Journal of Translational Medicine最新文献

Background: Cirrhosis is a severe liver disease characterized by inflammation, fibrosis, and immune dysregulation. This study exploits publicly available single-cell sequencing datasets to decipher disease-relevant specific immune cell subtypes, their gene expression profiles and altered transcription factor activity in cirrhosis, aiming to understand their roles in disease onset and progression.

Methods: To investigate the dynamic changes and underlying mechanisms of immune cells in cirrhosis, we employed a comprehensive bioinformatics approach integrating several advanced tools and analytical methods. Single-cell RNA sequencing data were processed and analyzed using Seurat for cell clustering and annotation. Monocle was used for pseudotime trajectory analysis to explore cellular differentiation pathways. CellChat enabled the assessment of cell‒cell communication networks among immune populations. Additionally, we conducted single-cell regulatory network inference to identify key transcriptional regulators. Immune response enrichment analysis (IREA) was performed to evaluate immune-related functional pathways, providing deeper insights into the immune landscape and disease progression in cirrhosis.

Results: We analyzed 35,017 cells, identifying 21 clusters and 12 major immune cell types. Macrophages, CD4⁺ T cells, and NK cells showed notable shifts in proportion in cirrhosis, suggesting key roles in disease progression. Pseudotime analysis revealed core macrophage and CD4⁺ T-cell subpopulations linked to cirrhosis. Functional analysis showed enrichment of IFN-α, IFN-β, and IL-1β in cirrhotic immune cells, primarily regulated by ETS2 and THRA. Fibroblast-mediated intercellular communication was enhanced, especially via increased macrophage migration inhibitory factor (MIF) signaling with B cells, indicating potential therapeutic targets in cytokine pathways and transcriptional regulation.

Conclusion: The analysis revealed three key axes influencing disease-related immune regulation: TNF-α/ETS2-driven polarization of macrophages toward a Mac-d phenotype, IL-1α/β/THRA-associated polarization of CD4⁺ T cells toward a T4-c phenotype, and enhancement of the fibroblast-to-B-cell MIF signaling axis. This network offers valuable insights and potential therapeutic targets for advancing cirrhosis research and clinical treatment.

Background: Liver fibrosis (LF) is a progressive pathological process that may lead to cirrhosis and liver failure. Human ion channel genes (HICGs) participate in hepatic mechanotransduction and immune regulation, but their contributions to LF remain insufficiently characterized. This study aimed to profile the expression of HICGs in LF and to identify key genes with diagnostic and therapeutic relevance.

Methods: Multiple transcriptomic datasets were integrated to identify differentially expressed HICGs in LF. Weighted gene co-expression network analysis and single-cell RNA sequencing were applied to identify fibrosis-associated gene modules and cell-type distribution. Functional enrichment and immune infiltration analyses were performed to explore biological relevance. The expression of key genes was validated in human cirrhotic tissues and bile duct ligation mouse models using immunohistochemistry. Potential therapeutic compounds targeting hub HICGs were predicted through molecular docking simulations.

Results: Three HICGs-AQP1, GJA1, and KCNN2-were identified as fibrosis-associated hub genes, showing distinct expression patterns and high diagnostic performance. GJA1 showed consistent upregulation in both experimental models and human cirrhosis. Functional analyses linked these genes to extracellular matrix remodeling, cell adhesion, and cytokine interactions, while immune infiltration analysis revealed significant associations with M0 macrophages, plasma cells, NK cells, and memory B cells. Molecular docking simulations further identified 16 candidate drugs targeting KCNN2 and GJA1.

Conclusions: This study demonstrates that AQP1, GJA1, and KCNN2 are closely associated with LF progression and immune remodeling. The consistent upregulation of GJA1, together with the identification of candidate drug interactions, provides potential avenues for biomarker development and therapeutic repurposing in LF.

Microplastics (MPs), ubiquitous environmental pollutants, can enter the human body through ingestion, inhalation, and dermal contact, accumulate in various organs, and exert harmful effects. Emerging evidence suggests that both the skin and the gut serve as key immunological and neuroendocrine organs, sharing structural and neuroanatomical similarities. The interaction between these two systems is referred to as the "gut-skin axis." Numerous studies have demonstrated that MPs not only induce gut microbiota dysbiosis and compromise intestinal barrier integrity but also impair skin barrier function. Thus, the gut-skin axis offers a novel perspective for understanding MP-induced toxicity. Although interactions between MPs and the gut-skin axis have garnered increasing scientific interest, the mechanistic understanding of how MPs may mediate crosstalk between the gut and skin remains limited, and the impact of MPs on skin damage is not yet fully elucidated. MPs can directly disrupt gut microbial homeostasis and epithelial barrier function, allowing harmful bacteria and microbial metabolites to translocate into the bloodstream and exert systemic effects, ultimately contributing to cutaneous inflammation, metabolic imbalance, and oxidative stress. This review summarizes the mechanisms by which MPs exposure induces gut microbiota dysbiosis and skin damage from an integrated gut-skin axis perspective, highlighting their interplay's relevance. Understanding changes in gut microbiota and its metabolites may represent a promising approach to mitigate MP-induced skin diseases via modulation of the gut-skin axis.

