Pentraxin 3 Promotes Glioblastoma Progression by Negative Regulating Cells Autophagy

Glioblastoma is the most malignancy tumor generated from the central nervous system along with median survival time less than 14.6 months. Pentraxin 3 has been proved its association with patients’ poor survival outcome in various tumor. Recently, several studies revealed its association with glioblastoma progression but the mechanism is remained unknown. Autophagy is a programmed cells death and acts critical role in tumor progression. In this study, pentraxin 3 is recognized as prognostic prediction biomarker of glioblastoma and can promote glioblastoma progression through negative modulating tumor cells autophagy. Transcription factor JUN is assumed to participate in cells autophagy modulation by regulating pentraxin 3 expression. This work reveals novel mechanism of pentraxin 3 mediated glioblastoma progression. Furthermore, JUN is identified as potential transcription factor involves in pentraxin 3 mediated tumor cells autophagy.


INTRODUCTION
Glioma is characterized as primary tumors that originate in brain parenchyma, which can be classified according to the type of glial cell involved in the tumor . World Health Organization characterizes glioma into four grades based on its malignancy. GBM, WHO grade IV, is the most vicious type with median survival time less than 15 months (Yang et al., 2011a;Hong et al., 2012;Ouyang et al., 2016). Current clinical treatment including maximum surgical resection followed by postoperative radio-therapy and concurrent chemo-therapy (Stupp et al., 2005;Gusyatiner and Hegi, 2018), but patients' survival outcome remains unsatisfactory. Recently, several factors have been identified and be applied to predict survival outcome in clinical such as the subtype of GBM (Verhaak et al., 2010) and the status of IDH1 (Wang et al., 2013). On account of GBM heterogeneity, insight on potential prognostic prediction factors are urgent need.
Pentraxin 3, known as TSG-14, is an inflammatory molecule belongs to the pentraxin family and mainly secreted by inflammatory cells like dendritic cells and macrophages (Liu et al., 2011;Bonavita et al., 2015). Recently, PTX3 has been proved its role in tumor progression. For example, PTX3 affects tumor proliferation and apoptosis by interacting with the PI3K/AKT/mTOR signaling pathway in lung cancer  and breast cancer (Thomas et al., 2017). PTX3 involves in the epithelial-mesenchymal transition in melanoma (Ronca et al., 2013) and breast cancer . Notably, PTX3 can interact with the fibroblast growth factor-2/fibroblast growth factor receptor system to promote tumor progression (Bonavita et al., 2015;Ying et al., 2016;Giacomini et al., 2018). In glioma, previous study confirmed that decreased the expression of PTX3 impaired glioma cells proliferation and invasion ability (Tung et al., 2016). However, the role of PTX3 in GBM is poorly understood.
Cells autophagy is a programmed cell death and act as a response to unfavorable factors like hypoxia and nutrient deficiency (Stavoe and Holzbaur, 2019). Therefore, by activating cells autophagy can prevent tumor progression and increase tumor sensitivity to chemo-or radio-therapy Perez-Hernandez et al., 2019). Previous studies proved PTX3 can affect cells autophagy but their relationship in GBM is unknown (Giorgi et al., 2015;Wu et al., 2015).
In this study, we analyzed the expression profile of PTX3, its ability to predict survival outcome and potential mechanisms in affecting GBM progression based on The Cancer Genome Atlas (TCGA) dataset. Results were verified in the Chinese Glioma Genome Atlas (CGGA) dataset. Then, we performed in vitro experiments to prove PTX3 affects tumor cells viability and autophagy. By integrating results from experiments in vitro and bioinformatics, we proved PTX3 negative modulates cells autophagy, and transcription factor JUN might regulate PTX3 expression.

