2026年 · 第139卷第01期

Long interspersed nucleotide element-1 (LINE-1) is the only known reverse transcriptional transposon in the human genome with autonomous transposition capabilities. It can replicate and insert itself into new gene sites through the reverse transcription of transposons. The activation of LINE-1 is closely related to the occurrence and development of aging, cancer, and neurological diseases, and therefore has received widespread attention. However, research on LINE-1 in the nervous system is still in its early stages. Emerging evidence suggests that LINE-1 can undergo reverse transcriptional translocation and be regulated in neurons and neuroglial cells, playing a crucial role in neuronal diversity, neural plasticity, and behavioral phenotypes. In this review, we summarize the multifaceted functions of LINE-1 in neuronal function and evolution, synapse formation, and its implications for various neurological conditions, including neurodevelopmental disorders, neurodegenerative diseases, and emotional disorders. In addition, we also discuss the potential role of LINE-1 as a diagnostic biomarker and a therapeutic target in these neurological disorders. A comprehensive understanding of LINE-1’s functions in the nervous system will enhance our insight into the pathogenesis of neurological diseases and may aid in the development of new therapeutic strategies.

Sepsis remains a leading cause morbidity and mortality worldwide; effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution. Existing frameworks often focus on either the diverse manifestations of sepsis or its progression over time, but fail to integrate these critical aspects. In this review, we propose a novel spatial-temporal framework that integrates both the heterogeneity and temporality of sepsis. The framework consists of two key dimensions: The cross-sectional (heterogeneity) dimension, which addresses pathogen variability, host factors, and pathogen-host interactions; The longitudinal (temporality) dimension, which explores the dynamic evolution of sepsis and the need for adaptive, real-time interventions. Given the complexity of multidimensional temporal data, big data techniques have the potential to integrate these data and decompose sepsis into distinct disease subtypes. Stratification facilitates the development of personalized therapeutic approaches tailored to specific subtypes. Moreover, methods, such as reinforcement learning, can track the dynamic transitions between these subtypes, enabling real-time adaptation of treatment strategies.

Gastrointestinal tumors are among the most prevalent and deadly cancers worldwide and have been increasingly associated with the gut microbiota. Particularly, colorectal cancer (CRC) has become a focal point for unraveling the complex interplay between microbial dynamics and gastrointestinal tumor development, as extensive studies have shown that gut microbiota dysbiosis is closely associated with CRC, affecting energy harvest, metabolism, and mucosal and systemic immune responses. Clostridioides difficile (C. difficile) is the major causative agent of gut microbiota dysbiosis, with toxins A and B being its main pathogenic factors. These toxins reportedly trigger a complex cascade of host cellular responses, leading to diarrhea, inflammation, and tissue necrosis. However, recent experimental evidence suggests that chronic infection with C. difficile is a previously unrecognized contributor to colonic tumorigenesis. In this concise review, we summarize the hypothetical models and provide a comprehensive overview of the mechanisms linking the microbiota to colorectal carcinogenesis, focusing on the reasonable extrapolation of the interaction between C. difficile and CRC. Understanding the significance of C. difficile as a potential pro-carcinogenic bacterium and its potential role as a biomarker in CRC is crucial for advancing our knowledge in preventing tumorigenesis, recurrence, and gastrointestinal tumor metastasis.

Background: Organoids have attracted enormous interest in disease modeling, drug screening, and precision medicine. However, developing robust patient-derived organoids (PDOs) was time-consuming, costly, and had low success rates for certain cancer types, which limited their clinical utility. This study aimed to develop an interpretable deep learning-based model to predict the cultivation outcome of ovarian cancer organoids in advance. Methods: Longitudinal microscopy images of 517 ovarian cancer organoid droplets were divided into training (n = 325), validation (n = 88), and test (n = 104) sets. Subsequently, growth prediction models were developed based on four neural network backbones (ResNet18, VGG11, ConvNeXt v2, and Swin Transformer v2), and specific optimization methods were designed for better prediction. Finally, 179 samples from multiple centers were collected for prospective validation, and the gradient-weighted class activation mapping (Grad-CAM) method was used for interpretability analysis of the deep model to reveal the basis of the model’s decisions. Results: The test set showed that the deep learning models could achieve high-performance prediction at the third stage with area under the curve (AUC) values greater than 0.8 for all four models. The homogeneous transfer learning optimization method improved the AUC from 0.833 to 0.884 (P = 0.0039). In prospective validation, the optimized model achieved an AUC of 0.832, a Brier score of 0.1919 in the calibration curve, and a greater net benefit in the decision curve. Interpretability analysis revealed that the area where organoids are being formed and have already formed is important for prediction. Conclusions: Our developed models achieved satisfactory results in predicting the growth of ovarian cancer organoids. There is potential for further development of the model toward process automation.

