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Research ArticleOriginal Article

A four-gene signature-derived risk score for glioblastoma: prospects for prognostic and response predictive analyses

Mianfu Cao, Juan Cai, Ye Yuan, Yu Shi, Hong Wu, Qing Liu, Yueliang Yao, Lu Chen, Weiqi Dang, Xiang Zhang, Jingfang Xiao, Kaidi Yang, Zhicheng He, Xiaohong Yao, Yonghong Cui, Xia Zhang and Xiuwu Bian
Cancer Biology & Medicine August 2019, 16 (3) 595-605; DOI: https://doi.org/10.20892/j.issn.2095-3941.2018.0277
Mianfu Cao
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Juan Cai
2Department of Kidney, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
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Ye Yuan
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Yu Shi
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Hong Wu
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Qing Liu
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Yueliang Yao
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Lu Chen
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Weiqi Dang
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Xiang Zhang
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Jingfang Xiao
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Kaidi Yang
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Zhicheng He
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Xiaohong Yao
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Yonghong Cui
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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Xia Zhang
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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  • For correspondence: zhangxia45{at}yahoo.com bianxiuwu{at}263.net
Xiuwu Bian
1Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China
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  • For correspondence: zhangxia45{at}yahoo.com bianxiuwu{at}263.net
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  • 1
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    1

    Differentially expressed genes between GBM and normal brain tissues. (A) Volcano plots showing the log2 (fold change) of mRNA in GBM compared to normal brain tissues, and the corresponding–log10 (adjusted P value) in TCGA, GSE4290 and GSE16011 datasets. Genes with adjusted P value below 0.05 and fold change above 2 (below -2) were marked with red (blue) dots. (B) Venn diagrams showing the gene numbers of the upregulated genes (left) and the downregulated genes (right) of GBM in TCGA, GSE4290 and GSE16011 cohorts. (C) Heatmaps of the overlapped genes in TCGA, GSE4290 and GSE16011 datasets. The overlapped genes that are from Figure 1B include 483 upregulated genes and 765 downregulated genes.

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    2

    The four-gene signature-derived risk score is high in GBM. (A) The calculation formula and the value of risk score (top), the corresponding expression of four key genes (middle), and the associated clinicopathological parameters (bottom). “ߔ indicates the regression coefficient derived from multivariate COX stepwise regression in TCGA GBM cohort; “E” represents the expression value of the corresponding gene. The P-value indicates the correlation between the risk score and the clinicopathological parameters (Supplementary Table S5). KPS, Karnofsky performance status; IDH, isocitrate dehydrogenase; MGMT, O(6)-methylguanine-DNA methyltransferase; NA, not available. (B-D) Risk scores in GBM and normal brain tissues (B), in IDH-wt GBM and IDH-mut GBM (C), and in mesenchymal GBM and non-mesenchymal (proneural and classical) GBM (D) in TCGA GBM cohort. IDH, isocitrate dehydrogenase; IDH-wt, IDH-wild type; IDH-mut, IDH-mutation. Data are shown as mean ± SEM, *P < 0.05, *** P < 0.001, ns, not significant.

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    S1

    Survival analysis of GBM patients. (A-B) Kaplan-Meier overall survival analysis of GBM patients stratified by IDH status (A), and expression subtypes (B). IDH, isocitrate dehydrogenase; IDH-wt, IDH-wild type; IDH-mut, IDH-mutation. Mes, mesenchymal; Non-Mes, non-mesenchymal, including proneural and classical subtypes.

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    Risk score predicts the prognosis and treatment response in TCGA GBM cohort. (A-C) Kaplan–Meier overall survival analysis among TCGA GBM patients stratified by risk score only (A), combined with IDH status (B), and combined with expression subtypes (C). Mes, mesenchymal; Non-Mes, non-mesenchymal, including proneural and classical subtypes. (D-E) Kaplan–Meier overall survival analysis of TCGA GBM patients with TMZ chemotherapy (D), or radiotherapy (E) according to the risk score. TMZ, temozolomide.

