Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, China.
Department of Thoracic Surgery, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei, 063000, China.
BMC Pulm Med. 2024 Jun 10;24(1):275. doi: 10.1186/s12890-024-03072-z.
Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis.
To investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients' tumor tissues compared to controls.
A total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients' tumor tissues compared to controls.
To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.
肺部纯磨玻璃结节（pGGN）是否存在侵袭性成分仍然是一个巨大的挑战。本研究旨在通过生物信息学分析方法，探讨和鉴定纯磨玻璃结节（pGGN）的潜在生物标志物基因。
为了研究差异表达基因（DEGs），首先使用基因表达综合数据库（GEO）数据库获得的数据。然后采用加权基因共表达网络分析（WGCNA）方法研究 DEGs 的共表达网络。选择黑色关键模块作为与 pGGN 相关的关键模块。对基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路进行分析。然后使用 STRING 构建蛋白质-蛋白质相互作用（PPI）网络，并用 Cytoscape 软件分析选定模块的基因。此外，聚合酶链反应（PCR）用于评估这些枢纽基因在 pGGN 患者肿瘤组织与对照之间的价值。
从 GSE193725 中筛选出 4475 个 DEGs，然后在黑色关键模块中鉴定出 225 个 DEGs，这些基因被发现与各种功能和途径有关，如细胞外小泡、囊泡、核糖体等。在这些 DEGs 中，有 6 个与高压力方法相关的枢纽基因重叠。这些枢纽基因包括 RPL4、RPL8、RPLP0、RPS16、RPS2 和 CCT3。最后，CCT3 和 RPL8 mRNA 的相对表达水平在 pGGN 患者的肿瘤组织中均受到调控。
综上所述，确定的 DEGs、途径、模块和重叠的枢纽基因可以为 pGGN 的潜在分子机制提供启示。
