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pmid:34220791

Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis.

Binghao Zhao · Yuekun Wang · Yaning Wang · Congxin Dai · Yu Wang · Wenbin Ma

Frontiers in immunology · 2021 doi:10.3389/fimmu.2021.557994

Paper

complete

Experiment

complete

Data

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partial

Execution

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title
Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis.
doi
10.3389/fimmu.2021.557994
pmid
34220791
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Binghao Zhao
[1]
Yuekun Wang
[2]
Yaning Wang
[3]
Congxin Dai
[4]
Yu Wang
[5]
Wenbin Ma
journal
Frontiers in immunology
year
2021
abstract
The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. These results contribute to classifying glioma subtypes, remodeling the immunosuppressive microenvironment and informing novel cancer immunotherapy in the era of precision immuno-oncology.
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Available clinicopathological factors (e.g., sex, age, treatment options, histological subtype, and classic mutations) were collected from the TCGA and CGGA datasets to estimate the association between these factors as well as the immune phenotypes and prognosis with univariable and multivariable Cox analysis (uniCox, multiCox) and proportional hazard models.
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sections[6] "Survival Analysis" paragraph 2
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Available clinicopathological factors (e.g., sex, age, treatment options, histological subtype, and classic mutations) were collected from the TCGA and CGGA datasets to estimate the association between these factors as well as the immune phenotypes and prognosis with univariable and multivariable Cox analysis (uniCox, multiCox) and proportional hazard models.
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Publicly available datasets were analyzed in this study. The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
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Expression Analysis of Juvenile Pilocytic Astrocytomas by Oligonucleotide Microarray Reveals Two Potential Subgroups
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Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology
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[3]
GSM1061664
[4]
GSM1061665
[5]
GSM1061666
[6]
GSM1061667
[7]
GSM1061668
[8]
GSM1061669
[9]
GSM1061670
[10]
GSM1061671
[11]
GSM1061672
[12]
GSM1061673
[13]
GSM1061674
[14]
GSM1061675
[15]
GSM1061676
[16]
GSM1061677
[17]
GSM1061678
[18]
GSM1061679
[19]
GSM1061680
[20]
GSM1061681
[21]
GSM1061682
[22]
GSM1061683
[23]
GSM1061684
[24]
GSM1061685
[25]
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[26]
GSM1061687
[27]
GSM1061688
[28]
GSM1061689
[29]
GSM1061690
[30]
GSM1061691
[31]
GSM1061692
[32]
GSM1061693
[33]
GSM1061694
[34]
GSM1061695
[35]
GSM1061696
[36]
GSM1061697
[37]
GSM1061698
[38]
GSM1061699
[39]
GSM1061700
[40]
GSM1061701
[41]
GSM1061702
[42]
GSM1061703
[43]
GSM1061704
[44]
GSM1061705
[45]
GSM1061706
[46]
GSM1061707
[47]
GSM1061708
[48]
GSM1061709
[49]
GSM1061710
sample_count
50
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Expression data from glioma patients
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 16 fields
id
asset:table:pmid:34220791:1
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table
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jats-table
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application/vnd.jats.table+json
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null
sha256
sha256:46993a95e2927dd95a25e7a7941a0fc8dda81e2b4eb32e23ac8b9ba8cf20ab6e
bytes
5142
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[16]/table-wrap[1]#T1
version
null
note
Results of univariable and multivariable analyses on overall survival of glioma patients from multiple cohorts.
structure object · 5 fields
pmcid
PMC8242587
section_index
16
section_title
The Immune-H Phenotype Is Associated With a Poor Prognosis
row_count
85
column_count
5
table object · 8 fields
label
Table 1
caption
Results of univariable and multivariable analyses on overall survival of glioma patients from multiple cohorts.