Cell Culture and Transfection
Human GBM cells (U87-MG) are purchased from the Chinese Academy of Sciences. Glioma cells are maintained in DMEM medium with 10% fetal bovine serum and 1% penicillinstreptomycin, 5% CO 2 and 37 • C. Cells are randomly divided into different groups, the control group, the siRNA-NC (si-NC) group, the siRNA-PTX3 (si-PTX3) group, the overexpression JUN group and the overexpression JUN with siRNA-PTX3 group.
The siRNA of PTX3 (5 -GGTCAAAGCCACAGATGTA-3 ) and JUN overexpression plasmid are obtained from RiboBio (Guangzhou, China). Five microliters of siRNA-PTX3 (or 2.5 µg overexpression JUN plasmid) and 5 µl lipofectamine (RiboBio, China) are added into 100 µl serum-free DMEM. Then, 1 ml DMEM is added and the mixed solution is incubated at 37 • C for 6 h. The medium is discarded after 72 h and cells are washed by PBS twice for further experiment.
Autophagic flux assay adopted similar process as previous depicted. Cells were separated into five groups and was processed with autophagy inhibitor Bafilomycin A1 (Baf A1; Abcam). A: the control group without Baf A1; B: the si-PTX3 group without Baf A1; C: the si-PTX3 group with Baf A1; D: the control group with Baf A1; E: the si-NC group with Baf A1.

CCK8 Assay
The logarithmic growth phase transfected tumor cells were obtained and digested for CCK8 assay. 5 × 10 3 glioma cells and 100 µl of medium were placed into 96-well plates. The absorbance at 450 nm was measured per 24 h during the following 3 days.

Colony Forming Assay
Cells were digested and plated in 6-well plates (300 cells per well) and cultured with 5% CO 2 at 37 • C for 2 weeks. The colonies were then fixed with 4% methanol (1 ml per well) for 15 min and stained with crystal violet for 30 min at room temperature. After photograph, discoloration was performed with 10% acetic acid, and cells were measured absorbance at 550 nm.

Immunofluorescence Assay
Cells were fixed with 4% paraformaldehyde, then added with 0.3% triton at 37 • C. After be blocked with 3% BSA for 60 min, cells were incubated with rabbit anti-LC3B (1:200, ab51520; Abcam) overnight at 4 • C. At the second day, cells were incubated with fluorescein isothiocyanate (FITC)-conjugated secondary antibodies (green) at 37 • C for 90 min, and stained with DAPI (blue) at 37 • C for 10 min. Observation and photograph were conducted by confocal microscopy.

Data Collection and Single-Cell Analysis
RNA-seq data of glioma and corresponding clinical information were acquired from the TCGA database 1 and the CGGA database 2 . All data were transferred into TPM data for further analysis.
For single-cell analysis, three GBM samples from GSE139448 are processed and normalized by R package "Seurat, " "NormalizeData, " and "FindVariableGenes" (Wang et al., 2020). The GO analysis based on PTX3 expression is perform as mentioned above. Expression profile of PTX3 and JUN are plotted by the R package "vlnplot." All data are obtained from online public database, and corresponding ethic statement can be found in their website.

GO Analysis and Gene Set Enrichment Analyses (GSEA)
Genes with the adjusted p-value < 0.05 and the absolute FC larger than 2.0 were considered to be statistically significant. Gene Ontology (GO) analysis on the aberrantly expressed genes were determined by the GSVA analysis, and false discovery rate (FDR) < 0.05 were considered statistically significant. The GSEA analysis was conducted to illustrate the relationship between PTX3 expression and hallmark gene sets from the Molecular Signatures Database (MSigDB).

Survival Analysis
Patients were subdivided into high and low groups according to median PTX3 expression. The overall survival (OS), progression free interval (PFI), and disease specific survival (DSS) rates of patients in low and high group were compared by the Kaplan-Meier method with log-rank test. ROC and AUC were performed to evaluate the prediction performance of PTX3 expression in various aspects, including 3,5-year OS, subtype of GBM (classical, mesenchymal, neural, proneural) and IDH status (wildtype, mutant).

