Background: NOTCH signaling has been shown to play a role in the production of interleukin-22 (IL-22) by CD4⁺ T cells. Multiple T-helper (Th) cell populations secrete IL-22. Th22 (CD4⁺IL22⁺IFNγ⁻IL17A⁻) cells are a subgroup of CD4⁺ effector T cells that primarily generate IL-22. The regulatory mechanisms of the NOTCH signaling pathway involved in differentiation of the Th22 cell subset have not been completely elucidated. This study aimed to further explore the involvement of NOTCH signaling in Th22 differentiation. Methods: In vitro combination of IL-6, IL-23, and tumor necrosis factor-α (TNF-α) treatment with naïve CD4⁺ T cells established the Th22 cell induced model. NOTCH signaling was activated by jagged-1 and inhibited by (2S)-N-[(3,5-difluorophenyl) acetyl]-L-alanyl-2-phenyl]glycine 1,1-dimethylethyl ester (DAPT). HES-1 siRNA and HES-1 vector were employed to knock down and induce overexpression of HES-1 to investigate the effect of NOTCH signaling on the differentiation of CD4⁺T cells into Th22 cells. Results: We observed that the proportion of Th22 cells, along with Hes-1, Ahr, and Il-22 mRNA and protein expression, was increased by both jagged-1 and overexpression of HES-1. On the other hand, after the combined cytokine treatment of cells, and exposure to jagged-1 and DAPT or HES-1 siRNA, there was a decrease in the Th22 cell proportion, mRNA and protein expression of HES-1, AHR, and IL-22. Conclusions: Our study demonstrates that HES-1 enhancement in AHR and IL-22 up-regulation of NOTCH signaling can promote the skewing of naïve CD4⁺T cells toward Th22 cells. Also, the results of our study show that HES-1 is a crucial factor in Th22 cell differentiation.

The prognostic significance of ferroptosis-related genes is well known. However, survival- and ferroptosis-related genes are not currently considered in risk scoring models for diffuse large B-cell lymphoma (DLBCL). Ferroptosis regulators and markers were downloaded from the FerrDb database. The transcriptome profiling data were collected from the cancer genome atlas (TCGA). Transcriptome data and corresponding clinical information of DLBCL were downloaded from the gene expression omnibus (GEO). The validation data were downloaded using the UCSC Xena browser. ConsensusClusterPlus was used to categorize DLBCL samples according to gene expression profiles. The survival function was plotted with the Kaplan-Meier plots. The nomogram was built using multivariate logistic regression analysis and the Cox proportional hazards regression model. Based on the GSE11318 dataset of 203 samples and 267 ferroptosis-related gene expression profiles, we identified four clusters. A total of 19 survival-related genes were found associated with ferroptosis. The prognostic risk scoring model was constructed based on the regression coefficients. The obtained area under the receiver operating characteristic curve (AUC) values were 0.769, 0.801, and 0.791 for 1-, 3-, and 5-year survival, respectively. DLBCL samples with cluster 2 or cancer stage IV have shorter survival. Correlations between the immune infiltration and risk scores of the 12 immune cells were demonstrated. The response of DLBCL to doxorubicin was effectively validated by the risk scoring model. In this study, a ferroptosis-based risk scoring model for patients with DLBCL was constructed and validated in an independent dataset. This risk score model has a better efficacy in predicting survival compared to clinical characteristics.

Ferroptosis is a new form of programmed cell death, which achieved great breakthroughs in cell biology during past decade. However, the regulation of ferroptosis is yet to be identified thoroughly. The latest study published on Nature cell biology by Nguyen and colleagues found a new NADPH sensor, MARCHF6 an E3 ubiquitin ligase, mediates ferroptosis in tumor growth and animal development. This finding provides a novel insight into ubiquitin system and energy metabolism in regulation of ferroptosis, which may open up new avenues for tumor treatment.