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Regulation of prognosis-related Siglecs in the glioma microenvironment

  • Original Article – Cancer Research
  • Published:
Journal of Cancer Research and Clinical Oncology Aims and scope Submit manuscript

Abstract

Purpose

The anti-inflammatory environment of glioma reduces the efficacy of immunotherapies. Therefore, it is vital to transform the immunosuppressive microenvironment of glioma into a pro-inflammatory environment. Sialic acid-binding immunoglobulin-type lectins (Siglecs) can serve as immune checkpoint targets that enhance the anti-tumor immune response. However, the roles of Siglecs in the glioma microenvironment are unknown. This study was conducted to identify targets to inhibit the anti-inflammatory environment to improve therapeutic outcomes in patients with glioma.

Methods

We analyzed the regulatory effect of prognosis-related Siglecs identified from data available in The Cancer Genome Atlas database (TCGA) and China Glioma Genome Atlas Data portal on the immunosuppressive microenvironment of glioma. The effects of prognosis-related Siglecs on the glioma microenvironment were investigated by determining the Pearson correlation coefficients of the Siglecs in transcriptome data from the TCGA database.

Results

Siglec-1, -9, -10, and -14 were closely associated with the prognosis of patients with glioma. The expression of these four Siglecs was significantly increased in the high-risk group and positively correlated with anti-inflammatory cytokine levels in the glioma microenvironment.

Conclusion

Our study provides insights into the effects of prognosis-related Siglecs in glioma immunotherapy, suggesting that targeted prognosis-related Siglecs can modify the microenvironment of glioma and improve the sensitivity of patients with glioma to immunotherapy.

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Abbreviations

CTLA-4:

Cytotoxic T-lymphocyte-associated protein 4

PD-1:

Programmed cell death protein 1

NK:

Natural killer cells

HIV:

Human immunodeficiency virus

HBV:

Hepatitis B virus

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Acknowledgements

Special thanks to Professor Dong Zhou for his guidance, Mr. Zhaohua Zeng for his help, and Dr. Yong Yang and Dr. Peng Wang for their financial support.

Funding

This work was financially supported by Natural Science Foundation of China (NO.81901250), High-level Hospital Construction Project of Guangdong Province of China (NO. DFJH201924) and Natural Science Foundation of Guangdong Province of China (NO.2018A0303130236).

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Authors and Affiliations

  1. School of Medicine, South China University of Technology, Guangzhou, 510006, China

    Rui Mao, Jiantao Zheng & Dong Zhou

  2. Department of Neurosurgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, No. 106, Zhongshan Second Road, Guangzhou, 510080, Guangdong, People’s Republic of China

    Rui Mao, Yong Yang, Peng Wang, Han Lin, Jiantao Zheng, Guangzhao Lv & Dong Zhou

  3. International Department, Affiliated High School of South China Normal University, Guangzhou, China

    Liting Zhou

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Contributions

HL and JZ: extracted the data; RM: methodology, formal analysis, recourses; LZ and GL: software; YY: project administration, funding acquisition; PW: supervision; RM: writing-original draft. All authors reviewed the final manuscript.

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Correspondence to Dong Zhou.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 5 KB) Supplementary Fig. 1 Forest plot of the multivariate Cox regression analysis in glioma.

432_2021_3762_MOESM2_ESM.pdf

Supplementary file2 (PDF 427 KB) Supplementary Fig. 2 Risk score analysis of Siglecs family four-gene prognostic model in CGGA glioma cohort. (A) Survival analysis according to risk score; (B) ROC analysis; (C) heat map; (D) risk score and (E)survival status of patients.

432_2021_3762_MOESM3_ESM.pdf

Supplementary file3 (PDF 323 KB) Supplementary Fig. 3 Risk score of glioma patients with different clinical characteristics (A-E). Multivariate Cox regression analyses of clinical characteristics and risk score associated with OS in the TCGA cohort(F). Multivariate Cox regression analyses of clinical characteristics and risk score associated with OS in the CGGA cohort(G).

432_2021_3762_MOESM4_ESM.pdf

Supplementary file4 (PDF 457 KB) Supplementary Fig. 4 Validation in CGGA. (A)The immune landscapes of high- and low-risk groups. (B) Comparison of immune cell infiltration between high- and low-risk groups.

432_2021_3762_MOESM5_ESM.pdf

Supplementary file5 (PDF 470 KB) Supplementary Fig.5 Analysis of immune cell infiltration. (A) The immune landscapes of IDH-mutant and IDH-wildtype groups. (B) Comparison of immune cell infiltration between IDH-mutant and IDH-wildtype groups.

432_2021_3762_MOESM6_ESM.pdf

Supplementary file6 (PDF 470 KB) Supplementary Fig.6 (A) The immune landscapes of 1p/19q-codel and 1p/19q non-codel groups. (B) Comparison of immune cell infiltration between 1p/19q-codel and 1p/19q non-codel groups.

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Mao, R., Zhou, L., Yang, Y. et al. Regulation of prognosis-related Siglecs in the glioma microenvironment. J Cancer Res Clin Oncol 147, 3343–3357 (2021). https://doi.org/10.1007/s00432-021-03762-9

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