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Research Paper|Volume 15, Issue 18|pp 9633—9660

A novel risk model of three gefitinib-related genes FBP1, SBK1 and AURKA is related to the immune microenvironment and is predicting prognosis of lung adenocarcinoma patients

Qiang Guo1,2, Kai Li3, Ni Jiang4, Rui Zhou5,6, Xin-Rui Rao5,6, Chuang-Yan Wu1
  • 1Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  • 2Department of Cardiothoracic Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
  • 3Department of Hepatobiliary and Pancreatic Surgery, The People’s Hospital of Jianyang City, Jianyang, China
  • 4Department of Obstetrics and Gynecology, Women and Children’s Hospital of Chongqing Medical University, Chongqing, China
  • 5Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  • 6Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
* Co-first authors
Received: May 24, 2023Accepted: August 21, 2023Published: September 21, 2023
https://doi.org/10.18632/aging.205040

Copyright: © 2023 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Purpose: Gefitinib, an anticancer drug, has been reported to potentially improve the prognosis of patients with lung adenocarcinoma (LUAD). This study aims to investigate the roles and mechanisms of Gefitinib.

Methods: The effects of Gefitinib on the growth and migration of LUAD cells were assessed using various methods, including CCK-8, flow cytometry, wound healing, and Transwell assays. To analyze the function and mechanisms of the differentially expressed Gefitinib target genes (GTGs), data from the TCGA database were utilized. Kaplan-Meier survival and ROC analysis identified prognostic-related GTGs and constructed a prognostic nomogram in LUAD. Consensus clustering, COX analysis and survival analysis evaluated the relationship between GTGs and the prognosis of LUAD patients. The mechanisms of the risk model involved LUAD progression, and the relationship between the risk model and immune microenvironment were investigated.

Results: Gefitinib could inhibit proliferation, migration and invasion and promote cell apoptosis. 84 DEGTGs were involved in RAS, MAPK, ERBB pathways. The DEGTGs (FBP1, SBK1, and AURKA) were the independent risk factors for dismal prognosis of LUAD patients and were used to establish risk model and nomogram. Gefitinib could promote the expression of FBP1 and inhibit the expression of SBK1 and AURKA. High-risk LUAD patients had the dismal prognosis, and the high-risk score group was significantly associated with the immune microenvironment.

Conclusion: FBP1, SBK1, and AURKA are prognostic risk factors, and the risk model and nomogram of FBP1, SBK1 and AURKA are associated with dismal prognosis and immune cell infiltration, and have huge prospects for application in evaluating the prognosis in LUAD.