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OBJECTIVE: The aim of this study was to explore the laboratory results in severe as asthma patients with omalizumab therapy and provide evidence for estimating omalizumab efficacy. METHODS: Retrospective study of 18 patients with severe asthma received omalizumab therapy in Shanghai General Hospital from 2020 to 2022 was performed. The basic data of patients were collected. The absolute number and the percentage of basophil and eosinophil in peripheral blood, total IgE level in serum, and as pulmonary function were detected at the beginning of treatment and 4 months after treatment. Differences between two groups were analyzed using Paired T test. RESULTS: The most common allergens collected from patients with moderate to severe asthma were dust mite (positive ratio 55.56%), mixed mold (16.67%), cat and dog dander, and Aspergillus fumigatus (11.11%). There was no significant difference in eosinophil and basophil counts in peripheral blood between the two groups. However, serum total IgE levels increased from (437.55±279.35) KU/L to (1071.42±721.28) KU/L (P=0.004), and FEV1/FVC ratio increased from (65.53±14.15)% to (73.91±13.63)% (P=0.005) after 4 months of treatment. CONCLUSIONS: The existing laboratory indicators for evaluation of omalizumab efficacy are still very limited, and new biomarkers need to be further developed. Elevated serum IgE levels at four weeks of treatment and FEV1/FVC may be potential indicators for omalizumab monitoring.
RESUMEN
Pituitary adenomas (PA) is one of the most frequent types of intracranial neoplasms. Long noncoding RNAs (lncRNAs) played important roles in the progression of human cancers, including PA. However, the roles of lncRNAs in PA remained to be further investigated. We performed analysis of GSE26966 dataset to identify differently expressed lncRNAs in PA. Co-expression network, lncRNA-RNA binding proteins network, and competing endogenous RNA networks were constructed. Moreover, we performed RT-qPCR assay to validate four key lncRNAs expression in PA. This study identified differently expressed mRNAs and lncRNAs by using GSE26966 database. Furthermore, we constructed lncRNA-mRNA co-expression, lncRNA-RBP interaction and ceRNA networks. Bioinformatics analysis showed these lncRNAs were involved in regulating mechanical stimulus, gene expression, JAK-STAT cascade, cell cycle arrest, FoxO signaling, HIF-1 signaling, Insulin signaling, Oxytocin signaling, and MAPK signaling. We also showed KCNQ1OT1, SNHG7, MEG3, and SNHG5 were down-regulated in PA. Our findings could provide a novel insight to understand the mechanisms of lncRNAs underlying PA pathogenesis and identify new biomarkers for PA.