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    4

    Performance of risk score in predicting the survival and treatment response in independent GEO GBM cohorts. (A) Comparison of risk scores between GBM and normal brain tissues in GSE4290, GSE16011, GSE59612 and GSE90604. (B) Kaplan-Meier overall survival analysis of high-risk and low-risk GBM patients in GSE16011, GSE43378 and GSE83300. (C) Kaplan-Meier overall survival analysis of GBM patients with radiotherapy in GSE16011 according to the risk score. (D) Risk scores in mesenchymal GBM and non-mesenchymal (proneural and classical) GBM (up), and Kaplan–Meier overall survival analysis of GBM patients stratified by risk scores combined with expression subtypes (down). Mes, mesenchymal; Non-Mes, non-mesenchymal, including proneural and classical subtypes. (E) Risk scores in IDH-wt GBM and IDH-mut GBM (up), and Kaplan–Meier overall survival analysis of GBM patients stratified by risk scores combined with IDH status (down). IDH, isocitrate dehydrogenase; IDH-wt, IDH-wild type; IDH-mut, IDH-mutation. Data are shown as mean ± SEM, *P < 0.05, ** P < 0.01, *** P < 0.001, ns, not significant.

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    5

    Four-gene signature associated biological pathways in high-risk GBM. (A) Enrichment map showing the pathways enriched in high-risk GBM through GSEA analysis. Nodes represent enriched gene sets with P-value below 0.05. Node with deep red color correlates with small P-value. Node size corresponds to the number of genes within gene set. Edge thickness corresponds to the number of shared genes between gene sets. (B) Representative enriched pathways in high-risk GBM through GSEA analysis. NES, normalized enrichment score.

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    6

    Validation of the differential expression of the four genes in GBM cells. (A and B) Western blot images (A) and the relevant quantification (B) of OSMR, HOXC10, SCARA3 and SLC39A10 in GBM cell line LN229, primary GBM cells (GBM-1 and GBM-2), and normal glial cell line HEB. The relative expression of target proteins is quantified in comparison with ݭActin and normalized to the corresponding expression in HEB cells. Data are shown as mean ± SEM from three independent experiments, *P < 0.05, ** P < 0.01, *** P < 0.001.