rows array · 85 items
[0] array · 3 items
[0]
[1]
Univariable Cox
[2]
Multivariable Cox
[1] array · 5 items
[0]
[1]
HR (95% CI)
[2]
P-value
[3]
HR (95% CI)
[4]
P-value
[2] array · 5 items
[0]
TCGA microarray cohort
[1]
[2]
[3]
[4]
[3] array · 5 items
[0]
Gender (male vs. female)
[1]
1.09 (0.87-1.35)
[2]
0.457
[3]
1.22 (0.98-1.53)
[4]
0.080
[4] array · 5 items
[0]
Radiation (yes vs. no)
[1]
0.13 (0.09-0.18)
[2]
< 0.001*
[3]
0.15 (0.10-0.21)
[4]
< 0.001*
[5] array · 5 items
[0]
Chemotherapy (yes vs. no)
[1]
0.43 (0.33-0.54)
[2]
< 0.001*
[3]
0.56 (0.43-0.72)
[4]
< 0.001*
[6] array · 5 items
[0]
Ethnicity (not Hispanic or Latino vs. Hispanic or Latino)
[1]
0.90 (0.46-1.75)
[2]
0.750
[3]
0.87 (9,44-1.72)
[4]
0.685
[7] array · 5 items
[0]
Race
[1]
[2]
[3]
[4]
[8] array · 5 items
[0]
White
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[9] array · 5 items
[0]
Asian
[1]
0.97 (0.48-1.96)
[2]
0.935
[3]
1.05 (0.52-2.16)
[4]
0.885
[10] array · 5 items
[0]
Black or African American
[1]
0.82 (0.54-1.24)
[2]
0.350
[3]
0.96 (0.63-1.46)
[4]
0.845
[11] array · 5 items
[0]
Phenotype
[1]
[2]
[3]
[4]
[12] array · 5 items
[0]
Immune-L
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[13] array · 5 items
[0]
Immune-M
[1]
0.82 (0.43-1.56)
[2]
0.550
[3]
0.68 (0.35-1.30)
[4]
0.246
[14] array · 5 items
[0]
Immune-H
[1]
0.94 (0.49-1.79)
[2]
0.849
[3]
0.81 (0.42-1.55)
[4]
0.525
[15] array · 5 items
[0]
TCGA GBM RNA-seq cohort
[1]
[2]
[3]
[4]
[16] array · 5 items
[0]
Age (y)
[1]
[2]
[3]
[4]
[17] array · 5 items
[0]
< 50
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[18] array · 5 items
[0]
50-59
[1]
1.26 (0.69-2.31)
[2]
0.445
[3]
1.37 (0.70-2.68)
[4]
0.358
[19] array · 5 items
[0]
60-69
[1]
0.98 (0.56-1.72)
[2]
0.944
[3]
0.93 (0.48-1.80)
[4]
0.831
[20] array · 5 items
[0]
70-79
[1]
1.97 (1.03-3.79)
[2]
0.042*
[3]
2.20 (1.01-4.79)
[4]
0.048*
[21] array · 5 items
[0]
Gender (male vs. female)
[1]
0.89 (0.57-1.38)
[2]
0.599
[3]
1.19 (0.72-1.98)
[4]
0.497
[22] array · 5 items
[0]
Radiation (yes vs. no)
[1]
0.31 (0.15-0.65)
[2]
0.002*
[3]
0.31 (0.10-0.94)
[4]
0.039*
[23] array · 5 items
[0]
Chemotherapy (yes vs. no)
[1]
0.34 (0.18-0.66)
[2]
0.002*
[3]
0.76 (0.25-2.28)
[4]
0.620
[24] array · 5 items
[0]
Adjuvant TMZ chemotherapy (yes vs. no)
[1]
0.64 (0.41-0.99)
[2]
0.050*
[3]
0.91 (0.53-1.58)
[4]
0.746
[25] array · 5 items
[0]
Histology type
[1]
[2]
[3]
[4]
[26] array · 5 items
[0]
Proneural
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[27] array · 5 items
[0]
Neural
[1]
0.94 (0.49-1.84)
[2]
0.866
[3]
0.96 (0.45-2.03)
[4]
0.907
[28] array · 5 items
[0]
Classical
[1]
0.88 (0.44-1.52)
[2]
0.534
[3]
1.10 (0.54-2.24)
[4]
0.794
[29] array · 5 items
[0]
Mesenchymal
[1]
0.99 (0.56-1.75)
[2]
0.964
[3]
0.92 (0.45-1.87)
[4]
0.814
[30] array · 5 items
[0]
Phenotype
[1]
[2]
[3]
[4]
[31] array · 5 items
[0]
Immune-L
