The present study aimed to investigate the correlation of long non-coding RNA nuclear-enriched abundant transcript 1 (lncRNA NEAT1) with microRNA (miR)-21, miR-124, and miR-125a, and their associations with disease risk, severity, and inflammatory cytokines of allergic rhinitis (AR). Totally 70 AR patients and 70 non-atopic obstructive snoring patients (as controls) were recruited. Inferior turbinate mucosa samples were collected from all participants for lncRNA NEAT1, its targets (miR-21, miR-124, and miR-125a), interleukin (IL)-4, IL-6, IL-10, and IL-17 detection via reverse transcription quantitative polymerase chain reaction. Disease severity of AR patients was assessed using individual nasal symptom score (INSS) and total nasal symptom score (TNSS). LncRNA NEAT1 was upregulated, while miR-21, miR-124, and miR-125a were downregulated in AR patients compared with controls. Additionally, lncRNA NEAT1, miR-21, and miR-125a displayed good values in differentiating AR patients from controls, while miR-124 could only slightly differentiate AR patients from controls. In AR patients, lncRNA NEAT1 was negatively associated with miR-21 and miR-125a, but not miR-124. However, in controls, no correlation of lncRNA NEAT1 with miR-21, miR-124, or miR-125a was observed. Furthermore, in AR patients, lncRNA NEAT1 was positively, while miR-21 and miR-125a was negatively associated with INSS (rhinorrhea, itching, congestion scores), TNSS and inflammatory cytokines; however, correlation of miR-124 with INSS, TNSS, and inflammatory cytokines was slight. LncRNA NEAT1 and its targets (miR-21 and miR-125a) present close correlations with disease risk, severity, and inflammation of AR, suggesting their potential as biomarkers for AR assessment.
In the field of modern information technology, how to find information quickly, accurately and comprehensively that users really needed has become the focus of research in this field. In this article, a feature selection method based on a complex network is proposed for the structure and content characteristics of large-scale web text information. The preprocessed web text is converted into a complex network. The nodes in the network correspond to the entries in the text. The edges of the network correspond to the links between the entries in the text, and the degree of nodes and the aggregation system are used. Second, the text classification method is studied from the point of view of data sampling, and a text classification method based on density statistics is proposed. This method uses not only the density information of the text feature set in the classification process, but also the use of statistical merging criteria to get the text. The difference information of each feature has a better classification effect for large text collections.
Given the importance of solute carrier (SLC) proteins in maintaining cellular metabolic homeostasis and that their dysregulation contributes to cancer progression, here we constructed a robust SLC family signature for lung adenocarcinoma (LUAD) patient stratification. Transcriptomic profiles and relevant clinical information of LUAD patients were downloaded from the TCGA and GEO databases. SLC family genes differentially expressed between LUAD tissues and adjacent normal tissues were identified using limma in R. Of these, prognosis-related SLC family genes were further screened out and used to construct a novel SLC family-based signature in the training cohort. The accuracy of the prognostic signature was assessed in the testing cohort, the entire cohort, and the external GSE72094 cohort. Correlations between the prognostic signature and the tumor immune microenvironment and immune cell infiltrates were further explored. We found that seventy percent of SLC family genes (279/397) were differentially expressed between LUAC tissues and adjacent normal. Twenty-six genes with p-values < 0.05 in univariate Cox regression analysis and Kaplan-Meier survival analysis were regarded as prognosis-related SLC family genes, six of which were used to construct a prognostic signature for patient classification into high- and low-risk groups. Kaplan-Meier survival analysis in all internal and external cohorts revealed a better overall survival for patients in the low-risk group than those in the high-risk group. Univariate and multivariate Cox regression analyses indicated that the derived risk score was an independent prognostic factor for LUAD patients. Moreover, a nomogram based on the six-gene signature and clinicopathological factors was developed for clinical application. High-risk patients had lower stromal, immune, and ESTIMATE scores and higher tumor purities than those in the low-risk group. The proportions of infiltrating naive CD4 T cells, activated memory CD4 T cells, M0 macrophages, resting dendritic cells, resting mast cells, activated mast cells, and eosinophils were significantly different between the high- and low-risk prognostic groups. In all, the six-gene SLC family signature is of satisfactory accuracy and generalizability for predicting overall survival in patients with LUAD. Furthermore, this prognostics signature is related to tumor immune status and distinct immune cell infiltrates in the tumor microenvironment.
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