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ITGB2 as a prognostic indicator and a predictive marker for immunotherapy in gliomas

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Abstract

Purpose

Glioma is the most common primary tumor in the brain, accounting for 81% of intracranial malignancies. Nowadays, cancer immunotherapy has become a novel and revolutionary treatment for patients with advanced, highly aggressive tumors. However, to date, there are no effective biomarkers to reflect the response of glioma patients to immunotherapy. In this study, we aimed to assess the clinical predictive value of ITGB2 in patients with glioma.

Methods

The correlation between ITGB2 expression levels and glioma progression was explored and validated using data from CGGA, TCGA, GEO datasets, and patient samples from our hospital. Univariate and multivariate cox regression models were developed to determine the predictive role of ITGB2 on the prognosis of patients with glioma. The relationship between ITGB2 and immune activation was then analyzed. Finally, we predicted the immunotherapy response in both high and low ITGB2 expression subgroups.

Results

ITGB2 was significantly elevated in gliomas with higher malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio for ITGB2 expression (low versus high) was 0.71 with 95% CI (0.59–0.85) (P < 0.001). Furthermore, we found that ITGB2 stratified glioma patients into high and low ITGB2 expression subgroups, exhibiting different clinical outcomes and immune activation status. At last, we demonstrated that glioma patients with high ITGB2 expression levels had better immunotherapy response.

Conclusions

This study demonstrated ITGB2 as a novel predictor for clinical prognosis and response to immunotherapy in gliomas. Assessing expression levels of ITGB2 is a promising method to discover patients that may benefit from immunotherapy.

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Data availability

Publicly available datasets were analyzed in this study. These data can be found here: http://gliovis.bioinfo.cnio.es/ and http://tisch.comp-genomics.org/home/. The supplementary material for this article can be found online. All processed data and R codes used in this study can be obtained from the corresponding author on reasonable request.

Abbreviations

ACC:

Adrenocortical carcinoma

BLCA:

Bladder urothelial carcinoma

BP:

Biological process

BRCA:

Breast invasive carcinoma

CC:

Cellular component

CESC:

Cervical squamous cell carcinoma and endocervical adenocarcinoma

CGGA:

Chinese glioma genome atlas

CHOL:

Cholangiocarcinoma

COAD:

Colon adenocarcinoma

CTLA4:

Cytotoxic T-lymphocyte-associated protein 4

DEG:

Differential expressed gene

DLBC:

Lymphoid neoplasm diffuse large B-cell lymphoma

DSS:

Disease-specific survival

ESCA:

Esophageal carcinoma

GBM:

Glioblastoma multiforme

GEO:

Gene expression omnibus

GO:

Gene ontology

GSEA:

Gene set enrichment analysis

HNSC:

Head and neck squamous cell carcinoma

ImmuCellAI:

Immune cells abundance identifier

ICB:

Immune checkpoint blockades

IDO1:

Indoleamine 2,3-dioxygenase

IHC:

Immunohistochemistry

ITGB2:

Integrin beta-2

KEGG:

Kyoto encyclopedia of genes and genomes

KICH:

Kidney chromophobe

KIRC:

Kidney renal clear cell carcinoma

KIRP:

Kidney renal papillary cell carcinoma

LCML:

Chronic myelogenous leukemia

LGG:

Brain lower-grade glioma

LIHC:

Liver hepatocellular carcinoma

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

MESO:

Mesothelioma

MF:

Molecular function

OS:

Overall survival

OV:

Ovarian serous cystadenocarcinoma

PAAD:

Pancreatic adenocarcinoma

PCPG:

Pheochromocytoma and paraganglioma

PD-1:

Programmed cell death 1

PDL-1 (CD274):

Programmed cell death 1 ligand 1

PFI:

Progression-free interval

PRAD:

Prostate adenocarcinoma

READ:

Rectum adenocarcinoma

SARC:

Sarcoma

SKCM:

Skin cutaneous melanoma

SubMap:

Subclass mapping

STAD:

Stomach adenocarcinoma

TCGA:

The cancer genome atlas

TGCT:

Testicular germ cell tumors

THCA:

Thyroid carcinoma

THYM:

Thymoma

TIICs:

Tumor-infiltrating immune cells

TIGIT:

T cell immunoreceptor with Ig and ITIM domains

TIM3 (HAVCR2):

(T-cell immunoglobulin and mucin domain-containing protein 3/hepatitis A virus cellular receptor 2)

TIS:

T-cell inflammation

UCEC:

Uterine corpus endometrial carcinoma

UCS:

Uterine carcinosarcoma

UVM:

Uveal melanoma

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Acknowledgements

The authors would like to thank Zhenyu Song from Fudan University for his generous and outstanding help in this research design. We thank openbiox community and Hiplot team (https://hiplot.com.cn) for providing technical assistance and valuable tools for data analysis and visualization.