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[32] array · 5 items
[0]
Immune-M
[1]
0.77 (0.28-2.13)
[2]
0.619
[3]
0.88 (0.30-2.59)
[4]
0.817
[33] array · 5 items
[0]
Immune-H
[1]
1.68 (0.96-2.92)
[2]
0.067
[3]
2.00 (1.04-3.86)
[4]
0.038*
[34] array · 5 items
[0]
CGGA microarray cohort
[1]
[2]
[3]
[4]
[35] array · 5 items
[0]
Age (y)
[1]
[2]
[3]
[4]
[36] array · 5 items
[0]
< 50
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[37] array · 5 items
[0]
50-59
[1]
2.80 (1.96-4.01)
[2]
< 0.001*
[3]
1.70 (1.13-2.55)
[4]
0.011*
[38] array · 5 items
[0]
60-69
[1]
2.61 (1.67-4.08)
[2]
< 0.001*
[3]
1.60 (0.99-2.59)
[4]
0.055
[39] array · 5 items
[0]
70-79
[1]
16.69 (2.24-)
[2]
0.006*
[3]
6.42 (0.77-53.42)
[4]
0.085
[40] array · 5 items
[0]
Gender (male vs. female)
[1]
1.27 (0.94-1.72)
[2]
0.125
[3]
1.08 (0.78-1.49)
[4]
0.640
[41] array · 5 items
[0]
PRS type
[1]
[2]
[3]
[4]
[42] array · 5 items
[0]
Primary
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[43] array · 5 items
[0]
Recurrent
[1]
1.89 (1.11-3.22)
[2]
0.020*
[3]
2.19 (1.17-4.10)
[4]
0.014*
[44] array · 5 items
[0]
Secondary
[1]
4.44 (2.25-8.77)
[2]
< 0.001*
[3]
2.83 (1.31-6.14)
[4]
0.008*
[45] array · 5 items
[0]
Histology (GBM vs. LGG)
[1]
4.44 (3.24-6.09)
[2]
< 0.001*
[3]
4.69 (2.81-7.85)
[4]
< 0.001*
[46] array · 5 items
[0]
Grade
[1]
[2]
[3]
[4]
[47] array · 5 items
[0]
WHO II
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[48] array · 5 items
[0]
WHO III
[1]
3.08 (1.94-4.90)
[2]
< 0.001*
[3]
2.77 (1.62-4.71)
[4]
< 0.001*
[49] array · 5 items
[0]
WHO IV
[1]
6.83 (4.60-10.12)
[2]
< 0.001*
[3]
NA
[4]
NA
[50] array · 5 items
[0]
Radiation (yes vs. no)
[1]
0.49 (0.31-0.78)
[2]
0.003*
[3]
0.48 (0.28-0.81)
[4]
0.006*
[51] array · 5 items
[0]
Chemotherapy (yes vs. no)
[1]
1.57 (1.16-2.14)
[2]
0.004*
[3]
0.83 (0.57-1.20)
[4]
0.314
[52] array · 5 items
[0]
IDH1 status (IDH1 MT vs IDH1 WT)
[1]
0.42 (0.31-0.58)
[2]
< 0.001*
[3]
0.88 (0.59-1.31)
[4]
0.533
[53] array · 5 items
[0]
Histology type
[1]
[2]
[3]
[4]
[54] array · 5 items
[0]
Proneural
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[55] array · 5 items
[0]
Neural
[1]
0.80 (0.51-1.27)
[2]
0.343
[3]
0.95 (0.58-1.56)
[4]
0.845
[56] array · 5 items
[0]
Classical
[1]
2.67 (1.50-4.74)
[2]
< 0.001*
[3]
1.15 (0.59-2.25)
[4]
0.673
[57] array · 5 items
[0]
Mesenchymal
[1]
2.61 (1.81-3.77)
[2]
< 0.001*
[3]
1.75 (1.05-2.91)
[4]
0.031*
[58] array · 5 items
[0]
Phenotype
[1]
[2]
[3]
[4]
[59] array · 5 items
[0]
Immune-L
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[60] array · 5 items
[0]
Immune-M
[1]
1.77 (1.20-2.61)
[2]
0.004*
[3]
1.14 (0.71-1.82)
[4]
0.584
[61] array · 5 items
[0]
Immune-H
[1]
2.31 (1.59-3.36)
[2]
< 0.001*
[3]
0.83 (0.48-1.44)
[4]
0.512
[62] array · 5 items
[0]
CGGA RNA-seq cohort
[1]
[2]
[3]
[4]
[63] array · 5 items
[0]
Age (y)
[1]
[2]
[3]
[4]
[64] array · 5 items
[0]
< 50