Mutation and Copy Number Variation Analysis
Single nucleotide polymorphisms (SNPs) and somatic CNVs were downloaded from the TCGA database. CNVs regions on chromosome associated with PTX3 expression were analyzed using GISTIC 2.0 3 . Venn diagram is generated by TBtools [Chengjie Chen, Rui Xia, Hao Chen & Yehua He. TBtools, a Toolkit for Biologists integrating various HTS-data handling tools with a user-friendly interface. Preprint at https://www. biorxiv.org/content/10.1101/289660v1 (2018)].

Statistical Analyses
PTX3 expression profile difference with in WHO grades, GBM subtypes and treatment outcome were analyzed using Wilcoxon rank testing. Kaplan-Meier survival curves were generated and compared by using the log-rank test. The Pearson correlation was applied to evaluate the linear relationship between gene expression levels. Univariate and multivariate Cox regression analyses, and LASSO regression analyses were performed by R/BioConductor (version 3.6.2 4 ).
Statistical analyses of the colony-forming assay and the CCK8 assay were carried out by GraphPad Prism (version 8.0 5 ). Two-way ANOVA analysis followed with Tukey's analysis for more than two groups. P-value < 0.05 was considered to be statistically significant.

PTX3 Expression Is Elevated in Glioblastoma
PTX3 expression profiles of pan-cancer and normal tissue were obtained from the TCGA database and the Getx database. PTX3 expression in glioma is higher than normal tissue (P < 0.001; Figure 1A). In glioma, the expression of PTX3 is increased along with the tumor grade elevated (P < 0.001; Figure 1B). Based on treatment outcome after first course, PTX3 expression is significantly increased in patients with PD than other three groups (CR, PR, and SD) in glioma from the TCGA database (P < 0.001, Supplementary Figure S1A). Therefore, high PTX3 expression indicates worse survival outcome.
The IDH status serves as prognostic prediction biomarkers in clinical (Chen et al., 2019), and the MGMT status can predict tumor sensitivity to temozolomide (Hegi et al., 2005). In our work, PTX3 is enriched in IDH wildtype GBM (TCGA: P < 0.001, Figure 1C; CGGA: P < 0.001, Figure 1D), unmethylated glioma (P < 0.001, Figure 1E). But no significantly expression difference is observed in GBM cased on the MGMT status (P > 0.05, Figure 1F) in the TCGA dataset. As for GBM subtypes, mesenchymal GBM exhibits the worst survival outcome and highest PTX3 expression while proneural GBM to the opposite in the TCGA microarray database (Figures 1G,H and Supplementary Figure S1B). Therefore, high PTX3 is associated with aggressive glioma.

PTX3 Acts as Prognostic Prediction Biomarker and Indicates Worse Survival Outcome
Patients were subdivided into high or low risk group based on median PTX3 expression to analyze survival outcome difference. In the TCGA database, high PTX3 expression suggested worse survival outcome than low PTX3 expression in glioma (P < 0.001; Figure 2A). Similarly, low risk group manifested better survival outcome relative to high risk group in GBM in the TCGA sequence (P = 0.007; Figure 2B) and microarray (P = 0.0024; Figure 2C) database. Same result was also confirmed in the CGGA database (P = 0.0012; Figure 2D). The survival outcome of patients receiving radiotherapy in low PTX3 group was better than high PTX3 group in the TCGA microarray database (B) Sequence data of PTX3 mRNA levels in WHO II, III, and IV from the TCGA dataset. PTX3 expression is related to the status of IDH in GBM from the TCGA microarray dataset (C, P-value < 0.001) and the CGGA dataset (D, P-value < 0.001). PTX3 expression is significant elevated in MGMT unmethylated group than MGMT methylated group in glioma (E, P < 0.001) from the TCGA sequence dataset, while similar difference is not observed in GBM (F, array, P < 0.001) from the TCGA array dataset. (G,H) PTX3 expression profiles in GBM subtypes from the TCGA microarray dataset. MES, mesenchymal; PN, proneural; NE, neural; CL, classical. NS, no significantly statistical; *P < 0.05; **P < 0.01; ***P < 0.001.
ROC and AUC were calculated to reveal the prognostic prediction ability of PTX3. The 3, 5 years survival probability of PTX3 expression were calculated (3-years: AUC = 0.84, 5-years: AUC = 0.792, Figure 2G). The AUC calculated according to the status of IDH (AUC = 0.852, Figure 2H) and GBM subtypes (AUC = 0.79, Figure 2I) in the TCGA microarray database were also calculated. Same results were verified in the TCGA sequence dataset (IDH: AUC = 0.839, subtypes: AUC = 0.842, Supplementary Figures S1G,H). Univariate and multivariate Cox regression analysis were also to evaluate the prognostic prediction ability of PTX3 (Supplementary Tables S1, S2). Therefore, PTX3 promotes tumor progression and its expression can predict survival outcome.

