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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1446273

Identification of Novel Markers for Neuroblastoma Immunoclustering Using Machine Learning

Provisionally accepted
Longguo Zhang Longguo Zhang 1Huixin Li Huixin Li 1Qiuping Wu Qiuping Wu 1Leigang Jin Leigang Jin 1Jiarui Chen Jiarui Chen 2Ranyao Yang Ranyao Yang 1*
  • 1 The University of Hong Kong, Pokfulam, Hong Kong, SAR China
  • 2 Sun Yat-sen University, Guangzhou, Guangdong Province, China

The final, formatted version of the article will be published soon.

    Background: Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated, and there is a lack of biomarkers to reflect the condition of the tumor immune microenvironment.The GEO Database was used to download transcriptome data (both training dataset and test dataset) on neuroblastoma. Immunity scores were calculated for each sample using ssGSEA, and hierarchical clustering was used to categorize the samples into high and low immunity groups. Subsequently, the differences in clinicopathological characteristics and treatment between the different groups were examined. Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.In the training set, there were 362 samples in the immunity_L group and 136 samples in the immunity_H group, with differences in age, MYCN status, etc. Additionally, the tumor microenvironment can also affect the therapeutic response of neuroblastoma. Six characteristic genes (BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM) were identified by machine learning, and these genes are associated with multiple immune-related pathways and immune cells in neuroblastoma.Conclusions: BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM may serve as biomarkers that reflect the conditions of the immune microenvironment of neuroblastoma and hold promise in guiding neuroblastoma treatment.

    Keywords: biomarker, Tumor Microenvironment, Immunoclustering, machine learning, Neuroblastoma

    Received: 09 Jun 2024; Accepted: 15 Oct 2024.

    Copyright: © 2024 Zhang, Li, Wu, Jin, Chen and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ranyao Yang, The University of Hong Kong, Pokfulam, Hong Kong, SAR China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.