ABSTRACT
Objective@#To screen the differentially expressed proteins (DEPs) in human bronchial epithelial cells (HBE) treated with atmospheric fine particulate matter (PM ).@*Methods@#HBE cells were treated with PM samples from Shenzhen and Taiyuan for 24 h. To detect overall protein expression, the Q Exactive mass spectrometer was used. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Perseus software were used to screen DEPs.@*Results@#Overall, 67 DEPs were screened in the Shenzhen sample-treated group, of which 46 were upregulated and 21 were downregulated. In total, 252 DEPs were screened in the Taiyuan sample-treated group, of which 134 were upregulated and 118 were downregulated. KEGG analysis demonstrated that DEPs were mainly enriched in ubiquitin-mediated proteolysis and HIF-1 signal pathways in Shenzhen PM samples-treated group. The GO analysis demonstrated that Shenzhen sample-induced DEPs were mainly involved in the biological process for absorption of various metal ions and cell components. The Taiyuan PM -induced DEPs were mainly involved in biological processes of protein aggregation regulation and molecular function of oxidase activity. Additionally, three important DEPs, including ANXA2, DIABLO, and AIMP1, were screened.@*Conclusion@#Our findings provide a valuable basis for further evaluation of PM -associated carcinogenesis.
Subject(s)
Humans , Air Pollutants , Bronchi , Metabolism , Computational Biology , Epithelial Cells , Metabolism , Gene Expression , Mass Spectrometry , Particle Size , Particulate Matter , ProteomicsABSTRACT
Objective@#Primary liver cancer is a kind of gastrointestinal malignant tumor with high morbidity and mortality. Early diagnosis is difficult, with postoperative high recurrence rate and high metastasis rate, poor prognosis, so it is particularly important to better understand the occurrence and development of liver cancer from the gene level, so as to provide theoretical basis for gene therapy or targeted drug therapy in the future.@*Methods@#TCGA database for liver cancer gene transcriptome data information and clinical phenotype information was downloaded, and R language edgeR package was used to standardize the transcriptome data. The log FC > 1, FDR < 0.01 gene was set to be expressing differences gene, and then the weighted correlation network analysis (WGCNA) methods was used to analyze the relationship between liver cancer stage (stage) and the differentially expressed genes to look for hub genes.@*Results@#Four hub genes related to tumor staging were identified (TPX2, HJURP, KIAA1524, SGO2). Kaplan-meier survival curve was drawn and it was found that their expression level was significantly correlated with patient survival time, and high expression level was an independent prognostic factor.@*Conclusions@#WGCNA method is used to mine the TCGA database information and find the hub genes related to tumor staging. The relationship between TPX2, HJURP, KIAA1524 and liver cancer has been reported in the literature, except SGO2. This study will provide important theoretical basis for the follow-up study of liver cancer.