Patients with cancer are at a significantly higher risk of sepsis, which is associated with substantially increased morbidity and mortality. However, the intrinsic molecular mechanisms driving sepsis susceptibility in this high-risk population remain unclear. Glucose-regulated protein 78 (GRP78), the master regulator of endoplasmic reticulum stress, is aberrantly overexpressed and is involved in cell membrane translocation and extracellular release driven by the tumor microenvironment and anticancer therapies. To date, no clinical cohort study has directly established a causal link between GRP78 dysregulation and sepsis incidence or mortality in patients with cancer. The present narrative review therefore relied predominantly on indirect evidence from in vitro studies, animal models and non-oncologic sepsis cohorts. Despite these limitations, the present study advanced the hypothesis that GRP78 dysregulation may increase sepsis susceptibility through two convergent mechanisms: i) Facilitating pathogen invasion via cell-surface GRP78, which serves as a critical coreceptor for specific viruses and Mucorales fungi and ii) orchestrating immunosuppression through secreted GRP78-mediated dampening of innate immune responses. Direct evidence for the function of cell-surface GRP78 as a bacterial adhesion receptor is limited; its contribution to bacterial sepsis, the predominant clinical form, is primarily indirect and mediated by host inflammatory dysregulation, phagocytic impairment and barrier disruption. The present review provided a preliminary theoretical framework for future investigations into GRP78-mediated sepsis susceptibility in patients with cancer, with hypothetical implications for risk stratification and targeted interventions, pending dedicated clinical validation in oncology-specific cohorts.
Hang Ruan, Meipeng Zhu, Shi-Yan Liu et al.· International Journal of Mol...· 0 citations
Sepsis remains a leading cause of mortality in intensive care units worldwide, a challenge exacerbated by pathophysiological and clinical heterogeneity that limits the effectiveness of uniform management strategies, motivating the development of phenotype-guided approaches to diagnosis and treatment. Artificial intelligence (AI)-assisted phenotyping can stratify patients with sepsis into distinct subpopulations with differential immune profiles and heterogeneous treatment responses. This narrative review synthesizes recent advances in AI-assisted sepsis phenotyping, focusing on three persistent research bottlenecks: terminological inconsistency, fragmented integration between prognostic stratification and treatment-response phenotyping, and ambiguous clinical translation pathways. A standardized nomenclature is proposed to improve cross-study comparability, accompanied by a delineation of mainstream AI methodological frameworks, critical care datasets, and multilevel validation systems tailored for intensive care unit scenarios. Eight complementary research dimensions are mapped, including transcriptomic endotyping, single-cell profiling, electronic health record-derived clinical phenotyping, dynamic trajectory modeling, organ dysfunction stratification, biomarker panels, multi-omic integration, and treatment-response phenotyping, with treatment-response phenotyping highlighted as the highest-priority translational frontier. Critical analysis of predominant translational barriers, such as limited model generalizability, insufficient interpretability, poor workflow compatibility, and regulatory uncertainty, is presented alongside stage-specific actionable roadmaps designed to promote real-world clinical deployment. By constructing a unified interdisciplinary framework, this review defines key future research priorities to accelerate the evidence-based transition from algorithmic prototypes to bedside precision sepsis management.
Hang Ruan, Jing-Kun Lee, Jie Xiong et al.· Digital Health· 0 citations