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

doi:10.1016/j.csbj.2021.08.019

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

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

Authors

Hao Zhang¹, Yue-Bei Luo², Wantao Wu³, Liyang Zhang¹, Zeyu Wang¹, Ziyu Dai¹, Songshan Feng¹, Hui Cao⁴, Quan Cheng^{1

5

6}, Zhixiong Liu^{1

6

7}

Affiliations

¹ Department of Neurosurgery, Xiangya Hospital, Central South University, China.
² Department of Neurology, Xiangya Hospital, Central South University, China.
³ Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.
⁴ Department of Psychiatry, The Second People's Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, China.
⁵ Department of Clinical Pharmacology, Xiangya Hospital, Central South University, China.
⁶ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, China.
⁷ Clinical Diagnosis and Therapy Center for Glioma of Xiangya Hospital, Central South University, China.

Abstract

Background: Gliomas 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.

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.

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.

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

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.