Background/Objectives: Breast cancer is the leading cause of cancer-related mortality in women, highlighting the urgent need for robust prognostic tools to enable individualized risk stratification. Methods: Transcriptomic data from 1075 breast cancer and 113 adjacent normal tissues in The Cancer Genome Atlas (TCGA) were integrated with clinical information. Differential expression analysis identified 531 immune-related genes, which were further selected by univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct a 13-gene prognostic signature. The model was validated in an independent cohort (n = 327). Tumor immune microenvironment and single-cell RNA sequencing data were analyzed to explore underlying biological differences. Results: The 13-gene signature effectively stratified patients into low- and high-risk groups with significantly different overall survival in both the TCGA cohort (log-rank p < 0.0001; C-index = 0.678; 5-year AUC = 0.72) and the validation cohort (log-rank p < 0.0001; C-index = 0.703; 3-year AUC = 0.81). Low-risk tumors exhibited an antitumor immune microenvironment enriched in CD8⁺ T cells, T follicular helper (Tfh) cells, and M1 macrophages, whereas high-risk tumors were dominated by immunosuppressive regulatory T cells and M2 macrophages (all p < 0.0001). Single-cell analysis revealed expansion of malignant epithelial cells and inflammatory cancer-associated fibroblasts (iCAFs) in high-risk tumors, with higher iCAF scores significantly associated with poorer survival (log-rank p = 0.00036). Conclusions: Collectively, this study delivers a rigorously validated 13-gene immune signature whose prognostic utility is rooted in distinct immune microenvironmental features, while unveiling iCAF-targeted therapeutic strategies as a promising intervention avenue.

Background/Objectives: Ustekinumab (UST) is an effective and safe drug for treating Crohn’s disease (CD), but data on disease progression after UST treatment is limited. This study aimed to develop a clinical decision support tool (CDST) to identify Chinese patients with CD less likely to experience disease progression during UST treatment. Methods: A multicenter, retrospective observational study was conducted among Chinese patients with CD who started UST treatment between 1 May 2020 and 20 October 2022. Baseline characteristics, defined as the measurements taken closest to, and prior to, the first dose of UST, were collected. Disease progression, defined as CD-related surgery, hospitalization, and complications, was evaluated by week 52 (±4 weeks). Predictors were identified using logistic regression, and a UST-specific CDST (UST-CDST) was developed. The UST-CDST was then internally and externally validated using the area under the receiver operating characteristic curve (AUC). Results: Among 602 enrolled patients, 533 were included in the analysis. Four factors were suggestively associated with disease progression: prior biologics usage, baseline disease severity, baseline C-reactive protein, and baseline hemoglobin. The prediction model demonstrated an AUC of 0.88 in internal validation and 0.66 in external validation. The UST-CDST effectively stratified patients into low-risk or high-risk groups for disease progression within one year. Conclusions: A UST-CDST was developed and validated to identify patients with CD less or more likely to experience disease progression around week 52 under UST therapy. The scoring system promises to facilitate clinical decision-making and personalized treatment.