Key immune-related gene ITGB2 as a prognostic signature for acute myeloid leukemia

Wei, Jie; Huang, Xunjun; Huang, Yan; Xiong, Mingyue; Yao, Xiang-You; Huang, Zhi-Ning; Li, Si-Nian; Zhou, Weijie; Fang, Dalang; Deng, Donghong; Pan, Cheng

doi:10.21037/atm-21-3641

Cited by 22 publications

(16 citation statements)

References 38 publications

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“…Breast tumors consist of cancerous and non-cancerous cells, which have rarely been studied; moreover, enriched pathways were found to be related to the tumor microenvironment. Therefore, we identified the composition of immune cells in breast tumors, and investigated the correlation between the key genes and infiltrating immune cells in breast cancer patients [ 6 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Genes and Key Pathways in Breast Cancer Based on Machine Learning

Bao

2022

Med Sci Monit

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Background Breast cancer is one of the most common malignant tumors among women worldwide. This study aimed to screen key genes and pathways for breast cancer diagnosis and treatment. Material/Methods We obtained public data from the NCBI GEO database. The data were divided into a control group (normal breast tissue) and a treatment group (breast cancer tissue). We screened 32 differentially expressed genes (DEGs) between normal breast and cancerous tissues and used GO analysis and GSEA to identify the key pathways. We then combined LASSO and SVM-RFE analyses to screen key genes, and used CIBERSORT to obtain the proportion of 22 types of immune cells. The relationships between key genes and immune-infiltrating cells were further explored. Results We screened 32 DEGs from the 2 groups, including 27 downregulated genes and 5 upregulated genes. GO analysis indicated that the DEGs were mainly correlated with collagen-containing extracellular matrix (ECM), Wnt signaling pathway, and glycosaminoglycan binding. GSEA indicated that the treatment group was correlated with chromosome segregation and cell cycle while the control group was correlated with cornification, intermediate filament, and nuclear transcription. Through machine learning, SYNM , TGFBR3 , and COL10A1 were screened as key genes. Numbers of CD8 T cells, gamma delta T cells, and M1 macrophages were significantly higher, while monocytes and follicular helper-T cells were significantly lower in the treatment group. The downregulated genes, SYNM and TGFBR3 , were positively correlated with CD8 T cells and monocytes, but were negatively correlated with gamma delta T cells and M1 macrophages. The upregulated gene, COL10A1 , was positively correlated with gamma delta T cells and M1 macrophages, and was negatively correlated with CD8 T cells, monocytes, and follicular helper-T cells. Conclusions SYNM , TGFBR3 , and COL10A1 are diagnostic genes of breast cancer. They affect breast cancer cells by modulating immune-infiltrating cells.

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Section: Introductionmentioning

confidence: 99%

Identification of Key Genes and Key Pathways in Breast Cancer Based on Machine Learning

Bao

2022

Med Sci Monit

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“…We found the co-association with four genes between the Romboutsia genus and Ruminococcus gnavus at the strict threshold ( p < 5 × 10 –4 ). It is reported that ITGB2 had a powerful influence on immune cell infiltration into the acute myeloid leukemia (AML) tumor microenvironment ( Wei et al, 2021 ). OTULIN , coding the M1-specific deubiquitinase, is essential for preventing TNF-associated systemic inflammation in humans and mice and critical for restraining life-threatening spontaneous inflammation and maintaining immune homeostasis ( Damgaard et al, 2016 ; Hoste et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

The host genetics affects gut microbiome diversity in Chinese depressed patients

Han

Ji²,

Wang³

et al. 2023

Front. Genet.

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The gut microbiome and host genetics are both associated with major depressive disorder (MDD); however, the molecular mechanisms among the associations are poorly understood, especially in the Asian, Chinese group. Our study applied linear discriminant analysis (LDA) effect size (LEfSe) and genome-wide association analysis in the cohort with both gut sequencing data and genomics data. We reported the different gut microbiota characteristics between MDD and control groups in the Chinese group and further constructed the association between host genetics and the gut microbiome. Actinobacteria and Pseudomonades were found more in the MDD group. We found significant differences in the ACE and Chao indexes of alpha diversity while no discrepancy in beta diversity. We found three associations between host genetics with microbiome features: beta diversity and rs6108 (p = 8.65 × 10–9), Actinobacteria and rs77379751 (p = 8.56 × 10–9), and PWY-5913 and rs1775633082 (p = 4.54 × 10–8). A species of the Romboutsia genus was co-associated with the species of Ruminococcus gnavus in an internetwork through four genes: METTL8, ITGB2, OTULIN, and PROSER3, with a strict threshold (p < 5 × 10–4). Furthermore, our findings suggested that the gut microbiome diversity might affect microRNA expression in the brain and influenced SERPINA5 and other spatially close genes afterward. These findings suggest new linkages between depression and gut microbiome in Asian, Chinese people, which might be mediated by genes and microRNA regulation in space distance.

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“…In addition, ITGB2 was proved to promote the effects of the migration capability and invasion capability of breast cancer via biological function experiments [ 30 ]. ITGB2 was identified to be the key immune-related gene in acute myeloid leukemia patients, while the high expression of ITGB2 was related to the poor prognosis of acute myeloid leukemia patients [ 31 ]. These results indicated that ITGB2 plays significant roles in tumorigenesis and progression.…”

Section: Discussionmentioning

confidence: 99%

The Profile and Clinical Significance of ITGB2 Expression in Non-Small-Cell Lung Cancer

Zhou

et al. 2022

JCM

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Integrins are involved in extracellular and intracellular signaling and are often aberrantly expressed in tumors. Integrin beta 2 (ITGB2) has previously been demonstrated to be correlated with the host defense. However, the expression profile and role of ITGB2 in non-small-cell lung cancer (NSCLC) remain unclear. Here, we found that the genetic alterations in ITGB2 was predominated by gene mutation and copy number deletion using cBioPortal analysis, and its expression was downregulated in the NSCLC tissues, as validated by the UALCAN, TCGA, and GEO databases and our tissue samples. Kaplan–Meier (KM) plotter analysis revealed that patients with a lower ITGB2 expression had a shorter overall survival (OS) time (p = 0.01). Moreover, 1089 differentially expressed genes (DEGs) in the NSCLC tissues were screened using the TCGA database. The GO and KEGG enrichment analysis showed that the DEGs were closely associated with immune processes and cell adhesion. The protein–protein interaction (PPI) network revealed that 10 of 15 EMT-related genes among the DEGs might lead to the metastasis of NSCLC. Concomitantly, the expression of ITGB2 was positively correlated with the infiltration of Treg cells and Myeloid-derived suppressor cells (MDSC). Biologically, the ectopic expression of ITGB2 significantly inhibited the proliferation and metastasis of NSCLC cells. Mechanistically, we demonstrated that ITGB2 suppressed the expression of N-cadherin, Vimentin, Slug, Snail, and Twist, while it promoted E-cadherin expression, according to gain-of-function studies. In conclusion, ITGB2 can inhibit the proliferation and migration of NSCLC cells, leading to a poor prognosis, via epithelial–mesenchymal transition (EMT) signaling.

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Key immune-related gene ITGB2 as a prognostic signature for acute myeloid leukemia

Cited by 22 publications

References 38 publications

Identification of Key Genes and Key Pathways in Breast Cancer Based on Machine Learning

Identification of Key Genes and Key Pathways in Breast Cancer Based on Machine Learning

The host genetics affects gut microbiome diversity in Chinese depressed patients

The Profile and Clinical Significance of ITGB2 Expression in Non-Small-Cell Lung Cancer

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