MXRA5 Is a Novel Immune-Related Biomarker That Predicts Poor Prognosis in Glioma

Hindawi Disease Markers Volume 2021, Article ID 6680883, 13 pages https://doi.org/10.1155/2021/6680883

Research Article MXRA5 Is a Novel Immune-Related Biomarker That Predicts Poor Prognosis in Glioma

Jin-Zhang Sun ,1,2 Jin-Hao Zhang,1,2 Jia-Bo Li,1,2 Feng Yuan,3 Lu-Qing Tong,4 Xu-Ya Wang,1,2 Lu-Lu Chen,1,2 Xiao-Guang Fan,1,2 Yi-Ming Zhang,1,2 Xiao Ren,1,2 Chen Zhang,1,2 Sheng-Ping Yu,1,2 and Xue-Jun Yang 1,2

1Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin 300052, China 2Laboratory of Neuro-Oncology, Tianjin Neurological Institute, Tianjin 300052, China 3Department of Neurosurgery, Aﬃliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China 4Department of Neurosurgery, The First Aﬃliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China

Correspondence should be addressed to Xue-Jun Yang; [email protected]

Received 8 December 2020; Revised 15 March 2021; Accepted 25 May 2021; Published 10 June 2021

Academic Editor: Anna Birková

Copyright © 2021 Jin-Zhang Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. Glioma is the most common primary intracranial tumor and is associated with poor prognosis. Identifying effective biomarkers for glioma is particularly important. MXRA5, a secreted glycoprotein, is involved in cell adhesion and extracellular matrix remodeling and has been reported to be expressed in many cancers. However, the role and mechanism of action of MXRA5 in gliomas remain unclear. This study was aimed at investigating the role of MXRA5 at the transcriptome level and its clinical prognostic value. Methods. In this study, RNA microarray data of 301 glioma patients from the Chinese Glioma Genome Atlas (CGGA) were collected as a training cohort and RNA-seq data of 702 glioma samples from The Cancer Genome Atlas (TCGA) were used for validation. We analyzed the clinical and molecular characteristics as well as the prognostic value of MXRA5 in glioma. In addition, the expression level of MXRA was evaluated in 28 glioma tissue samples. Results. We found that MXRA5 expression was significantly upregulated in high-grade gliomas and IDH wild-type gliomas compared to controls. Receiver operating characteristic (ROC) analysis showed that MXRA5 is a potential marker of the mesenchymal subtype of glioblastoma multiforme (GBM). We found that MXRA5 expression is highly correlated with immune checkpoint molecule expression levels and tumor-associated macrophage infiltration. High MXRA5 expression could be used as an independent indicator of poor prognosis in glioma patients. Conclusion. Our study suggests that MXRA5 expression is associated with the clinicopathologic features and poor prognosis of gliomas. MXRA5 may play an important role in the immunosuppressive microenvironment of glioma. As a secreted glycoprotein, MXRA5 is a potential circulating biomarker for glioma, deserving further investigation.

