ArticleJournal of thoracic disease2024
Bioinformatic analysis of differentially expressed genes in lung cancer bone metastasis and their implications for disease progression in lung cancer patients.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Identification of PD-1-Related Genes as Prognostic Biomarkers in Lung Adenocarcinoma.Human mutation · 2026Article
- Factors associated with survival in patients with spinal metastases from lung cancer.American journal of cancer research · 2025Article
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Authors and funding
6 authors.
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Abstract
Background: Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death worldwide. Moreover, it is highly susceptible to distant metastasis, which is the main cause of pain in advanced lung cancer, and frequently occurs in the bone. This study aimed to identify the differentially expressed genes (DEGs) related to metastatic bone disease in lung cancer using bioinformatics methods and to analyze the risk factors influencing the incidence of secondary bone metastasis in lung cancer. Methods: Gene expression profiles from the GSE175601 and GSE10799 datasets in the Gene Expression Omnibus (GEO) database were analyzed to screen for the DEGs associated with lung cancer bone metastasis. The STRING database was used to construct a protein-protein interaction (PPI) network, and the MCODE plugin was used to identify the key genes. The expression of these important genes in lung tumor tissues and their correlation with prognosis were validated in The Cancer Genome Atlas (TCGA) database. An examination of clinical data from patients diagnosed with stage IV lung adenocarcinoma treated at the Anhui No. 2 Provincial People's Hospital was conducted. Immunohistochemistry was used to examine the expression of key genes in lung cancer tumor tissues. A binary logistic regression analysis was conducted to examine the interactions in the expression of critical genes associated with bone metastasis in lung carcinoma patients. Results: In total, 59 DEGs were identified in the GSE175601 and GSE10799 datasets through Venn diagram construction. The PPI network analysis revealed two significant modules and eight candidate genes ( Conclusions: The key gene
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