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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Respiratory Pharmacology
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1441233
This article is part of the Research Topic Applications of AI, Machine Learning, Computational Medicine, and Bioinformatics in Respiratory Pharmacology View all 4 articles

Therapeutic targets for lung cancer: Genome-wide Mendelian randomization and colocalization analyses

Provisionally accepted
Yi Luan Yi Luan 1Wenjian Wang Wenjian Wang 1*Wenjun Song Wenjun Song 1*Desheng Xian Desheng Xian 2Changwen Zhao Changwen Zhao 2Xin Qing Xin Qing 3Hanlin He Hanlin He 4*Kaixuan Zheng Kaixuan Zheng 4*Taijiao Jiang Taijiao Jiang 1*Chaohui Duan Chaohui Duan 1*
  • 1 Sun Yat-sen Memorial Hospital, Guangzhou, China
  • 2 Key Laboratory for Nanomaterials of the Ministry of Education, Beijing University of Chemical Technology, Beijing, Beijing Municipality, China
  • 3 West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
  • 4 Guangzhou National Laboratory, Guangzhou, China

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

    Background: Lung cancer, categorized into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), remains a significant global health challenge. The development of drug resistance and the heterogeneity of the disease necessitate the identification of novel therapeutic targets to improve patient outcomes. Methods: We conducted a genome-wide Mendelian randomization (MR) and colocalization analysis using a comprehensive dataset of 4,302 druggable genes and cis-expressed quantitative trait loci (cis-eQTLs) from 31,884 blood samples. The study integrated genomic analysis with eQTL data to identify key genes associated with lung cancer risk. Results: The analysis revealed five actionable therapeutic targets for NSCLC, including LTB4R, LTBP4, MPI, PSMA4, and TCN2. Notably, PSMA4 demonstrated a strong association with both NSCLC and SCLC risks, with odds ratios of 3.168 and 3.183, respectively. Colocalization analysis indicated a shared genetic etiology between these gene expressions and lung cancer risk. Conclusion: Our findings contribute to precision medicine by identifying druggable targets that may be exploited for subtype-specific lung cancer therapies.

    Keywords: lung cancer, drug target, Mendelian randomization, GWAS, colocalization analyses

    Received: 30 May 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 Luan, Wang, Song, Xian, Zhao, Qing, He, Zheng, Jiang and Duan. 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:
    Wenjian Wang, Sun Yat-sen Memorial Hospital, Guangzhou, China
    Wenjun Song, Sun Yat-sen Memorial Hospital, Guangzhou, China
    Hanlin He, Guangzhou National Laboratory, Guangzhou, China
    Kaixuan Zheng, Guangzhou National Laboratory, Guangzhou, China
    Taijiao Jiang, Sun Yat-sen Memorial Hospital, Guangzhou, China
    Chaohui Duan, Sun Yat-sen Memorial Hospital, Guangzhou, 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.