Songyun Zhao

Cellular senescence exerts context‐dependent effects on breast cancer progression and therapeutic response through dynamic interactions with the tumor microenvironment; however, its pathological manifestations, cellular organization, and clinical significance remain incompletely understood. In this study, we integrated whole‐slide pathology images, bulk transcriptomic profiles, single‐cell ribonucleic acid (RNA) sequencing, spatial transcriptomics, and clinical data to systematically characterize senescence‐associated features in breast cancer. Quantitative pathology and deep‐learning features were extracted from hematoxylin and eosin‐stained images, and a senescence‐related signature (SRS) was constructed using machine‐learning survival models. The SRS effectively stratified patients into distinct prognostic groups and showed consistent performance across the internally divided TCGA‐BRCA subsets, with high‐SRS patients exhibiting significantly poorer survival. Single‐cell analyses revealed that senescence activity was predominantly enriched in epithelial and endothelial cells, whereas fibroblasts acted as major signal‐sending populations and mediated enhanced collagen‐related communication with tumor cells. Low‐SRS tumors displayed greater immune‐cell infiltration, higher immune‐checkpoint expression, higher immunophenoscores, and more favorable Tumor Immune Dysfunction and Exclusion (TIDE)/immunophenoscore (IPS)‐based predicted response profiles, while also exhibiting distinct drug‐sensitivity profiles. Integrative pathological, transcriptomic, and spatial analyses further identified STARD10 as a senescence‐associated candidate enriched in tumor regions and spatially concordant with senescence‐marker expression at the spot level. Functional experiments showed that STARD10 knockdown reduced cellular metabolic activity in CCK‐8 assays and suppressed migration and invasion, attenuated senescence‐associated β‐galactosidase staining and senescence‐associated secretory phenotype (SASP)‐related cytokine expression, and suppressed tumor growth in vivo. Collectively, these findings establish an integrated pathology‐ and multi‐omics‐derived senescence‐related signature for prognostic stratification and characterization of TIDE/IPS‐based predicted immunotherapy‐response profiles in breast cancer, while identifying STARD10 as a candidate biomarker and a potential target for further mechanistic and therapeutic investigation.

We comment on the recent study by Dong et al. that identified the amphiregulin (AREG)–epidermal growth factor receptor (EGFR) axis as a key driver of fibroblast activation and extracellular matrix deposition in keloids. We expand their findings by discussing the potential roles of immune–stromal interactions, EGFR downstream signalling complexity, extracellular matrix mechanobiology and ligand-specific targeting within the EGFR network. These perspectives may help refine the mechanistic understanding of AREG signalling and guide the development of more precise therapeutic strategies for keloids.

Diabetic wounds, particularly diabetic foot ulcers, represent a significant clinical challenge owing to impaired vascularization, persistent inflammation, and dysfunctional extracellular matrix remodeling. Although adipose‐derived stem cells offer therapeutic potential, their heterogeneity and functional impairment within the diabetic microenvironment limit their efficacy. Using single‐cell RNA sequencing of human adipose and diabetic wound tissues, we identified a distinct CCL2‐expressing ADSC subpopulation that is enriched in obese individuals and exhibits elevated stemness, unique metabolic profiles, and enrichment in pathways related to ECM organization and tissue development. This subpopulation functions as a key communication node, engaging with fibroblasts, macrophages, and endothelial cells through ligand–receptor interactions such as CCL2‐ACKR1, TGFB1‐TGFBR1, and IL34‐CSF1R. Exosomes secreted by these CCL2‐positive ADSCs were found to be enriched in CCL2, TGFB1, and IL34. In a diabetic mouse wound model, CCL2‐ADSC‐derived exosomes significantly accelerated wound closure compared with conventional exosomes, promoting angiogenesis, collagen deposition, and M2‐macrophage polarization while reducing pro‐inflammatory cytokines. In vitro, these exosomes reversed high‐glucose‐induced suppression of endothelial cell proliferation, migration, and tube formation. Mechanistically, CCL2 carried by the exosomes activates the PI3K/AKT/mTOR/HIF‐1α signaling axis in endothelial cells via ACKR1, an effect abolished by CCL2 neutralization or ACKR1 knockdown. Together, these results demonstrate that the CCL2‐positive ADSC subpopulation exerts multi‐cellular and multi‐target therapeutic actions, and that exosomes derived from this subpopulation offer a potent cell‐free strategy to enhance diabetic wound healing by improving vascularization, modulating immune responses, and supporting ECM remodeling.

This letter critically evaluates the recent study by Hu et al. on collagen I–discoidin domain receptor 1 (DDR1) signalling in keloid inflammation, highlighting its role in activating NF-κB and STAT3 pathways. We discuss the potential of dual-target therapy involving DDR1 and adenosine diphosphate ribosylation factor 6 (ARF6) inhibition and identify areas for further exploration, including the involvement of co-receptors, intermediate signalling pathways and collagen remodelling. These insights may guide future therapeutic strategies for keloid treatment.

