Bioinformatics-based screening of hub genes for prostate cancer bone metastasis and analysis of immune infiltration.

IF 1.3 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL
Shu-Kun Lin, Chen-Ming Zhang, Bo Men, Zhong Hua, Si-Cheng Ma, Fang Zhang
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引用次数: 0

Abstract

Bioinformatics analysis of genes and immune cells that influence prostate cancer (PCa) bone metastases. Using the gene expression omnibus database, we analyzed a PCa bone metastasis dataset. Differentially expressed genes were identified through the utilization of GEO2R and weighted gene co-expression network analysis. Gene set enrichment analysis software was used to identify important pathways. In addition to creating a network of protein-protein interactions, functional enrichment analyses were conducted using Kyoto encyclopedia of genes databases. To screen hub genes, Cytoscape software was used with the CytoHubba plug-in and performed mRNA and survival curve validation analysis of key genes using the cBioPortal website and GEPIA2 database. Immune infiltration analysis was performed using the CIBERSORTx website, and finally, immune cell correlation analysis was performed for key genes according to the TIMER database. A total of 197 PCa bone metastasis risk genes were screened, "G2M_CHECKPOINT" was significantly enriched in PCa bone metastasis samples according to genomic enrichment analysis. Based on the protein interactions network, we have identified 10 alternative hub genes, and 3 hub genes, CCNA2, NUSAP1, and PBK, were validated by the cBioPortal website and the GEPIA2 database. T cells regulatory and macrophages M0 may influence PCa to metastasize to bones, according to CIBERSORTx immune cell infiltration analysis. TIMER database analysis found different degrees of correlation between 3 key genes and major immune cells. PCa bone metastasis has been associated with CCNA2, NUSAP1, and PBK. T cells regulatory and macrophages (M0) may also be involved.

基于生物信息学的前列腺癌骨转移枢纽基因筛选和免疫浸润分析。
对影响前列腺癌(PCa)骨转移的基因和免疫细胞进行生物信息学分析。我们利用基因表达总括数据库分析了 PCa 骨转移数据集。利用 GEO2R 和加权基因共表达网络分析确定了差异表达基因。基因组富集分析软件用于识别重要通路。除了创建蛋白质-蛋白质相互作用网络外,还利用京都基因百科全书数据库进行了功能富集分析。为了筛选枢纽基因,使用了带有CytoHubba插件的Cytoscape软件,并利用cBioPortal网站和GEPIA2数据库对关键基因进行了mRNA和生存曲线验证分析。利用 CIBERSORTx 网站进行了免疫浸润分析,最后根据 TIMER 数据库对关键基因进行了免疫细胞相关性分析。共筛选出197个PCa骨转移风险基因,根据基因组富集分析,"G2M_CHECKPOINT "在PCa骨转移样本中显著富集。基于蛋白质相互作用网络,我们发现了10个备选的枢纽基因,其中CCNA2、NUSAP1和PBK这3个枢纽基因得到了cBioPortal网站和GEPIA2数据库的验证。根据CIBERSORTx免疫细胞浸润分析,T细胞调节和巨噬细胞M0可能会影响PCa向骨骼转移。TIMER数据库分析发现,3个关键基因与主要免疫细胞之间存在不同程度的相关性。PCa 骨转移与 CCNA2、NUSAP1 和 PBK 相关。T细胞调节和巨噬细胞(M0)也可能参与其中。
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来源期刊
Medicine
Medicine 医学-医学:内科
CiteScore
2.80
自引率
0.00%
发文量
4342
审稿时长
>12 weeks
期刊介绍: Medicine is now a fully open access journal, providing authors with a distinctive new service offering continuous publication of original research across a broad spectrum of medical scientific disciplines and sub-specialties. As an open access title, Medicine will continue to provide authors with an established, trusted platform for the publication of their work. To ensure the ongoing quality of Medicine’s content, the peer-review process will only accept content that is scientifically, technically and ethically sound, and in compliance with standard reporting guidelines.
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