1. Introduction lymphatic system, which has brought a new theoretical basis for brain tumor immunotherapy [5, 6]. Gliomas account for the majority of primary malignant brain Matrix-remodeling associated protein 5 (MXRA5), a tumors in adults, and GBM is the most invasive and incur- secreted glycoprotein, is a member of the MXRA protein able type, which is characterized by a high recurrence rate family that participates in cell adhesion and extracellular and a high fatality rate [1, 2]. Despite the improvement of matrix (ECM) remodeling [7]. This protein contains 7 conventional treatments, including surgical resection and leucine-rich repeats and 12 immunoglobulin-like C2-type radiotherapy with concomitant temozolomide chemother- domains related to perlecan. MXRA5 is expressed in pri- apy, the treatment of GBM remains a tremendous challenge mates and some mammals but not in rats or mice [8]. [3, 4]. Recently, immunotherapies have changed cancer treat- MXRA5 mRNA is upregulated in human chronic ischemic ment strategies, especially the discovery of the intracranial myocardium and chronic kidney disease [9, 10]. In addition 2 Disease Markers to ECM remodeling, MXRA5 has been found to be involved tissues were used for IHC. A172 and TJ 905 glioma cell in tumorigenesis. For instance, somatic mutations of MXRA5 lines were used for IF staining. The cells were seeded in are observed in patients with non-small-cell lung cancer 24-well plates (1×104 cells/well in 1 mL of medium) and (NSCLC) [11]. MXRA5 protein is aberrantly expressed in incubated for 24 hours. The paraffin-embedded glioma sec- colorectal cancer (CRC) tissues and serves as a biomarker tions were incubated with a MXRA5 primary antibody for the early diagnosis of CRC [12]. (ab99463, Abcam, USA, 1 : 100 dilution). Markers of IHC However, little information is available concerning were detected with a two-step detection kit (PV-9003, MXRA5 expression in glioma. Therefore, in the present ZSGB-Bio, Beijing, China). An Alexa Fluor®594-labeled study, we aimed to conduct a comprehensive analysis to anti-goat antibody (ZF-0517, ZSGB-Bio, 1 : 500) was added explore the molecular and clinical characteristics of MXRA5 to the cell samples. The nuclei were stained with DAPI in glioma. We employed the CGGA mRNA microarray data (Sigma-Aldrich). Images were obtained with a VANOX as a training cohort, and our findings were validated in microscope (Olympus, Japan). IHC staining was assessed TCGA RNA-seq dataset successfully. We found that MXRA5 by the sum of the intensity and quantity scores. The inten- mRNA and protein expression was upregulated in glioma, sity score was graded as 0 (no staining), 1 (light brown especially GBM, and was an unfavorable prognostic bio- staining), 2 (brown staining), or 3 (dark brown staining). marker for patients with glioma. This is the first integrative The quantity score was graded as 0 (no staining), 1 (1– study molecularly and clinically characterizing MXRA5 25%), 2 (26–50%), 3 (51–75%), or 4 (>75%). The samples expression in whole WHO-graded gliomas. were defined as negative (0-2) or positive (>2) based on the final score. The stained sections were blindly evaluated 2. Materials and Methods by three independent pathologists who had no knowledge of the clinical information. 2.1. Patients and Tissue Samples. Gene expression microarray data of 301 glioma samples, ranging from WHO grade II to grade IV, were obtained from the CGGA (http://www.cgga 2.5. Quantitative Real-Time PCR (RT-qPCR) Analysis. The .org.cn). The RNA-seq data of 702 glioma samples, ranging TRIzol® reagent (Invitrogen, USA) was used to extract total from WHO grade II to grade IV, were acquired from TCGA RNA from glioma tissues and cell lines according to the (http://cancergenome.nih.gov/). Twenty-eight glioma tissue manufacturer’s instructions. The obtained RNA was reverse samples were obtained from the Department of Neurosur- transcribed into cDNA using the First Strand cDNA Synthe- gery, Tianjin Medical University General Hospital (1 grade sis Kit (Promega, USA). RT-PCR was performed based on I case, 7 grade II cases, 7 grade III cases, and 13 grade IV the Fast SYBRTM Green Master Mix according to the man- cases; grades were determined according to the 2016 WHO ufacturer’s protocol (Promega, USA). The primer sequences classification of tumors of the central nervous system). Writ- designed for MXRA5 (NM_015419.4) were 5′-TCCAAG ten informed consent was obtained from the patients or their AGGATGATCGACGC-3′ (forward) and 5′-TGTGTT families. This study was conducted in compliance with the CAATCTTGGCCGGT-3′ (reverse). GAPDH was applied principles of the Helsinki Declaration and approved by the as an endogenous control. The primer sequences for Ethics Committee of the General Hospital of Tianjin Medical GAPDH were 5′-TCACTGGCATGGCCTTCCGT-3′ (for- University. ward) and 5′-CTTACTCCTTGGAGGCCAT-3′ (reverse). 2.2. Cell Lines and Culture. A172 and TJ905 glioma cell lines Amplification was performed with 45 cycles of PCR in the were purchased from the Chinese Academy of Sciences Cell MJ Real-Time PCR Detection System (Bio-Rad, USA). For -ΔΔCT Bank (China). The A172 glioma cell line was cultured in Dul- relative quantification, 2 , which was calculated by becco’s modified Eagle’s medium (DMEM, Gibco, USA). The subtracting the CT values of the control gene from the CT TJ905 glioma cell line was cultured in DMEM/F-12 (1 : 1) values of MXRA5, was used to determine the relative medium (Gibco, USA). Cells were not contaminated with expression levels. mycoplasma. All cell culture media were supplemented with antibiotics (100 U/mL penicillin-streptomycin, Gibco, USA) 2.6. Statistical Analysis. The overall survival differences and 10% fetal bovine serum (FBS, Gibco, USA) and incu- ° between groups were estimated by Kaplan-Meier analysis. bated in 5% CO 2 at 37 C. Cox regression analysis was performed with SPSS statistical 2.3. Online Analysis of MXRA5 mRNA Expression in Tumors software (version 19). One-way ANOVA was used to test ff ’ t and Cell Lines. MXRA5 mRNA expression in tumors and for di erences among at least three groups. Student s -test ff normal tissues was explored by Gene Expression Profiling was used to determine di erences in comparisons between χ2 Interactive Analysis (GEPIA, http://gepia.cancer-pku.cn/ two groups. The Pearson test was used to evaluate the cor- index.html). MXRA5 mRNA expression in cell lines was relation between MXRA5 expression level and clinicopatho- fi explored in the Cancer Cell Line Encyclopedia (CCLE, logical parameters. Most gures and statistical analyses https://portals. http://broadinstitute.org/ccle). were performed with several packages (ggplot2, pROC, clus- terProfiler, enrichplot, circlize, and corrgram) based on the R 2.4. Immunohistochemistry (IHC) and Immunofluorescence language version 3.6.2 (http://www.r-project.org) and (IF) Staining. IHC and IF staining were performed as previ- GraphPad Prism 8.0. p <0:05 was considered statistically ously described by Li et al. [13]. Paraffin-embedded glioma significant. ies Markers Disease Figure xrsini 09cne ellns c )R-PRaayi fMR5i loatsu c n ellns(d). lines cell and (c) tissue glioma in MXRA5 of analysis RT-qPCR d) (c, lines. cell cancer 1019 in expression and p <0 :MR5mN xrsinpro expression mRNA MXRA5 1: : respectively. 01,