Background: Gut microbiota are important for uric acid (UA) metabolism in hyperuricemia (HUA); however, the underlying mechanisms of how the gut microbiota regulate intestinal UA metabolism remain unclear. This study aimed to explore the function of the intestine in HUA and to further reveal the possible mechanism. Methods: We conducted gut microbiota depletion to validate the role of gut microbiota in UA metabolism. A mouse model of HUA was established, and the gut microbiota and microbiome-derived metabolites were analyzed via 16S RNA gene sequencing and metabolomics analysis. The mechanism of the gut microbiota in HUA was elucidated by in vivo and in vitro experiments. Results: Antibiotic treatment elevated serum UA, disturbed purine metabolism, and decreased the relative abundance of Lactobacillus. HUA mice had a lower relative abundance of Lactobacillus johnsonii (L. johnsonii) and decreased gut butyrate concentration. Supplementation of L. johnsonii significantly reduces serum UA in hyperuricemia mice by preventing UA synthesis and promoting the excretion of gut purine metabolites. In addition, L. johnsonii enhanced intestinal UA excretion by heightening the urate transporter ABCG2 (adenosine triphosphate-binding cassette transporter, subfamily G, member 2) expression, and increasing the levels of butyrate, which upregulated ABCG2 expression via the Wnt5a/b/β-catenin signaling pathway. Conclusion: Our results suggest that gut microbiota and microbiota-derived metabolites directly regulate gut UA metabolism, highlighting potential applications in the treatment of diet-induced HUA by targeting gut microbiota and its metabolites.

Background: Non-communicable diseases (NCDs) are the leading cause of disease burden worldwide. Amid rapid population aging, China faces a substantial NCD burden. This study aimed to assess the NCD burden in China in 2023 using data from the Global Burden of Disease Study 2023. Methods: This study used data from the Global Burden of Disease Study 2023, which covers 31 provinces in Chinese mainland and the Hong Kong and Macao Special Administrative Regions. Age-standardized and all-age mortality and disability-adjusted life year (DALY) rates for NCDs were estimated and compared between 1990 and 2023. Analyses were conducted according to sex, age group, and region. Level 3 NCD causes were ranked according to mortality and DALY rates. Results: NCDs were the major contributor to China’s disease burden in 2023. Cardiovascular diseases (316.08/100,000), neoplasms (180.02/100,000), and chronic respiratory diseases (73.15/100,000) were the leading causes of NCD-related mortality rates. Cardiovascular diseases (6262.68/100,000) and neoplasms (4456.46/100,000) were the top contributors to DALY rates. Age-standardized mortality and DALY rates for major NCDs have declined since 1990; however, the absolute numbers continue to rise because of population aging. Notable increases in disease burden were observed. Compared to 1990, the mortality rate increased by 239.14% and the DALY rate by 77.04% for neurological disorders. For mental disorders, mortality increased by 382.34% and the DALY rate by 25.82%. For musculoskeletal disorders, the DALY rate increased by 48.17%. Geographic disparities persisted, with higher NCD burdens concentrated in the western and northeastern provinces, whereas the more developed eastern regions showed relatively lower rates. Conclusions: NCDs remain the leading cause of disease burden in China and vary significantly by disease type, sex, age, and region. Strengthening prevention, improving the management of high-risk populations, and enhancing data accuracy are essential for more effective and equitable NCD control.

