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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1501486
This article is part of the Research Topic Deciphering Cancer Metabolism: A New Frontier in Tumor Immunology with Computational Innovation View all 9 articles

Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma cell

Provisionally accepted
Xiaoxia Zeng Xiaoxia Zeng 1Dunhui Yang Dunhui Yang 1Jin Zhang Jin Zhang 2Kang Li Kang Li 1Xijia Wang Xijia Wang 1Fang Ma Fang Ma 1Xianqin Liao Xianqin Liao 1Zhen Wang Zhen Wang 1Xianhai Zeng Xianhai Zeng 1Peng Zhang Peng Zhang 1*
  • 1 Longgang ENT Hospital, Institute of ENT and Shenzhen Key Laboratory of ENT, Shenzhen, China
  • 2 Second People’s Hospital of Yibin, Yibin, China

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

    Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in close association with the tumor immune microenvironment (TIME). Therefore, identifying effective targets related to HNSC and TIME is of paramount importance. Here, we employed the ESTIMATE algorithm to compute immune and stromal cell scores for HNSC samples from the TCGA database and identified differentially expressed genes (DEGs) based on these scores. Subsequently, we utilized four machine learning algorithms to identify four key genes: ITM2A, FOXP3, WIPF1, and RSPO1 from DEGs. Through a comprehensive pan-cancer analysis, our study identified aberrant expression of ITM2A across various tumor types, with a significant association with the TIME. Specifically, ITM2A expression was markedly reduced and correlated with poor prognosis in HNSC. Functional enrichment analysis revealed that ITM2A is implicated in multiple immune-related pathways, including immune-infiltrating cells, immune checkpoints, and immunotherapeutic responses. ITM2A expression was observed in various immune cell populations through single-cell analysis. Furthermore, we showed that ITM2A overexpression inhibited the growth of HNSC cells. Our results suggest that ITM2A may be a novel prognostic marker associated with TIME.

    Keywords: ITM2A, immune microenvironment, hNSC, machine learning, prognostic

    Received: 25 Sep 2024; Accepted: 25 Nov 2024.

    Copyright: © 2024 Zeng, Yang, Zhang, Li, Wang, Ma, Liao, Wang, Zeng and Zhang. 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: Peng Zhang, Longgang ENT Hospital, Institute of ENT and Shenzhen Key Laboratory of ENT, Shenzhen, 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.