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. 2021 Aug 14:19:4603-4618.
doi: 10.1016/j.csbj.2021.08.019. eCollection 2021.

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

Hao Zhang  1 , Yue-Bei Luo  2 , Wantao Wu  3 , Liyang Zhang  1 , Zeyu Wang  1 , Ziyu Dai  1 , Songshan Feng  1 , Hui Cao  4 , Quan Cheng  1   5   6 , Zhixiong Liu  1   6   7
Affiliations

"VSports app下载" The molecular feature of macrophages in tumor immune microenvironment of glioma patients

Hao Zhang et al. Comput Struct Biotechnol J. .

Abstract

Background: Gliomas are one of the most common types of primary tumors in central nervous system VSports手机版. Previous studies have found that macrophages actively participate in tumor growth. .

Methods: Weighted 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. V体育安卓版.

Results: Macrophages 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. V体育ios版.

Conclusion: Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies. VSports最新版本.

Keywords: ACC, Adrenocortical carcinoma; BBB, brain blood barrier; BLCA, Bladder Urothelial Carcinoma; BRCA, Breast invasive carcinoma; CDF, cumulative distribution function; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; CGGA, Chinese Glioma Genome Atlas; CHOL, Cholangiocarcinoma; CNA, copy number alternations; CNV, copy number variation; COAD, Colon adenocarcinoma; CSF-1, colony-stimulating factor-1; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; DMP, differentially methylated position; ESCA, Esophageal carcinoma; GBM, glioblastoma; GEO, Gene Expression Omnibus; GO, gene ontology; GSEA, gene set enrichment analysis; GSVA, gene set variation analysis; Glioma microenvironment; HNSC, Head and Neck squamous cell carcinoma; IGR, intergenic region; IHC, immunohistochemistry; IL, interleukin; Immunotherapy; KEGG, Kyoto Encyclopaedia of Genes and Genomes; KICH, Kidney Chromophobe; KIRC, Kidney renal clear cell carcinoma; KIRP, Kidney renal papillary cell carcinoma; LGG, low grade glioma; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; LUSC, Lung squamous cell carcinoma; MMP-2, matrix metalloproteinase-2; MT1, MMP membrane type 1 matrix metalloprotease; Machine learning; Macrophage; OV, Ovarian serous cystadenocarcinoma; PAAD, Pancreatic adenocarcinoma; PAM, partition around medoids; PCA, principal component analysis; PCPG, Pheochromocytoma and Paraganglioma; PRAD, Prostate adenocarcinoma; Prognostic model; READ, Rectum adenocarcinoma; SARC, Sarcoma; SKCM, Skin Cutaneous Melanoma; SNP, single-nucleotide polymorphism; SNV, single-nucleotide variant; STAD, Stomach adenocarcinoma; SVM, Support Vector Machines; TAM, tumor associated macrophage; TCGA, The Cancer Genome Atlas; TGF-β, tumor growth factor-β; THCA, Thyroid carcinoma; THYM, Thymoma; TIMP-2, tissue inhibitor of metalloproteinase-2; TLR2, toll-like receptor 2; TME, tumor microenvironment; TNFα, tumor necrosis factor α; TSS, transcription start site; UCEC, Uterine Corpus Endometrial Carcinoma; UCS, Uterine Carcinosarcoma; WGCNA, weighted gene co-expression network analysis; pamr, prediction analysis for microarrays. V体育平台登录.