Background: Family history (FH) of cancer is an established risk factor for early onset of cancer. However, reliable estimates on the difference in onset age between familial and sporadic cancers remain scarce in the Chinese population. Methods: This multicenter, hospital-based, cross-sectional study included 23 hospitals across 12 provinces in China. Patients diagnosed with cancers of the lung, stomach, esophagus, or colorectum between January 1, 2016 and December 31, 2017 were identified. Detailed information on sociodemographic characteristics, lifestyle factors, stage at diagnosis, and onset age was collected. We analyzed the association between FH and onset age across different cancer types using quantile regressions. Results: Among 41,072 eligible patients, 3054 (7.44%) reported a first-degree FH of cancer, and they were diagnosed at younger ages than those without FH (median difference: -1.19, 95% confidence interval [CI]: -1.59 to -0.79). Stratified by cancer type, the most pronounced difference was observed in colorectal cancer (median difference: -2.25, 95% CI: -3.31 to -1.19). Failure to account for lead time bias resulted in an overestimation of the FH effect, ranging from 3.4% to 15.4% across cancer types. Quantile regression analysis revealed that the impact of FH on age at diagnosis was more pronounced at the upper tail of the age distribution for all cancers combined and for each cancer type individually. Conclusions: Our findings suggest that FH of cancer is associated with the early onset of lung, stomach, esophageal, and colorectal cancers in China. Cancer screening at earlier ages is needed for individuals with an FH.

Background: Gastric cancer (GC) prognosis and treatment depend on tumor burden and gastric function, yet tumor progression and therapy resistance are influenced by intratumoral G protein-coupled receptors (GPCRs) and the tumor microenvironment (TME). This study aims to examine GPCR- and TME-related factors to enhance the understanding of GC prognostic and therapeutic predictions. Methods: This study analyzed single-cell RNA sequencing data from the GEO dataset GSE167297 for stomach adenocarcinoma (STAD), bulk transcriptome data from the GEO cohort GSE62254 and TCGA-STAD cohort. Differentially expressed GPCR-related genes (GPCRRGs) were identified using limma, and immune cell proportions were estimated via Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Prognostic GPCRRGs were selected through univariable/multivariable Cox proportional hazards regression and least absolute shrinkage and selector operator (LASSO) regression to build a risk model, validated by Kaplan-Meier analysis. A GPCR-TME classifier integrated GPCR signatures and TME scores. Functional enrichment employed gene set enrichment analysis (GSEA) and weighted gene co-expression network analysis (WGCNA). scRNA-seq processing via Seurat included CellChat for cell interactions and tumor mutational burden (TMB) estimation. Tumor immune dysfunction and exclusion (TIDE) predicted immune checkpoint blockade (ICB) response. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed on 10 STAD tumor tissues collected from patients undergoing surgical resection at Shanxi Province Cancer Hospital. Results: In TCGA, 209 GPCRRGs were identified (145 upregulated, 64 downregulated). CIBERSORT revealed 20 immune cell types, with 11 prognostic. A model with 14 GPCRRGs and 11 TME immune cells stratified risk. The GPCR-TME classifier categorized patients into four subgroups: GPCRlow/TMEhigh (best prognosis), GPCRlow/TMElow, GPCRhigh/TMEhigh, and GPCRhigh/TMElow. GSEA showed extracellular matrix (ECM)-receptor and cytokine-receptor pathways enriched in high GPCR/TME groups. WGCNA linked modules to vasculature, cell cycle, and metabolism. scRNA-seq confirmed GPCR signatures, with CD8+ T and B cells as key expressors, and strong interactions between GPCRhigh immune clusters and tumor cells. GPCRlow/TMEhigh had the highest TMB and best prognosis; GPCRhigh/TMElow showed more TP53 mutations. Immune checkpoint patterns varied, aiding ICB response prediction. The classifier stratified ICB patients, with GPCRlow/TMEhigh demonstrating superior response rates. Proteomap analysis highlighted differential enrichment in immune signaling and metabolic pathways between responders and non-responders. qRT-PCR confirmed upregulation of c-x-c motif chemokine receptor 4, lysophosphatidic acid receptor 2, frizzled class receptor 2, and apelin receptor in STAD tissues. Conclusion: The GPCR-TME classifier offers pretreatment predictive value for prognosis and therapeutic responses, potentially enabling novel patient stratification for targeted therapies.

