Immunity has vital research value and promising applications in triple-negative breast cancer (TNBC). Nevertheless, few bibliometric analyses have systematically investigated this area. This study aimed to comprehensively review the collaboration and impact of countries, institutions, authors, and journals on the role of immunity in TNBC from a bibliometric perspective, evaluate the keyword co-occurrence of the knowledge structure, and identify hot trends and emerging topics. Articles and reviews related to immunity in TNBC were retrieved from the Web of Science core collection using subject search. A bibliometric study was conducted primarily using CiteSpace and VOSviewer. A total of 3,104 articles and reviews were included from January 1, 2014, through December 31, 2024. The number of articles on immunization in TNBC is rising. These publications are mainly from 415 institutions in 82 countries, led by China and the USA. Among these publications, Lajos Pusztai published the most papers, while Peter Schmid was co-cited the most. The most productive journals focused on molecular biology, biological immunology, and clinical medicine. Furthermore, co-citation analysis revealed that tumor microenvironment, biomarkers, and immune checkpoint inhibitors are current and developing research areas. The keywords "immunotherapy" and "nanoparticles" are also likely to be new trends and focal points for future research. This study adopted bibliometric and visualization methods to provide a comprehensive review of the research on immunization in TNBC. This article will help researchers better understand the dynamic evolution of the role of immunity in TNBC and identify areas for future research.

Metabolic alterations and immune dysfunction within the gastric tumor microenvironment critically drive gastric cancer (GC) progression and therapeutic resistance. Although single‐cell RNA sequencing (scRNA‐seq) has unveiled cellular heterogeneity in GC, the metabolic landscapes of tumor cells and their interplay with immune components remain underexplored. By integrating scRNA‐seq data from 35,633 cells across 23 GC tissues (GSE150290), bulk RNA‐seq data from UCSC Xena, and two independent microarray cohorts (GSE26899, GSE62254), we systematically characterized metabolic heterogeneity and identified immune‐related prognostic biomarkers. Reclustering of malignant epithelial cells revealed distinct metabolic phenotypes, with the citrate cycle and oxidative phosphorylation pathways emerging as key drivers of intratumoral diversity and T cell differentiation. Through machine learning and survival analyses, we discovered a novel risk score model composed of 6 T cell differentiation signatures, which stratified patients into high‐ and low‐risk groups with significant differences in overall survival. Notably, this model outperformed traditional clinicopathological factors in predicting prognosis, validated in both bulk RNA‐seq and microarray datasets. Immunohistochemistry further confirmed the prognostic value of key regulatory proteins (RGS1, CXCR4, CTLA4, ARPP19, ZNRF1, and ZNF207). Our findings highlight the metabolic immune crosstalk in GC and provide a promising biomarker panel for precision risk stratification and potential immunotherapeutic targets.