MXRA5 expression 10 12

4 6 8 Transcripts per million (TPM) 10 0 1 2 3 4 5 6 7 8 9

Giant_cell_tumour (3) T (n=163) N (n=207) GBM Tyroid (12) n T ( =518) LGG Upper_aerodigestive (33) N (n=207) T (n=77) Chondrosarcoma (4) N (n=128) ACC T (n=404) Relative mRNA expression of MXRA5 Esophagus (27) N (n=28) BLCA 0 1 2 3 4 Breast (60) T (n=1085) BRCA N (n=291) Medulloblastoma (4) T (n=306) CESC

ﬁ n Glioma (66) N ( =13) e a XA xrsinpro expression MXRA5 (a) le. T (n=36) CHOL WHO I Sof_tissue (20) N (n=9) T (n=275) COAD Pancreas (46) N (n=349) Ovary (55) T (n=47) DLBC N (n=337) n (c) Urinary_tract (28) T ( =182) ESCA WHO II N (n=286) ⁎⁎⁎ Other (8) T (n=519) HNSC Mesothelioma (11) N (n=44) T (n=66) KICH ⁎⁎ Lymphoma_hodgkin (13) N (n=53) WHO III Osteoscarcoma (10) T (n=523) KIRC N (n=100) ⁎⁎ Osteoscarcoma (10) T (n=286) KIRP N (n=60) Meningioma (3) n (b) LAML (a) T ( =173) Neuroblastoma (17) N (n=70) WHO IV T (n=369) LIHC ﬁ Melanoma (63) N (n=160) e cosaltmrsmlsi CAdtst b XA mRNA MXRA5 (b) dataset. TCGA in samples tumor all across les LUAD Kidneu (37) T (n=483) N (n=347) Bile_duct (8) T (n=486) LUSC N (n=338) Relative mRNA expression of MXRA5 Stomach (39) n MESO 10 T ( =87) 0 2 4 6 8 NA (387) T (n=426) OV N (n=88) Ewings_sarcoma (12) T (n=179) PAAD Lung_NSC (136) N (n=171) T (n=182) PCPG Liver (29) N (n=3) (d) n TJ 905 Lung_small_cell (54) T ( =492) PRAD N (n=152) ⁎⁎⁎ Colorectal (63) T (n=92) READ N (n=318) Multiple_myeloma (29) N (n=2) SARC B-cell_lymphoma_other (16) T (n=461) A 172 N (n=558) SKCM B-cell_all (13) T (n=408) N (n=211) STAD T-cell_all (16) T (n=137) Leukemia_other (5) N (n=165) TGCT T (n=512) ∗∗ Lymphoma_burkitt (11) N (n=337) THCA Lymphoma_DLBCL (18) T (n=118) and N (n=339) THY M T-cell_lymphoma_other (11) T (n=174) N (n=91) UCEC ∗∗∗ Prostate (8) T (n=57) UCS CML (15) N (n=78) T (n=79) UVM

indicate AML (39) p <0 : 05 3 4 Disease Markers

MXRA5 expression in CGGA dataset ⁎⁎⁎⁎ ⁎⁎⁎⁎ 10 ⁎⁎ ⁎⁎ 6

4 5

2 MXRA5 expression

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WHO II WHO III WHO IV Mutant Wild type Grade IDH mutation status (a) (b)

MXRA5 expression in TCGA dataset ⁎⁎⁎⁎ ⁎⁎⁎⁎ 15 ⁎ ⁎⁎

10 MXRA5 expression MXRA5 expression

5 5

WHO II WHO III WHO IV Mutant Wild type Grade IDH mutation status (c) (d)

Figure 2: Comparison of MXRA5 mRNA expression levels in CGGA and TCGA cohorts with diﬀerent WHO grades (a, c) and IDH mutation statuses (b, d). MXRA5 was signiﬁcantly increased in WHO IV and IDH wild-type gliomas in the CGGA and TCGA datasets. ∗, ∗∗, and ∗∗∗∗ indicate p <0:05, p <0:01, and p <0:0001, respectively.

