Abstract
Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.
Highlights
Lung cancer is a serious and common disease that causes approximately 1.6 million deaths annually and ranks first in causing mortality and morbidity among all cancer types [1]
The GSE10072, GSE31210, and GSE40791 gene expression profiles were integrated from the Gene Expression Omnibus (GEO) database to explore novel biomarkers associated with the pathogenesis and prognosis of lung adenocarcinoma (LUAD) and identify the differentially expressed genes (DEGs) in LUAD and adjacent normal tissue
In prognostic analysis, integrated bioinformatics analysis plays a significant role in hub node discovery from the PPI network and screening of DEGs [34]
Summary
Lung cancer is a serious and common disease that causes approximately 1.6 million deaths annually and ranks first in causing mortality and morbidity among all cancer types [1]. Various intracellular signaling molecules play a significant role in tumor invasion and metastasis, leading to poor prognosis in patients with LUAD [8,9,10,11]. The identification of novel biomarkers and understanding their molecular mechanisms will contribute to enhancing our knowledge regarding the initiation and progression of LUAD. Seven novel biomarkers were identified that might contribute to understanding the molecular mechanisms associated with the occurrence and progression of LUAD. These biomarkers are significant in the diagnosis and prognosis of patients with LUAD. This study aimed to provide new insights into this multi-gene hereditary disease and/or the diagnosis, prognosis, and targeted treatments of LUAD based on the novel biomarkers.
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