Background: Immunoglobulin A nephropathy (IgAN) has a heterogeneous clinical presentation. Comparison of different IgAN subgroups may facilitate the application of more targeted therapies. This study was aimed to distinct disease phenotypes in IgAN and to develop prognostic models for renal composite outcomes. Methods: Clinical and pathological data were from 2000 patients with biopsy-proven primary IgAN from four centers, including the First Affiliated Hospital of Sun Yat-sen University (SYSU), the Fifth Affiliated Hospital of Sun Yat-sen University, the Huadu District People’s Hospital of Guangzhou, and Jieyang Affiliated Hospital of SYSU in China between January 2009 and December 2018 (training cohort: 1203 patients, validation cohort: 797 patients). Components from principal components analysis (PCA) were used to fit a k-means clustering algorithm and identify distinct subgroups. A subgroup-based prediction model was developed to assess prognosis and therapeutic efficacy in each subgroup. Results: The PCA-k-means clustering algorithm identified four subgroups. Subgroup 1 had significantly better long-term renal survival upon administration of a renin-angiotensin system blocker (adjusted hazard ratio [aHR]: 0.16, 95% confidence interval [CI]: 0.10-0.27, P <0.001). Subgroup 2 had a significant improvement from corticosteroid therapy (aHR: 0.19, 95% CI: 0.06-0.61, P = 0.005). Subgroups 3 and 4 had milder pathological changes and relatively stable kidney function for several years. Subgroup 3 (predominantly males) had a high incidence of metabolic risk factors, necessitating more intensive monitoring; subgroup 4 (predominantly females) had a high incidence of recurrent macroscopic hematuria. These patterns were similar in the validation cohort. A subgroup-based prognosis prediction model demonstrated an area under the curve of 0.856 in the validation dataset. Conclusions: The unsupervised clustering method provided reliable classification of IgAN patients into different subgroups according to clinical features, prognoses, and treatment responsiveness. Our subgroup-based prediction model has significant clinical utility for the assessment of risk and treatment in patients with IgAN.

Background: The activation of hepatic stellate cells (HSCs) plays a crucial role in the progression of liver fibrosis, and eliminating activated HSCs is regarded as an effective strategy for combating fibrosis. Ferroptosis has emerged as a potential mechanism for HSC depletion. Dihydroartemisinin (DHA), a derivative of artemisinin, has shown anti-fibrotic effects, but its role in HSC ferroptosis remains unclear. This study aimed to investigate the molecular mechanism by which DHA regulates HSC ferroptosis through histone modifications to suppress liver fibrosis. Methods: In vitro experiments were conducted using human hepatic stellate cell line HSC-LX2 and mouse hepatic stellate cell line mHSC, with DHA treatment to induce ferroptosis, and ferrostatin-1 as a ferroptosis inhibitor for control. RNA sequencing was performed to analyse differentially expressed genes in DHA-treated HSCs, and real-time polymerase chain reaction, Western blotting, and immunofluorescence were used to verify glutathione-specific gamma-glutamylcyclotransferase 1 (CHAC1) expression. Chromatin immunoprecipitation quantitative polymerase chain reaction was used to detect histone acetylation at the CHAC1 promoter, and luciferase reporter assays with wild-type or mutated CHAC1 promoter were used to confirm activating transcription factor 4 (ATF4) binding sites. In vivo experiments used male C57BL/6J mice induced with carbon tetrachloride (CCl4) to establish a liver fibrosis model. Histopathological staining, serum biochemical index detection, and ferroptosis-related assays in isolated liver cells were performed to evaluate the therapeutic effect of DHA and the roles of CHAC1 and ATF4. Results: DHA inhibited HSC activation through the ferroptosis pathway, DHA treatment elevated CHAC1 levels in HSCs, inhibition of CHAC1 prevented DHA-induced HSC ferroptosis, and DHA regulated the expression of CHAC1 at the transcriptional level rather than at the post-transcriptional level in HSC-LX2 and mHSC cells. Mechanistically, upregulated H3K9 acetylation was essential for the DHA-mediated transcriptional upregulation of CHAC1 through increased histone acetyltransferase P300 in HSCs. Inhibiting histone acetylation attenuated DHA-induced CHAC1 upregulation and ferroptosis. ATF4 was identified as a key transcription factor in the transcriptional activation of CHAC1. Interfering with ATF4 inhibited the transcriptional upregulation of CHAC1 by DHA. Notably, the -212 to -199 bp and -269 to -257 bp promoter regions in CHAC1 were essential for the initiation of transcription of ATF4. In mice, treatment with DHA alleviated murine liver fibrosis by inducing HSC ferroptosis. Inhibition of CHAC1 or ATF4 impaired DHA-induced HSC ferroptosis in murine liver fibrosis. Conclusion: The transcriptional activation of CHAC1, which is regulated by H3K9 acetylation, was essential for the ability of DHA to trigger HSC ferroptosis and, consequently, to suppress liver fibrosis.