Biofunction Prediction of PTX3
Next, we predicted the potential biological functions of PTX3 by conducting the GO analysis (Figures 3A-C), the single-cell analysis (Figure 3D), and the GSEA analysis (Figures 3E-G and Supplementary Figure S1I). Results suggested that PTX3 involve in negative modulating cells autophagy and extracellular matrix disassembly. Therefore, PTX3 might promote tumor progression by inhibiting tumor cells autophagy. In order to explicit the association between PTX3 and genes involved in negative modulating autophagy pathway, we first identified differential expression genes (DEGs) between high and low PTX3 expression group. Then genes related to negative modulating autophagy pathway were obtained from the MSigDB 6 . Three genes, HMOX1, IL10RA, and TREM2, were identified by intersecting DEGs and autophagy related genes 6 http://www.gsea-msigdb.org/gsea/msigdb/cards/GO_NEGATIVE_ REGULATION_OF_AUTOPHAGY ( Figure 3H). The correlation coefficient was also calculated (Supplementary Figure S2).

PTX3 Affects Tumor Cell Viability by Negative Modulating Cell Autophagy
We next prove PTX3 can affect GBM cells viability. The CCK8 assay indicates that cells proliferation is inhibited by silencing PTX3 expression ( Figure 4A). The Western-blotting assay suggests the expression level of autophagy related proteins, beclin1 and LC3B, are elevated in the si-PTX3 group ( Figure 4B). Notably, the expression of LC3B-II is higher in the si-PTX3 group relative to other groups indicating activated cells autophagy.  Next, the autophagic flux assay is performed to determine the source of LC3B-II. Increased LC3B-II expression in the si-PTX3 group cannot be reversed by adding autophagy inhibitor Baf A1 ( Figure 4C). Therefore, PTX3 can negative modulate cells autophagy. The LC3B expression profile is also examined by immunofluorescence suggesting U87MG cells in the si-PTX3 group tends to accumulate more LC3B in cytoplasm (Figure 4D). The colony forming assay also suggests the viability of U87MG cells is inhibited when PTX3 expression is decreased ( Figure 4E). Thus, PTX3

Expression Profile and Biofunction of Transcription Factor JUN
According to gene expression correlation, we identify positive correlation between JUN expression and PTX3 expression ( Figure 5A and Supplementary Figure S3A). Likewise, JUN can predict survival outcome based on its expression (Supplementary Figures S3B,C). JUN can bind to chromosome 3 like polymerase (RNA) II (DNA directed) polypeptide A (regulator of message RNA synthesis) (Figure 5B). Previous study also proved that JUN binds to the promoter of PTX3 to regulate PTX3 expression (Chang et al., 2015). Next, we explore  Figure S3E) and single-cell analysis (Figure 5E) also support that high JUN expression is associated with negative modulating cells autophagy.

