figure 4H. The the IC50 of parental cells was shown to be lower than that of electrostatic map of the protein surface was calculated and TMZ-resistant cells (Figure 4A). In addition, as presented in plotted in Figure 4F. Figure 4B, the genes related to drug resistance, comprising As indicated by the binding modes, the ligand TMZ bound P-gp and GST-π, were augmented in TMZ-resistant cells strongly to the receptor, mainly through hydrogen bonds and compared to those in the parental cells (Figure 4B). We hydrophobic interactions. It formed hydrogen bonds with the also investigated the expression of SNRPG in drug-resistant sidechains of Asn22 and Glu47 as well as with the backbone of GBM cells by Western blot and RT-qPCR, which showed Val61 (Figure 4H). In addition, hydrophobic interactions with that the expression of SNRPG was significantly increased the surrounding nonpolar residues, including Leu21, Val27, in TMZ-resistant cells compared to that in parental cells Ile57, Val60, and Ile62, were observed. Cancer Biol Med Vol 17, No 1 February 2020 121

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Figure 3 Effects of SNRPG knockdown on xenograft tumorigenicity in vivo in nude mice. U87MG cells were injected into the flanks of the nude mice. (A) Tumor growth was measured on the indicated days. (B) Tumor volumes (upper panel) and body weights (lower panel) were measured on the indicated days (mean ± SD, n = 10, ns, not significant;*** P < 0.001; ****P < 0.0001 vs. the shCtrl group on the indicated days). (C) Representative bioluminescence images of shCtrl- and shSNRPG-infected U87MG cells injected into the flanks of nude mice. Mice were imaged 28 days after implantation (n = 10 in the shCtrl group and n = 20 in the shSNRPG group). In vivo imaging detection clearly showed that mice in the shCtrl groups had significantly better tumor growth delay and survival rates than mice in the shSNRPG groups. (D) Tumor weights were measured 30 days after injection (mean ± SD, n = 10, ***P < 0.001). (E) hematoxylin and eosin staining and immunohistochemistry analysis of proliferating cell nuclear antigen expression in tumors developed from U87MG/shSNRPG cells and from U87MG/shCtrl-transfected cells. Semiquantitative analysis of the stained sections was performed using light microscopy to calculate the immunoreactive score (mean ± SD, n = 3, **P < 0.001). 122 Lan et al. SNRPG in glioblastoma

Z Z ABC 150 U87/TMZ U87 U87/TM U251 U251/TM 100 U87 Z SNRPG 50 IC50: 253 µM IC50: 49 µM 0 U87 U87/TM U251 U251/TMZ 0123 β-actin n Cell survival rate (% ) Log concentration of TMZ (µmol/L) P-gp

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Polar Sidechain acceptor Solvent residue Arene-arene Acidic Sidechain donor Metal complex Arene-h Basic Backbone acceptor Solvent contact Arene-cation Greasy Backbone donor Metal/ion contact Proximity Ligand Receptor contour exposure exposure Cancer Biol Med Vol 17, No 1 February 2020 123

Figure 4 SNRPG suppression sensitizes human glioblastoma multiforme cells to temozolomide (TMZ). (A) The MTT assay was used to determine the viability of U87/TMZ and U251/TMZ cells (IC50 for U87, 49 μM vs. 253 μM; IC50 for U251, 54 μM vs. 285 μM). (B) Western blot analysis of the drug resistance-related genes P-gp and GST-π in U251/TMZ and U87/TMZ cells. (C) Western blot analysis (upper panel) and RT-qPCR analysis (lower panel) of SNRPG in U251/TMZ and U87/TMZ cells. (D) The MTT assay was used to determine the cell viability after

SNRPG knockdown. Downregulation of SNRPG significantly reduced the IC50 compared with that of the control group (113 μM vs. 253 μM in U87/TMZ cells; 123 μM vs. 285 μM in U251/TMZ cells; *P < 0.05; **P < 0.01). (E) A colony formation assay was used to determine the cell growth after SNRPG knockdown and TMZ treatment. *P < 0.05; **P < 0.01. The colonies were counted after staining with 0.1% crystal violet (Original magnification, 1×). (F–H) The binding mode of TMZ bound to SNRPG by molecular docking. (F) The electrostatic map surface of the SNRPG protein. Positively charged areas are colored blue, and negatively charged areas are colored red. (G) The three-dimensional binding mode of TMZ bound to SNRPG. Gray ribbons: receptor backbone. Cyan sticks: ligand TMZ. Green sticks: binding pocket residues. Blue dashes: electrostatic interactions (e.g., hydrogen bonds). (H) The two-dimensional binding mode of TMZ bound to SNRPG.

SNRPG suppression may critically affect the confirmed (Figure 6B). Specifically, p53, CKD2, and cell cycle progression in GBM cells via Myc and CCNB1 expression was found to be significantly changed p53 signaling in SNRPG-silenced U87MG cells. These experimental and bioinformatics analyses showed that SNRPG regulated To identify whether any specific gene sets were possibly the cell cycle control of chromosomal replication, cyclins, augmented in differentially expressed genes controlled by and the cell cycle regulation pathway in human U87MG SNRPG, GSEA was performed using GSEA tools35. Notably, glioblastoma cells, and suggested that the effects of SNRPG SNRPG suppression significantly enriched the G2/M downregulation on the cell cycle as well as on apoptosis were checkpoint (Figure 5A), and numerous GSEA gene set most likely dependent on the comprehensive regulation of categories, as determined by the GSEA collections, which cellular functions. were possibly augmented. Intriguingly, SNRPG suppression Myc is a transcription factor that binds to DNA at E-Box caused significant Myc inhibition (Figure 5B and 5C) and p53 sequences to initiate the transcription of numerous target activation (Figure 5D–5F), indicating that both Myc and p53 genes36. We next examined the effect of SNRPG knockdown were downstream targets of SNRPG. on Myc localization. Myc was observed predominantly in the nucleus (Figure 6C). In SNRPG-knockdown cells, however, SNRPG influences Myc cellular localization Myc was mainly present in the cytoplasm, while its expression and Myc-dependent p53 activation was decreased in the nucleus (Figure 6C). Incubation of cells with leptomycin, a pharmacological blocker of nuclear Bioinformatic analysis further revealed downstream export37, had no effect on the nuclear localization of Myc in molecular targets regulated by Myc (Figure 6A). The control cells, but blocked the cytoplasmic accumulation of knowledge-dependent interactome associated with Myc Myc in SNRPG-knockdown cells (Figure 6D). These results regulation was analyzed by ingenuity pathway analysis (IPA) showed that loss of SNRPG expression increased the export and overlaid with microarray data with a 1.5-fold change of Myc to the cytoplasm. In addition, we also investigated cut-off. In addition, downstream targets of Myc and p53 whether the effects of SNRPG loss on cell cycle progression were assessed via bioinformatics analysis (Supplementary were Myc-dependent. The results indicated that the cell cycle Table S2). To confirm the outcomes of microarray and arrest caused by SNRPG knockdown was reversed in lentivirus- IPA analyses, Western blot was performed to evaluate infected U87MG cells overexpressing Myc (*P < 0.05) the expression of a group of representative proteins. The (Figure 6E). These results showed that loss of SNRPG led to outcomes suggested that knockdown of SNRPG in U87MG decreased levels of Myc, which might significantly contribute cells diminished Myc expression (Figure 6B), confirming the to the lack of cell cycle progression observed in SNRPG- microarray data, which further verified the regulatory effect knockdown cells. of SNRPG on Myc. In addition, changes in the expression RT-qPCR together with Western blot further confirmed levels of various other cell cycle-associated proteins were that the expression levels of p53 and its target genes, 124 Lan et al. SNRPG in glioblastoma