3. Results we performed RT-qPCR with A172 cells and TJ905 cells (Figure 1(d)). 3.1. MXRA5 mRNA Expression in Glioma and Other Cancers. In glioma and many malignancies, MXRA5 is more highly 3.2. MXRA5 Is Highly Expressed in GBM and IDH Wild-Type expressed in tumor tissues than in normal tissues Glioma. MXRA5 mRNA expression was analyzed according (Figure 1(a)). To determine the expression of MXRA5 in can- to the WHO grading system in glioma. In the CGGA dataset, cer cells, we evaluated MXRA5 expression in the CCLE with the expression of MXRA5 was signiﬁcantly higher in GBM 1019 cancer cell lines. We found that MXRA5 expression was than in LGG (Figure 2(a)). This result was well validated in higher in glioma than in most other cancers (Figure 1(b)). TCGA dataset (Figure 2(c)), which indicated that MXRA5 MXRA5 mRNA expression was upregulated in GBM com- was closely linked to the malignancy of glioma. We also pared to lower-grade gliomas (LGG), which was veriﬁed by explored the relationship between MXRA5 mRNA expres- RT-qPCR in twenty-eight glioma samples from Tianjin Med- sion level and IDH mutation status and found that MXRA5 ical University General Hospital (Figure 1(c)). To further was highly expressed in IDH wild-type glioma in both the verify the expression level of MXRA5 in glioma cell lines, CGGA and TCGA datasets (Figures 2(b) and 2(d)). Disease Markers 5

WHO II WHO III WHO IV

10 �M

(a) MXRA5 DAPI Merge

A172

10 �M

TJ905

(b)

Figure 3: MXRA5 protein expression in glioma tissue and cell lines. (a) Immunohistochemical staining of MXRA5 in different grades of gliomas. Positive cells are stained brown. Magnification: 40x objective lens. (b) Immunofluorescence staining of MXRA5 protein (red) in the A172 and TJ905 glioma cell lines. Nuclei were stained with DAPI (blue). Magnification: 40x objective lens.

Table 3.3. MXRA5 Protein Expression in Glioma Tissue and Cell 1: MXRA5 immunohistochemical expression in 28 glioma tissue samples. Lines. To further characterize the relationship of MXRA5 expression level and tumor specimens, we measured the Expression of MXRA5 Samples N p value MXRA5 protein level in glioma tissue samples from Positive Negative twenty-eight patients (1 with grade I glioma, 7 with grade II LGG 15 2 (13.3%) 13 (86.6%) 0.042 glioma, 7 with grade III glioma, and 13 with GBM) using GBM 13 7 (53.8%) 6 (46.2%) IHC analysis. Representative IHC staining of MXRA5 in gliomas is illustrated in Figure 3(a). MXRA5 expression levels were significantly higher in GBM than in LGG (p <0:05, Table 1). These results suggested that MXRA5 expression mesenchymal-subtype GBM to generate ROC curves. In the was more prevalent in aggressive glioma. To determine the CGGA dataset, the area under the curve (AUC) was 0.804 subcellular localization of MXRA5, we performed IF staining (Figure 4(b)). In TCGA dataset, the AUC was 0.709 (Figure 4(d)). These results showed that MXRA5 was highly with A172 and TJ905 cancer cells (Figure 3(b)). Our results fi showed that it could be detected in the cytoplasm. speci cally expressed in mesenchymal-subtype GBM and may serve as a biomarker to predict mesenchymal-subtype 3.4. MXRA5 Is a Biomarker of the Mesenchymal Subtype of GBM. GBM. To investigate the relationship between MXRA5 mRNA expression and TCGA-defined molecular subtypes, 3.5. Gene Ontology (GO) and Pathway Enrichment Analysis we analyzed the distribution of MXRA5 mRNA expression of MXRA5-Associated Genes in GBM. To explore the biolog- in different TCGA molecular subtypes. The MXRA5 mRNA ical function and key pathway of MXRA5 in GBM, we per- expression level was significantly increased in the mesenchy- formed GO analysis and Kyoto Encyclopedia of Genes and mal subtype compared with the other three subtypes in both Genomes (KEGG) pathway analysis. First, we identified the the CGGA and TCGA cohorts (Figures 4(a) and 4(c)). Then, genes that strongly correlated with MXRA5 by Pearson cor- we used data for MXRA5 mRNA expression in relation analysis (Pearson ∣ R ∣≥0:6 in CGGA dataset, 6 Disease Markers

MXRA5 expression in CGGA dataset ROC curve in CGGA dataset ⁎⁎⁎⁎ 120 9 ⁎⁎⁎⁎

⁎⁎ 100

6 80

60 3 40 AUC: 0.804 Sensitivity (%) MXRA5 expression 20 0

−3 –20 Classical Mesenchymal Neural Proneural 100 80 60 40 20 0 GBM subtypes Specifcity (%) (a) (b) MXRA5 expression in TCGA dataset ROC curve in TGGA dataset ⁎⁎⁎⁎ 120 ⁎⁎⁎⁎ ⁎⁎⁎⁎ 100