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[65] array · 5 items
[0]
50-59
[1]
1.65 (1.33-2.05)
[2]
< 0.001*
[3]
1.11 (0.88-1.39)
[4]
0.376
[66] array · 5 items
[0]
60-69
[1]
2.40 (1.85-3.11)
[2]
< 0.001*
[3]
1.26 (0.96-1.67)
[4]
0.099
[67] array · 5 items
[0]
70-79
[1]
4.19 (2.53-6.95)
[2]
< 0.001*
[3]
2.35 (1.38-3.98)
[4]
0.002*
[68] array · 5 items
[0]
Gender (male vs. female)
[1]
1.01 (0.85-1.20)
[2]
0.922
[3]
1.12 (0.94-1.33)
[4]
0.217
[69] array · 5 items
[0]
PRS type
[1]
[2]
[3]
[4]
[70] array · 5 items
[0]
Primary
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[71] array · 5 items
[0]
Recurrent
[1]
2.23 (1.86-2.67)
[2]
< 0.001*
[3]
2.30 (1.90-2.79)
[4]
< 0.001*
[72] array · 5 items
[0]
Secondary
[1]
4.37 (2.92-6.54)
[2]
< 0.001*
[3]
3.11 (2.00-4.83)
[4]
< 0.001*
[73] array · 5 items
[0]
Histology (GBM vs. LGG)
[1]
4.38 (3.66-5.25)
[2]
< 0.001*
[3]
5.85 (4.25-8.06)
[4]
< 0.001*
[74] array · 5 items
[0]
Grade
[1]
[2]
[3]
[4]
[75] array · 5 items
[0]
WHO II
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[76] array · 5 items
[0]
WHO III
[1]
2.04 (2.24-3.87)
[2]
< 0.001*
[3]
2.68 (2.00-3.59)
[4]
< 0.001*
[77] array · 5 items
[0]
WHO IV
[1]
8.33 (6.39-10.85)
[2]
< 0.001*
[3]
NA
[4]
NA
[78] array · 5 items
[0]
Radiation (yes vs. no)
[1]
0.97 (0.77-1.23)
[2]
0.817
[3]
0.83 (0.64-1.06)
[4]
0.130
[79] array · 5 items
[0]
Chemotherapy (yes vs. no)
[1]
1.59 (1.30-1.94)
[2]
< 0.001*
[3]
0.72 (0.57-0.89)
[4]
0.003*
[80] array · 5 items
[0]
IDH1 status (IDH1 MT vs IDH1 WT)
[1]
0.32 (0.27-0.38)
[2]
< 0.001*
[3]
0.50 (0.40-0.62)
[4]
< 0.001*
[81] array · 5 items
[0]
Phenotype
[1]
[2]
[3]
[4]
[82] array · 5 items
[0]
Immune-L
[1]
NA
[2]
NA
[3]
NA
[4]
NA
[83] array · 5 items
[0]
Immune-M
[1]
1.44 (1.18-1.74)
[2]
< 0.001*
[3]
1.04 (0.86-1.27)
[4]
0.685
[84] array · 5 items
[0]
Immune-H
[1]
1.94 (1.54-2.44)
[2]
< 0.001*
[3]
0.94 (0.73-1.20)
[4]
0.607
locator
section[16]/table-wrap[1]#T1
[6] object · 16 fields
id
asset:table:pmid:34220791:2
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table
format
jats-table
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application/vnd.jats.table+json
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sha256:e8f4dc9de4b9f36e3be030948e49f7d9c9a2301469aebd0b82a84e7744095854
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reader
read_table
reader_status
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identifier
section[20]/table-wrap[1]#T2
version
null
note
Distribution of IDH status, type and grade of glioma among immune phenotypes in CGGA dataset.
structure object · 5 fields
pmcid
PMC8242587
section_index
20
section_title
Genomic Alterations, Tumor Mutation Burden, and Histological Characteristics
row_count
13
column_count
9
table object · 8 fields
label
Table 2
caption
Distribution of IDH status, type and grade of glioma among immune phenotypes in CGGA dataset.