Background: Tumor necrosis factor-α-induced protein 8-like 2 (TIPE2) is an intracellular immune checkpoint protein known to suppress T cell activation and effector function. Despite its role in limiting T cell responses, CAR-T cells are prone to TIPE2-mediated inhibitory signaling. We therefore hypothesized that inhibiting this immune checkpoint would enhance CAR-T cell anti-tumor function.

Methods: To overcome TIPE2-mediated negative regulation, we engineered a novel second-generation NKG2D-based CAR-T cell by incorporating TIPE2-targeting shRNA sequences directly into the CAR construct. TIPE2 knockdown efficiency in the CAR constructs was measured by qPCR and western blot analysis. The functional and mechanistic properties of TIPE2-downregulated CAR-T cells were evaluated in vitro by flow cytometry, including analysis of activation, cytotoxicity, exhaustion, apoptosis, proliferation, and differentiation. Antitumor efficacy was further validated in vivo using a preclinical pancreatic cancer mouse model.

Results: Flow cytometry analysis revealed that TIPE2-deficient CAR-T cells exhibited significantly higher expression of activation (CD69), degranulation (CD107a), cytotoxic (GzmB), and cytokine (IFN-γ) markers, resulting in more efficient tumor cell elimination compared to conventional CAR-T cells. TIPE2 silencing also reduced T cell exhaustion, lowered susceptibility to apoptosis, and enhanced proliferation when co-cultured with Panc-28 pancreatic cancer cells. Moreover, TIPE2 inhibition skewed CAR-T cells differentiation towards an effector phenotype (T_EFF), characterized by higher T-bet expression and reduced Eomes production. Mechanistically, these functional enhancements were mediated by increased NF-κB signaling, as confirmed by elevated p-p65 expression and functional reversal upon NF-κB inhibition. Consistently, TIPE2-deficient CAR-T cells exhibited significantly improved anti-tumor efficacy in vivo compared to wild-type CAR-T cells.

Conclusion: We successfully developed TIPE2-downregulated NKG2D-CAR-T cells that exhibited enhanced activation and cytotoxicity while limiting apoptosis and exhaustion against NKG2D ligand-expressing pancreatic tumors, highlighting TIPE2 as a promising intracellular immune checkpoint target for optimizing CAR-T cell therapy in solid tumors.

Background: Current preoperative assessment faces limitations, including PI-RADS scoring subjectivity and diagnostic uncertainty in distinguishing high-risk prostate cancer from benign and low-risk lesions. To develop an interpretable ensemble learning framework integrating habitat-based radiomics and peritumoral analysis from multiparametric MRI for preoperative high-risk prostate cancer prediction.

Methods: This retrospective, multi-institutional study included 896 patients with suspected prostate lesions and histopathologically confirmed diagnoses across three centers (January 2018-December 2024). Intratumoral habitat analysis used K-means clustering; peritumoral analysis evaluated 1 mm, 3 mm, and 5 mm expansion rings. Feature selection used minimum Redundancy Maximum Relevance (mRMR) and LASSO regression. Models were validated externally with SHAP analysis for interpretability.

Results: The cohort comprised 398 training, 171 internal validation, and 327 external validation patients. The habitat signature achieved superior performance with AUCs of 0.827 (95% CI: 0.768-0.886) and 0.855 (95% CI: 0.795-0.915) in external validation cohorts, significantly outperforming intratumoral signatures (AUCs: 0.774 and 0.629, p < 0.001) and clinical signatures (AUCs: 0.791 and 0.712, p < 0.001). The 3 mm peritumoral signature performed best (AUC: 0.782-0.793). The combined model achieved the highest performance (AUC: 0.860-0.876). SHAP analysis showed ADC-derived features dominated importance, with habitat region H3 contributing > 70% of selected features.

Conclusion: Integrated habitat and peritumoral radiomics provide robust preoperative risk stratification for prostate cancer, with superior performance from ADC-derived habitat features.

Trial registration: Not applicable. This was a retrospective observational study without prospective trial registration.