A BD Enrichment plot: Enrichment plot: Enrichment plot: HALLMARK_G2M_CHECKPOINT HALLMARK_MYC_TARGET_V1 HALLMARK_P53_PATHWAY

NES = 1.52 NES = 1.5 NES = –1.78 P < 0.001 P < 0.001 P < 0.001

C Enrichment plot: HALLMARK_MYC_TARGET_V2

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E F Enrichment plot: Enrichment plot: P53_DN.V1_DN P53_DN.V1_UP

NES = –2.16 NES = 1.74 P < 0.001 P < 0.001

Figure 5 Bioinformatics analysis indicated that SNRPG inhibition affected Myc and downstream p53. (A) Gene set enrichment analysis (GSEA) was performed with GSEA tools to determine whether particular gene sets were significantly enriched in differentially expressed genes regulated by SNRPG. The normalized enrichment scores (NES) are shown in the plot. The genes affected by silencing SNRPG in glioblastoma multiforme cells were enriched in the G2/M checkpoint pathway signature. (B–F) GSEA plot of the Cancer Genome Atlas gene signatures in shCtrl or shSNRPG glioma cells. Gene expression profile data were obtained by cDNA microarray using glioma cells expressing shCtrl or shSNRPG. The NES is shown in the plot. Further GSEA revealed that several gene set classifications, as annotated by GSEA collections, were significantly enriched. Specifically, SNRPG inhibition could cause significant Myc inhibition (B–C) and p53 activation (D–F).

especially p21, were upregulated in SNRPG-silenced U87MG expression, both control and SNRPG-suppressed U87MG cells (Figure 6F). In addition, silencing SNRPG repressed cells grown in vitro were examined by Western blot. The the phosphorylation and expression of cdc2, which may p53 was primarily cytosolic in control U87MG cells grown have led to its effect on prompting G2/M phase arrest in culture, with a relative lack of p53 staining. In contrast, in (**P < 0.01) (Figure 6F). In addition, to more fully addressing SNRPG-knockdown cells, p53 exhibited nuclear localization the relationships between SNRPG and p53 localization and in association with increased expression (**P < 0.01) Cancer Biol Med Vol 17, No 1 February 2020 125

ACU87 U251 U118 shSNRPG ––+ + ––+ + ––++ CCN N C N CCN N CN Myc Tubulin Histone 1.8 0.9 3 B 1.6 0.8 y 2.5 1.4 0.7 G G 1.2 ol ol 0.6 2 1 0.5 Contr shSNRP Contr shSNRP 1.5 0.8 0.4 Myc CDK2 0.6 0.3 1 GAPDH GAPDH lative intensit 0.4 0.2

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shSNRPG+lenti-Myc shSNRPG+lenti-Myc shSNRPG+lenti-vector shSNRPG+lenti-vector 126 Lan et al. SNRPG in glioblastoma

Figure 6 The results of analyzing microarray data regarding Myc function and downstream Myc-mediated cell cycle-associated proteins were further validated. (A) The knowledge-based interactome surrounding the regulation of Myc was examined using ingenuity pathway analysis (IPA) and overlaid with microarray data with a 1.5-fold change cut-off. (B) The expression levels of downstream cell cycle-associated proteins were further validated. First, Western blot confirmed that Myc and p53 were downregulated in SNRPG-silenced U87 cells compared with that in shCtrl-transduced cells. In addition, the knockdown of SNRPG in U87MG cells also decreased the expression of other cell cycle-associated proteins, such as CDK2 and CCNB1. (C) SNRPG controls the subcellular localization of Myc. The levels of Myc, histone H3, and tubulin in the cytoplasmic (Cy) and nuclear (Nu) fractions of cells expressing a scramble or SNRPG-targeted shRNA, as assessed by Western blot, are shown, n = 3. (D) Western blot analysis of Myc, tubulin, and histone in the nuclear and cytoplasmic fractions of control and shSNRPG-expressing U87MG cells incubated with 0 or 5 ng of leptomycin B (LMB, 8 h). (E) SNRPG suppression increases cell cycle arrest via a Myc-dependent pathway. The cell cycle distribution was confirmed in SNRPG knockdown cells and in lentivirus-infected U87MG cells overexpressing Myc. *P < 0.05, n = 3. (F–I) The levels of SNRPG correlated inversely with p53 expression in vitro and in vivo, which affected the sensitivity of GBM cells to TMZ. (F) Western blot analysis showed that p53 and its target genes, especially p21, were upregulated in SNRPG-silenced U87MG cells compared with those in shCtrl-transduced cells (mean ± SD, n = 3, **P < 0.001 vs. Ctrl or shCtrl). (G, left panel) Representative Western blot analyses of p53 nuclear/cytoplasmic expression in control and shSNRPG-transfected U87MG cells in vitro. Bar = 50 μm, n = 3. (G, right panel) Average subcellular distribution of p53 (nuclear and cytosolic protein expression) and total p53 expression in U87MG cells from U87 tumor cells grown as xenografts in the shCtrl and shSNRPG groups. **P < 0.01, n = 3. (H–I) SNRPG suppression increased the p53 levels via a Myc-dependent pathway. The protein levels (H) and mRNA levels (I) of p53 were confirmed inSNRPG knockdown cells and in lentivirus-infected U87MG cells overexpressing Myc. *P < 0.05 and **P < 0.01 vs. the control group; #P < 0.05 vs. the shSNRPG group, n = 3.