Funding

This research was funded by Natural Science Foundation of Shanghai (18ZR1430400).

Author information

Author notes

  1. Houshi Xu, Anke Zhang and Xiaying Han have contributed equally.

Authors and Affiliations

  1. Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China

    Houshi Xu, Anke Zhang & Meiqing Lou

  2. Department of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Zhejiang, China

    Houshi Xu, Anke Zhang & Zeyu Zhang

  3. Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, 650 New Songjiang Road, Shanghai, 200080, China

    Xiaying Han

  4. Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yanning Li

  5. Shanxi Medical University, Taiyuan, 030001, Shanxi, China

    Liying Song

  6. Department of Urology, First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China

    Wei Wang

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  1. Houshi Xu
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Contributions

HX, AZ and XH collected clinical data. HX, WW performed data analysis. HX, AZ and ML designed and wrote the manuscript. WW and ML contributed to discussion.

Corresponding authors

Correspondence to Wei Wang or Meiqing Lou.

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Conflict of interests

The authors declare that they have no competing interests.

Ethical approval

The study was approved by the Human Investigation Ethical Committee of Shanghai General Hospital, and the written informed consent was obtained from all patients.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Figure 1.

ITGB2 expression and correlation with immune signatures in pan-cancer. (a) ITGB2 has a significant relationship with tumor microenvironment in various tumors. Supplementary Figure 2. ITGB2 involved in remodeling tumor immune microenvironment.(a) U-MAP plot demonstrating clustering obtained from single cell RNA sequencing (GSE131928). Clusters annotations: CD8Tex, Mono/Macro, AC-like Malignant, MES-like Malignant, OPC-like Malignant, NPC-like Malignant, Oligodendrocyte. (b-c) ITGB2 specifically expressed in Mono/Macro and CD8Tex. (d-e) GEPIA2021 demonstrated that ITGB2's expression was significantly higher in M2 macrophages compared to M0 and M1 macrophages. Supplementary Figure 3. Gene function enrichment analysis between ITGB2 high and low subgroups. (a) KEGG results for differential expression genes between ITGB2 high and low subgroups. The X-axis represents gene counts and the Y-axis represents different enriched pathways (log2 fold change> 1.5). (b) GO (Gene Ontology) results for differential expression genes (Cut-off criteria for DEGs significance was adj. p value< 0.05 and the absolute value of the log2 fold change> 1.5). The X-axis represents gene ratio and the Y-axis represents different enriched pathways (BP: biological progress; CC: cellular component; MF: molecular function). (c) The plot of top three GO pathways for differential expression genes. (d) Rank-based gene set enrichment analysis shows significantly activated immune related pathways in ITGB2 high subgroup compared with ITGB2 low subgroup (LFC, log fold change). Supplementary Figure 4. Validation of correlation between ITGB2 and immune checkpoints in CGGA and TCGA cohorts. (a) The correlation between PDCD1, CD274, CTLA4 and ITGB2 in TCGA dataset. (b) Correlation between mRNA expression of ITGB2 and PDCD1, CTLA4, TIM3 (HAVCR2), IDO1 and TIGIT in tumor tissues obtained from glioma patients (n = 20). (c) The correlation between TIGIT, IDO1, TIM3(HAVCR2) and ITGB2 in CGGA dataset. (d) The correlation between TIGIT, IDO1, TIM3(HAVCR2) and ITGB2 in TCGA dataset. Supplementary Figure 5. Association between ITGB2 expression and immunotherapeutic response. (a) ROC curves for ITGB2 in predicting the immunotherapy response of glioma patients. (PDF 10377 KB)

Supplementary Sheet 1.

Marker genes of 28 immune cells. Supplementary Sheet 2. Marker genes of 25 immune-related pathways. Supplementary Sheet 3. Marker genes of the steps of the cancer immunity cycle. Supplementary Sheet 4. Marker genes of immunotherapy-predicted pathways. Supplementary Sheet 5. Marker genes of TIICs (Based on ImmuCellAI). Supplementary Sheet 6. Marker genes of TIS scores. Supplementary Sheet 7. GSEA result of ITGB2 high vs low subgroup. (XLSX 66 KB)

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Xu, H., Zhang, A., Han, X. et al. ITGB2 as a prognostic indicator and a predictive marker for immunotherapy in gliomas. Cancer Immunol Immunother 71, 645–660 (2022). https://doi.org/10.1007/s00262-021-03022-2

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