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

Front Immunol. 2024 Oct 30:15:1481753. doi: 10.3389/fimmu.2024.1481753. eCollection 2024.

Abstract

Introduction: Lung cancer is a leading cause of cancer-related deaths, with its incidence continuing to rise. Chromatin remodeling, a crucial process in gene expression regulation, plays a significant role in the development and progression of malignant tumors. However, the role of chromatin regulators (CRs) in lung adenocarcinoma (LUAD) remains underexplored.

Methods: This study developed a chromatin regulator-related signature (CRRS) using a 429-combination machine learning approach to predict survival outcomes in LUAD patients. The CRRS model was validated across multiple independent datasets. We also investigated the impact of CRRS on the immune microenvironment, focusing on immune cell infiltration. To identify potential therapeutic targets, TFF1, a chromatin regulator, was knocked down using siRNA in LUAD cells. We assessed its impact through apoptosis analysis, proliferation assays, and in vivo tumor growth studies. Additional validation was performed using Ki67 expression and TUNEL assays.

Results: The CRRS accurately predicted survival outcomes and was shown to modulate immune cell infiltration in the tumor microenvironment. High-risk patients demonstrated increased activity in cell cycle regulation and DNA repair pathways, along with distinct mutation profiles and immune responses compared to low-risk patients. TFF1 emerged as a key therapeutic target. Knockdown of TFF1 significantly inhibited LUAD cell proliferation, induced apoptosis, and suppressed in vivo tumor growth. Ki67 and TUNEL assays confirmed the role of TFF1 in regulating tumor growth and cell death.

Discussion: These findings highlight the potential of chromatin regulators in prognostic modeling and immune modulation in LUAD. TFF1 was identified as a promising therapeutic target, suggesting that targeting TFF1 could provide new treatment strategies. Further research is warranted to explore its full potential and therapeutic applicability.

Keywords: TFF1; chromatin regulator; lung adenocarcinoma; machine learning; multi-omics.

MeSH terms

  • Adenocarcinoma of Lung* / genetics
  • Adenocarcinoma of Lung* / immunology
  • Adenocarcinoma of Lung* / mortality
  • Adenocarcinoma of Lung* / pathology
  • Animals
  • Apoptosis / genetics
  • Biomarkers, Tumor / genetics
  • Cell Line, Tumor
  • Cell Proliferation / genetics
  • Chromatin / genetics
  • Chromatin / metabolism
  • Female
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Lung Neoplasms* / genetics
  • Lung Neoplasms* / immunology
  • Lung Neoplasms* / mortality
  • Lung Neoplasms* / pathology
  • Lung Neoplasms* / therapy
  • Machine Learning*
  • Mice
  • Multiomics
  • Prognosis
  • Trefoil Factor-1* / genetics
  • Tumor Microenvironment* / genetics
  • Tumor Microenvironment* / immunology
  • Xenograft Model Antitumor Assays

Substances

  • Trefoil Factor-1
  • TFF1 protein, human
  • Biomarkers, Tumor
  • Chromatin

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.