(Figure 6G, left panel). The same results were observed in Myc. Furthermore, we evaluated whether SNRPG deficiency vivo, as U87MG tumor cells grown as xenografts expressed sensitized GBM cells to TMZ through Myc. Overexpression cytosolic p53, but little nuclear p53, whereas tumors derived of Myc significantly augmented the endurance rate of from U87MG cells in which SNRPG was knocked down TMZ-resistant cells after TMZ treatment, whereas SNRPG exhibited increased expression of p53 in the nucleus (**P < suppression significantly inhibited the survival of TMZ- 0.01) (Figure 6G, right panel). resistant cells following TMZ treatment (Figure 7A). However, The transcriptional activity of Myc involves its binding the inhibition induced by SNRPG suppression was abolished to DNA at E-Box sequences to initiate the transcription of by Myc overexpression (*P < 0.05; **P < 0.01 vs. the shCtrl numerous target genes, a process that might involve p5336. + vector group; #P < 0.05; ##P < 0.01 vs. the shSNRPG + We also investigated whether the effects of SNRPG loss on vector group) (Figure 7A). The evidence for Myc and p53 p53 levels were Myc-dependent, which showed that the overexpression is shown in Figure 7B. Comparable outcomes increased p53 levels caused by SNRPG knockdown were were also obtained in the colony formation assay (Figure 7C). reversed in lentivirus-infected U87MG cells overexpressing In addition, we overexpressed p53 in U87/TMZ cells, and as Myc (Figure 6H). In addition, overexpression of Myc in shown in Figure 7C, activation of p53 signaling reversed the SNRPG-knockdown cells decreased the expression of p53 oncogenic effects of Myc as determined by the cell viability (Figure 6I). These results showed that the loss of SNRPG (**P < 0.01 vs. the vector group; ##P < 0.01 vs. the Myc group) led to decreased levels of Myc. Subsequent stimulation of (Figure 7D), apoptosis (*P < 0.05) (Figure 7E), and colony p53 and increased p53 protein levels could significantly formation ability (*P < 0.05) (Figure 7F) of U87/TMZ cells. contribute to the lack of cell cycle progression in SNRPG­ - These results indicated that SNRPG suppression sensitized knockdown cells. GBM cells to TMZ by targeting Myc via the p53 signaling cascade (Figure 8). SNRPG suppression sensitizes GBM cells to TMZ through Myc and p53 signaling Discussion

We previously showed that Myc is a target of SNRPG and Current data suggest that SNRPG expression in glioma tissues is that downregulating SNRPG decreases the expression of higher than that in healthy control tissues and that suppressing Cancer Biol Med Vol 17, No 1 February 2020 127

A B shCtrl+vector Control Lenti-vector Lenti-p53 120 shCtrl+vector shSNRPG+vector 120 shSNRPG+vector shCtrl+Myc p53 100 ) 100 shCtrl+Myc shSNRPG+Myc shSNRPG+Myc 80 80 β-actin 60 60 al rate (% al rate (%) iv iv 40 40 c 20 20 Control Lenti-vector Lenti-My Su rv Su rv 0 0 Мус 25 50 100 200 300 400 25 50 100 200 300 400

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Figure 7 Overexpression of Myc reverses the inhibitory effects of SNRPG downregulation, and activation of p53 signaling reverses the oncogenic effects of Myc in temozolomide-resistant glioblastoma multiforme cells. (A) The MTT assay was used to determine the cell viability after knocking down SNRPG and overexpressing Myc. The IC50 was determined in U87 (shCtrl + vector group: 297 μM; shSNRPG + vector group: 201 μM; shCtrl + Myc group: 372 μM; shSNRPG + Myc group: 289 μM), and U251 cells (shCtrl + vector group: 321 μM; shSNRPG + vector group: 226 μM; shCtrl + Myc group: 409 μM; shSNRPG + Myc group: 313 μM). *P < 0.05; **P < 0.01 vs. the shCtrl+vector group; ##P < 0.01 vs. the shSNRPG + vector group. (B) The Myc and p53 protein abundances in GBM cells, as indicated, were determined by immunoblot analysis. β-actin was used as the loading control. (C) A colony formation assay performed in the absence of TMZ was used to determine the cell growth after knocking down SNRPG and overexpressing Myc. The colonies were counted after staining with 0.1% crystal violet (Original magnification, 1×). (D) The MTT assay was used to determine the cell viability after Myc transfection and p53 overexpression ** ## (IC50 for Vector, 253 μM; IC50 for Myc, 497 μM; IC50 for Myc + p53, 295 μM). P < 0.01 vs. the vector group; P < 0.01 vs. the Myc group. (E) An apoptosis assay was used to assess cell apoptosis after Myc transfection and p53 overexpression. *P < 0.05. (F) A colony formation assay was used to determine cell growth after Myc transfection and p53 overexpression. *P < 0.05. The colonies were counted after staining with 0.1% crystal violet (Original magnification, 1×).

SNRPG expression reduces the proliferation of GBM by further investigation, and further studies should thus focus inhibiting cell proliferation and promoting apoptosis and on elucidating the exact mechanisms controlling the effects of G2/M cell cycle arrest. The SNRPG-mediated inhibitory effect SNRPG on Myc, and whether the effects of Myc suppression on glioma cells might have been due to the direct suppression are documented in different types of tumors remains to be of Myc as well as p53 and the ensuing modification of their recognized. downstream molecules, possibly promoting apoptosis and cell Intriguingly, the p53 protein halts the cell cycle at numerous cycle arrest by interacting directly or indirectly with related points under certain conditions, such as beyond the DNA proteins. Furthermore, knockdown of SNRPG in U87MG cells damage restoration point and upon activation of apoptotic promoted the inhibitory effect of TMZ on cell proliferation, cascades44. The stimulation of p53 can transcriptionally suggesting its regulatory effect on TMZ sensitivity; current augment the expression levels of its target genes, especially studies indicate that SNRPG suppression sensitized GBM cells p2145. In the current study, silencing SNRPG augmented to TMZ by targeting Myc through the p53 signaling pathway. G2/M phase arrest and amplified expression of the p53 and These findings suggest the high potential ofSNRPG as a p21Waf1/Cip1 proteins. In addition, cdc2 activity is reportedly chemotherapeutic target for treating malignant glioma. vital for progression into mitosis and into the S phase of Myc is overexpressed in numerous tumors and plays leading the cell cycle46. We have also reported that silencing SNRPG roles in both tumor cell cycle regulation and pathogenesis4,38,39. represses the phosphorylation and expression of cdc2, which Myc promotes tumor cell production by regulating the cell may lead to its promotion of G2/M phase arrest. These results cycle4, and Myc suppression is a favorable approach for treating are consistent with previous results. Furthermore, current data both lung cancer40,41 and insulinoma42 by promoting intense indicated that silencing p53 reversed the decreased expression tumor deterioration. Only a few slight side effects in normal induced by SNRPG knockdown. Although p53 may exert tissues have been documented, and the complications of an important effect on regulating the mesenchymal (MES) resistance with other targeted treatments are avoided. Although identity of GBM cells mediated by SNRPG, other probable the direct pharmacological suppression of Myc is not currently processes participating in the effect of silencing SNRPG on cell an option, the function of Myc can potentially be directly production in p53 mutant or p53 null cells must exist, and this prevented, as recent studies have reported the use of peptides topic needs further investigation. derived from Myc itself 43. Myc function can be indirectly Importantly, resistance to chemotherapy remains an prevented, as established by bromodomain blockers38. Current enormous clinical problem47. The current study also indicated data suggest that the loss of SNRPG could lead to decreased that SNRPG expression was increased in TMZ-resistant GBM levels of Myc, which might significantly contribute to cell cells, and downregulating SNRPG made the cells resistant cycle progression deficits, and this phenomenon could be to TMZ. Overall, the current study confirmed that SNRPG induced by increasing the export of Myc to the cytoplasm. suppression sensitized GBM cells to TMZ by targeting Myc However, how SNRPG regulates Myc localization requires via the p53 signaling cascade (Figure 8). Our findings showed Cancer Biol Med Vol 17, No 1 February 2020 129

