Background: Fusobacterium nucleatum can cause opportunistic and chronic infections and has recently been shown to be involved in colorectal cancer. However, the specific mechanism by which F. nucleatum induces colorect...Background: Fusobacterium nucleatum can cause opportunistic and chronic infections and has recently been shown to be involved in colorectal cancer. However, the specific mechanism by which F. nucleatum induces colorectal carcinoma remains unclear. Methods: We downloaded the GSE110223, GSE110224, GSE113513 and GSE122183 microarray datasets from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) related to F. nucleatum in CRC by overlapping data sets was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome pathway (KEGG) analyses were used for enrichment analysis. Moreover, Cytoscape software constructed a protein-protein interaction (PPI) network of differentially expressed genes. Finally, western blot and RT-qPCR analysis identified the relative protein and mRNA expression of hub genes in the cell model. Results: In total, 118 DEGs in F. nucleatum-associated CRC were screened from nonoverlapping microarray data, among which 20 upregulated and 98 downregulated DEGs were identified. The 118 DEGs were significantly correlated with diverse functions and pathways. The hub gene MUC1 had higher centrality scores in the PPI network, and the top 5 closely interacting hub genes, SLC7A11, AGR2, KRT18, CARTPT and TSPYL5, were identified. Conclusion: Our evidence suggests that the identified DEGs associated with F. nucleatum enhance our comprehension of the molecular Mechanisms underlying the tumorigenesis and development of CRC and might be used as molecular targets and diagnostic biomarkers for the treatment of CRC.展开更多
A blind deconvolution algorithm with modified Tikhonov regularization is introduced.To improve the spectral resolution,spectral structure information is incorporated into regularization by using the adaptive term to d...A blind deconvolution algorithm with modified Tikhonov regularization is introduced.To improve the spectral resolution,spectral structure information is incorporated into regularization by using the adaptive term to distinguish the spectral structure from other regions.The proposed algorithm can effectively suppress Poisson noise as well as preserve the spectral structure and detailed information.Moreover,it becomes more robust with the change of the regularization parameter.Comparative results on simulated and real degraded Raman spectra are reported.The recovered Raman spectra can easily extract the spectral features and interpret the unknown chemical mixture.展开更多
文摘Background: Fusobacterium nucleatum can cause opportunistic and chronic infections and has recently been shown to be involved in colorectal cancer. However, the specific mechanism by which F. nucleatum induces colorectal carcinoma remains unclear. Methods: We downloaded the GSE110223, GSE110224, GSE113513 and GSE122183 microarray datasets from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) related to F. nucleatum in CRC by overlapping data sets was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome pathway (KEGG) analyses were used for enrichment analysis. Moreover, Cytoscape software constructed a protein-protein interaction (PPI) network of differentially expressed genes. Finally, western blot and RT-qPCR analysis identified the relative protein and mRNA expression of hub genes in the cell model. Results: In total, 118 DEGs in F. nucleatum-associated CRC were screened from nonoverlapping microarray data, among which 20 upregulated and 98 downregulated DEGs were identified. The 118 DEGs were significantly correlated with diverse functions and pathways. The hub gene MUC1 had higher centrality scores in the PPI network, and the top 5 closely interacting hub genes, SLC7A11, AGR2, KRT18, CARTPT and TSPYL5, were identified. Conclusion: Our evidence suggests that the identified DEGs associated with F. nucleatum enhance our comprehension of the molecular Mechanisms underlying the tumorigenesis and development of CRC and might be used as molecular targets and diagnostic biomarkers for the treatment of CRC.
基金the Project of the Program for New Century Excellent Talents in University(NCET-11-0654)the National Key Technology Research and Development Program(2013BAH72B01)+3 种基金the National Key Technology Research and Development Program(2013BAH18F02)the Scientific R&D Project of State Education Ministry and China Mobile(MCM20121061)the National Social Science Fund of China(14BGL131)and the New PhD Researcher Award from the Chinese Ministry of Education.
文摘A blind deconvolution algorithm with modified Tikhonov regularization is introduced.To improve the spectral resolution,spectral structure information is incorporated into regularization by using the adaptive term to distinguish the spectral structure from other regions.The proposed algorithm can effectively suppress Poisson noise as well as preserve the spectral structure and detailed information.Moreover,it becomes more robust with the change of the regularization parameter.Comparative results on simulated and real degraded Raman spectra are reported.The recovered Raman spectra can easily extract the spectral features and interpret the unknown chemical mixture.