12 80

60 9 AUC:0.709 40 Sensitivity (%) MXRA5 expression 20 6 0

–20 Classical Mesenchymal Neural Proneural 100 80 60 40 20 0 GBM subtypes Specifcity (%) (c) (d)

Figure 4: MXRA5 expression pattern in different GBM subtypes in the CGGA and TCGA datasets. (a, c) MXRA5 was significantly enriched in mesenchymal-subtype GBM. (b, d) ROC analysis showed that MXRA5 had high sensitivity and specificity for predicting mesenchymal- subtype GBM. ∗∗ and ∗∗∗∗ indicate p <0:01 and p <0:0001, respectively.

Pearson ∣ R ∣≥0:5 in TGGA dataset). Finally, 321 and 262 that the biological function of MXRA5 may be related to genes that were highly correlated with MXRA5 expression tumor invasion and the immune microenvironment of in GBM in the CGGA and TCGA datasets, respectively, were glioma. eligible for subsequent analysis. Significantly related genes were chosen for GO and KEGG analyses with R. We found 3.6. The Relationship between MXRA5 and the Immune that genes most relevant to MXRA5 expression were more Microenvironment in GBM. To further clarify the role of involved in extracellular matrix organization, extracellular MXRA5 in the immune response in GBM, we selected the structure organization, and leukocyte migration in both immunosuppressive gene TGFB1 and six gene sets that rep- CGGA and TCGA datasets. KEGG pathway analysis revealed resent different types of inflammation and immune response that the most related genes were mainly enriched in the and were defined as metagenes [14]. Corrgrams were derived PI3K-AKT signaling pathway, human papillomavirus infec- according to the Pearson r value between MXRA5 and meta- tion, and focal adhesion (Figure 5). These analyses indicate genes. As shown in Figures 6(a) and 6(b), most of the Disease Markers 7

GO analysis in CGGA P GO analysis in TCGA adjust P adjust Extracellular matrix organization Extracellular structure organization

Extracellular structure organization Extracellular matrix organization Leukocyte migration Leukocyte migration Negative regulation of cell adhesion Calcium ion homeostasis Connective tissue development Cellular calcium ion homeostasis Cell−substrate adhesion Cellular divalent inorganic cation homeostasis Cartilage development Cell−substrate adhesion 1e−05 1e−04 Skeletal system morphogenesis Connective tissue development

Collagen metabolic process Positive regulation of cytosolic calcium ion concentration Extracellular matrix disassembly Collagen metabolic process T cell-mediated immunity Cartilage development Bone morphogenesis Cell chemotaxis

Endochondral bone morphogenesis Regulation of leukocyte migration 2e−05 Endochondral bone growth Regulation of cell−substrate adhesion e Cartilage development 2 −04 Collagen fbril organization Bone growth Chondrocyte diferentiation Endodermal cell diferentiation Chemokine−mediated signaling pathway

Endoderm formation Endochondral bone morphogenesis Collagen fbril organization Chondrocyte development 3e−05 Growth plate cartilage development Collagen biosynthetic process

0.05 0.10 0.05 0.10 0.15 Gene ratio Gene ratio Count Count (10) (30) (10) (30) (20) (40) (20) (40)

KEGG analysis in CGGA KEGG analysis in TCGA P P adjust adjust PI3K−Akt signaling pathway Human papillomavirus infection

Human papillomavirus infection PI3K−Akt signaling pathway Focal adhesion Cytokine−cytokine receptor interaction Cytokine−cytokine receptor interaction 0.01 0.01 Focal adhesion ECM−receptor interaction

Protein digestion and absorption ECM−receptor interaction

Regulation of actin cytoskeleton 0.02 Protein digestion and absorption Transcriptional misregulation in cancer Chemokine signaling pathway 0.02 Proteoglycans in cancer Hematopoietic cell lineage Hematopoietic cell lineage 0.03 Hypertrophic cardiomyopathy (HCM) Viral protein interaction cytokine receptor

Viral protein interaction with cytokine receptor Amoebiasis 0.03 Arrhythmogenic right ventricular cardiomyopathy 0.04 Complement and coagulation cascades Dilated cardiomyopathy (DCM)

AGE−RAGE signaling pathway in diabetic complications Primary immunodefciency

0.050 0.075 0.100 0.125 0.150 0.03 0.06 0.09 0.12 0.15 Gene ratio Gene ratio Count Count (8) (16) (8) (16) (12) (20) (12) (20)