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Figure 8 Schematic diagram of the molecular mechanisms of SNRPG-mediated p53 targeting of TMZ-induced glioblastoma multiforme (GBM) growth inhibition. The SNRPG-mediated inhibitory effect on glioma cells might have been due to the direct suppression of Myc as well as p53 and the ensuing modification of their downstream molecules including p21, CDK2, CCNB1, and cdc2, possibly promoting apoptosis and cell cycle arrest by interacting directly or indirectly with related proteins. Specifically, SNRPG suppression promoted the cytosolic translocalization of Myc, as well as the nuclear translocalization of p53. Furthermore, knockdown of SNRPG in GBM cells promoted the inhibitory effect of TMZ on cell proliferation by targeting Myc through the p53 signaling pathway, suggesting its regulatory effect on TMZ sensitivity. Besides, SNRPG decreased the protein expression of the mismatch repair (MMR) protein MLH1 and increased MGMT methylation, which could all be well-established mechanisms of TMZ resistance. These findings could indicate that SNRPG-driven systemic Myc-regulated p53 activation and sensitization of GBM cells to TMZ are highly promising strategies for treating glioma. that suppression of SNRPG in resistant GBM cells might be a in future studies. Finally, SNRPG is known to play important valuable method for overcoming essential drug resistance. roles in the processing of the 3′ ends of histone transcripts In this study, various problems potentially need to be or histone modifications (http://www.genecards.org/) and addressed. First, the present study established that SNRPG potentially in protein translation during replication. As expedited cell cycle development and thus stimulated cell histone modifications can impact alternative splicing, we production. Moreover, this function was the outcome of believe that an SNRPG-driven histone-based system might the regulation of various signaling cascades, as suggested help elucidate details regarding the collection of alternative by microarray data. Although we have not studied the splicing arrays in cells and tissues48-50, which may be the functions of other genes, other genes with altered expression direction of our future studies. in SNRPG-silenced cells are potentially important in GBM, and further investigation is warranted. Second, the Conclusions xenograft experimental results demonstrated that reducing SNRPG expression decelerated tumor development in an Although substantial efforts have been made in exploiting new animal model, but the effects of silencing SNRPG on mouse treatment agents, novel treatments for glioma, an illness with mortality were not assessed and should be further addressed a particularly negative prognosis, are urgently needed, as this 130 Lan et al. SNRPG in glioblastoma disease remains one of the most fatal malignancies. Assessment 7. Oktay Y, Ülgen E, Can Ö, Akyerli CB, Yüksel Ş, Erdemgil Y, et al. of the SNRPG-derived oncogenic effect might contribute to IDH-mutant glioma specific association of rs55705857 located at the discovery of new targets for the treatment of malignant 8q24.21 involves MYC deregulation. Sci Rep. 2016; 6: 27569. 8. Tateishi K, Iafrate AJ, Ho Q, Curry WT, Batchelor TT, Flaherty KT, glioma. Our work was designed to elucidate the anticancer et al. Myc-driven glycolysis is a therapeutic target in glioblastoma. mechanisms of SNRPG suppression in malignant glioma. In Clin Cancer Res. 2016; 22: 4452-65. conclusion, our data indicated that SNRPG-driven systemic 9. Annibali D, Whitfield JR, Favuzzi E, Jauset T, Serrano E, Cuartas I, Myc-regulated p53 activation and sensitization of GBM cells et al. Myc inhibition is effective against glioma and reveals a role for to TMZ are highly promising strategies for treating glioma. Myc in proficient mitosis. Nat Commun. 2014; 5: 4632. Additionally, recognition of SNRPG-specific inhibitors may 10. Aubrey BJ, Kelly GL, Janic A, Herold MJ, Strasser A. How does p53 induce apoptosis and how does this relate to p53-mediated tumour create a novel model for the detection and advancement of suppression? Cell Death Differ. 2018; 25: 104-13. molecularly targeted treatments for cancers, including GBM. 11. Sullivan KD, Galbraith MD, Andrysik Z, Espinosa JM. Mechanisms These conclusions provide robust evidence for the potential of transcriptional regulation by p53. Cell Death Differ. 2018; 25: of SNRPG as a new candidate for malignant glioma treatment. 133-43. 12. Kaiser AM, Attardi LD. Deconstructing networks of p53-mediated tumor suppression in vivo. Cell Death Differ. 2018; 25: 93-103. Acknowledgements 13. 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Supplementary materials

Table S1 General feature of patients History of History of Tumor location Sample Family history of Tumor size ≥ Age Gender smoking drinking Diagnosis (supratentorial/ No. cancer (Y/N) 4.5cm (Y/N) (Y/N) (Y/N) subtentorial)

G-0004 33 Female No No No GBM (WHO IV) No Supratentorial

G-0115 60 Male No No No GBM (WHO IV) No Supratentorial

G-0132 48 Male No Yes Yes GBM (WHO III-IV) Yes Supratentorial

G-0266 41 Female No No No GBM (WHO IV) Yes Supratentorial

G-0285 62 Female No No No GBM (WHO IV) No Supratentorial

G-0396 55 Female No No No GBM (WHO IV) Yes Supratentorial

G-0558 55 Female No No No GBM (WHO IV) Yes Supratentorial

G-0666 65 Male No No No GBM (WHO IV) Yes Supratentorial

G-0729 36 Male No Yes No GBM (WHO IV) Yes Supratentorial

G-0821 54 Male No No No GBM (WHO IV) Yes Supratentorial

G-0839 61 Male No No No GBM (WHO IV) Yes Supratentorial

G-0940 57 Female No No No GBM (WHO IV) No Supratentorial

G-0944 60 Female No No No GBM (WHO IV) Yes Supratentorial

G-0996 67 Male No No No GBM (WHO IV) Yes Supratentorial 2 Lan et al. SNRPG in glioblastoma

Table S2 Downstream targets of Myc and p53 assessed via Continued bioinformatics analysis Downstream targets of Myc assessed via bioinformatic analysis Downstream targets of Myc assessed via bioinformatic analysis after knocking down SNRPG after knocking down SNRPG Target molecules Regulation Fold_change P Target molecules Regulation Fold_change P in dataset in dataset

