Identification and validation of novel biomarkers for cold-dampness syndrome of rheumatoid arthritis based on integration of multiple bioinformatics methods / 中国中药杂志

This study aims to identify the novel biomarkers of cold-dampness syndrome(RA-Cold) of rheumatoid arthritis(RA) by gene set enrichment analysis(GSEA), weighted gene correlation network analysis(WGCNA), and clinical validation. Firstly, transcriptome sequencing was carried out for the whole blood samples from RA-Cold patients, RA patients with other traditional Chinese medicine(TCM) syndromes, and healthy volunteers. The differentially expressed gene(DEG) sets of RA-Cold were screened by comparison with the RA patients with other TCM syndromes and healthy volunteers. Then, GSEA and WGCNA were carried out to screen the key DEGs as candidate biomarkers for RA-Cold. Experimentally, the expression levels of the candidate biomarkers were determined by RT-qPCR for an independent clinical cohort(not less than 10 cases/group), and the clinical efficacy of the candidates was assessed using the receiver operating characteristic(ROC) curve. The results showed that 3 601 DEGs associated with RA-Cold were obtained, including 106 up-regulated genes and 3 495 down-regulated genes. The DEGs of RA-Cold were mainly enriched in the pathways associated with inflammation-immunity regulation, hormone regulation, substance and energy metabolism, cell function regulation, and synovial pannus formation. GSEA and WGCNA showed that recombinant proteasome 26S subunit, ATPase 2(PSMC2), which ranked in the top 50% in terms of coefficient of variation, representativeness of pathway, and biological modules, was a candidate biomarker of RA-Cold. Furthermore, the validation results based on the clinical independent sample set showed that the F1 value, specificity, accuracy, and precision of PSMC2 for RA-Cold were 70.3%, 61.9%, 64.5%, and 81.3%, respectively, and the area under the curve(AUC) value was 0.96. In summary, this study employed the "GSEA-WGCNA-validation" integrated strategy to identify novel biomarkers of RA-Cold, which helped to improve the TCM clinical diagnosis and treatment of core syndromes in RA and provided an experimental basis for TCM syndrome differentiation.

Characteristics of Atmospheric Fine Particulate Matter (PM ) Induced Differentially Expressed Proteins Determined by Proteomics and Bioinformatics Analyses / 生物医学与环境科学（英文）

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.

Mining hub genes related to liver cancer stage in TCGA database with weighted correlation network analysis method / 中国医师进修杂志

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.