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Conflict of interest statement (VSports)

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures (V体育平台登录)

None
Graphical abstract
Fig. 1
Fig. 1
Construction and validation of M2 macrophage-related clusters based on machine learning. A, clustering dendrogram demonstrating good separation of the two clusters determined via PAM algorithm by traits. B, sample clustering by PCA in the TCGA dataset. C, Kaplan-Meier survival analysis of the two clusters. D, validation of clustering by pamr. E, selection of optimal threshold and exhibition of misclassification error. F, heatmap illustrating the differentiation power of 13 genes, red dots and green dots representing samples classified by genes. G, validation of clustering by neural network. H, validation of clustering by SVM. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Characterization of the two clusters. A, dendrogram correlating the levels of 64 cell types and clusters in TCGA. B, ESTIMATEScores, ImuneScores and StromalScores of the two clusters in TCGA. C, molecule levels in the pathways involved in immune checkpoint pathways in TCGA.
Fig. 3
Fig. 3
Genomic features of the two clusters. A, global CNV profile of the two cluster. B, distribution of gain or loss of function mutation in the two clusters. C, list of the most frequently altered genes in clusters 1 and 2.
Fig. 4
Fig. 4
Methylation characteristics of the two clusters. A, clustering dendrogram by DMPs showing good separation of the two clusters by clinical and genetic traits. B, volcano plot of DMPs and their position in genes. C, Manhattan plot of the genome-wide DNA differential methylation. D, GSEA of the two clusters. E, GO functional enrichment analysis. F, comparison of enrichment scores of several immune cell types in the two clusters. *** p < 0.001.
Fig. 5
Fig. 5
Characterization of the MScore. A, dendrogram correlating the MScores and 64 cell types. B, GO functional enrichment analysis correlating different immune regulatory processes with MScores. C, survival analyses of MScores in pan-glioma, LGG and GBM groups from TCGA. D, Hazard ratios of MScores in different cancer types based on univariate Cox regression analysis. GBM, Glioblastoma multiforme; LGG, Brain Lower Grade Glioma; CHOL, Cholangiocarcinoma; OV, Ovarian serous cystadenocarcinoma; LIHC, Liver hepatocellular carcinoma; ESCA, Esophageal carcinoma; PAAD, Pancreatic adenocarcinoma; STAD, Stomach adenocarcinoma; COAD, Colon adenocarcinoma; KIRC, Kidney renal clear cell carcinoma; READ, Rectum adenocarcinoma; PCPG, Pheochromocytoma and Paraganglioma; HNSC, Head and Neck squamous cell carcinoma; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; LUSC, Lung squamous cell carcinoma; KIRP, Kidney renal papillary cell carcinoma; KICH, Kidney Chromophobe; BRCA, Breast invasive carcinoma; THCA, Thyroid carcinoma; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; SKCM, Skin Cutaneous Melanoma; BLCA, Bladder Urothelial Carcinoma; SARC, Sarcoma; THYM, Thymoma; LUAD, Lung adenocarcinoma; UCEC, Uterine Corpus Endometrial Carcinoma; UCS, Uterine Carcinosarcoma; ACC, Adrenocortical carcinoma; PRAD, Prostate adenocarcinoma. E, GO enrich functional analysis of MScores in several cancer types. F, MScore discriminating survival probabilities in Xiangya glioma cohort. G, CD163 staining for 25 LGG samples and 15 GBM samples. IHC against CD163 molecule demonstrating the different M2 macrophage densities in the two MScore groups.
Fig. 6
Fig. 6
MScores discriminating survival probabilities in the majority of glioma cohorts.
Fig. 7
Fig. 7
Predictive value of MScore in immunotherapy response. A, Kaplan-Meier curve of high and low MScore groups in IMvigor210 cohort. B, rain-cloud plot showing MScores of CR, PR, PD and SD groups. CR, complete response; PR, partial response; PD, progressive disease; SD, stable disease. C, the bar chart showing proportions of high and low MScores. D, the bar chart showing proportions of CR/PR and SD/PD patients in high and low MScore groups. E, Constitution of the four therapeutic response types in high and low MScore groups. F. comparison of collective CD274 levels in the two MScore groups. G, Survival analysis of MScores in a melanoma cohort. H, Proportions of high and low MScores in different response groups. I, proportions of different response groups in high and low MScore groups. J, physiologic functions of M2 macrophages. CR, complete response; NS, not significant; PD, progressive disease; PR, partial; SD, stable disease. * p < 0.05; *** p < 0.001.
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