YAP1 Down -2.2004654 0.000899037 PSAT1 Down -2.7941062 4.30426E-05

WLS Up 2.726611 6.40171E-07 PRDM1 Up 2.4411225 3.35728E-05

WISP1 Up 3.8561678 6.47337E-06 PLK1 Down -2.3198814 0.000164844

VEGFA Down -2.4606857 5.41824E-07 PFAS Down -2.2056475 1.46729E-06

VCAM1 Up 31.123684 2.31657E-06 PDGFRA Up 3.8465552 0.000319564

VARS Down -2.152252 0.00037761 PCDH18 Up 4.690889 4.71284E-05

UBE2S Down -2.0267875 1.7197E-05 NRP1 Up 2.192101 3.39564E-05

UBE2C Down -3.1527188 1.35925E-06 NDRG1 Down -2.0377207 5.49884E-05

TYMS Down -2.8719566 2.08657E-06 MYC Down -2.3531933 6.84799E-06

TXNIP Up 2.5055113 2.4035E-06 MTFR2 Down -3.1721334 7.32363E-06

TP53I3 Up 3.2173285 3.44497E-05 MRE11A Down -2.422145 9.68115E-06

TMEM97 Down -2.6563091 0.00019609 MKI67 Down -3.6224332 2.8842E-06

STMN1 Down -2.428075 3.16331E-05 MIR17HG Down -2.792616 0.000221759

SPARC Up 2.983737 5.69005E-06 MGP Up 22.994284 3.3942E-07

SOD2 Up 2.3653908 5.75501E-08 MFAP1 Down -2.154815 1.66954E-05

SMAD1 Up 2.5486372 8.24186E-06 MCM7 Down -3.4240937 1.39583E-06

SLC38A1 Down -2.1260705 0.000338194 MCM6 Down -2.7770162 1.12485E-06

SGK1 Up 2.060982 1.9894E-05 MCM5 Down -2.7076702 0.000152585

SERPINH1 Up 2.0949132 3.29312E-06 MAN2A1 Up 2.584694 5.13991E-06

SERPINE1 Down -2.776294 1.63496E-06 MAD2L1 Down -3.0069954 3.91006E-05

SERPINA1 Up 3.655164 3.9032E-07 LXN Up 2.1453402 2.22587E-06

SAT1 Up 2.3773856 1.38163E-06 LGMN Up 2.3995295 1.73229E-05

RRM2 Down -2.6255248 7.52328E-07 KRT7 Up 2.1850588 0.000203507

RCC1 Down -2.737947 1.65928E-07 ITM2B Up 2.4764104 1.41161E-08

RBP1 Up 2.9123752 6.93317E-05 ID3 Up 4.5758276 2.72157E-06

RARRES1 Up 4.272019 0.000169545 ICAM1 Up 2.7644155 1.96913E-05

RAD51 Down -2.2705245 9.49069E-05 HMGA1 Down -4.1570177 6.52401E-07

RAB40B Up 3.1059167 2.69713E-06 HES1 Up 4.7940874 8.81984E-05 Continued Cancer Biol Med Vol 17, No 1 February 2020 3

Table S2 Continued Continued

Downstream targets of Myc assessed via bioinformatic analysis Downstream targets of Myc assessed via bioinformatic analysis after knocking down SNRPG after knocking down SNRPG Target molecules Regulation Fold_change P Target molecules Regulation Fold_change P in dataset in dataset

HAS2 Down -2.077807 0.000111374 CTBS Down -2.0393775 0.000428111

GLG1 Up 2.0773172 4.01057E-06 CSRP2 Down -2.0059276 2.51977E-06

GFPT1 Down -4.121707 3.02735E-05 CRYAB Up 7.0759397 3.75693E-05

GCSH Down -2.9161048 1.13144E-05 CPD Up 2.2487748 1.51939E-06

GCLM Down -2.076191 6.14521E-07 COL5A2 Up 5.0252233 1.22958E-06

GBP2 Up 2.9921346 0.000191824 COL5A1 Up 2.1802685 7.51787E-06

GART Down -2.694237 0.000257483 COL3A1 Up 6.0942416 2.7127E-05

FOXM1 Down -3.0084047 2.38538E-05 COL1A2 Up 3.3868077 2.98091E-07

FOSL1 Down -3.0467093 2.21821E-05 COL14A1 Up 3.668856 0.00015045

FOS Up 2.3506725 1.93163E-05 CLEC3B Up 3.7030663 1.25343E-06

FN1 Up 3.2369347 1.12496E-05 CKS2 Down -2.22107 2.06846E-07

FGF5 Down -2.0261834 0.000121213 CHEK1 Down -2.2617967 6.8628E-05

FCGRT Up 2.0499787 2.14653E-05 CEBPD Up 8.443227 6.37108E-06

FAS Up 2.1131759 2.28379E-05 CDKN1A Up 2.0020273 3.1422E-05

EZH2 Down -2.6572704 3.36057E-05 CDK6 Up 2.2414362 2.83847E-05

EXOSC8 Down -2.339027 6.42808E-07 CDK2 Down -2.3414066 0.000723858

EMP1 Down -2.6436226 5.68407E-05 CDK1 Down -3.3894331 3.41338E-08

EIF4EBP1 Down -2.1822093 4.25906E-05 CDCA7 Down -2.5425134 3.25259E-05

EIF3G Up 2.0444875 0.000525781 CDC25C Down -2.9542015 1.06227E-05

EIF2S1 Down -2.4059823 2.03661E-06 CDC25A Down -2.0147529 0.002159371

EGR1 Up 2.6178222 7.71939E-06 CDC20 Down -3.6122062 1.22727E-07

EFEMP1 Up 3.8927963 3.04738E-05 CD274 Down -2.3226845 0.002961726

EBI3 Up 5.3947735 0.00029493 CCNB2 Down -2.8234894 1.19585E-06

E2F1 Down -2.6413052 0.000395762 CCNB1 Down -3.2859113 2.33511E-07

DUSP6 Down -2.6694174 1.71495E-05 CCNA2 Down -3.3991127 1.46568E-06

DKK1 Down -2.4766245 0.000258911 C9orf3 Up 2.142429 0.000119559

DHFR Down -2.5963402 2.21914E-05 BUB1B Down -3.9987106 4.8566E-06

CXCL10 Up 6.316949 8.62745E-06 BUB1 Down -3.664866 1.47275E-06 Continued 4 Lan et al. SNRPG in glioblastoma

Table S2 Continued Continued

Downstream targets of Myc assessed via bioinformatic analysis Downstream targets of p53 assessed via bioinformatic analysis after knocking down SNRPG after knocking down SNRPG Target molecules Regulation Fold_change P Target molecules Regulation Fold_change PPe in dataset in dataset

