Objective To explore the role of versican (VCAN) in ESCC prognosis based on bioinformatics data. MethodsFirst, three RNA microarray datasets of ESCC were downloaded from GEO database, which were then integrated and used to explore differentially expressed genes (DEGs). The subsequent analysis was conducted based on the results of these DEGs: (1) The STRING database was used to construct a protein-protein interaction (PPI) network; (2) molecular complex detection software was used to analyze the modules of the PPI network, of which the most significant modules were chosen, and hub genes were the genes included in the chosen modules; (3) high-throughput RNA sequencing data from TCGA and GTEx databases were used to verify the expression of these hub genes to confirm whether they were differentially expressed; (4) the survival curve analysis of confirmed DEGs was conducted to select genes that had significant influence on the survival of ESCC; (5) TIMER database was used to analyze the relationship between the gene expression of VCAN and the abundance of tumor-infiltrating immune cells (TIICs) and gene markers in these cells; (6) Targetscan and miRDB software were used to predict the miRNAs that could regulate VCAN, and Cytoscape software was used to construct the regulatory network. ResultsA total of 630 DEGs and 32 hub genes were found, of which VCAN was an up-regulated DEG, and high expression of VCAN was significantly associated with poor prognosis of ESCC. Moreover, VCAN could also play a role in the immune microenvironment of ESCC, which was mainly manifested by a significant positive correlation between the abundance of VCAN and the abundance of M2 macrophages gene markers, some of which had been reported to be associated with poor prognosis of ESCC. Finally, we also found that VCAN could be regulated by 15 miRNAs in ESCC, some of which had been reported to be associated with ESCC prognosis. ConclusionThis study provides direct and indirect comprehensive evidence for the role of VCAN in ESCC prognosis. The direct evidence is that the survival curve shows that highly expressed VCAN is significantly associated with the poor prognosis of ESCC, and the indirect evidence is that VCAN is positively related to some markers which indicate poor prognosis in the ESCC immune microenvironment, and VCAN can be regulated by some prognostic miRNAs in ESCC.
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.
Objective By using metagenomic next-generation sequencing (mNGS), we aimed to analyze the microbes characteristics of lower respiratory tract of patients with pulmonary infection, so as to improve the further understanding of clinical etiological characteristics of patients with pulmonary infection. Methods A total of 840 patients with suspected pulmonary infection were enrolled from August 2020 to October 2021 in West China Hospital of Sichuan University. mNGS was used to detect the microbiome of bronchoalveolar lavage fluid of all patients, and the microbial characteristics of lower respiratory tract of all patients were retrospectively analyzed. Results A total of 840 patients were enrolled, of which 743 were positive for microbiome, with bacterial infection accounting for 35.13% (261/743). Acinetobacter baumannii accounted for 18.98% (141/743), followed by Streptococcus pneumoniae (14.13%, 105/743), Klebsiella pneumoniae (13.46%, 100/743), Enterococcus faecium (12.11%, 90/743) and Mycobacterium tuberculosis complex (11.98%, 89/743). Acinetobacter baumannii had the highest average reads (2607.48). In addition, some specific pathogens were detected, such as 9 cases of Chlamydia psittaci. The main fungal infections were Candida albicans (12.38%, 92/743), Pneumocystis jirovecii (9.02%, 67/743) and Aspergillus fumigatus (7.40%, 55/743), among which the average reads of Pneumocystis jirovecii was higher (141.86) than Candida albicans and Aspergillus fumigatus. In addition, some special pathogens were also detected, such as a case of Talaromyces marneffei. The main viral infections included human β herpevirus 5 (17.90%, 133/743), human γ herpevirus 4 (17.36%, 129/743), human β herpevirus 7 (16.15%, 120/743) and human α herpevirus 1 (13.59%, 101/743), among which the average reads of human herpesvirus type 1 (367.27) was the highest. Parasitic infection was least, with only 2 cases of Echinococcus multilocularis, 2 cases of Angiostrongylus cantonensis, 2 cases of Dermatophagoides pteronyssinus and 1 case of Dermatophagoides farinae, which were mainly infected with bacteria and viruses. In addition, a total of 407 patients were diagnosed with mixed infection, of which virus and bacteria mixed infection was the most (22.61%, 168/743). The distribution of microorganisms in different seasons also has certain characteristics. For example, bacteria (Acinetobacter baumannii) were most frequently detected in autumn and winter, while viruses (human gamma-herpesvirus type 4) were most frequently detected in spring and summer. Conclusions In the lower respiratory tract of patients with pulmonary infection, the main gram-negative bacteria are Acinetobacter baumannii and Klebsiella pneumoniae, while the main gram-positive bacteria are Streptococcus pneumoniae, Enterococcus faecium and Mycobacterium tuberculosis complex; the main fungi are Candida albicans, Pneumocystis jirovecii and Aspergillus fumigatus; the main viruses are human β herpevirus 5, human γ herpevirus 4 and human β herpevirus 7. However, parasites are rarely detected and have no obvious characteristics. Bacterial infection and bacterial virus mixed infection are the main co-infections; the microbial characteristics of autumn and winter are different from those of spring and summer. In addition, attention should be paid to special pathogenic microorganisms, such as Chlamydia psittaci and Talaromyces marneffei. These characteristics could be used as reference and basis for the pathogenic diagnosis of pulmonary infection.