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ORIGINAL RESEARCH ARTICLE

Integrated bioinformatics analysis of key genes and pathways in lung adenocarcinoma occurrence, development, and survival

Wenyang He1 ,  Chao Liu1 ,  Ce Zhang1 ,  Chaochao Wang1*
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1 Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan , China
EJMO, 026260301 https://doi.org/10.36922/EJMO026260301
Received: 23 June 2026 | Revised: 2 September 2026 | Accepted: 8 October 2026 | Published online: 23 October 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Introduction: Lung adenocarcinoma (LUAD) remains a major cause of cancer mortality, and robust molecular markers are still needed to improve risk stratification.

Objective: This study aimed to identify reproducible genes and pathways associated with LUAD development and patient outcome through an integrated analysis of public transcriptomic datasets.

Methods: Four Gene Expression Omnibus (GEO) datasets (GSE10072, GSE18842, GSE19804, and GSE33532) were used as discovery cohorts to identify common tumor-versus-non-tumor differentially expressed genes (DEGs), followed by LUAD-specific external evaluation. Common DEGs were integrated by protein–protein interaction analysis and functional enrichment. Candidate hub genes were subsequently evaluated for expression, pathological stage, overall survival, promoter methylation, immune-cell infiltration, and smoking-related expression using Gene Expression Profiling Interactive Analysis, University of Alabama at Birmingham Cancer Data Analysis Portal, and Tumor Immune Estimation Resource.

Results: We identified 151 common DEGs, including 33 upregulated and 118 downregulated genes. Enrichment analysis highlighted extracellular matrix-related functions and the phosphoinositide 3-kinase–protein kinase B signaling pathway. Eight hub genes were associated with overall survival. Among them, ZWINT, TOP2A, RRM2, NEK2, and AGER showed additional associations with promoter methylation and/or immune infiltration. Smoking-related expression differences were observed for ZWINT, TOP2A, RRM2, and NEK2, whereas AGER showed no significant association with smoking status.

Conclusion: Integrating four independent GEO datasets with complementary validation analyses identified ZWINT, TOP2A, RRM2, NEK2, and AGER as reproducible LUAD-associated candidates. These findings support further experimental and clinical validation of these genes as potential components of biomarker panels rather than as established therapeutic targets.

Keywords
Bioinformatics analysis
Differentially expressed genes
Lung adenocarcinoma
Funding
None.
Conflict of interest
All authors declare no conflicts of interest in this paper.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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