Ferroptosis-Related Gene Signature for Prognosis Prediction in Acute Myeloid Leukemia and Potential Therapeutic Options.

Background: Limited data were available to understand the significance of ferroptosis in leukemia prognosis, regardless of the genomic background. Methods: RNA-seq data from 151 AML patients were analyzed from The Cancer Genome Atlas (TCGA) database, along with 70 healthy samples from the Genotype-Tissue Expression (GTEx) database. Ferroptosis-related genes (FRGs) features were constructed by multivariate COX regression analysis and risk scores were calculated for each sample and a novel prediction model was identified. The validation was carried out using data from 35 AML patients and 13 healthy controls in our cohort. Drug sensitivity analysis was conducted on various chemotherapeutic drugs. Results: A signature of 10 FRGs was identified, as prognostic predictors for AML, and the risk scores were calculated to constructed the prognostic features of FRGs. Significantly lower overall survival was observed in the high-risk group. The predictive ability of these features for AML prognosis was confirmed using Cox regression analysis, ROC curves, and DCA. The prediction model performed well in our clinical practices, and had its potential superiority when comparing to classical NCCN risk stratification. Multiple chemotherapy drugs, including paclitaxel, dactinomycin, cisplatin, etc. had a lower IC50 in FRGs high-risk group than low-risk group. Conclusion: The AML prognosis model based on FRGs accurately predicts AML prognosis and drug sensitivity, and the drugs identified worthy further investigation.

miR-664a-5p promotes experimental membranous nephropathy progression through HIPK2/Calpain1/GSα-mediated autophagy inhibition.

We previously found that miR-664a-5p is specifically expressed in urinary exosomes of idiopathic membranous nephropathy (IMN) patients. Homeodomain-interacting protein kinase 2 (HIPK2), a nuclear serine/threonine kinase, plays an important role in nephropathy. But the function of these factors and their connection in MN are unclear. To investigate the function and mechanism of miR-664a-5p in MN, the miR-664a-5p expression in HK-2 cells, exosomes, podocytes and renal tissues were studied, as well as cell growth and apoptosis of these cells, the binding of miR-664a-5p to HIPK2 mRNA, the levels of relative proteins and autophagy. The MN progression in MN mice model was also studied. Albumin increased the miR-664a-5p content and apoptosis of HK-2 cells, which was blocked by miR-664a-5p antagomir. miR-664a-5p bound to the 3' UTR of HIPK2 mRNA, resulting in the up-regulation of Calpain1, GSα shear and the inhibition of autophagy level. Autophagy inhibitor CQ blocked the protective effect of miR-664a-5p antagomir, HIPK2 overexpression, Calpain inhibitor SJA6017 on albumin-mediated injury. MiR-664a-5p from albumin-treated HK-2 cells could be horizontally transported to podocytes through exosomes. Exosomes from albumin-treated HK-2 cells promoted progression of MN mice, AAV-Anti-miR-664-5p (mouse homology miRNA) could improve them. Albumin increases the miR-664a-5p level and causes changes of HIPK2/Calpain1/GSα pathway, which leads to autophagy inhibition and apoptosis up-regulation of renal tubular epithelial cells. miR-664a-5p can horizontally enter podocytes through exosomes resulting in podocytes injury. Targeted inhibition of miR-664a-5p can reduce the apoptosis of renal tubule cells and podocytes, and may improve the MN progression.

A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning.

Objective: Due to the increased likelihood of progression of severe pneumonia, the mortality rate of the elderly infected with coronavirus disease 2019 (COVID-19) is high. However, there is a lack of models based on immunoglobulin G (IgG) subtypes to forecast the severity of COVID-19 in elderly individuals. The objective of this study was to create and verify a new algorithm for distinguishing elderly individuals with severe COVID-19. Methods: In this study, laboratory data were gathered from 103 individuals who had confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using a retrospective analysis. These individuals were split into training (80%) and testing cohort (20%) by using random allocation. Furthermore, 22 COVID-19 elderly patients from the other two centers were divided into an external validation cohort. Differential indicators were analyzed through univariate analysis, and variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression. The severity of elderly patients with COVID-19 was predicted using a combination of five machine learning algorithms. Area under the curve (AUC) was utilized to evaluate the performance of these models. Calibration curves, decision curves analysis (DCA), and Shapley additive explanations (SHAP) plots were utilized to interpret and evaluate the model. Results: The logistic regression model was chosen as the best machine learning model with four principal variables that could predict the probability of COVID-19 severity. In the training cohort, the model achieved an AUC of 0.889, while in the testing cohort, it obtained an AUC of 0.824. The calibration curve demonstrated excellent consistency between actual and predicted probabilities. According to the DCA curve, it was evident that the model provided significant clinical advantages. Moreover, the model performed effectively in an external validation group (AUC=0.74). Conclusion: The present study developed a model that can distinguish between severe and non-severe patients of COVID-19 in the elderly, which might assist clinical doctors in evaluating the severity of COVID-19 and reducing the bad outcomes of elderly patients.

Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome.

Introduction: Proteomics technology has been used in various fields in recent years for the Q6 exploration of novel markers and the study of disease pathogenesis, and has become one of the most important tools for researchers to explore unknown areas. However, there are fewer studies related to the construction of clinical models using proteomics markers. Methods: In our previous study we used DIA proteomics to screen for proteins that were significant in 31 PCOS patients compared to women of normal reproductive age. In this study, we used logistic regression among these protein markers to screen out variables with diagnostic value and constructed logistic regression models. Results: We constructed a logistic model using these protein markers, where HIST1H4A (OR=1.037) was an independent risk factor for polycystic ovary syndrome and TREML1 (OR=0.976) were protective factors for the disease. The logistic regression model equation is: Logit (PCOS) =0.036*[HIST1H4A]-0.024*[TREML1]-16.368. The ROC curve analyzing the diagnostic value of the model has an AUC value of 0.977 and a Youden index of0.903, which gives a cutoff value of 0.518 at this point. The model has a sensitivity of 93.5% and a specificity of 96.8%. Calibration curves show fair consistency of the model. Discussion: Our study is the first to use proteomic results with clinical biochemical data to construct a logistic regression model, and the model is consistent. However, our study still needs a more complete sample to confirm our findings.

Untargeted metabolomic approach to study the serum metabolites in women with polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome (PCOS) is not only a kind of common endocrine syndrome but also a metabolic disorder, which harms the reproductive system and the whole body metabolism of the PCOS patients worldwide. In this study, we aimed to investigate the differences in serum metabolic profiles of the patients with PCOS compared to the healthy controls. MATERIAL AND METHODS: 31 PCOS patients and 31 matched healthy female controls were recruited in this study, the clinical characteristics data were recorded, the laboratory biochemical data were detected. Then, we utilized the metabolomics approach by UPLC-HRMS technology to study the serum metabolic changes between PCOS and controls. RESULTS: The metabolomics analysis showed that there were 68 downregulated and 78 upregulated metabolites in PCOS patients serum compared to those in the controls. These metabolites mainly belong to triacylglycerols, glycerophosphocholines, acylcarnitines, diacylglycerols, peptides, amino acids, glycerophosphoethanolamines and fatty acid. Pathway analysis showed that these metabolites were enriched in pathways including glycerophospholipid metabolism, fatty acid degradation, fatty acid biosynthesis, ether lipid metabolism, etc. Diagnosis value assessed by ROC analysis showed that the changed metabolites, including Leu-Ala/Ile-Ala, 3-(4-Hydroxyphenyl) propionic acid, Ile-Val/Leu-Val, Gly-Val/Val-Gly, aspartic acid, DG(34:2)_DG(16:0/18:2), DG(34:1)_DG(16:0/18:1), Phe-Trp, DG(36:1)_DG(18:0/18:1), Leu-Leu/Leu-Ile, had higher AUC values, indicated a significant role in PCOS. CONCLUSION: The present study characterized the difference of serum metabolites and related pathway profiles in PCOS patients, this finding hopes to provide potential metabolic markers for the prognosis and diagnosis of this disease.

DIA proteomics analysis through serum profiles reveals the significant proteins as candidate biomarkers in women with PCOS.

