Associations of Serum Iron Status with MAFLD and Liver Fibrosis in the USA: a Nationwide Cross-Section Study.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a new terminology characterized by liver steatosis. Iron status is related to many metabolic diseases. However, the researches on the associations of serum iron status with MAFLD are limited. The objective of this study was to investigate the associations of serum iron status biomarkers with MAFLD and liver fibrosis. A total of 5892 adults were enrolled in the current cross-sectional study using the 2017-March 2020 National Health and Nutrition Examination Survey. Liver steatosis and liver fibrosis were defined by the median values of controlled attenuation parameter ≥ 274 dB/m and liver stiffness measurement ≥ 8 kPa, respectively. The multivariable logistic/linear regression and restricted cubic spline analysis were conducted. After adjusting for potential confounders, higher ferritin levels were associated with higher odds of MAFLD (OR 4.655; 95% CI 2.301, 9.418) and liver fibrosis (OR 7.013; 95% CI 3.910, 12.577). Lower iron levels were associated with a higher prevalence of MAFLD (OR 0.622; 95% CI 0.458, 0.844) and liver fibrosis (OR 0.722; 95% CI 0.536, 0.974). Lower transferrin saturation (TSAT) was associated with a higher prevalence of MAFLD (OR 0.981; 95% CI 0.970, 0.991) and liver fibrosis (OR 0.988; 95% CI 0.979, 0.998). Higher ferritin levels, lower iron levels, and TSAT were associated with a higher prevalence of MAFLD and liver fibrosis. This study extended the knowledge of modifying iron status to prevent MAFLD and liver fibrosis. More prospective and mechanism studies were warranted to confirm the conclusions.

MIF promotes Th17 cell differentiation in Hashimoto's thyroiditis by binding HVEM and activating NF-κB signaling pathway.

Hashimoto's thyroiditis is a typical thyroid autoimmune disease and Th17 cells are crucial in its development. In recent years, MIF (Macrophage Migration Inhibitory Factor) has been found to promote the secretion of IL-17A and the production and differentiation of Th17 cells. However, the specific mechanism of it remains unclear. Here, we found that the expression of MIF, IL-17A and HVEM (Herpes Virus Entry Mediator) were up-regulated in HT patients. The proportion of Th17 cells in peripheral blood mononuclear cells was positively correlated with the serum MIF protein level. We further found that the expression of HVEM and the phosphorylation level of NF-κB in peripheral blood mononuclear cells of HT patients were significantly increased. Therefore, we speculated that MIF promotes Th17 cell differentiation through HVEM and NF-κB signaling pathways. Further mechanism studies showed that MIF could directly bind to HVEM, and the stimulation of rhMIF in vitro could increase the expression of HVEM and activate NF-κB signaling pathways to promote Th17 cell differentiation. After blocking HVEM with HVEM antibody, the effect of MIF on Th17 cell differentiation disappeared. The results above show that the differentiation of Th17 cells is promoted by MIF combined with HVEM through NF-κB signaling pathways. Our research provides a new theory to the regulation mechanism of Th17 cell differentiation and gives hint to new potential therapeutic targets for HT.

Associations of metal profiles in blood with thyroiditis: a cross-sectional study.

Autoimmune thyroiditis (AIT) is increasingly common, and serological markers include thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TgAb). To determine if selected metals influence thyroiditis antibody positivity, this cross-sectional study investigated associations between metals and thyroiditis antibody status. Healthy individuals (n = 1104) completed a questionnaire and underwent checkups of anthropometric parameters, thyroid function status, and levels of seven metals in blood (magnesium, iron, calcium, copper, zinc, manganese, and lead). Associated profiles of glyco- and lipid metabolism were also established. Logistic regression and restricted cubic spline (RCS) regression analysis were applied to adjudge associations between metals and TPOAb and TgAb status. It was found that, after adjusting for likely cofounding factors, participants with antibody positivity had significantly lower serum concentrations of magnesium and iron. When serum magnesium levels were analyzed in quartiles, the odds ratios of quartile 4 were 0.329-fold (95% confidence interval (CI): 0.167-0647) and 0.259-fold (95% CI 0.177-0.574) that of quartile 1 regarding TPOAb and TgAb positivity (P = 0.004, 0.003). After adjustment, the RCS analysis detected nonlinear associations between iron and TPOAb and TgAb positivity (P < 0.01, both). In stratified analyses, these associations regarding magnesium and iron remained for women of reproductive age, but not for postmenopausal women and men. We conclude that lower serum levels of magnesium and iron are associated with incremental positivity of thyroiditis antibodies and may be among the most important metals contributing to AIT in women of reproductive age.

Development of a prognostic gene signature for hepatocellular carcinoma.

Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns. Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package "limma". Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan-Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the "rms" package. Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes, IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. In conclusion, the prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

Macrophage migration inhibitory factor in the pathogenesis of leukemia (Review).