rows array · 13 items
[0] array · 6 items
[0]
[1]
[2]
Immune-L Phenotype
[3]
Immune-M Phenotype
[4]
Immune-H Phenotype
[5]
Chi-square test (1)
[1] array · 9 items
[0]
CGGA RNA-seq cohort
[1]
IDH Status
[2]
IDH MT(%)
[3]
280 (72.0)
[4]
IDH MT(%)
[5]
203 (51.4)
[6]
IDH MT(%)
[7]
45 (25.3)
[8]
χ2 = 110.855; P < 0.001
[2] array · 6 items
[0]
IDH WT (%)
[1]
109 (28.0)
[2]
IDH WT (%)
[3]
192 (48.6)
[4]
IDH WT (%)
[5]
133 (74.7)
[3] array · 8 items
[0]
Glioma Type
[1]
Primary (%)
[2]
314 (77.0)
[3]
Primary (%)
[4]
249 (61.9)
[5]
Primary (%)
[6]
85 (49.7)
[7]
χ2 = 45.058; P < 0.001
[4] array · 6 items
[0]
Recurrent (%)
[1]
94 (23.0)
[2]
Recurrent (%)
[3]
153 (38.1)
[4]
Recurrent (%)
[5]
86 (50.3)
[5] array · 8 items
[0]
Glioma Grade
[1]
LGG (%)
[2]
322 (78.9)
[3]
LGG (%)
[4]
240 (59.9)
[5]
LGG (%)
[6]
61 (35.7)
[7]
χ2 = 101.384; P < 0.001
[6] array · 6 items
[0]
GBM (%)
[1]
86 (21.1)
[2]
GBM (%)
[3]
161 (40.1)
[4]
GBM (%)
[5]
110 (64.3)
[7] array · 9 items
[0]
CGGA microarray cohort
[1]
IDH Status
[2]
IDH MT(%)
[3]
62 (59.6)
[4]
IDH MT(%)
[5]
47 (52.2)
[6]
IDH MT(%)
[7]
25 (23.8)
[8]
χ2 = 29.941; P < 0.001
[8] array · 6 items
[0]
IDH WT (%)
[1]
42 (40.4)
[2]
IDH WT (%)
[3]
43 (47.8)
[4]
IDH WT (%)
[5]
80 (76.2)
[9] array · 8 items
[0]
Glioma Type
[1]
Primary (%)
[2]
92 (91.1)
[3]
Primary (%)
[4]
83 (95.4)
[5]
Primary (%)
[6]
88 (88.9)
[7]
χ2 = 2.625; P = 0.269
[10] array · 6 items
[0]
Recurrent (%)
[1]
9 (8.9)
[2]
Recurrent (%)
[3]
4 (4.6)
[4]
Recurrent (%)
[5]
11 (11.1)
[11] array · 8 items
[0]
Glioma Grade
[1]
LGG (%)
[2]
82 (78.1)
[3]
LGG (%)
[4]
53 (58.9)
[5]
LGG (%)
[6]
39 (37.9)
[7]
χ2 = 34.592; P < 0.001
[12] array · 6 items
[0]
GBM (%)
[1]
23 (21.9)
[2]
GBM (%)
[3]
38 (42.2)
[4]
GBM (%)
[5]
64 (62.1)
locator
section[20]/table-wrap[1]#T2
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asset:table:pmid:34220791:3
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jats-table
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sha256:62deb2fde6b74dcaa2a73c53bb13cfbf46a51cf420b5257dd680ebfbec28fb13
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section[22]/table-wrap[1]#T3
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note
Summary of the molecular and biological functions of T cell costimulatory molecules.
structure object · 5 fields
pmcid
PMC8242587
section_index
22
section_title
Discussion
row_count
16
column_count
6
table object · 8 fields
label
Table 3
caption
Summary of the molecular and biological functions of T cell costimulatory molecules.
rows array · 16 items
[0] array · 6 items
[0]
Molecular marker
[1]
Aliase(s)
[2]
Ligand(s)
[3]
Receptor expression pattern
[4]
Biological function
[5]
Molecular function
[1] array · 6 items
[0]
Coinhibitory
[1]
[2]
[3]
[4]
[5]
[2] array · 6 items
[0]
PD-1
[1]
PDCD1, CD279, SLEB2, hPD-1
[2]
PD-L1, PD-L2
[3]
Activated T cells, NK cells, NKT cells, B cells, macrophages, subsets of DCs
[4]
Negative T cells costimulation (primarily in periphery), attenuate peripheral activity, preserve T-cell function in the context of chronic antigen
[5]
Inhibition of proximal TCR signaling, attenuate CD28 signaling
[3] array · 6 items
[0]
CTLA-4
[1]
CD152, ALPS5, CELIAC3, GRD4
[2]
B7-1 (CD80), B7-2 (CD86)
[3]
Activated T cells, Tregs
[4]
Negative T-cell costimulation (primarily at priming); prevent tonic signaling, attenuate high-affinity clones
[5]
Competitive inhibition of CD28 costimulation (binding to B7-1 and B7-2)
[4] array · 6 items
[0]
PD-L1
[1]
CD274, PDCD1L1, B7-H, B7H1
[2]
PD-1, B7-1 (CD80)
[3]
Monocytes, macrophages, mast cells, inducible in DCs, T cells, B cells, NK cells
[4]
Attenuate T cells activity in inflamed peripheral tissues
[5]
PD-1 ligation; cell-intrinsic mechanism unclear
[5] array · 6 items
[0]
LAG-3
[1]
CD223, Ly66
[2]
MHC-II, LSECtin
[3]
Activated CD4+ and CD8+ T cells, NK cells, Tregs
[4]
Negative regulator of T cells expansion; control T cells homeostasis; DCs activation
[5]