Figure 5: GO and KEGG pathway analyses of MXRA5-associated genes in GBM. metagenes were positively associated with MXRA5 expres- been identiﬁed as therapeutic targets in clinical or preclinical sion except for IgG, which was mainly correlated with the trials and additional immune genes and immune checkpoints activities of B lymphocytes. Among them, lymphocyte- were included in the analysis, such as B7-H3, PD-L1, and speciﬁc kinase (LCK), TGFB1, and STAT1 had the highest IDO1. PD-L1 was reported to be upregulated in high-grade positive correlations. Many checkpoint members that have glioma compared with LGG. Circos plots demonstrated that 8 Disease Markers

Corrgram of MXRA5 and infammatory metagenes CGGA dataset TCGA dataset

MXRA5 MXRA5

LCK LCK

HCK HCK

MHC-I MHC-I

MHC-II MHC-II

IgG IgG

TGFB1 TGFB1

STAT1 STAT1

(a) (b)

Figure 6: MXRA5-related inﬂammatory activities in GBM samples of the CGGA and TCGA cohorts. In pie charts, positive correlations are displayed in green and negative correlations are displayed in red. The color intensity and the size of the circle are proportional to the correlation coeﬃcient.

MXRA5 expression was tightly associated with B7-H3 and (p <0:001). No significant correlation was found between PD-L1 (Figures 7(a)–7(d)). MXRA5 mRNA expression and sex (p =0:125), MGMT promoter methylation status (p =0:118), radiotherapy 3.7. MXRA5 Positively Correlates with Tumor-Associated (p =0:156), or chemotherapy status (p =0:965) (Table 2). Macrophages (TAMs) in GBM. To further investigate the role Furthermore, univariate and multivariate Cox regression of MXRA5 in the glioma immune microenvironment, we hazard analyses showed that the MXRA5 mRNA expression performed Pearson correlation analysis of key markers of level was an independent prognostic factor for glioma TAMs and Treg cells. The analysis showed that in GBM, (p =0:018) (Table 3). MXRA5 closely positively correlated with M2 tumor- associated macrophage infiltration but had little correlation 4. Discussion with M1 and Treg infiltration (Figures 8(a) and 8(b)), which indicates that MXRA5 may play a role in tumor The prognosis of glioma is poor, and numerous traditional immunosuppression. antitumor drugs fail to prolong the survival time of patients with glioma, especially GBM [15]. The rise of immune and 3.8. MXRA5 Is Highly Expressed in GBM and Could Predict targeted therapy has brought new hope to patients. At pres- Worse Survival in Glioma. To further analyze the prognostic ent, a series of biomarkers have been identified in local tumor value of MXRA5, we divided glioma patients into the tissues, but circulating markers that could be detected in MXRA5 high-expression group and low-expression group body fluids are still lacking [16]. For a long time, we have based on the MXRA5 mRNA expression level (median). As been committed to finding suitable therapeutic targets shown in Figures 9(a) and 9(d), in both CGGA and TCGA through bioinformatics analysis. datasets, glioma patients in the high-expression group MXRA5, a secreted adhesion proteoglycan with VEGF experienced significantly shorter overall survival than their receptor activity, is elevated in the cartilage of patients with low-expression counterparts. Similar Kaplan-Meier curves osteoarthritis and can be detected in synovial fluid [17, 18]. were significantly observed in LGG and GBM patients Moreover, it is upregulated by TGFβ-1 in chronic renal tubu- (Figures 9(b), 9(c), 9(e), and 9(f)). lar disease and involved in the progression of chronic renal disease to end-stage renal disease. Studies have shown that 3.9. MXRA5 Expression and Its Relationship with MXRA5 also plays an important role in the development Pathological Features of Glioma. The relationship between and progression of cancers [19–22]. However, the prognostic MXRA5 mRNA expression and clinicopathological features value and role of MXRA5 in glioma remain unclear. was analyzed. Statistical analysis indicated that high mRNA In the present study, we found that MXRA5 mRNA and expression of MXRA5 was correlated with age (p <0:001), protein expression was closely related to tumor grade, with WHO grade (p <0:001), and IDH mutation status the highest expression in GBM among other glioma Disease Markers 9

Correlation of immune checkpoints in glioma of CGGA dataset Correlation of immune checkpoints in GBM of CGGA dataset

B7-H3 1 1.5 2 MXRA5 0.5 2.5 1.2 1.6 2 0 0 0.8 2.4 2.5 0 0.5 0.4 2 1 0 0.4 2.4 0.8 1 2 1.5 MXRA5 1.2 PDL1 CEACAM1 1.5 1.6 1 1.6

B7-H3 1.2 2 2

0.5

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1.5 CD80 1 0.8 1.6

CE ACAM1 1.2 1 IDO1 1.5 PDL1 1.2 1.6 0.5 2 0.8 2 0 0.4 0 2.5 2.5 0 0 0.4 2 2.8 0.5 – 2.4 0.8 1.5 1 1 2 1.6 1.2 IDO1 CD80

(a) (b)

