ObjectiveTo find the hub genes related to bone metastasis of breast cancer by weighted geneco-expression network analysis (WGCNA) method, and provide theoretical support for the development of new targeted therapeutic drugs.MethodsThe basic clinical features of 286 breast cancer patients and the gene expression information of tumor specimens were downloaded from the GSE2034 dataset from the Gene Expression Omnibus. R software was used to analyze the gene microarray. The WGCNA package embedded in the R software was used for various analysis in weighted correlation network analysis. Cox proportional hazard regression was performed by using SPSS software.ResultsThe top one quarter genes with the greatest variance variability were selected by WGCNA, and a total of 5 000 genes were used for further enrichment analysis. Finally, 15 gene co-expression modules were constructed, and the magenta module (r=0.94, P<0.001) was significantly positively correlated with bone metastasis of breast cancer. It was further found that six hub genes highly associated with bone metastasis in the magenta module were: Ral GTPase-activating protein subunitalpha-1 (RALGAPA1), B-cell antigen receptor complex-associated protein alpha chain (CD79A), immunoglobulin kappa chain C region (IGKC), arrestin beta 2 (ARRB2), differentially expressed in FDCP 6 homolog (DEF6), and immunoglobulin lambda variable 2 (IGLV2).ConclusionWe found that RALGAPA1, CD79A, IGKC, ARRB2. DEF6, and IGLV2 may play an important role in bone metastasis of breast cancer.
ObjectiveTo screen differential expression of genes in hepatocellular carcinoma (HCC) by bioinformatics method, and analyze its clinical significance and its possible molecular mechanism in HCC.MethodsThe HCC gene expression profile GSE101728 was picked out to analyze the differential expression genes. The hub genes were identified by STRING and Cytoscape. GO and KEGG analysis were carried out by using DAVID and PPI network were constructed by STRING. The relationship among the hub genes were analyzed by using GEPIA.ResultsA total of 1 082 DEGs were captured (354 up-regulated genes and 728 down-regulated genes). Meantime, 10 hub genes ［cyclin dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin A2 (CCNA2), polo-like kinase 1 (PLK1), laser kinase B (AURKB), cyclin of cell division 20 (CDC20), centromere protein A (CENPA), mitotic arrest defective protein 2 (MAD2L1), cyclin B2 (CCNB2), and kinesin family 2C (KIF2C)］ were identified, and its expression and clinical significance were verified by GEPIA. GO and KEGG analysis showed 10 hub genes were mainly enriched in cell division and cell cycle. Expressions of AURKB, CCNB1, and MAD2L1 were obviously positively correlated (P<0.05).ConclusionThis study analyzes the hub genes in the development of HCC by bioinformatics methods and provides valuable information for further research on the mechanism of HCC.
Objective To screen the lapatinib resistance-related hub genes of breast cancer by bioinformatics initially in order to lay the foundation for further study. Methods We screened and downloaded the gene expression profile data of GSE16179 and GSE38376 from the gene expression omnibus (GEO), and used the limma package of R software to identify the differential expressed genes (DEGs) in breast cancer cells. Then we used the DAVID online website for pathway and function enrichment. With the usage of STRING and Cytoscape, the protein-protein interaction network (PPI) was constructed, and the plug-in app MCODE in Cytoscape was applied to screen hub genes. Then we performed the function enrichment and co-expression analysis of hub genes by DAVID and GeneMANIA. Kaplan-Meier Plotter was used to conduct survival analysis of hub genes. Results A total of 206 kinds of DEGs were screened, and there were 126 kinds of up-regulated genes and 80 kinds of down-regulated genes. DAVID results showed that DEGs were mainly enriched in the biological processes of extracellular space and extracellular region, including extracellular matrix organization, oxygen binding, integrin binding, cell adhesion, positive regulation of angiogenesis, Hippo signaling pathway, transforming growth factor-β signaling pathway and so on. PPI network visualized 74 nodes, the top 10 kinds of hub genes with high connectivity in the gene expression network were screened by MCODE. The Kaplan-Meier Plotter analysis confirmed that 6 of the 10 kinds of hub genes, including peroxisome proliferator activated receptor gamma, transforming growth factor beta receptor 2, tissue inhibitor of metalloproteinase 1, transforming growth factor beta induced, serpin family E member 1, and thrombospondin 1, were correlated with the prognosis of breast cancer patients. Conclusion This 6 kinds of genes may play a significant role in lapatinib resistance of breast cancer.
Objective To explore the role of cyclin B1 (CCNB1), cyclin B2 (CCNB2) and cyclin dependent kinase 1 (CDK1) in lung adenocarcinoma (LUAD) using bioinformatic data. Methods First, RNA expression data were downloaded from two datasets in Gene Expression Omnibus (GEO), and DESeq2 software was used to identify deferentially expressed genes (DEGs). Subsequent analyses were conducted based on the results of these DEGs: protein-protein interaction (PPI) network was constructed with STRING database; the modules in PPI network were analyzed by Molecular Complex Detection software, and the most significant modules were selected, the genes included in these modules were the hub genes; high-throughput RNA sequencing data from other databases were used to verify the expression of these hub genes to confirm whether they were DEGs; survival curve analyses of the confirmed DEGs were conducted to select genes that had significant influence on the survival of LUAD; the expression of these hub genes in different stages of LUAD were also analyzed. Then, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for these selected hub genes using KOBAS database. MuTarget tool was used to analyze the correlations between the expression of these selected hub genes and gene mutation status in LUAD. The potential value of these hub genes in the treatment of LUAD was explored based on the drug information in GDSC database. Finally, immunohistochemical data from Human Protein Atlas (HPA) database were used to verify the expression of these hub genes in LUAD again. Results According to the expression data in GEO, 594 up-regulated genes and 651 down-regulated genes were identified (P<0.05), among which 30 hub genes were selected for subsequent analyses. The RNA high-throughput sequencing data of other databases verified that 18 genes were DEGs, among which 8 hub genes had significant impact on disease-free survival in LUAD (P<0.05). Moreover, the 8 genes were differentially expressed in different stages of LUAD, which were higher in the middle and late stage of LUAD. Among the 8 genes. CCNB1, CCNB2 and CDK1 were significantly enriched in the cell cycle pathway. The expression of CCNB1, CCNB2 and CDK1 in LUAD was closely related to the TP53 mutation status. In addition, CDK1 was associated with four drugs, revealing the potential value of CDK1 in the treatment of LUAD. Finally, immunohistochemical data from HPA database verified that CCNB1, CCNB2 and CDK1 were highly expressed in LUAD in the protein level. Conclusion Overexpression of CCNB1, CCNB2 and CDK1 are associated with poor prognosis of LUAD, indicating that the three genes may be prognostic biomarkers of LUAD and CDK1 is a potential therapeutic target for LUAD.