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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Thoracic Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1441235

AGTR1: a potential biomarker associated with the occurrence and prognosis of lung adenocarcinoma

Provisionally accepted
Rui Xiao Rui Xiao Jiajia Han Jiajia Han Yongjian Deng Yongjian Deng Ling Zhang Ling Zhang Ying Qian Ying Qian Nan Tian Nan Tian Zhen Yang Zhen Yang Lin Zhang Lin Zhang *
  • Zhejiang Chinese Medical University, Hangzhou, China

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

    Lung adenocarcinoma, a disease with complex pathogenesis, high mortality and poor prognosis, is one of the subtypes of lung cancer. Hence, it is very crucial to find novel biomarkers as diagnostic and therapeutic targets for LUAD. We obtained 631 differentially expressed genes (DEGs) in the GSE10072 expression profile. 623 intersection genes were obtained by intersecting DEGs with major module genes selected through WGCNA. Based on intersection genes, five key genes, DUOX1, CD36, AGTR1, FHL5, and SSR4, were selected by three machine learning methods. Then the ROC, COX and qRT-PCR were employed to verify key genes, which revealed a significant relationship between AGTR1 and LUAD occurrence and prognosis. Enrichment analysis showed that AGTR1 was significantly enriched in the GPCR-related pathways, which implied that AGTR1 is likely to play a role in the growth and proliferation of LUAD cells. Immune infiltration analysis showed that the development of LUAD was related to the changes of immune cells such as M2 macrophages and neutrophils, and AGTR1 was involved in the regulation of M2 macrophages and neutrophils, as well as immune chemokines and immune checkpoints such as PECAM1 and ADARB1. Further, the druggene interaction network screened out 13 potential drugs such as Benazepril, Valsartan, Eprosartan, and so on. In summary, AGTR1 is a potential biomarker for the occurrence and development of LUAD and can be regarded as a new target for LUAD diagnosis and treatment.

    Keywords: lung adenocarcinoma1, bioinformatics2, biomarkers3, Machine Learning methods4, AGTR15

    Received: 30 May 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Xiao, Han, Deng, Zhang, Qian, Tian, Yang 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: Lin Zhang, Zhejiang Chinese Medical University, Hangzhou, 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.