Correlation of immune checkpoints in glioma of TGGA dataset Correlation of immune checkpoints in GBM of TGGA dataset

MXRA5 0.6 0.3 0.9 MXRA5 0 1.2 1.2 0.4 0.8 1.6 1 1.5 1.5 0 2 0 2.4 1.2 2.4 0 0.3 2 0.9 0.4 B7-H3 1.6 0.6 PDL1 0.8 B7-H3 0.6 1.2 PDL1 1.2 0.9

0.8 0.3 1.6

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0.3 0.8 1.2 0.9 IDO1 0.4 1.6 0 IDO1 1.5 1.2 0 2 2.8 –1 1.2 1.5 2.4 2.4 0.9 0 1.8 0.6 0.3 2 0 2.8 1.6 CD80 1.2 0.8 0.4 CD80 (c) (d)

Figure 7: Correlation of MXRA5 and immune checkpoint molecules in glioma (a, c) and GBM (b, d).

GBM in CGGA dataset GBM in TGGA dataset MXRA5 MXRA5 CD80 CD80 CD86 CD86 CD163 CD163 CD14 CD14 CD68 CD68 CD4 CD4 FOXP3 FOXP3 MMP2 MMP2 MMP9 MMP9 CDH1 CDH1 CDH2 CDH2 VIM VIM SNAI1 SNAI1

Row min Row max Row min Row max (a) (b)

Figure 8: MXRA5 is positively correlated with the inﬁltration of tumor-associated macrophages and the expression of tumor invasion-related genes in GBM. 10 Disease Markers

Survival analysis for Survival analysis for Survival analysis for MXRA5 in glioma of CGGA dataset MXRA5 in LGG of CGGA dataset MXRA5 in GBM of CGGA dataset Log rank p<0.0001 100 100 100 Log rank p=0.0256 Log rank p<0.0001 n(high)=79 n n(high)=62 (high)=149 n(low)=80 n n(low)=149 (low)=61 50 50 50 Percent survival Percent survival 0 0 Percent survival 0 0 2000 4000 6000 0 2000 4000 6000 0 1000 2000 3000 4000 5000 OS (days) OS (days) OS (days) Low expression Low expression Low expression High expression High expression High expression (a) (b) (c) Survival analysis for Survival analysis for Survival analysis for MXRA5 in glioma of TCGA dataset MXRA5 in LGG of TCGA dataset MXRA5 in GBM of TCGA dataset 100 100 p Log rank p<0.0001 Log rank =0.0099 100 Log rank p=0.02 n n(high)=261 n(high)=83 (high)=349 n n(low)=346 (low)=263 n(low)=83 50 50 50 Percent survival Percent survival Percent survival 0 0 0 0 2000 4000 6000 8000 0 2000 4000 6000 8000 0 1000 2000 3000 OS (days) OS (days) OS (days) Low expression Low expression Low expression High expression High expression High expression (d) (e) (f)

Figure 9: The MXRA5 survival curves of glioma and GBM in the CGGA and TCGA cohorts. Kaplan-Meier survival analysis showed that, compared to low expression, high expression of MXRA5 conferred a signiﬁcantly worse prognosis in glioma and GBM patients.

Table 2: Correlation between the MXRA5 expression and the subtypes, and the expression in mesenchymal-subtype GBM clinicopathologic characteristics of glioma patients in the CGGA was higher than that in other subtypes. Mesenchymal- dataset. subtype GBMs are more invasive and have a worse prognosis than other subtypes [23]. MXRA5 is highly expressed in MXRA5 expression Variable p value Low (n) High (n) mesenchymal-subtype GBM and can be used as a predictor. This suggests that MXRA5 is a marker of poor prognosis. Age <0.001 Analysis of clinical survival data also confirmed that the ≥45 42 80 survival time of glioma patients with high expression of <45 105 69 MXRA5 was significantly shorter than that of patients with Sex 0.125 low expression. This prognostic value was also observed in Male 82 95 patients with GBM. Female 67 54 IDH mutation status is of great significance to the molec- < WHO grade 0.001 ular classification of glioma and significantly affects the prog- GBM 40 83 nosis of patients. The prognoses of IDH wild-type glioma LGG 109 66 and GBM are worse than those of IDH mutant glioma and < IDH 0.001 GBM [24, 25]. However, the molecular mechanisms are not Mutation 93 42 well understood. Our results suggest that MXRA5 is more Wild type 56 107 highly expressed in IDH wild-type glioma, which indirectly MGMT promoter 0.118 suggests that MXRA5 is more highly expressed in glioma Unmethylated 89 97 with poor prognosis. In addition, IDH wild-type glioma Methylated 57 42 may also regulate the occurrence and progression of the Radiotherapy 0.156 tumor through the MXRA5 pathway, leading to poor Untreated 19 27 prognosis. Treated 125 112 Additionally, Cox univariate and multivariate analyses of Chemotherapy 0.965 MXRA5 expression and clinicopathological features revealed Untreated 74 70 that MXRA5 expression has a correlation with age and WHO Treated 68 65 pathological stage, suggesting that MXRA5 could be an inde- GBM: glioblastoma multiforme; LGG: lower-grade glioma; IDH: isocitrate pendent prognostic factor for glioma patients. These results dehydrogenase; MGMT: methylguanine methyltransferase. indicate that MXRA5 is involved in the malignant biological Disease Markers 11

Table 3: Univariate and multivariate analyses of clinical prognostic parameters in the CGGA dataset. MXRA5 expression was an independent prognostic factor in the CGGA dataset.

