RESUMO

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment. METHODS: Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes. Meanwhile, univariate and multivariate Cox regression analyses were used to evaluate independent prognostic factors of DLBCL. Finally, the prognostic model, including some key genes screened by Lasso regression, Random Forest algorithm, and point-biserial correlation, was constructed by an optimal classifier from seven machine learning algorithms and validated by another three external datasets (GSE34171, GSE87371, GSE31312). RESULTS: Comprehensive genomic analysis of 1,143 DLBCL samples identify 2 molecularly, prognostically relevant subtypes: immune-enriched (IME) and cell-cycle-enriched (CCE). Then a new predictive model including seven key genes (SERPING1, TIMP2, NME1, DCTPP1, RFC4, POLE2, and SNRPD1) was developed with high prediction accuracy (88.6%) and strong predictive power (AUC = 0.973) based on the Support Vector Machine (SVM) algorithm in 414 patients from GSE10846. The predictive power was similar in another three testing sets (HR > 1.400, p < 0.05). CONCLUSION: This model could evaluate survival independently with strong predictive power compared with other clinical risk factors. Our study constructed a reliable model to predict two new subtypes of DLBCL patients, which could guide the implementation of individualized treatment.

Assuntos

Linfoma Difuso de Grandes Células B , Humanos , Ciclo Celular/genética , Linfoma Difuso de Grandes Células B/genética , Algoritmos , Análise por Conglomerados , Aprendizado de Máquina , Prognóstico

RESUMO

Abstract Objective To analyze the effect of mast cells (MCs) in neurogenic inflammation and the neuroimmune response of trigeminal ganglia (TG) due to pulpitis and detect the regulatory effect of curcumin (Cur) on neuroimmune responses induced by pulpitis. Methodology Immunohistochemistry, toluidine blue staining (TB), and other methods were used to detect the dynamic changes of MCs, as well as tryptase expression changes and protease activated receptor 2 (PAR2) and calcitonin gene-related peptide (CGRP) levels in the neuroimmune response induced by pulpitis. After administering Cur by intraperitoneal injection, the expression levels of Toll-like receptor 4 (TLR4), CGRP, glial fibrillary acidic protein (GFAP), fractalkine (CX3CL1), Tumor necrosis factor (TNF-α), and other factors were examined in the TG of pulpitis-induced rats. Results After pulpitis induction, the expression of CGRP-positive neurons and GFAP-positive soluble guanylate cyclase (SGC) in the TG significantly increased. A large number of MCs underwent degranulation. MCs were scattered between the CGRP-positive nerve fibers. MCs showing a typical degranulated state within the TG significantly increased and tryptase-positive MCs surrounded the TG nerve fibers and neurons. After treatment with Cur, the inflammatory response in the periodontal bone induced by pulpitis decreased and promoted early tissue repair. The expression of TNF-α significantly decreased as did degranulation of MCs. In contrast, the expression of CGRP, TLR4-positive neurons, activated SGCs, and PAR2-positive TG neurons significantly decreased. MCs could participate in the neuroimmune response induced by pulpitis by the tryptase signaling pathway. Conclusion Importantly, Cur inhibited the degranulation of MCs, downregulated the expression of tryptase and PAR2 in the TG, and attenuated the activation response of osteoclasts in the apical periodontium.