Tables

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    S1

    TCGA samples used in the study

    Sample ID (normal)Sample ID (GBM)Sample ID (GBM)Sample ID (GBM)Sample ID (GBM)Sample ID (GBM)
    TCGA-06-0675-11TCGA-02-0047-01TCGA-06-0646-01TCGA-06-5856-01TCGA-19-1389-02TCGA-28-2513-01
    TCGA-06-0678-11TCGA-02-0055-01TCGA-06-0649-01TCGA-06-5858-01TCGA-19-1390-01TCGA-28-2514-01
    TCGA-06-0680-11TCGA-02-2483-01TCGA-06-0686-01TCGA-06-5859-01TCGA-19-1787-01TCGA-28-5204-01
    TCGA-06-0681-11TCGA-02-2485-01TCGA-06-0743-01TCGA-08-0386-01TCGA-19-2619-01TCGA-28-5207-01
    TCGA-06-AABW-11TCGA-02-2486-01TCGA-06-0744-01TCGA-12-0616-01TCGA-19-2620-01TCGA-28-5208-01
    TCGA-06-0125-01TCGA-06-0745-01TCGA-12-0618-01TCGA-19-2624-01TCGA-28-5209-01
    TCGA-06-0125-02TCGA-06-0747-01TCGA-12-0619-01TCGA-19-2625-01TCGA-28-5213-01
    TCGA-06-0129-01TCGA-06-0749-01TCGA-12-0821-01TCGA-19-2629-01TCGA-28-5215-01
    TCGA-06-0130-01TCGA-06-0750-01TCGA-12-1597-01TCGA-19-4065-01TCGA-28-5216-01
    TCGA-06-0132-01TCGA-06-0878-01TCGA-12-3650-01TCGA-19-4065-02TCGA-28-5218-01
    TCGA-06-0138-01TCGA-06-0882-01TCGA-12-3652-01TCGA-19-5960-01TCGA-28-5220-01
    TCGA-06-0141-01TCGA-06-1804-01TCGA-12-3653-01TCGA-26-1442-01TCGA-32-1970-01
    TCGA-06-0152-02TCGA-06-2557-01TCGA-12-5295-01TCGA-26-5132-01TCGA-32-1980-01
    TCGA-06-0156-01TCGA-06-2558-01TCGA-12-5299-01TCGA-26-5133-01TCGA-32-1982-01
    TCGA-06-0157-01TCGA-06-2559-01TCGA-14-0736-02TCGA-26-5134-01TCGA-32-2615-01
    TCGA-06-0158-01TCGA-06-2561-01TCGA-14-0781-01TCGA-26-5135-01TCGA-32-2616-01
    TCGA-06-0168-01TCGA-06-2562-01TCGA-14-0787-01TCGA-26-5136-01TCGA-32-2632-01
    TCGA-06-0171-02TCGA-06-2563-01TCGA-14-0789-01TCGA-26-5139-01TCGA-32-2634-01
    TCGA-06-0174-01TCGA-06-2564-01TCGA-14-0790-01TCGA-27-1830-01TCGA-32-2638-01
    TCGA-06-0178-01TCGA-06-2565-01TCGA-14-0817-01TCGA-27-1831-01TCGA-32-4213-01
    TCGA-06-0184-01TCGA-06-2567-01TCGA-14-0871-01TCGA-27-1832-01TCGA-32-5222-01
    TCGA-06-0187-01TCGA-06-2569-01TCGA-14-1034-01TCGA-27-1834-01TCGA-41-2571-01
    TCGA-06-0190-01TCGA-06-2570-01TCGA-14-1034-02TCGA-27-1835-01TCGA-41-2572-01
    TCGA-06-0190-02TCGA-06-5408-01TCGA-14-1402-02TCGA-27-1837-01TCGA-41-3915-01
    TCGA-06-0210-01TCGA-06-5410-01TCGA-14-1823-01TCGA-27-2519-01TCGA-41-4097-01
    TCGA-06-0210-02TCGA-06-5411-01TCGA-14-1825-01TCGA-27-2521-01TCGA-41-5651-01
    TCGA-06-0211-01TCGA-06-5412-01TCGA-14-1829-01TCGA-27-2523-01TCGA-76-4925-01
    TCGA-06-0211-02TCGA-06-5413-01TCGA-14-2554-01TCGA-27-2524-01TCGA-76-4926-01
    TCGA-06-0219-01TCGA-06-5414-01TCGA-15-0742-01TCGA-27-2526-01TCGA-76-4927-01
    TCGA-06-0221-02TCGA-06-5415-01TCGA-15-1444-01TCGA-27-2528-01TCGA-76-4928-01
    TCGA-06-0238-01TCGA-06-5416-01TCGA-16-0846-01TCGA-28-1747-01TCGA-76-4929-01
    TCGA-06-0644-01TCGA-06-5417-01TCGA-16-1045-01TCGA-28-1753-01TCGA-76-4931-01
    TCGA-06-0645-01TCGA-06-5418-01TCGA-19-0957-02TCGA-28-2509-01TCGA-76-4932-01
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    S2

    The clinical characteristics of GBM patients in TCGA cohort

    CharacteristicGBM patients (n = 165)
    Gender
    Male106
    Female59
    Age
    =50128
    <5037
    Grade
    I-III0
    IV165
    KPS score
    =8088
    <8036
    Missing41
    IDH status
    Mutation11
    Wild type142
    Missing12
    MGMT status
    Methylated58
    Unmethylated67
    Missing40
    Expression subtypes
    Mesenchymal48
    Classical68
    Proneural49
    Radiotherapy
    Yes127
    No4
    Missing34
    Chemotherapy
    Yes112
    No19
    Missing34
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    S3