JUN Affect Glioblastoma Cells Proliferation, Viability and Autophagy
The CCK8 assay indicate that increased JUN expression can promote tumor cells proliferation, but by silencing PTX3 expression can reverse that process ( Figure 6A). Next, the Western-blotting assay is performed to examine relationship between JUN, PTX3 and autophagy related proteins ( Figure 6B). JUN can significantly increase PTX3 expression relative to the control group. In the meantime, less LC3B-I is transferred into LC3B-II indicating cells autophagy is inhibited. Cells autophagy is re-activated when PTX3 expression is inhibited. The colony forming assay also supported JUN can affect tumor viability through affecting PTX3 expression ( Figure 6C). Therefore, JUN can affect U87MG cells proliferation, viability and autophagy.

DISCUSSION
Previous studies confirmed that elevated PTX3 level in tumor tissue as a biomarker of poor survival outcome (Locatelli et al., 2013;Tarassishin et al., 2014). In this work, we also prove PTX3 expression is associated with aggressive type of glioma. High PTX3 expression indicates worse survival outcome. By silencing PTX3 expression can impair tumor cells colony-forming and proliferation ability in vitro. Thus, PTX3 acts as a prognostic prediction biomarker of glioma.
PTX3 was proved as an inflammatory factor belonging to the pentraxin family at the beginning (Bonavita et al., 2015). Its expression also enriches in immune cells based on the single-cell analysis. Therefore, PTX3 might able to affect tumor immune landscape (Netti et al., 2020). Tumor can be labeled as 'hot' or 'cold' according to their response to immunotherapy, and immunocytes infiltration degree decides tumor sensitivity to immunotherapy (Doni et al., 2019;Tomaszewski et al., 2019). Previous study proved PTX3 deficiency tumor manifested high macrophage infiltration, more cytokine production and high complement activation (Bonavita et al., 2015). However, the association between PTX3 and immunocytes is unclear and requires more researches.
JUN oncogene, also known as c-Jun or AP-1, belongs to the Jun family and encodes the component of the activator protein-1 complex (Meng and Xia, 2011). Previous study has already confirmed that JUN can bind to PTX3 promoter to regulate its expression (Chang et al., 2015). Other studies illustrated JUN serves as critical role in tumor progression. For example, the MAPK/JNK pathway can regulate cells autophagy, and c-jun is recognized as one of its downstream target (Zhou et al., 2015). The PI3K/Art pathway and the NF-κB pathway activation can initiate JUN expression in head and neck cancer (Chang et al., 2015). Factors like astrocyte elevated gene 1 (Liu et al., 2017), microRNA-4476 (Lin et al., 2020), 3-phosphoinositide dependent protein kinase 1 (Luo et al., 2018) can also activate c-jun expression. Therefore, JUN participates in cells autophagy regulation by affecting PTX3 expression.
Single nucleotide polymorphisms suggests mutation ratio of EGFR and PTEN are higher in high PTX3 expression group relative to low PTX3 expression group, while IDH1, ATRX and TP53 mutation are enriched in low PTX3 expression group. High EGFR and PTEN mutation are common in GBM, and actively participate in promoting tumor progression (Brennan et al., 2013;Han et al., 2016). TP53 is recognized as tumor suppressor and able to induce tumorigenesis (Wang et al., 2014). IDH1 and ATRX mutation have been confirmed as biomarker indicating better survival outcome in clinical (Wang et al., 2013;Aquilanti et al., 2018). Therefore, SNPs supported low PTX3 expression group indicates better survival outcome relative to high PTX3 expression group. CNVs indicates EGFR is amplificated in high PTX3 expression group while deletion regions like ERRFI1 and NF1 are mainly observed in high PTX3 expression group. Previous studies identified high EGFR expression promote tumor progression (Hatanpaa et al., 2010), and high ERRFI1 (Duncan et al., 2010) expression can slow tumor progression. In general, PTX3 is a prognostic prediction biomarker in GBM, and PTX3 promote GBM progression through negative modulating cells autophagy.

AUTHOR CONTRIBUTIONS
ZW, XW, and NZ prepared the manuscript, analyzed the data, and performed the experiments. HZ and ZD analyzed the data. MZ modified the manuscript. SF and QC designed the project and finally approved the manuscript to publish. All authors contributed to the article and approved the submitted version.