Leukemia is a group of malignant diseases of clonal hematopoietic stem­progenitor cells and its pathological mechanisms remain to be elucidated. Genetic and epigenetic abnormalities, as well as microenvironmental factors, including cytokines, serve critical roles in leukaemogenesis. Macrophage migration inhibitory factor (MIF) has been presented as one of the key regulators in tumorigenesis, angiogenesis and tumor metastasis. This article focuses on the functional role of MIF and its pathway in cancer, particularly in leukemia. MIF/CD74 interaction serves prominent roles in tumor cell survival, such as upregulating BCL­2 and CD84 expression, and activating receptor­type tyrosine phosphatase ζ. Furthermore, MIF upregulation forms a pro­tumor microenvironment in response to hypoxia­induced factors and promotes pro­inflammatory cytokine production. Additionally, polymorphisms of the MIF promoter sequence are associated with leukemia development. MIF signal­targeted early clinical trials show positive results. Overall, these efforts provide a promising means for intervention in leukemia.

UHRF1 regulates the transcriptional repressor HBP1 through MIF in T acute lymphoblastic leukemia.

Macrophage migration inhibitory factor (MIF) has been confirmed as an oncogene in solid tumor development, and its overexpression causes cell proliferation in T acute lymphoblastic leukemia (T­ALL); however, the underlying mechanisms remain unclear. The overexpression of MIF promotes cellular transformation and proliferation, in part, through interaction with UHRF1. Nevertheless, overexpression of UHRF1 cannot upregulate MIF expression in T­ALL. New insights into MIF regulation in T­ALL are imperative to offer the opportunity for therapeutic intervention. In the present study, using RT­qPCR, western blot analysis, confocal microscopy and RNA sequence, we report the identification and validation of UHRF1 as a negative regulator of MIF, which functions to downregulate MIF expression by binding to the CATT repeat sequence of the MIF promoter. By contrast, HMG­box protein 1 (HBP1) functions as a positive regulator of MIF. Moreover, we demonstrated that HBP1 suppressive signaling is reduced by UHRF1 through promotion of the interaction between MIF and HBP1. MIF deficiency caused by UHRF1 knockdown resulted in enhanced apoptosis in T­ALL as compared with that caused by decreased MIF or increased HBP1 expression alone. These results identify UHRF1 as a key regulator of MIF transcription in T­ALL, although these transcription factors possess opposite regulatory functions. Thus, this mechanism may provide insight into how to effectively prevent MIF­dependent oncogenic activity. Finally, T­ALL mice possessing high HBP1 or low UHRF1 expression levels are associated with longer survival as compared with control mice, with UHRF1­knockdown mice living the longest. Taken together, these findings indicate that MIF and its regulators are potential treatment targets and biomarkers for the prediction of prognosis in T­ALL.

Establishment of magnetic resonance imaging 3D reconstruction technology of orbital soft tissue and its preliminary application in patients with thyroid-associated ophthalmopathy.

OBJECTIVE: Effective management of thyroid-associated ophthalmopathy (TAO) requires precise identification of the disease activity period as it is responsive to immunosuppressive treatment. Quantitative evaluations of orbital soft tissue are useful for analysing disease stages. We aimed to establish a method for orbital soft tissue volume calculation based on magnetic resonance imaging (MRI) data using 3D reconstruction technology. Furthermore, we validated the accuracy and precision of this method and investigated volume differences between patients with TAO and healthy individuals. MATERIALS AND METHODS: Using Mimics software for 3D reconstruction based on orbital MRI data, we quantitatively measured orbital fat volume (FV) and extraocular muscle volume (MV) using a manual phantom, and in patients with TAO and healthy volunteers (n = 10 each). The phantom was made using a combination of butter and chicken muscle and 2 observers measured its volume. Volume calculations were compared to a previously established standard volume. One observer measured a typical TAO case 10 times to calculate intra-observer variability while 3 observers independently measured 10 patients with TAO each to calculate interobserver variability. Orbital soft tissue volumes between 10 patients with TAO and 10 healthy individuals were compared. RESULTS: The precision of calculations for the phantom between the 2 observers varied from -4.60% to -2.78% for FV and between -4.13% to 0.71% for MV. Mean differences among repetitive calculations were lower than 4%, except during measurement of MV, which was 5.84%. The intraclass correlation coefficient varied from 0.976 to 0.996. FV was 15.53 ± 3.06 mL in patients with TAO and 11.32 ± 1.68 mL(P = .001)in healthy individuals, while MV was 3.19 ± 0.82 mL in patients with TAO and 2.45 ± 0.57 mL(P = .030)in healthy individuals. CONCLUSIONS: This method of calculating orbital soft tissue volumes based on MRI data and 3D reconstruction is both reliable and accurate as it yielded significant differences in tissue volume between patients with TAO and healthy individuals.