CTL4

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses.

ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients.

The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Autosomal dominant loss-of-function (LoF) variants in cytotoxic T-lymphocyte associated protein 4 (CTLA4) cause immune dysregulation with autoimmunity, immunodeficiency and lymphoproliferation (IDAIL). Incomplete penetrance and variable expressivity are characteristic of IDAIL caused by CTLA-4 haploinsufficiency (CTLA-4h), pointing to a role for genetic modifiers. Here, we describe an IDAIL proband carrying a maternally inherited pathogenic CTLA4 variant and a paternally inherited rare LoF missense variant in CLEC7A, which encodes for the β-glucan pattern recognition receptor DECTIN-1. The CLEC7A variant led to a loss of DECTIN-1 dimerization and surface expression. Notably, DECTIN-1 stimulation promoted human and mouse regulatory T cell (T_reg) differentiation from naïve αβ and γδ T cells, even in the absence of transforming growth factor-β. Consistent with DECTIN-1's T_reg-boosting ability, partial DECTIN-1 deficiency exacerbated the T_reg defect conferred by CTL4-4h. DECTIN-1/CLEC7A emerges as a modifier gene in CTLA-4h, increasing expressivity of CTLA4 variants and acting in functional epistasis with CTLA-4 to maintain immune homeostasis and tolerance.

Ferroptosis and lncRNAs both play crucial roles in cancers. But the roles of ferroptosis-related lncRNAs (FRLncs) in HBV-related HCC (HBV-HCC) remain ambiguous.

The gene expression profile and clinical data were originated from the Cancer Genome Atlas (TCGA) database. The risk signature was constructed by FRLncs based on the Cox regression analysis. The survival curve, Cox regression analysis, and time-dependent receiver operating characteristic (ROC) curve were adopted to verify the independence and reliability of the signature. A nomogram was established. Immune-infiltrating cells, immune functions, and checkpoints were analyzed.

A risk signature composed of 7 FRLncs (LINC00942, AC131009.1, POLH-AS1, AC090772.3, MKLN1-AS, AC009403.1, AL031985.3) was constructed and divided HBV-HCC patients into high- and low-risk groups. Patients in the high-risk group showed a poor prognosis. The area under curves (AUC) of the signature for 1-, 3-, and 5-year was satisfactory. A nomogram composed of gender, stage, age, grade, and risk signature was established. The risk signature and nomogram displayed appreciable independence and reliability in HBV-HCC patients. The T-cell CD8 + , monocyte, and macrophage M1 were expressed differently significantly in HCC patients, while macrophage M2 showed an obvious difference in the HBV-HCC patients between the different risk groups. PDCD1 and CTL4 were expressed higher in the high-risk group of HCC patients.

A 7-lncRNA signature was identified as a potential prognostic predictor for HBV-HCC patients. Immune therapy may be a promising strategy for HCC patients, especially HBV-HCC patients.