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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1481753
This article is part of the Research Topic Advancements in Multi-Omics and Bioinformatics for the Management of Solid Malignancies View all 13 articles

Comprehensive Multi-omics Analysis Identifies Chromatin Regulator-Related Signatures and TFF1 as a Therapeutic Target in Lung Adenocarcinoma Through a 429-Combination Machine Learning Approach

Provisionally accepted
Jun Fan Jun Fan 1BoGuang Chen BoGuang Chen 2*Hao Wu Hao Wu 3Liang XQ Liang XQ 4*Xiaye Miao Xiaye Miao 5*Wen Shen Wen Shen 6*
  • 1 First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
  • 2 Oncology Department I, Huai'an 82 hospital, Jiangsu, China
  • 3 Department of Oncology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Jiangsu, China
  • 4 Capital Medical University, Beijing, Beijing Municipality, China
  • 5 Department of Laboratory Medicine, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, China
  • 6 Department of Respiratory Diseases,the Affiliated Huai'an Hospital of Xuzhou Medical University, Jiangsu, China

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

    Lung cancer is a leading cause of cancer-related deaths, with its incidence on the rise. Chromatin remodeling, a key process in gene expression, plays a vital role in the development and progression of malignant tumors. This study highlights the role of chromatin regulators (CRs) in lung adenocarcinoma (LUAD) and introduces a novel prognostic model. Using a 429-combination machine learning approach, we developed a chromatin regulator-related signature (CRRS) that accurately predicts survival outcomes in LUAD patients, validated across multiple independent datasets. CRRS was also found to influence the immune microenvironment, affecting immune cell infiltration in LUAD. High-risk patients showed increased activity in cell cycle and DNA repair processes, with significant differences in mutations and immune responses compared to low-risk patients. Among the identified chromatin regulators, TFF1 emerged as a promising therapeutic target. To confirm its role, we used siRNA to reduce TFF1 expression in LUAD cells, followed by apoptosis analysis, proliferation assays, and in vivo tumor growth studies. Further tests, including Ki67 expression and TUNEL assays, confirmed the impact of TFF1 on tumor growth and cell death. These findings suggest that TFF1 is a key target for new treatment strategies in LUAD.

    Keywords: Lung Adenocarcinoma, machine learning, TFF1, multi-omics, chromatin regulator

    Received: 16 Aug 2024; Accepted: 07 Oct 2024.

    Copyright: © 2024 Fan, Chen, Wu, XQ, Miao and Shen. 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:
    BoGuang Chen, Oncology Department I, Huai'an 82 hospital, Jiangsu, China
    Liang XQ, Capital Medical University, Beijing, 100069, Beijing Municipality, China
    Xiaye Miao, Department of Laboratory Medicine, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Jiangsu, China
    Wen Shen, Department of Respiratory Diseases,the Affiliated Huai'an Hospital of Xuzhou Medical University, Jiangsu, 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.