Competitive binding to MHC-II; proximal LSECtin mechanism unclear
[6] array · 6 items
[0]
TIM-3
[1]
HAVCR2, CD366, KIM-3, SPTCL, TIMD-3
[2]
Galectin-9, PtdSer, HMGB1, CEACAM-1
[3]
Th1 CD4+ and Tc1 CD8+, Tregs, DCs, NK cells, monocytes
[4]
Negative regulation of Type immunity; preserve peripheral tolerance
[5]
Negative regulation ofproximal TCR components; differences between ligands unknown
[7] array · 6 items
[0]
TIGIT
[1]
VSIG9, VSTM3, WUCAM
[2]
PVR (CD155), PVRL2 (CD112)
[3]
CD4+ and CD8+ T cells, Tregs, TFH, NK cells
[4]
Inhibition of T cells activity; DC tolerization
[5]
Competitive inhibition of DNAM1 (CD226) costimulation (binding of PVR), binding of DNAM1 in cis; cell-intrinsic ITIM-negative signaling
[8] array · 6 items
[0]
VISTA
[1]
VSIR, B7-H5, B7H5, C10orf54, PD-1H
[2]
Counterreceptor unknown
[3]
T cells and activated Tregs, myeloid cells, mature APCs
[4]
Negative regulation of T cells activity; suppression of CD4+ T cells, shaping naive CD4+ T cells compartment
[5]
Increase threshold for TCR signaling, induce FOXP3 synthesis; proximal signaling unknown
[9] array · 6 items
[0]
Costimulatory
[1]
[2]
[3]
[4]
[5]
[10] array · 6 items
[0]
ICOS
[1]
AILIM, CCLP, CRP-1
[2]
ICOSL
[3]
Activated T cells, B cells, ILC2
[4]
Positive costimulation; Type I and II immunity; Tregs maintenance; TFH differentiation
[5]
p50 PI3K recruitment (AKT signaling); enhance calcium signaling (PLCγ)
[11] array · 6 items
[0]
OX40
[1]
TNFRSF4, ACT35, CD134, TXGP1L
[2]
OX40L
[3]
Activated T cells, Tregs, NK cells, NKT cells, neutrophils
[4]
Sustain and enhance CD4+ T cell immunity; role in CD8+ T cells and Tregs
[5]
Regulation of BCL2/XL (survival); enhance PI3K/AKT signaling
[12] array · 6 items
[0]
GITR
[1]
TNFRSF18, AITR, CD357, ENERGEN, GITR
[2]
GITRL
[3]
Activated T cells, Tregs, B cells, NK cells, macrophages
[4]
Attenuate Tregs; costimulation of activated T cells, NK cell activation
[5]
Signal through TRAF5
[13] array · 6 items
[0]
CD137
[1]
TNFRSF9, 4-1BB, CDw137, ILA
[2]
4-1BBL (CD137L)
[3]
Activated T cells, Tregs, NK cells, monocytes, DCs, B cells
[4]
Positive T cells costimulation; DC activation
[5]
Signal through TRAF1, TRAF2
[14] array · 6 items
[0]
CD40
[1]
TNFRSF5, Bp50, CDW40, p50
[2]
CD40L
[3]
APCs, B cells, monocytes, non hematopoietic cells (e.g., fibroblasts, endothelial cells)
[4]
APC licensing
[5]
Signal through TRAF2, 3, 5, 6; TRAF-independent mechanisms unclear
[15] array · 6 items
[0]
CD27
[1]
TNFRSF7, S152, LPFS2, Tp55
[2]
CD70
[3]
CD4+ and CD8+ T cells, B cells, NK cells
[4]
Lymphocyte and NK cell costimulation; generation of T-cell memory
[5]
Signal through TRAF2, TRAF5
locator
section[22]/table-wrap[1]#T3
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asset:data:geo:GSE12907 not downloaded
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asset:data:geo:GSE12907 licence not read
[2]
asset:data:geo:GSE16011 not downloaded
[3]
asset:data:geo:GSE16011 licence not read
[4]
asset:data:geo:GSE23806 not downloaded
[5]
asset:data:geo:GSE23806 licence not read
[6]
asset:data:geo:GSE43378 not downloaded
[7]
asset:data:geo:GSE43378 licence not read
analysis object · 4 fields
state
partial
methods array · 1 item
[0] object · 10 fields
id
method-1
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null
specific
null
label
Univariable and multivariable Cox analysis with proportional hazard models
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parameters object · 0 fields
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evidence object · 3 fields
score
1
band
verbatim
quote
Available clinicopathological factors (e.g., sex, age, treatment options, histological subtype, and classic mutations) were collected from the TCGA and CGGA datasets to estimate the association between these factors as well as the immune phenotypes and prognosis with univariable and multivariable Cox analysis (uniCox, multiCox) and proportional hazard models.
