Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein–protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.

Background: RAD51, a critical protein for DNA repairment, has been found to associate with multiple cancer types, but, so far, a systematic pan-cancer analysis of RAD51 has not been done yet. Methods: Data were obtained from multiple open databases and genetic alteration, gene expression, survival association, functional enrichment, stemness, mutation association, immunity association, and drug therapy association of RAD51were analyzed. A prognostic model of RAD51 for overall glioma was constructed as an example application of RAD51 as a biomarker. Results: RAD51 was overexpressed in 28 types of cancers and was associated with worse overall survival in 11 cancer types. RAD51 correlated genes were enriched in cell cycle terms. RAD51 was associated with cancer stemness, tumor mutational burden, and multiple immunomodulators in different cancer types. RAD51 expression was different across immune subtypes in 11 cancer types. RAD51 was closely associated with cancer immune microenvironments in some cancer types. Proliferating T cells was the cell type that expressed highest RAD51 across most of the cancer samples analyzed. RAD51 expression had an AUC of over 0.5 in 12 of the 23 ICB subcohorts. The Tumor Immune Dysfunction and Exclusion of 9 cancer types were different between RAD51 high and low groups. RAD51 expression showed negative correlations with the sensitivity of most drugs. A prognostic nomogram was constructed with a high confidence. Conclusion: RAD51 is a clinical valuable biomarker for multiple cancer types, regarding its potential power for diagnosis, prognosis, and therapeutic prediction.

Background: COVID-19, including its post-acute sequelae (Long COVID), is increasingly recognized as involving persistent immune dysregulation and chronic inflammation. Severe and prolonged disease states are often accompanied by sustained cytokine release, immune cell exhaustion, and ongoing cell-cell communication that shapes the inflammatory milieu. Among immune subsets, CD8 ⁺ T cells play a central role in antiviral defense, yet the molecular mechanisms linking their dysfunction to prolonged inflammation remain incompletely understood. Methods: We analyzed 73,110 peripheral blood mononuclear cells (PBMCs) from individuals across four disease states (Healthy, Exposed, Infected, and Hospitalized) using single-cell RNA sequencing. Immune cell subsets were annotated, and T cell heterogeneity was profiled. Cytokine and inflammatory scores were calculated to assess immune activation. Differentially expressed genes (DEGs) underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Cell-cell communication was evaluated to map ligand-receptor networks. Additionally, nine machine learning models were trained on a bulk RNA-seq cohort, and the SHapley Additive exPlanations (SHAP) framework was applied to interpret key predictive genes. Results: Progressive disease severity was associated with a decline in T cell proportions, enrichment of pro-inflammatory myeloid cells, and elevated cytokine expression, particularly IL-32. Memory CD8 ⁺ T cells showed increased exhaustion and inflammatory scores while maintaining a central position in MHC-I-mediated communication networks. Persistent activation of immune and metabolic pathways, including antigen presentation and oxidative phosphorylation, was observed in prolonged disease states. Seven genes (RPS26, RPS29, RPL36, RPL39, RPS28, RPS21, and CD3E) were identified as strong predictors of chronic immune dysregulation, with the XGBoost model achieving the highest AUC. SHAP analysis confirmed their contributions to disease classification. Conclusion: This study maps the immune landscape of COVID-19 and Long COVID at single-cell resolution, revealing that persistent immune cell communication, particularly involving memory CD8 ⁺ T cells, may sustain chronic inflammation beyond the acute phase. The identified molecular signatures offer potential biomarkers and therapeutic targets for mitigating post-viral inflammatory syndromes.

[This corrects the article DOI: 10.3389/fimmu.2025.1689507.].