BRCA1 Down -3.013453 2.99571E-05 ASNS Down -2.0371935 4.61309E-06

BIRC5 Down -4.0315733 1.76944E-06 ASPM Down -4.345241 2.71048E-06

BDNF Up 2.0975358 3.10914E-06 ATAD2 Down -3.2450206 2.73272E-05

BCL6 Up 2.1456041 0.000507154 AURKA Down -3.0987566 6.72217E-07

BCAT1 Down -2.603652 1.53501E-06 AURKB Down -2.6026242 7.70047E-06

BBC3 Up 2.1616032 0.000223194 AXIN2 Up 2.3022826 0.000557813

AURKB Down -2.6026242 7.70047E-06 BAK1 Up 2.0840688 0.000638436

ASNS Down -2.0371935 4.61309E-06 BBC3 Up 2.1616032 0.000223194

ARL6IP1 Down -2.4931567 2.49777E-07 BCL2L11 Up 5.340361 1.4619E-05

ARHGAP22 Down -2.2010329 1.57562E-06 BDKRB2 Up 2.5592594 4.44557E-05

ANGPT1 Up 3.9937463 0.000218086 BID Up 2.4654102 9.44967E-07

AKAP12 Down -2.8823323 1.32047E-06 BIRC5 Down -4.0315733 1.76944E-06

ADAMTS1 Down -2.3186843 3.74103E-07 BMP1 Up 3.4533818 2.53152E-05

ABCE1 Down -2.2096348 4.0258E-05 BRCA1 Down -3.013453 2.99571E-05

BRCA2 Down -2.852761 2.28877E-05

BTG2 Up 2.1304529 3.59177E-05 Downstream targets of p53 assessed via bioinformatic analysis after knocking down SNRPG BUB1 Down -3.664866 1.47275E-06

Target molecules Regulation Fold_change PPe BUB1B Down -3.9987106 4.8566E-06 in dataset CA9 Down -2.1404824 2.70905E-06 ACP2 Up 2.5898085 3.46048E-06 CCL2 Up 6.9680066 2.9082E-07 ACSL3 Down -2.747913 6.88464E-06 CCNA2 Down -3.3991127 1.46568E-06 ACTA2 Up 2.0995708 6.24381E-06 CCNB1 Down -3.2859113 2.33511E-07 ADCK3 Up 2.347324 0.003031234 CCNB2 Down -2.8234894 1.19585E-06 AKAP12 Down -2.8823323 1.32047E-06 CDC20 Down -3.6122062 1.22727E-07 ALDH4A1 Up 2.4930303 0.000318248 CDC25A Down -2.0147529 0.002159371 ANGPT1 Up 3.9937463 0.000218086 CDC25C Down -2.9542015 1.06227E-05 ANLN Down -4.2728977 1.81942E-05 CDC6 Down -3.687949 9.97116E-07 ARL6IP1 Down -2.4931567 2.49777E-07 CDC7 Down -2.1907902 0.000464492 ASF1B Down -2.924686 3.86935E-06 Continued Cancer Biol Med Vol 17, No 1 February 2020 5

Table S2 Continued Continued

Downstream targets of p53 assessed via bioinformatic analysis Downstream targets of p53 assessed via bioinformatic analysis after knocking down SNRPG after knocking down SNRPG Target molecules Regulation Fold_change PPe Target molecules Regulation Fold_change PPe in dataset in dataset

CDK1 Down -3.3894331 3.41338E-08 DUSP6 Down -2.6694174 1.71495E-05

CDK2 Down -2.3414066 0.000723858 DUT Down -2.1344292 0.000372482

CDKN1A Up 2.0020273 3.1422E-05 E2F1 Down -2.6413052 0.000395762

CDKN3 Down -4.096533 1.0441E-06 E2F8 Down -2.1755638 1.73999E-05

CDT1 Down -2.8250837 8.81031E-05 EGF Up 2.2057219 0.00019899

CENPF Down -2.5941923 7.81122E-05 EGR1 Up 2.6178222 7.71939E-06

CEP55 Down -4.4314423 1.77357E-05 ENG Up 2.5714345 0.000294865

CHEK1 Down -2.2617967 6.8628E-05 ENPP2 Up 3.4637127 2.19034E-06

CKAP2 Down -2.1922245 1.45445E-05 EPHX1 Up 2.0975645 6.01894E-05

CKB Up 2.0246646 0.00010062 ESPL1 Down -2.6732528 1.67595E-06

CKS1B Down -2.887445 4.66962E-05 EXO1 Down -2.5579734 0.002804422

CNN2 Down -2.6031473 1.1283E-05 EZH2 Down -2.6572704 3.36057E-05

COL14A1 Up 3.668856 0.00015045 EZR Down -2.5168595 4.72436E-05

COL1A2 Up 3.3868077 2.98091E-07 F11R Up 2.318464 2.48687E-05

COL3A1 Up 6.0942416 2.7127E-05 FAM83D Down -3.2122777 2.72169E-05

COL5A2 Up 5.0252233 1.22958E-06 FANCI Down -3.0667968 1.31616E-05

CRYAB Up 7.0759397 3.75693E-05 FAS Up 2.1131759 2.28379E-05

CTSF Up 2.1767623 0.000418674 FBXW7 Up 2.2539053 8.28855E-06

CXCL1 Up 2.8209019 7.21376E-06 FEN1 Down -2.4864187 1.55089E-05

DBF4 Down -3.6854699 2.00798E-05 FERMT2 Down -2.0035534 5.38065E-06

DDIAS Down -2.9275298 0.002405864 FN1 Up 3.2369347 1.12496E-05

DDIT4L Up 5.2929754 9.59775E-07 FOS Up 2.3506725 1.93163E-05

DDR1 Up 3.3824682 1.23232E-06 FOSL1 Down -3.0467093 2.21821E-05

DHFR Down -2.5963402 2.21914E-05 FOXM1 Down -3.0084047 2.38538E-05

DICER1 Up 2.3602622 2.18034E-05 FUCA1 Up 4.7821565 1.45953E-06

DKK1 Down -2.4766245 0.000258911 GART Down -2.694237 0.000257483

DLGAP5 Down -3.9265223 1.49922E-05 GLIPR1 Down -2.279712 1.72457E-06

DSN1 Down -2.786994 1.21996E-05 GMNN Down -2.4207106 1.38595E-06 Continued 6 Lan et al. SNRPG in glioblastoma

Table S2 Continued Continued

Downstream targets of p53 assessed via bioinformatic analysis Downstream targets of p53 assessed via bioinformatic analysis after knocking down SNRPG after knocking down SNRPG Target molecules Regulation Fold_change PPe Target molecules Regulation Fold_change PPe in dataset in dataset

