PDF(Pubmed)
Abstract：
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible disease with a high mortality rate worldwide. However, the etiology and pathogenesis of IPF have not yet been fully described. Moreover, lung cancer is a significant complication of IPF and is associated with increased mortality. Nevertheless, identifying common genes involved in developing IPF and its progression to lung cancer remains an unmet need. The present study aimed to identify hub genes related to the development of IPF by meta-analysis. In addition, we analyzed their expression and their relationship with patients\' progression in lung cancer.
METHODS: Microarray datasets GSE24206, GSE21369, GSE110147, GSE72073, and GSE32539 were downloaded from Gene Expression Omnibus (GEO). Next, we conducted a series of bioinformatics analysis to explore possible hub genes in IPF and evaluated the expression of hub genes in lung cancer and their relationship with the progression of different stages of cancer.
RESULTS: A total of 1888 differentially expressed genes (DEGs) were identified, including 1105 upregulated and 783 downregulated genes. The 10 hub genes that exhibited a high degree of connectivity from the PPI network were identified. Analysis of the KEGG pathways showed that hub genes correlate with pathways such as the ECM-receptor interaction. Finally, we found that these hub genes are expressed in lung cancer and are associated with the progression of different stages of lung cancer.
CONCLUSIONS: Based on the integration of GEO microarray datasets, the present study identified DEGs and hub genes that could play an essential role in the pathogenesis of IPF and its association with the development of lung cancer in these patients, which could be considered potential diagnostic biomarkers or therapeutic targets for the disease.
METHODS: Microarray datasets GSE24206, GSE21369, GSE110147, GSE72073, and GSE32539 were downloaded from Gene Expression Omnibus (GEO). Next, we conducted a series of bioinformatics analysis to explore possible hub genes in IPF and evaluated the expression of hub genes in lung cancer and their relationship with the progression of different stages of cancer.
RESULTS: A total of 1888 differentially expressed genes (DEGs) were identified, including 1105 upregulated and 783 downregulated genes. The 10 hub genes that exhibited a high degree of connectivity from the PPI network were identified. Analysis of the KEGG pathways showed that hub genes correlate with pathways such as the ECM-receptor interaction. Finally, we found that these hub genes are expressed in lung cancer and are associated with the progression of different stages of lung cancer.
CONCLUSIONS: Based on the integration of GEO microarray datasets, the present study identified DEGs and hub genes that could play an essential role in the pathogenesis of IPF and its association with the development of lung cancer in these patients, which could be considered potential diagnostic biomarkers or therapeutic targets for the disease.
摘要：
背景：特发性肺纤维化（IPF）是一种慢性，进步,和全球高死亡率的不可逆疾病。然而,IPF的病因和发病机制尚未完全描述。此外，肺癌是IPF的重要并发症,并与死亡率增加相关.然而，鉴定与IPF发展及其发展为肺癌有关的常见基因仍未满足。本研究旨在通过荟萃分析鉴定与IPF发生发展相关的hub基因。此外,我们分析了它们的表达及其与肺癌患者进展的关系。
方法:微阵列数据集GSE24206、GSE21369、GSE110147、GSE72073和GSE32539从基因表达Omnibus(GEO)下载。接下来,我们进行了一系列生物信息学分析，以探索IPF中可能的hub基因，并评估hub基因在肺癌中的表达及其与癌症不同阶段进展的关系。
结果：共鉴定出1888个差异表达基因（DEG），包括1105个上调基因和783个下调基因。鉴定了表现出来自PPI网络的高度连通性的10个hub基因。KEGG途径的分析表明，hub基因与ECM-受体相互作用等途径相关。最后,我们发现这些hub基因在肺癌中表达，并且与肺癌不同阶段的进展有关。
结论：基于GEO微阵列数据集的集成，本研究确定了DEGs和hub基因，这些基因可能在IPF的发病机制中起重要作用，并与这些患者的肺癌发展有关。这可能被认为是该疾病的潜在诊断生物标志物或治疗靶标。
方法:微阵列数据集GSE24206、GSE21369、GSE110147、GSE72073和GSE32539从基因表达Omnibus(GEO)下载。接下来,我们进行了一系列生物信息学分析，以探索IPF中可能的hub基因，并评估hub基因在肺癌中的表达及其与癌症不同阶段进展的关系。
结果：共鉴定出1888个差异表达基因（DEG），包括1105个上调基因和783个下调基因。鉴定了表现出来自PPI网络的高度连通性的10个hub基因。KEGG途径的分析表明，hub基因与ECM-受体相互作用等途径相关。最后,我们发现这些hub基因在肺癌中表达，并且与肺癌不同阶段的进展有关。
结论：基于GEO微阵列数据集的集成，本研究确定了DEGs和hub基因，这些基因可能在IPF的发病机制中起重要作用，并与这些患者的肺癌发展有关。这可能被认为是该疾病的潜在诊断生物标志物或治疗靶标。