    The clinical information of GBM patients from GEO database

    CharacteristicGSE4290GSE16011GSE43378GSE59612GSE83300GSE90604
    Normal tissues23801707
    GBM cases8115932755016
    GBM_gender
    Male10820442410
    Female511231256
    Missing8100010
    GBM_age
    =50NA107226719NA
    <50NA5210831NA
    GBM_grade
    I-III000000
    IV8115532755016
    GBM_cases with survivalNA15532NA50NA

    NA, not available.

    Notes:

    (1) All the GBM cases in the above GEO datasets were included in our study except for 4 of 159 GBM cases in GSE16011 without survival information.

    (2) The glioma samples but not GBM samples in GSE4290 (26 astrocytoma and 50 oligodendroglioma), GSE16011 (29 astrocytoma, 8 pilocytic astrocytoma, 52 oligodendroglioma and 28 mixed glioma) and GSE43378 (5 astrocytoma and 13 anaplastic oligodendroglioma) were not included in our study.

    (3) In GSE90604, the data from 7 normal brain tissues were included while the data from 2 normal astrocyte cell lines were excluded in our study.

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      S4

      The clinical information of GBM specimens used for establishment of primary GBM cells

      CasesPrimary cellsGenderAgeTumor locationWHO gradeHistologyIDH status
      Case 1GBM-1Female22Right temporal, parietal and occipital lobeIVGlioblastomaWild type
      Case 2GBM-2Female37Left temporal and parietal lobeIVGlioblastomaWild type
      • View popup
      1

      Univariate and multivariate Cox regression analysis of the overlapped DEGs of GBM

      Gene nameUp/DownUnivariate analysisMultivariate analysis
      HR (95%CI)P HR (95%CI)P
      OSMRUP0.3121.366 (1.181, 1.581)< 0.0010.2791.321 (1.136, 1.537)< 0.001
      PDIA4UP0.5111.667 (1.246, 2.231)0.001
      GJB2UP0.1511.164 (1.063, 1.273)0.001
      FKBP9UP0.3201.378 (1.135, 1.672)0.001
      STEAP3UP0.2221.248 (1.086, 1.434)0.002
      HOXC10UP0.1001.105 (1.037, 1.177)0.0020.0891.093 (1.024, 1.167)0.008
      ISG20UP0.2971.346 (1.112, 1.629)0.002
      SCARA3UP0.3371.401 (1.127, 1.742)0.0020.2381.268 (1.018, 1.580)0.034
      ZNF540Down–0.3480.706 (0.558, 0.893)0.004
      IKBIPUP0.4041.498 (1.131, 1.984)0.005
      KDELC2UP0.3261.385 (1.104, 1.738)0.005
      SPAG4UP0.1601.174 (1.048, 1.315)0.006
      C1RLUP0.2161.241 (1.065, 1.446)0.006
      SLC39A10Down–0.4940.611 (0.430, 0.868)0.006–0.4240.655 (0.445, 0.963)0.031
      GNSUP0.4561.578 (1.140, 2.185)0.006
      KHDRBS2Down–0.1470.863 (0.775, 0.961)0.007
      SLC2A10UP0.2411.273 (1.062, 1.525)0.009
      DENND2AUP0.2631.301 (1.066, 1.588)0.010

      Abbreviations: ݬ regression coefficient; HR, hazard ratio; CI, confidence interval.

