As an H+-gated subgroup of the degenerin/epithelial Na+ channel family, acid-sensing ion channels (ASICs) were reported to be involved in various physiological and pathological processes in neurons. However, little is known about the role of ASICs in the function of dendritic cells (DCs). In this study, we investigated the expression of ASICs in mouse bone marrow-derived DCs and their possible role in the function of DCs. We found that ASIC1, ASIC2, and ASIC3 are expressed in DCs at the mRNA and protein levels, and extracellular acid can evoke ASIC-like currents in DCs. We also demonstrated that acidosis upregulated the expression of CD11c, MHC class II, CD80, and CD86 and enhanced the Ag-presenting ability of DCs via ASICs. Moreover, the effect of acidosis on DCs can be abolished by the nonsteroidal anti-inflammatory drugs ibuprofen and diclofenac. These results suggest that ASICs are involved in the acidosis-mediated effect on DC function.

High-mobility group box 1 (HMGB1) is a nuclear factor released extracellularly as an early endogenous alarmin of inflammation following injury and as a late mediator of lethality in sepsis. Although HMGB1 has been implicated in acute lung injury, rheumatoid arthritis, and allograft rejection, its role in T-cell mediated hepatitis remains obscure. Here, we investigated the role and the underlying mechanisms of HMGB1 in concanavalin A (Con A) induced hepatic injury. We demonstrate that high levels of HMGB1 were detected in the necrotic area and in the cytoplasm of hepatocytes after Con A treatment. Administration of exogenous recombinant HMGB1 enhanced Con A-induced hepatitis, while blockade of HMGB1 protected animals from T cell-mediated hepatitis as evidenced by decreased serum transaminase, associated with reduced hepatic necrosis and mortality. Blockade of HMGB1 by a neutralizing antibody inhibited proinflammatory cytokine production, NFκB activity, and the late stage of T/NKT cell activation. These finding thus suggest a pivotal factor of HMGB1 in Con A-induced hepatitis. Blockage of extracellular HMGB1 may represent a novel therapeutic strategy to prevent hepatic injury in T cell-mediated hepatitis.

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PurposePapillary thyroid carcinoma (PTC), the most frequent type of malignant thyroid tumor, lacks novel and reliable biomarkers of patients’ prognosis. In the current study, we mined The Cancer Genome Atlas (TCGA) to develop lncRNA signature of PTC.Patients and methodsThe intersection of PTC lncRNAs was obtained from the TCGA database using integrative computational method. By the univariate and multivariate Cox analysis, key lncRNAs were identified to construct the prognostic model. Then, all patients were divided into the high-risk group and low-risk group to perform the Kaplan–Meier (K–M) survival curves and time-dependent receiver operating characteristic (ROC) curve, estimating the prognostic power of the prognostic model. Functional enrichment analysis was also performed. Finally, we verified the results of the TCGA analysis by the Gene Expression Omnibus (GEO) databases and quantitative real-time PCR (qRT-PCR).ResultsAfter the comprehensive analysis, a three-lncRNA signature (PRSS3P2, KRTAP5-AS1 and PWAR5) was obtained. Interestingly, patients with low-risk scores tended to gain obviously longer survival time, and the area under the time-dependent ROC curve was 0.739. Furthermore, gene ontology (GO) and pathway analysis revealed the tumorigenic and prognostic function of the three lncRNAs. We also found three potential transcription factors to help understand the mechanisms of the PTC-specific lncRNAs. Finally, the GEO databases and qRT-PCR validation were consistent with our TCGA bioinformatics results.ConclusionWe built a three-lncRNA signature by mining the TCGA database, which could effectively predict the prognosis of PTC.

Purpose: Gastric cancer (GC) is aggressive cancer with a high mortality rate worldwide. N6-methyladenosine (m6A) RNA methylation is related to tumorigenesis, which is dynamically regulated by m6A modulators ("writer," "eraser," and "reader"). We conducted a comprehensive analysis of the m6A genes of GC patients in TCGA datasets to identify the potential diagnostic biomarkers. Materials and Methods: We analyzed the expression profile of m6A genes in the TCGA cohort and constructed a diagnostic-m6A-score (DMS) by the LASSO-logistic model. In addition, by consensus cluster analysis, we identified two different subgroups of GC risk individuals by the expression profile of m6A modulators, revealing that YTHDF1's expression variation profile in GC diagnosis. We also performed RT-qPCR and WB verification in 17 pairs of GC specimens and paired adjacent non-tumor tissues and GC cell lines, and verified the expression trend of YTHDF1 in five GEO GC datasets. YTHDF1 expression and clinical features of GC patients were assessed by the UALCAN. Results: The DMS with high specificity and sensitivity (AUC = 0.986) is proven to distinguish cancer from normal controls better. Moreover, we found that the expression profile variation of YTHDF1 was significantly associated with the high-risk subtype of GC patients. RT-qPCR and Western blot results are consistent with silicon analysis, revealing that YTHDF1's potential oncogene role in GC tumor. Conclusion: In conclusion, we developed the m6A gene-based diagnostic signature for GC and found that YTHDF1 was significantly correlated with the high-risk subtype of GC patients, suggesting that YTHDF1 might be a potential target in GC early diagnosis.