A novel disulfide death-related genes prognostic signature identifies the role of IPO4 in glioma progression.

BACKGROUND: "Disulfide death," a form of cellular demise, is triggered by the abnormal accumulation of intracellular disulfides under conditions of glucose deprivation. However, its role in the prognosis of glioma remains undetermined. Therefore, the main objective of this study is to establish prognostic signature based on disulfide death-related genes (DDRGs) and to provide new solutions in choosing the effective treatment of glioma. METHODS: The RNA transcriptome, clinical information, and mutation data of glioma samples were sourced from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), while normal samples were obtained from the Genotype-Tissue Expression (GTEx). DDRGs were compiled from previous studies and selected through differential analysis and univariate Cox regression analysis. The molecular subtypes were determined through consensus clustering analysis. Further, LASSO analysis was employed to select characteristic genes, and subsequently, a risk model comprising seven DDRGs was constructed based on multivariable Cox analysis. Kaplan-Meier survival curves were employed to assess survival differences between high and low-risk groups. Additionally, functional analyses (GO, KEGG, GSEA) were conducted to explore the potential biological functions and signaling pathways of genes associated with the model. The study also explored immune checkpoint (ICP) genes, immune cell infiltration levels, and immune stromal scores. Finally, the effect of Importin-4(IPO4) on glioma has been further confirmed through RT-qPCR, Western blot, and cell functional experiments. RESULTS: 7 genes associated with disulfide death were obtained and two subgroups of patients with different prognosis and clinical characteristics were identified. Risk signature was subsequently developed and proved to serve as an prognostic predictor. Notably, the high-risk group exhibited an immunosuppressive microenvironment characterized by a high concentration of M2 macrophages and regulatory T cells (Tregs). In contrast, the low-risk group showed lower half-maximal inhibitory concentration (IC50) values. Therefore, patients in the high-risk group may benefit more from immunotherapy, while patients in the low-risk group may benefit more from chemotherapy. In addition, in vitro experiments have shown that inhibition of the expression of IPO4 leads to a significant reduction in the proliferation, migration, and invasion of glioma cells. CONCLUSION: This study identified two glioma subtypes and constructed a prognostic signature based on DDRGs. The signature has the potential to optimize the selection of patients for immune- and chemotherapy and provided a potential therapeutic target for glioma.

A machine learning model for distinguishing Kawasaki disease from sepsis.

KD is an acute systemic vasculitis that most commonly affects children under 5 years old. Sepsis is a systemic inflammatory response syndrome caused by infection. The main clinical manifestations of both are fever, and laboratory tests include elevated WBC count, C-reactive protein, and procalcitonin. However, the two treatments are very different. Therefore, it is necessary to establish a dynamic nomogram based on clinical data to help clinicians make timely diagnoses and decision-making. In this study, we analyzed 299 KD patients and 309 sepsis patients. We collected patients' age, sex, height, weight, BMI, and 33 biological parameters of a routine blood test. After dividing the patients into a training set and validation set, the least absolute shrinkage and selection operator method, support vector machine and receiver operating characteristic curve were used to select significant factors and construct the nomogram. The performance of the nomogram was evaluated by discrimination and calibration. The decision curve analysis was used to assess the clinical usefulness of the nomogram. This nomogram shows that height, WBC, monocyte, eosinophil, lymphocyte to monocyte count ratio (LMR), PA, GGT and platelet are independent predictors of the KD diagnostic model. The c-index of the nomogram in the training set and validation is 0.926 and 0.878, which describes good discrimination. The nomogram is well calibrated. The decision curve analysis showed that the nomogram has better clinical application value and decision-making assistance ability. The nomogram has good performance of distinguishing KD from sepsis and is helpful for clinical pediatricians to make early clinical decisions.

A Novel Defined Pyroptosis-Related Gene Signature for Predicting Prognosis and Treatment of Glioma.

Pyroptosis, a form of programmed cell death, that plays a significant role in the occurrence and progression of tumors, has been frequently investigated recently. However, the prognostic significance and therapeutic value of pyroptosis in glioma remain undetermined. In this research, we revealed the relationship of pyroptosis-related genes to glioma by analyzing whole transcriptome data from The Cancer Genome Atlas (TCGA) dataset serving as the training set and the Chinese Glioma Genome Atlas (CGGA) dataset serving as the validation set. We identified two subgroups of glioma patients with disparate prognostic and clinical features by performing consensus clustering analysis on nineteen pyroptosis-related genes that were differentially expressed between glioma and normal brain tissues. We further derived a risk signature, using eleven pyroptosis-related genes, that was demonstrated to be an independent prognostic factor for glioma. Furthermore, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to implement functional analysis of our gene set, and the results were closely related to immune and inflammatory responses in accordance with the characteristics of pyroptosis. Moreover, Gene Set Enrichment Analysis (GSEA) results showed that that the high-risk group exhibited enriched characteristics of malignant tumors in accordance with its poor prognosis. Next, we analyzed different immune cell infiltration between the two risk groups using ssGSEA. Finally, CASP1 was identified as a core gene, so we subsequently selected an inhibitor targeting CASP1 and simulated molecular docking. In addition, the inhibitory effect of belnacasan on glioma was verified at the cellular level. In conclusion, pyroptosis-related genes are of great significance for performing prognostic stratification and developing treatment strategies for glioma.

Integrative analysis of a novel 5 methylated snoRNA genes prognostic signature in patients with glioma.

Aim: To explore the prognostic value of methylated snoRNA genes in glioma and construct a prognostic risk signature. Materials & methods: We retrieved clinical information and 450K methylation data from The Cancer Genome Atlas and obtained five methylated snoRNA genes. Then we established a risk signature and verified the effect of SNORA71B on glioma cells with functional assays. Results: A risk signature containing five methylated snoRNA genes was constructed and demonstrated to be an independent predictor of glioma prognosis. Silencing SNORA71B restrained the proliferation, migration and invasion of glioma cells and reduced the expression of mesenchymal and cell cycle marker proteins. Conclusion: This study constructed a methylated snoRNA gene risk signature, which may provide a reference for glioma patients' prognosis assessment.