GNAI1 Up 2.3712525 1.11948E-05 MAP3K8 Up 3.197822 7.80446E-05

GTSE1 Down -3.179631 0.000123705 MCM2 Down -3.3188963 4.27989E-07

HAS2 Down -2.077807 0.000111374 MCM3 Down -2.6946843 1.45363E-06

HJURP Down -2.9257886 4.46607E-06 MCM4 Down -2.8259888 2.29587E-05

HMGB2 Down -2.4225783 1.96645E-06 MCM5 Down -2.7076702 0.000152585

HMGCS1 Down -2.1236782 1.71473E-06 MCM6 Down -2.7770162 1.12485E-06

HMMR Down -4.2627034 3.08806E-06 MCM7 Down -3.4240937 1.39583E-06

ICAM1 Up 2.7644155 1.96913E-05 MELK Down -3.3216226 6.23091E-07

ID3 Up 4.5758276 2.72157E-06 MGST2 Up 3.2562354 1.54722E-06

IGDCC4 Up 3.4510975 2.31546E-06 MIR17HG Down -2.792616 0.000221759

IGFBP3 Up 4.6044054 4.75433E-07 MIS18BP1 Down -2.1686413 3.06466E-05

IGFBP4 Up 2.587116 0.000130696 MKI67 Down -3.6224332 2.8842E-06

IGFBP5 Up 14.235849 9.1868E-06 MMP1 Down -2.77545 9.05794E-06

IL1B Down -3.6559737 2.24477E-06 MTDH Down -2.0708454 0.000109479

IL6 Up 2.2604623 5.30168E-07 MYC Down -2.3531933 6.84799E-06

ITGA2 Down -2.00359 3.0551E-06 MYO10 Down -2.056971 0.000290178

KIAA0101 Down -3.5872898 7.89813E-05 NCAPG Down -3.466131 8.75417E-06

KIF23 Down -3.642677 0.000104343 NCAPH Down -3.277411 3.37501E-07

KIFC1 Down -3.3392606 2.3573E-05 NDC80 Down -3.2344527 4.81342E-06

KIT Up 2.1119153 0.000167271 NDRG1 Down -2.0377207 5.49884E-05

KNTC1 Down -2.299394 2.53516E-06 NDRG4 Up 2.0044627 5.06301E-05

KRT15 Down -3.0376554 2.42634E-05 NEK2 Down -4.0638337 4.49023E-05

LAMA5 Up 2.2744126 2.76063E-05 NMU Down -2.0142093 0.000157581

LBR Down -2.9253728 1.75678E-06 NR2F1 Up 2.3057506 2.93534E-05

LPIN1 Down -3.7294765 6.3445E-07 NRP1 Up 2.192101 3.39564E-05

MAD2L1 Down -3.0069954 3.91006E-05 NUSAP1 Down -4.121419 1.35285E-06

MAFB Up 3.5815496 1.60634E-07 PBK Down -3.1371903 3.61418E-07

MAN2A1 Up 2.584694 5.13991E-06 PCDH7 Up 3.6979644 3.16436E-05 Continued Cancer Biol Med Vol 17, No 1 February 2020 7

Table S2 Continued Continued

Downstream targets of p53 assessed via bioinformatic analysis Downstream targets of p53 assessed via bioinformatic analysis after knocking down SNRPG after knocking down SNRPG Target molecules Regulation Fold_change PPe Target molecules Regulation Fold_change PPe in dataset in dataset

PDGFRA Up 3.8465552 0.000319564 S100A2 Down -2.0636077 2.59487E-06

PDIA5 Up 2.6322782 1.0041E-05 SAT1 Up 2.3773856 1.38163E-06

PGM3 Down -2.1608644 4.79481E-05 SERPINB2 Down -5.9836097 4.76691E-06

PHGDH Down -2.5204659 2.53071E-06 SERPINE1 Down -2.776294 1.63496E-06

PIK3R1 Up 3.5230632 2.80978E-06 SERPINH1 Up 2.0949132 3.29312E-06

PIK3R3 Up 4.3986635 7.12702E-07 SESN1 Up 4.083665 1.25736E-07

PLK1 Down -2.3198814 0.000164844 SFRP2 Up 6.6169605 4.69607E-05

PODXL Down -2.613001 3.50673E-05 SGK1 Up 2.060982 1.9894E-05

POLA1 Down -2.0860755 2.76132E-06 SHISA5 Up 2.085424 3.4949E-06

POLE2 Down -3.2632394 1.19058E-05 SIDT2 Up 2.217279 0.000121514

POSTN Up 6.2268686 2.24186E-05 SLC2A12 Up 2.389174 7.79558E-06

PPM1D Up 2.0515914 0.002066009 SMAD6 Up 2.2160177 1.37075E-06

PRC1 Down -3.2625606 2.8762E-06 SMAD7 Up 2.0452683 9.80671E-05

PRDM1 Up 2.4411225 3.35728E-05 SMC4 Down -2.0496526 1.14819E-05

PRIM1 Down -3.0388367 2.39556E-05 SMURF2 Down -2.0765357 1.56039E-05

PTCHD4 Up 2.135742 0.000167485 SOD2 Up 2.3653908 5.75501E-08

PTPN12 Down -2.4150302 8.74551E-06 SPATA18 Up 2.062645 0.000359106

PTTG1 Down -2.9523795 1.28204E-06 SPC25 Down -4.1329875 8.29516E-07

RACGAP1 Down -2.4381306 4.61863E-05 SPHK1 Down -3.0104914 1.14056E-05

RAD51 Down -2.2705245 9.49069E-05 STMN1 Down -2.428075 3.16331E-05

RAD51AP1 Down -3.490721 6.10245E-05 TDO2 Up 4.0305943 1.39229E-07

RARRES3 Up 4.570907 6.67711E-07 TFPI2 Down -5.1304183 2.30218E-07

RBL1 Down -2.6507204 0.000324717 TGFB3 Up 7.6097136 5.65463E-05

RBPJ Down -3.2799733 4.3E-05 TIGAR Up 2.8314755 6.22895E-07

RFC3 Down -3.0650783 6.62013E-06 TMEM97 Down -2.6563091 0.00019609

RFC4 Down -2.6281676 3.20178E-06 TNFAIP2 Up 3.5823321 1.6619E-06

RRM1 Down -2.0249104 3.14287E-05 TNFRSF10C Up 3.342336 0.000138792

RRM2 Down -2.6255248 7.52328E-07 TNFRSF10D Up 2.337418 6.22351E-06 Continued 8 Lan et al. SNRPG in glioblastoma

Table S2 Continued

Downstream targets of p53 assessed via bioinformatic analysis after knocking down SNRPG Target molecules Regulation Fold_change PPe in dataset

