Objective To reveal the differences in gene expression levels between Th2-driven classical asthma (CA) and Th2-driven cough variant asthma (CVA) in order to investigate the pathogenesis of asthma further. Methods Clinical data were collected from asthmatic patients in the Department of Respiratory and Critical Care of Sichuan Provincial People's Hospital from June 1, 2018, to December 31, 2019. The healthy control (HC) group was healthy adults from the physical examination center. Gene expression of peripheral blood mononuclear cells (PBMCs) in the CA group, CVA group, and HC group was determined by full-length transcriptome sequencing. Differential genes were screened by GO, KEGG analysis, and protein-protein interaction (PPI) network analysis. The results of interaction network analysis were visualized by Cytoscape. Finally, the candidate genes were verified by real-time quantitative polymerase chain reaction (RT-PCR). ResultsA total of 31 patients with asthma were included in the study, including 20 patients in the CA group and 11 patients in the CVA group. According to serum total IgE > 60 IU/mL and fractional exhaled nitric oxide (FeNO) > 40 ppb as the screening condition, 9 cases of Th2-driven CA and 5 cases of Th2-driven CVA were screened for analysis. Gene expression analysis showed 300 differentially expressed genes between the Th2-driven CA group and the Th2-driven CVA group, among which 155 genes were up-regulated, and 145 were down-regulated. GO enrichment analysis showed that differential genes were mainly enriched in drug response, nitrogen compound biosynthesis, cytoplasmic matrix, protein binding, ATP binding, etc. KEGG pathway analysis showed that differential genes were mainly concentrated in 2-oxy-carboxylic acid metabolism and cytotoxic signaling pathways mediated by natural killer cells. PPI analysis revealed extensive protein interactions between different genes. Ten candidate genes were screened for RT-PCR verification and finally found that CLU, GZMB, PPBP, PRF1, PTGS1, and TMSB4X were significantly differentially expressed between the Th2-driven CA group and the Th2-driven CVA group. Conclusions Asthma's occurrence results from the interaction of many genes and pathways. CLU, GZMB, PPBP, PRF1, PTGS1, and TMSB4X genes may be essential in developing Th2-driven CVA to Th2-driven CA.
Objective To explore potential host-derived biomarkers for active pulmonary tuberculosis and optimize early diagnosis and treatment monitoring strategies for tuberculosis. Methods Integrated analysis of two whole blood gene expression datasets (GSE19444 and GSE42830) from the GEO database (37 tuberculosis patients and 50 healthy controls) was performed. Differentially expressed genes (DEGs) were screened using GEO2R, and gene ontology enrichment, pathway analysis, and protein-protein interaction network construction were conducted using Metascape and STRING to identify key gene modules. Core genes were identified based on MCODE scores (≥10) and T-tests/ANOVA, and their expression trends and diagnostic efficacy were validated in independent datasets (GSE152532, GSE19435). The sensitivity and specificity of candidate biomarkers were assessed using receiver operating characteristic (ROC) curves. Results A total of 70 common DEGs were identified (59 upregulated and 11 downregulated), significantly enriched in type Ⅱ interferon signaling pathways and innate immune responses. Module analysis revealed 16 core genes (e.g., RTP4, GBP4, TRIM22) forming a high-confidence interaction network. Validation showed that the three genes were significantly overexpressed in tuberculosis patients (P<0.05) and dynamically downregulated with treatment. ROC curve analysis indicated that RTP4 had the best diagnostic efficacy with an area under curve (AUC) of 0.952 (sensitivity 100%, specificity 79%), while the three-gene combination (TBscore) had an AUC of 0.933 (sensitivity 100%, specificity 81%). Conclusion RTP4, GBP4, and TRIM22 are potential diagnostic biomarkers for active pulmonary tuberculosis, and their combination model (TBscore) exhibits high sensitivity and specificity, with dynamic expression levels reflecting treatment efficacy, providing new targets for precise diagnosis and treatment of tuberculosis.