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        S5

        Correlation between the risk score and clinicopathological parameters in TCGA GBM cohort

        FactorRisk score P value
        Low (n = 77) High (n = 88)
        Gender 0.633
        Male48 (62.3%)58 (65.9%)
        Female29 (37.7%)30 (34.1%)
        Age 0.517
        =5058 (75.3%)70 (79.5%)
        <5019 (24.7%)18 (20.5%)
        KPS score 0.124
        =8039 (50.6%)49 (55.7%)
        <8022 (28.6%)14 (15.9%)
        Missing16 (20.8%)25 (28.4%)
        IDH status 0.004**
        Mutation10 (13.0%)1 (1.1%)
        Wild type64 (83.1%)78 (88.6%)
        Missing3 (3.9%)9 (10.2%)
        MGMT status 0.214
        Methylated28 (36.4%)30 (34.1%)
        Unmethylated35 (45.5%)32 (36.4%)
        Missing14 (18.2%)26 (29.5%)
        Expression subtypes 0.028*
        Mesenchymal16 (20.8%)32 (36.4%)
        Non-mesenchymal61 (79.2%)56 (63.6%)
        Radiotherapy 0.180
        Yes62 (80.5%)65 (73.9%)
        No0 (0.0%)4 (4.5%)
        Missing15 (19.5%)19 (21.6%)
        Chemotherapy 0.946
        Yes53 (68.8%)59 (67.0%)
        No9 (11.7%)10 (11.4%)
        Missing15 (19.5%)19 (21.6%)

        KPS, Karnofsky performance status; IDH, isocitrate dehydrogenase; MGMT, O(6)-methylguanine-DNA methyltransferase. *, P < 0.05; **, P < 0.01.

          • View popup
          S6

          Univariate and multivariate Cox regression analysis of clinicopathological parameters and risk score for overall survival in TCGA GBM cohort

          FactorUnivariate analysisMultivariate analysis
          HR (95%CI)P HR (95%CI)P
          Gender0.995 (0.691, 1.432)0.977
          Age1.029 (1.013, 1.044)< 0.0011.034 (1.013, 1.055)0.001
          KPS score0.893 (0.568, 1.405)0.625
          IDH status0.229 (0.092, 0.568)0.001
          MGMT status0.568 (0.368, 0.875)0.0100.600 (0.382, 0.941)0.026
          Expression subtypes1.509 (1.015, 2.245)0.042
          Radiotherapy0.467 (0.171, 1.279)0.138
          Chemotherapy0.672 (0.396, 1.141)0.141
          Risk score2.714 (1.924, 3.829)< 0.0012.046 (1.321, 3.168)0.001

          KPS, Karnofsky performance status; IDH, isocitrate dehydrogenase; MGMT, O(6)-methylguanine-DNA methyltransferase; HR, hazard ratio; CI, confidence interval.

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          A four-gene signature-derived risk score for glioblastoma: prospects for prognostic and response predictive analyses
          Mianfu Cao, Juan Cai, Ye Yuan, Yu Shi, Hong Wu, Qing Liu, Yueliang Yao, Lu Chen, Weiqi Dang, Xiang Zhang, Jingfang Xiao, Kaidi Yang, Zhicheng He, Xiaohong Yao, Yonghong Cui, Xia Zhang, Xiuwu Bian
          Cancer Biology & Medicine Aug 2019, 16 (3) 595-605; DOI: 10.20892/j.issn.2095-3941.2018.0277

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          A four-gene signature-derived risk score for glioblastoma: prospects for prognostic and response predictive analyses
          Mianfu Cao, Juan Cai, Ye Yuan, Yu Shi, Hong Wu, Qing Liu, Yueliang Yao, Lu Chen, Weiqi Dang, Xiang Zhang, Jingfang Xiao, Kaidi Yang, Zhicheng He, Xiaohong Yao, Yonghong Cui, Xia Zhang, Xiuwu Bian
          Cancer Biology & Medicine Aug 2019, 16 (3) 595-605; DOI: 10.20892/j.issn.2095-3941.2018.0277
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          Keywords

          • Differentially expressed genes
          • gene set enrichment analysis
          • glioblastoma prognosis
          • radiotherapy
          • temozolomide chemotherapy

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