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gaps array · 1 item
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method.reader_named_unmapped
capability object · 3 fields
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complete
requirements array · 1 item
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capability_id
capability:computational_analysis
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assay_families array · 9 items
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meta_analysis
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machine_learning
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survival_analysis
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statistical_regression
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pca
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molecular_docking
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network_analysis
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Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
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R4
blocking_axis
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blocked_on
method:not_determinable
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R4
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false
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basis
The reader quoted the method the paper names (Univariable and multivariable Cox analysis with proportional hazard models), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 4 items
[0]
GSE16011
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GSE43378
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GSE23806
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GSE12907
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[0]
GSE16011
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GSE43378
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GSE23806
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GSE12907
statements array · 1 item
[0]
Publicly available datasets were analyzed in this study. The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
basis
The paper names GSE16011, GSE43378, GSE23806, GSE12907 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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source:pmid:34220791
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asset:table:pmid:34220791:2
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asset:table:pmid:34220791:3
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run_analysis
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data:geo:GSE12907
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method:not_determinable
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partial
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MiniMax-M3.1-Flash
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minimax
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minimax
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2026-10-31T18:31:47Z
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single_reader
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method
verbatim
availability
verbatim
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extraction
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pmid:34220791
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PMID 34220791 full text
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data:geo:GSE12907
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GEO GSE12907
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PMID 34220791 full text
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GEO GSE12907
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agent:corpus-assay-engine
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Complete file
{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34220791", "kind": "science-record", "revision": 4, "content_hash": "sha256:87916d2470d2bd8c289f17f0de6e93afb385e7343b72882374fb831c5fe12952", "created_at": "2026-11-01T15:41:41Z", "updated_at": "2026-11-01T21:25:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis.", "doi": "10.3389/fimmu.2021.557994", "pmid": "34220791", "authors": ["Binghao Zhao", "Yuekun Wang", "Yaning Wang", "Congxin Dai", "Yu Wang", "Wenbin Ma"], "journal": "Frontiers in immunology", "year": 2021, "abstract": "The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. 