Multiple sclerosis (MS) is a common autoimmunity disease of the central nervous system (CNS) that mostly happens in young adults. The chronic clinical features of MS include inflammatory demyelination, infiltration of immune cells, and secretion of inflammatory cytokines, which have been proved to be associated with CD4⁺ T cells. Ferroptosis is a newly discovered programmed cell death mediated by the massive lipid peroxidation and more sensitive to CD4⁺ T cells. However, the effect of ferroptosis of CD4⁺ T cells on the occurrence and progression of MS retains unclear. Here, the experimental autoimmune encephalomyelitis (EAE) model was used to investigate the role of GPX4, a leading inhibitor of ferroptosis, which plays in the function of CD4⁺ T cells. Our results showed that GPX4 was highly expressed in CD4⁺ T cells of MS patients based on existing databases. Strikingly, conditional knockout of GPX4 in CD4^cre mice (cKO mice) significantly alleviated the average symptom scores and immunopathology of EAE. The infiltration of immune cells, including CD4⁺ T and CD8⁺ T cells, and the generation of GM-CSF, TNF-α, and IL-17A, were remarkably reduced in the CNS from cKO mice compared with WT mice. These findings further revealed the vital role of GPX4 in the expansion and function of CD4⁺ T cells. Moreover, GPX4-deficient CD4⁺ T cells were susceptible to ferroptosis in EAE model. Overall, this study provided novel insights into therapeutic strategies targeting GPX4 in CD4⁺ T cells for inhibiting CNS inflammation and treating MS.

Purpose: Psoriasis is a systemic inflammatory disease, and the mechanism that links psoriasis to depression is still elusive. Hence, this study aimed to elucidate the potential pathogenesis of psoriasis and depression comorbidity. Methods: The gene expression profiles of psoriasis (GSE34248, GSE78097 and GSE161683) and depression (GSE39653) were downloaded from the Gene Expression Omnibus (GEO) DataSets. Functional annotation, protein‐protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis were performed, following identification of the common differentially expressed genes (DEGs) of psoriasis and depression. Results: A total of 115 common DEGs (55 up-regulated and 60 down-regulated) were identified between psoriasis and depression. Functional analysis indicated that T cell activation and differentiation were predominantly implicated in the potential pathogenesis of these two diseases. In addition, Th17 cell differentiation and cytokines is closely related to both. Finally, 17 hub genes were screened, including CTLA4, LCK, ITK, IL7R, CD3D, SOCS1, IL4R, PRKCQ, SOCS3, IL23A, PDGFB, PAG1, TGFA, FGFR1, RELN, ITGB5 and TNXB, which re-emphasized the importance of the immune system in psoriasis and depression. Conclusion: Our study reveals the common pathogenesis of psoriasis and depression. These common pathways and hub genes may apply to a molecular screening tool for depression in psoriasis patients, which could help dermatologists optimize patient management in routine care.

Background. The objective of this study is to determine the prognostic factors of keratinizing squamous cell carcinoma of the tongue (KTSCC) and to establish a prognostic nomogram of KTSCC to assist clinical diagnosis and treatment. Methods. This study identified 3874 patients with KTSCC from the Surveillance, Epidemiology, and End Results (SEER) database, and these patients were randomly divided into the training (70, (n=2711) and validation (30, n=1163) cohorts. Cox regression was then used to filter variables. Nomograms were then constructed based on meaningful variables. Finally, the concordance index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration charts, and decision-curve analysis (DCA), were used to evaluate the discrimination, accuracy and effectiveness of the model. Results. A nomogram model was established for predicting the 3-, 5-, and 8-year overall survival (OS) probabilities of patients with KTSCC. The model indicated that age, radiotherapy sequence, SEER stage, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, radiotherapy status, race, lymph node dissection status, and sex were factors influencing the OS of patients with KTSCC. Verified by C-index, NRI, IDI, calibration curve, and DCA curve, our model has better discrimination, calibration, accuracy and net benefit compared to the AJCC system. Conclusions. This study identified the factors that affect the survival of KTSCC patients and established a prognostic nomogram that can help clinicians predict the 3-, 5-, and 8-year survival rates of KTSCC patients.

Background: Psoriasis and metabolic dysfunction–associated fatty liver disease (MAFLD) share pathological features such as chronic inflammation, immune dysregulation, and metabolic disturbance. Increasing evidence suggests biological crosstalk between the two conditions, offering new insights into their shared mechanisms and comanagement. Early-stage MAFLD, characterized by hepatic steatosis without evident inflammation or fibrosis, provides a crucial window for intervention. This study aimed to identify early diagnostic biomarkers linking psoriasis and MAFLD. Methods: Transcriptomic datasets of psoriasis and MAFLD were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify MAFLD-related modules. Shared genes were obtained by intersecting module genes with differentially expressed genes (DEGs) from psoriasis datasets. Machine learning algorithms, including random forest (RF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), were applied to identify hub genes. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis, immune infiltration assessment, Spearman correlation, and experimental validation in psoriasis and MAFLD mouse models. Results: Twenty-nine shared genes were identified and found to be enriched in immune and metabolic pathways. Six hub genes—ADRB2, WNT5A, S100A9, FAM110C, S100A12, and TUBB6—were selected through integrated machine learning analysis and experimental validation. These genes exhibited high diagnostic accuracy and significant correlations with disease severity and immune cell infiltration. Conclusions: This study identified six hub genes—ADRB2, WNT5A, S100A9, FAM110C, S100A12, and TUBB6—as potential cross-disease biomarkers for the comorbidity of psoriasis and MAFLD, and these genes are significantly associated with disease severity. These findings provide new targets for early diagnosis and potential treatment strategies for the comorbidity.