Evidence map›Paper›PMID 34471502›Full record

ArticleComputational and structural biotechnology journal2021

The molecular feature of macrophages in tumor immune microenvironment of glioma patients.

Hao Zhang, Yue-Bei Luo, Wantao Wu, Liyang Zhang, Zeyu Wang, Ziyu Dai, Songshan Feng, Hui Cao, Quan Cheng, Zhixiong Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed
14.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

105 citing papers in PubMed, 142 citations in OpenAlex.

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45 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Hao ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, China.
Yue-Bei LuoDepartment of Neurology, Xiangya Hospital, Central South University, China.
Wantao WuDepartment of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
Liyang ZhangDepartment of Neurosurgery, Xiangya Hospital, Central South University, China.
Zeyu WangDepartment of Neurosurgery, Xiangya Hospital, Central South University, China.
Ziyu DaiDepartment of Neurosurgery, Xiangya Hospital, Central South University, China.
Songshan FengDepartment of Neurosurgery, Xiangya Hospital, Central South University, China.
Hui CaoDepartment of Psychiatry, The Second People's Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, China.
Quan ChengDepartment of Neurosurgery, Xiangya Hospital, Central South University, China.
Zhixiong LiuDepartment of Neurosurgery, Xiangya Hospital, Central South University, China.
Central South University · CNSecond Affiliated Hospital of Hunan University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth.

methodsWeighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore.

resultsMacrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate.

conclusionOur findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.

Indexed as

ACC, Adrenocortical carcinomaBBB, brain blood barrierBLCA, Bladder Urothelial CarcinomaBRCA, Breast invasive carcinomaCDF, cumulative distribution functionCESC, Cervical squamous cell carcinoma and endocervical adenocarcinomaCGGA, Chinese Glioma Genome AtlasCHOL, CholangiocarcinomaCNA, copy number alternationsCNV, copy number variationCOAD, Colon adenocarcinomaCSF-1, colony-stimulating factor-1DLBC, Lymphoid Neoplasm Diffuse Large B-cell LymphomaDMP, differentially methylated positionESCA, Esophageal carcinomaGBM, glioblastomaGEO, Gene Expression OmnibusGlioma microenvironmentGO, gene ontologyGSEA, gene set enrichment analysisGSVA, gene set variation analysisHNSC, Head and Neck squamous cell carcinomaIGR, intergenic regionIHC, immunohistochemistryIL, interleukinImmunotherapyKEGG, Kyoto Encyclopaedia of Genes and GenomesKICH, Kidney ChromophobeKIRC, Kidney renal clear cell carcinomaKIRP, Kidney renal papillary cell carcinomaLGG, low grade gliomaLIHC, Liver hepatocellular carcinomaLUAD, Lung adenocarcinomaLUSC, Lung squamous cell carcinomaMachine learningMacrophageMMP-2, matrix metalloproteinase-2MT1, MMP membrane type 1 matrix metalloproteaseOV, Ovarian serous cystadenocarcinomaPAAD, Pancreatic adenocarcinomaPAM, partition around medoidspamr, prediction analysis for microarraysPCA, principal component analysisPCPG, Pheochromocytoma and ParagangliomaPRAD, Prostate adenocarcinomaPrognostic modelREAD, Rectum adenocarcinomaSARC, SarcomaSKCM, Skin Cutaneous MelanomaSNP, single-nucleotide polymorphismSNV, single-nucleotide variantSTAD, Stomach adenocarcinomaSVM, Support Vector MachinesTAM, tumor associated macrophageTCGA, The Cancer Genome AtlasTGF-β, tumor growth factor-βTHCA, Thyroid carcinomaTHYM, ThymomaTIMP-2, tissue inhibitor of metalloproteinase-2TLR2, toll-like receptor 2TME, tumor microenvironmentTNFα, tumor necrosis factor αTSS, transcription start siteUCEC, Uterine Corpus Endometrial CarcinomaUCS, Uterine CarcinosarcomaWGCNA, weighted gene co-expression network analysis

Identifiers

PMID34471502
PMCPMC8383063
OpenAlexW3193942139

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.