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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1478491
This article is part of the Research Topic Lung Adenocarcinoma: From Genomics to Immunotherapy, Volume II View all articles

Gene expression-based modeling of overall survival in Black or African American patients with lung adenocarcinoma

Provisionally accepted
Bin Zhu Bin Zhu Stephanie S. Mchale Stephanie S. Mchale Michelle V. Scoyk Michelle V. Scoyk Gregory Riddick Gregory Riddick Pei-Ying Wu Pei-Ying Wu Chu- Fang Chou Chu- Fang Chou Ching-Yi Chen Ching-Yi Chen Robert A. Winn Robert A. Winn *
  • Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, United States

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

    Lung cancer is a leading cause of cancer-related deaths worldwide. Black/African American (B/AA) populations, in particular, exhibit the highest incidence and mortality rates of lung adenocarcinoma (LUAD) in the United States. This study aims to explore gene expression patterns linked to LUAD in B/AA and case-matched white patients, with the goal of developing predictive models for prognosis. Leveraging RNA sequencing data from The Cancer Genome Atlas (TCGA) database, genes and pathways associated with overall survival (OS) were identified. The OSassociated genes in B/AA patients were distinct from those in white patients, showing predominant enrichment in immune-related pathways. Furthermore, mRNA co-expression network analysis revealed that OS-associated genes in B/AA patients had higher levels of interaction with various pathways, including those related to immunity, cell-ECM interaction, and specific intracellular signaling pathways. Notably, a potential B/AA-specific biomarker, C9orf64, demonstrated significant correlations with genes involved in immune response. Unsupervised machine learning algorithms stratified B/AA patients into groups with distinct survival outcomes, while supervised algorithms demonstrated a higher accuracy in predicting survival for B/AA LUAD patients compared to white patients. Further validation and clinical application of these findings are warranted to address disparities and improve outcomes in diverse patient populations.

    Keywords: Lung Adenocarcinoma, Race disparity, African American, immune response, machine learning, overall survival, Expression Network

    Received: 09 Aug 2024; Accepted: 16 Oct 2024.

    Copyright: © 2024 Zhu, Mchale, Scoyk, Riddick, Wu, Chou, Chen and Winn. 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: Robert A. Winn, Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, United States

    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.