TNFRSF9 Up 2.0453916 1.28021E-06

TNFSF10 Up 2.4190438 3.8299E-05

TOP2A Down -3.2117655 4.4514E-06

TP53I3 Up 3.2173285 3.44497E-05

TPD52L1 Up 5.191617 2.63324E-05

TPX2 Down -3.0621989 3.22177E-07

TRIM22 Up 2.3660667 1.25788E-06

TSPAN6 Up 2.2814004 6.81893E-06

TTC28 Up 2.1429846 1.7392E-05

TTK Down -3.4548926 2.0953E-05

TUBB Down -2.0580049 1.89709E-06

TYMS Down -2.8719566 2.08657E-06

UBE2C Down -3.1527188 1.35925E-06

UBE2T Down -2.235396 8.42079E-07

UHRF1 Down -2.9184792 2.20236E-05

USP14 Down -3.0199425 3.90779E-05

VAMP4 Down -2.2361658 5.1771E-07

VEGFA Down -2.4606857 5.41824E-07

VRK1 Down -3.4682982 7.9567E-06

WDHD1 Down -3.1272936 4.82984E-05

XAF1 Up 2.917754 0.000314485

ZMAT3 Up 2.7310157 1.09655E-06

ZNF175 Down -2.184365 0.000152832 Cancer Biol Med Vol 17, No 1 February 2020 9

A ** B 6

l 0.9 1.2 shCtrl shSNRPG shCtrl shSNRPG ) 0.8 5 1.0 0.7 0.8 0.6 4 0.5 0.6 3

OD490 0.4 0.3 2 0.4 OD490/fold 0.2 1

lative mRNA leve 0.2

(SNRPG/GAPDH 0.1

Re 0.0 0 0 12345 12345 shCtrl shSNRPG Time (day) Time (day)

C Day 1 Day 2 Day 3 Day 4 Day 5

shCtrl

shSNRPG

7,000 8 shCtrl shSNRPG shCtrl shSNRPG 6,000 7 6 5,000 5 4,000 4 3,000 3 Cell count 2,000 2

1,000 Cell count/fold 1 0 0 day 1 day 2 day 3 day 4 day 5 day 1 day 2 day 3 day 4 day 5

D ** shCtrl 80 shSNRPG 70 shCtrl ) 60 50 ** 40 30 ** centage (% r shSNRPG 20 Pe 10 0 G1 S G2/M Phase

E **

shCtrl 16 ) 14 ANNEXIN-V ANNEXIN-V ANNEXIN-V 12 10 8 centage (% r 6 shSNRPG Pe 4 2 0 ANNEXIN-V ANNEXIN-V ANNEXIN-V shCtrl shSNRPG 10 Lan et al. SNRPG in glioblastoma

Figure S1 Effects of knocking down SNRPG on cell proliferation in U251 cells. (A) SNRPG knockdown was performed using shRNAs in U251 cells. The downregulated expression of SNRPG by shSNRPG in U251 was further confirmed via RT-qPCR. (B) MTT assay showed that SNRPG knockdown significantly increased cell proliferation in U251 cells compared with the control cells. The OD values were measured at indicated days (mean ± SD, n = 3). (C) Celigo assay was performed to detect the cell viability over 5 days after transfection. The colony numbers of U251 cells transfected with shCtrl were evidently higher than those transfected with shSNRPG (mean ± SD, n = 3). (D–E) Effects of knocking down SNRPG on cell apoptosis and cell cycle progression in U251 cells. (D) Flow cytometric analysis was performed to further examine the effect of SNRPG on proliferation of GBM cells by altering cell cycle progression. Error bars, standard error. **P < 0.01. (E) To further determine the physiological role of SNRPG in cells growth, U251 cells were transfected with shSNRPG. After 48 h, cell apoptosis were analyzed by flow cytometric analysis. Graphs show the mean percentages of three biological replicates. Error bars, standard error. **P < 0.01.

es 1 es 2 1 2 ABospher ospher Normal GBM neur GBM neur Con shSNRPG# shSNRPG# SNRPG SNRPG

β-actin β-actin

C D E y tumors ent tumors 250 Primar Recurr 1.6 Control Passage 8 SNRPG 1.4 shSNRPG#1 200 Passage 10 1.2 shSNRPG#2

( OD 450) 1 150 β-actin 0.8

ance 100 0.6 G 0.4 Cell counts 50 F Con shSNRP 0.2 Absorb 0 0 MLH1 0 h 24 h 48 h 72 h 1 2 -actin 0 nM β

shSNRPG# shSNRPG# % Methylation of MGMT % SNRPG expression G 0 0–25 25 25–50 50 50–75 75 75–100 100

LN229

U251

U87

A172

U118 50 60 70 80 90 100 110

Figure S2 Expression of SNRPG in human GBM neurospheres and effect of silencing SNRPG on the proliferation and invasion of glioma cells. (A) The expression level of SNRPG in human normal brain tissue and GBM neurospheres were quantified by Western blot. (B) SNRPG protein levels in GBM neurospheres, as indicated, were determined by a Western blot analysis. β-actin was used as the internal control (C) CCK8 test to examine the influence of silencing SNRPG on the proliferation ability of GBM neurospheres. (D) Transwell invasion assay to test the influence of SNRPG inhibition on the invasion ability of GBM neurospheres. (**P < 0.01). (E) The protein expression levels of SNRPG in samples of primary and recurrent GBM patients. n = 3. (F) Expression of mismatch repair (MMR) protein MLH1. MLH1 expression was significantly increased in shSNRPG compared to control group. (G) The possible correlation between SNRPG protein expression and MGMT promoter methylation percentage in GBM cell lines. Cancer Biol Med Vol 17, No 1 February 2020 11

AB 120 LN229/TMZ LN229 LN229/TMZ 100

) LN229 80 P-gp

al rate (% 60 iv IC : 183 M rv 50 µ 40 GST-π

Cell su IC : 64 M 20 50 µ

0 0123 β-actin Log concentration of TMZ (µmol/L)

C D LN229 LN229/TMZ 120 LN229-control

SNRPG LN229-shSNRPG 100

) 80 β-actin 60 al rate (% iv 40 ∗ Su rv ession 2 ) ** IC50: 183 µM 1.5 20 ∗∗

IC50: 151 µM 1 ∗∗

SNRPG (% 0

of 0.5 25 50 100 200 300 400 lative mRNA expr 0 Concentration of TMZ (µmol/L) Re LN229 LN229/TMZ

Figure S3 SNRPG suppression sensitizes human GBM cells to TMZ. (A) The MTT assay was used to determine the viability of LN229/TMZ

(IC50, 64 μM vs. 183 μM). (B) Western blot analysis of the drug resistance-related genes P-gp and GST-π in LN229/TMZ cells. (C) Western blot analysis (upper panel) and RT-qPCR analysis (lower panel) of SNRPG in LN229/TMZ cells. (D) The MTT assay was used to determine the cell viability after SNRPG knockdown. Downregulation of SNRPG significantly reduced the IC50 compared with that of the control group (151 μM vs. 183 μM; *P < 0.05, **P < 0.01).