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{"pmcid": "PMC8242587", "section_index": 20, "section_title": "Genomic Alterations, Tumor Mutation Burden, and Histological Characteristics", "row_count": 13, "column_count": 9}, "table": {"label": "Table 2", "caption": "Distribution of IDH status, type and grade of glioma among immune phenotypes in CGGA dataset.", "rows": [["", "", "Immune-L Phenotype", "Immune-M Phenotype", "Immune-H Phenotype", "Chi-square test (1)"], ["CGGA RNA-seq cohort", "IDH Status", "IDH MT(%)", "280 (72.0)", "IDH MT(%)", "203 (51.4)", "IDH MT(%)", "45 (25.3)", "χ2 = 110.855; P < 0.001"], ["IDH WT (%)", "109 (28.0)", "IDH WT (%)", "192 (48.6)", "IDH WT (%)", "133 (74.7)"], ["Glioma Type", "Primary (%)", "314 (77.0)", "Primary (%)", "249 (61.9)", "Primary (%)", "85 (49.7)", "χ2 = 45.058; P < 0.001"], ["Recurrent (%)", "94 (23.0)", "Recurrent (%)", "153 (38.1)", "Recurrent (%)", "86 (50.3)"], ["Glioma Grade", "LGG (%)", "322 (78.9)", "LGG (%)", "240 (59.9)", "LGG (%)", "61 (35.7)", "χ2 = 101.384; P < 0.001"], ["GBM (%)", "86 (21.1)", "GBM (%)", "161 (40.1)", "GBM (%)", "110 (64.3)"], ["CGGA microarray cohort", "IDH Status", "IDH MT(%)", "62 (59.6)", "IDH MT(%)", "47 (52.2)", "IDH MT(%)", "25 (23.8)", "χ2 = 29.941; P < 0.001"], ["IDH WT (%)", "42 (40.4)", "IDH WT (%)", "43 (47.8)", "IDH WT (%)", "80 (76.2)"], ["Glioma Type", "Primary (%)", "92 (91.1)", "Primary (%)", "83 (95.4)", "Primary (%)", "88 (88.9)", "χ2 = 2.625; P = 0.269"], ["Recurrent (%)", "9 (8.9)", "Recurrent (%)", "4 (4.6)", "Recurrent (%)", "11 (11.1)"], ["Glioma Grade", "LGG (%)", "82 (78.1)", "LGG (%)", "53 (58.9)", "LGG (%)", "39 (37.9)", "χ2 = 34.592; P < 0.001"], ["GBM (%)", "23 (21.9)", "GBM (%)", "38 (42.2)", "GBM (%)", "64 (62.1)"]], "row_count": 13, "column_count": 9, "numeric_cells": 43, "header_rows": 1, "locator": "section[20]/table-wrap[1]#T2"}}, {"id": "asset:table:pmid:34220791:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": 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T-cell function in the context of chronic antigen", "Inhibition of proximal TCR signaling, attenuate CD28 signaling"], ["CTLA-4", "CD152, ALPS5, CELIAC3, GRD4", "B7-1 (CD80), B7-2 (CD86)", "Activated T cells, Tregs", "Negative T-cell costimulation (primarily at priming); prevent tonic signaling, attenuate high-affinity clones", "Competitive inhibition of CD28 costimulation (binding to B7-1 and B7-2)"], ["PD-L1", "CD274, PDCD1L1, B7-H, B7H1", "PD-1, B7-1 (CD80)", "Monocytes, macrophages, mast cells, inducible in DCs, T cells, B cells, NK cells", "Attenuate T cells activity in inflamed peripheral tissues", "PD-1 ligation; cell-intrinsic mechanism unclear"], ["LAG-3", "CD223, Ly66", "MHC-II, LSECtin", "Activated CD4+ and CD8+ T cells, NK cells, Tregs", "Negative regulator of T cells expansion; control T cells homeostasis; DCs activation", "Competitive binding to MHC-II; proximal LSECtin mechanism unclear"], ["TIM-3", "HAVCR2, CD366, KIM-3, SPTCL, TIMD-3", "Galectin-9, PtdSer, HMGB1, CEACAM-1", "Th1 CD4+ and Tc1 CD8+, Tregs, DCs, NK cells, monocytes", "Negative regulation of Type immunity; preserve peripheral tolerance", "Negative regulation ofproximal TCR components; differences between ligands unknown"], ["TIGIT", "VSIG9, VSTM3, WUCAM", "PVR (CD155), PVRL2 (CD112)", "CD4+ and CD8+ T cells, Tregs, TFH, NK cells", "Inhibition of T cells activity; DC tolerization", "Competitive inhibition of DNAM1 (CD226) costimulation (binding of PVR), binding of DNAM1 in cis; cell-intrinsic ITIM-negative signaling"], ["VISTA", "VSIR, B7-H5, B7H5, C10orf54, PD-1H", "Counterreceptor unknown", "T cells and activated Tregs, myeloid cells, mature APCs", "Negative regulation of T cells activity; suppression of CD4+ T cells, shaping naive CD4+ T cells compartment", "Increase threshold for TCR signaling, induce FOXP3 synthesis; proximal signaling unknown"], ["Costimulatory", "", "", "", "", ""], ["ICOS", "AILIM, CCLP, CRP-1", "ICOSL", "Activated T cells, B cells, ILC2", "Positive costimulation; Type I and II immunity; Tregs maintenance; TFH differentiation", "p50 PI3K recruitment (AKT signaling); enhance calcium signaling (PLCγ)"], ["OX40", "TNFRSF4, ACT35, CD134, TXGP1L", "OX40L", "Activated T cells, Tregs, NK cells, NKT cells, neutrophils", "Sustain and enhance CD4+ T cell immunity; role in CD8+ T cells and Tregs", "Regulation of BCL2/XL (survival); enhance PI3K/AKT signaling"], ["GITR", "TNFRSF18, AITR, CD357, ENERGEN, GITR", "GITRL", "Activated T cells, Tregs, B cells, NK cells, macrophages", "Attenuate Tregs; costimulation of activated T cells, NK cell activation", "Signal through TRAF5"], ["CD137", "TNFRSF9, 4-1BB, CDw137, ILA", "4-1BBL (CD137L)", "Activated T cells, Tregs, NK cells, monocytes, DCs, B cells", "Positive T cells costimulation; DC activation", "Signal through TRAF1, TRAF2"], ["CD40", "TNFRSF5, Bp50, CDW40, p50", "CD40L", "APCs, B cells, monocytes, non hematopoietic cells (e.g., fibroblasts, endothelial cells)", "APC licensing", "Signal 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