This letter highlights recent mechanistic insights into Sweet syndrome, focusing on the IL-36–PADI4 inflammatory circuit that links neutrophil activation, NETosis and keratinocyte signalling. We discuss how these findings redefine the immunopathogenesis of neutrophilic dermatoses and identify remaining challenges in translating IL-36-targeted therapies to clinical practice.

Introduction: Keloids are a complex pathological condition of the skin characterized by abnormal proliferation of fibrous tissue and excessive accumulation of extracellular matrix, typically following inflammation after skin injury. Understanding the regulatory mechanisms of immune cells involved in keloid formation is essential for the development of effective treatments. Methods: This study integrated publicly available single-cell RNA sequencing (scRNA-seq) data with our own keloid scRNA-seq samples to investigate the role of triaptosis in shaping the immune microenvironment of keloids. We analyzed the composition and functional status of fibroblast and immune cell subpopulations. Results: Immune cells in keloids, especially CD8⁺ T cells and macrophages, showed significant heterogeneity under the influence of triaptosis regulatory patterns. These triaptosis-associated immune cell clusters exhibited distinct signaling interference compared to mesenchymal fibroblasts and contributed to keloid development. Furthermore, ELMO2 was identified as a key gene with a potential causal relationship to keloids using Summary-data-based Mendelian Randomization and validated through immunofluorescence staining. Conclusion: Our findings reveal the complexity of cell–cell interactions in the keloid immune microenvironment and highlight triaptosis as a potential regulatory mechanism in keloid pathogenesis. The identification of ELMO2 as a key factor offers a promising therapeutic target. This study lays a foundation for developing novel therapeutic strategies and encourages future investigations into the clinical application of triaptosis-related interventions for keloid treatment.

Following complete mesocolic excision (CME), heart failure (HF) emerges as a significant complication, exerting substantial impacts on both short-term and long-term patient prognoses. The primary objective of our investigation was to develop a machine learning model capable of discerning preoperative and intraoperative high-risk factors, facilitating the prediction of HF occurrence subsequent to CME. A cohort comprising 1158 patients diagnosed with colon cancer was enrolled in our study, encompassing 172 individuals who developed postoperative HF. We compiled 37 feature variables, spanning patient demographic traits, foundational medical histories, preoperative examination characteristics, surgery types, and intraoperative details. Four distinct machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)—were employed to construct the model. The k-fold cross-validation method, ROC curve, calibration curve, decision curve analysis (DCA), and external validation were employed for comprehensive model evaluation. The XGBoost algorithm exhibited superior performance compared to the other three prediction models. Specifically, within the training set of the internal validation set, the XGBoost algorithm demonstrated an AUC value of 0.990 (0.983–0.996), accuracy of 0.929 (0.923–0.935), sensitivity of 0.983 (0.976–0.990), specificity of 0.918 (0.910–0.927), and F1 value of 0.799 (0.784–0.814). In the validation set of the internal validation set, the XGBoost algorithm recorded an AUC value of 0.941 (0.890–0.991), accuracy of 0.897 (0.882–0.911), sensitivity of 0.898 (0.860–0.937), specificity of 0.875 (0.845–0.905), and F1 value of 0.711 (0.656–0.766). The AUC value for the external validation set was 0.93, indicating robust extrapolative capabilities of the XGBoost prediction model. The HF prediction model post-CME, derived from the XGBoost machine learning algorithm in this study, attests to its elevated predictive accuracy and clinical utility.

Keloids are complex pathological skin scars characterised by excessive growth of fibrous tissue and abnormal accumulation of extracellular matrix (ECM). Despite various treatment options available, the treatment of keloids remains a major clinical challenge due to high recurrence rates and inconsistent therapeutic outcomes. By collecting three keloid tissues and three normal skin samples and utilising single‐cell RNA sequencing (scRNA‐seq), we delved into the cellular heterogeneity and molecular mechanisms of keloids. Our study identified multiple fibroblast subpopulations within keloid tissue. Enrichment and cell–cell communication analyses revealed that POSTN‐positive mesenchymal fibroblasts (POSTN+ mesenchymal fibs) are more prevalent in keloids and exhibit higher transforming growth factor β (TGF‐β) signalling activity, potentially playing a central role in excessive fibrosis. In contrast, IGFBP2‐positive fibroblasts (IGFBP2+ fibs) are more abundant in normal skin, insensitive to TGF‐β and Periostin signalling, and possess anti‐fibrotic potential, possibly related to limited tissue repair and regenerative capacity. Trajectory analysis inferred the differentiation states and patterns of different fibroblast subpopulations. Additionally, we explored the heterogeneity of endothelial cells, finding an endothelial cell subpopulation (EC10) exhibiting mesenchymal activation characteristics, which may work with specific fibroblasts to promote abnormal angiogenesis and endothelial‐to‐mesenchymal transition processes. Spatial transcriptomics analysis has shown that the proportion of IGFBP2+ fibroblasts relatively increases in acne keloidalis after hormonal treatment, further demonstrating their value as potential therapeutic targets. Ultimately, we separated these two subpopulations using flow cytometry, highlighting their opposing roles in the secretion of the ECM. These findings provide new insights into the pathogenesis of keloids and lay the theoretical foundation for the development of innovative anti‐fibrotic treatment strategies.