Univariate analysis Multivariate analysis Variable HR (95% CI) p value HR (95% CI) p value <0.001 0.018 MXRA5 expression 2.310 (1.717-3.107) 1.491 (1.071-2.077)

<0.001 0.042 Age at diagnosis 2.079 (1.555-2.779) 1.376 (1.011-1.873)

<0.001 <0.001 WHO grade 4.552 (3.356-6.176) 3.402 (2.382-4.857)

<0.001 0.317 IDH mutation 2.524 (1.859-3.426) 1.212 (0.832-1.765) process of glioma. However, the specific mechanism by of ideal circulating markers. As a secreted glycoprotein, which MXRA5 mediates glioma development remains MXRA5 is mainly expressed in the extracellular stroma and unclear. cytoplasm and can be detected in body fluids such as articular Minafra et al. confirmed that MXRA5 was involved in fluid and urine [18, 32]. Therefore, MXRA5 could be a poten- epithelial-mesenchymal transition (EMT) and matrix tial circulating marker. remodeling in breast cancer [26]. Ding et al. found that However, our study had several limitations. The analysis MXRA5 expression is decreased in preeclampsia and affects in our study was mainly based on the transcriptional level of trophoblast cell invasion through the MAPK pathway [27]. MXRA5, and the protein level of MXRA5 was only prelimi- Through CGGA and TCGA transcriptome analysis, we narily detected by immunohistochemistry. The direct found that the expression of MXRA5 was highly positively relationship between MXRA5 protein levels and patient out- correlated with the expression of immune checkpoints, the comes needs to be further verified. In addition, the role of immunosuppressive factor TGFB1, and tumor invasion MXRA5 in glioma was only preliminarily speculated through markers. TAMs are divided into M1 and M2 macrophages, bioinformatics analysis. These hypotheses need to be tested which are important immune cells in the glioma microenvi- in real molecular biology experiments. ronment. M1 macrophages express high levels of cluster of In conclusion, our study has shown that MXRA5 is aber- differentiation (CD) 80 and CD86, which are reportedly anti- rantly expressed in mesenchymal-subtype GBM and that tumoral and are associated with better prognosis. M2 macro- high MXRA5 expression indicates poor overall survival. phages are defined by high cell surface levels of CD163, These results reveal the potential value of MXRA5 as a novel CD204, and CD14 and mediate the immunosuppressive biomarker and immunotherapy target. We will continue to response [28]. The majority of glioma-associated microglia/- explore the molecular mechanism by which MXRA5 influ- macrophages have been identified as M2 macrophages with ences glioma biology. immunosuppressive and tumor supportive action. We performed Pearson correlation analysis of key markers of TAMs and Treg cells. The analysis showed that in GBM, MXRA5 Data Availability expression closely correlated with M2 tumor-associated macrophage infiltration but had little correlation with M1 and The data used to support the findings of this study are Treg infiltration. Therefore, we hypothesized that MXRA5 included within the article. is likely to promote the malignant progression of GBM by participating in the immunosuppression of the tumor microenvironment. However, the exact molecular mechanism Conflicts of Interest remains to be clarified through further experimental research. The authors declare that they have no conflict of interest. The study of biomarkers has always been a hot spot in the field of glioma. However, until now, clinically significant circulating markers have been scarce. Sreekanthreddy et al. [29] Authors’ Contributions found that the expression levels of OPN and TIMP1 in serum of GBM patients were higher than those in LGG patients and J.Z.S. performed the data analysis and drafted the manu- the survival time of GBM patients with higher OPN expres- script. J.Z.S., J.H.Z., and J.B.L. performed the experiments. sion level was shorter. It has been reported that there are F.Y., L.Q.T., X.Y.W., L.L.C., X.G.F., Y.M.Z., X.R., C.Z., and many kinds of molecules in the cerebrospinal fluid of glioma S.P.Y. contributed to the design of this study. X.J.Y. managed patients, such as IL-6 and miR-21, that have significantly the research design, reviewed the manuscript, and provided higher levels than those in normal people [30, 31]. However, funding support. All authors read and approved the final due to the lack of sensitivity and specificity, there is still a lack manuscript. 12 Disease Markers

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