BACKGROUND: The aim of this study was to apply proteomic methodology for the analysis of proteome changes in women with polycystic ovary syndrome (PCOS). MATERIAL AND METHODS: All the participators including 31 PCOS patients and 31 healthy female as controls were recruited, the clinical characteristics data was recorded at the time of recruitment, the laboratory biochemical data was detected. Then, a data-independent acquisition (DIA)-based proteomics method was performed to compare the serum protein changes between PCOS patients and controls. In addition, Western blotting was used to validate the expression of identified proteomic biomarkers. RESULTS: There were 80 proteins differentially expressed between PCOS patients and controls significantly, including 54 downregulated and 26 upregulated proteins. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that downregulated proteins were enriched in platelet degranulation, cell adhesion, cell activation, blood coagulation, hemostasis, defense response and inflammatory response terms; upregulated proteins were enriched in cofactor catabolic process, hydrogen peroxide catabolic process, antioxidant activity, cellular oxidant detoxification, cellular detoxification, antibiotic catabolic process and hydrogen peroxide metabolic process. Receiver operating characteristic curves analysis showed that the area under curve of Histone H4 (H4), Histone H2A (H2A), Trem-like transcript 1 protein (TLT-1) were all over than 0.9, indicated promising diagnosis values of these proteins. Western blotting results proved that the detected significant proteins, including H4, H2A, TLT-1, Peroxiredoxin-1, Band 3 anion transport protein were all differently expressed in PCOS and control groups significantly. CONCLUSION: These proteomic biomarkers provided the potentiality to help us understand PCOS better, but future studies comparing systemic expression and exact role of these candidate biomarkers in PCOS are essential for confirmation of this hypothesis.

The Diagnostic Value of Autoantibodies to Asialoglycoprotein Receptor Combined with Antinuclear Antibody in Autoimmune Hepatitis.

BACKGROUND: This study aimed to analyze the combined diagnostic value of autoantibodies to asialoglycoprotein receptor (anti-ASGPR) and antinuclear antibody (ANA) for autoimmune hepatitis (AIH), and to further explore the role of anti-ASGPR in autoimmune hepatitis. METHODS: According to the clinical diagnosis, the patients were divided into AIH group, viral hepatitis group, alcoholic hepatitis group, fatty liver group, and normal group, then the four groups were compared with the normal group, and the sensitivity and specificity of Anti-ASGPR, ANA and their combination in the diagnosis of AIH were analyzed. Then AIH patients were divided into anti-ASGPR positive group and negative group. The two groups were compared regarding the difference of biochemical and immunological indicators. RESULTS: Only the positive rate of anti-ASGPR and ANA in the AIH group and normal disease group were statistically significant (p < 0.05); in the AIH group, the positive rate of anti-ASGPR and ANA was 63.16% and 71.93%, respectively, the sensitivity and specificity of anti-ASGPR and ANA in parallel were 87.72% and 79.02%, respec-tively, and the Youden index was 0.6674. AIH patients with anti-ASGPR positive had higher levels of immunoglobin G (IgG), alanine aminotransferase (ALT), interleukin-6 (IL-6), interleukin-10 (IL-10), and lower complement C3 than AIH patients with anti-ASGPR negative. CONCLUSIONS: The combined positive of anti-ASGPR and ANA in serum has diagnostic value for AIH, and anti-ASGPR may be related to the disease activity, inflammatory reaction, and pathogenesis of AIH.

Imbalance of Th17/Treg cells in pathogenesis of patients with human leukocyte antigen B27 associated acute anterior uveitis.

Th17 and regulatory T cells, involved in the pathogenesis of several autoimmune diseases, are new lineages of CD4+ T helper cells. However, the role of their imbalance in human leukocyte antigen B27-associated acute anterior uveitis has not been elucidated. In our study, the percentages of Th17 and Treg cells, their molecular markers and related factors in peripheral blood of patients and healthy controls were measured by flow cytometry, real-time RT-PCR and ELISA. We observed a remarkable increase of CD4+ and CD4+IL-17+ T cells in peripheral blood of patients compared to controls. The molecular markers and related factors of Th17 cell were also showed a distinct elevation. Interestingly, we observed an obvious decrease of CD4+CD25+Foxp3+ T cells and Foxp3 mRNA level in patients. The ratio of Th17/Treg in patients was dramatically higher than controls. Moreover, the ratio of Th17/Treg cells had a more significantly positive correlation with the disease activity score than Th17 cells whereas Treg cells had a negative correlation. Our findings demonstrated a distinct increase of Th17 cells and a significant decrease of Treg cells in patients compared to controls. The imbalance of Th17 and Treg cells may play a vital role in the pathogenesis of the disease.