ObjectiveProtein-protein interactions (PPIs) are fundamental to the execution of biological functions within living cells. However, traditional biochemical methods, such as co-immunoprecipitation (Co-IP), often fail to capture transient, weak, or membrane-associated interactions due to the stringent detergent requirements for cell lysis. Proximity labeling (PL) has emerged in recent years as a transformative technology for mapping the proteomes of specific subcellular compartments and identifying dynamic interactomes in situ. Golgi protein 73 (GP73, also known as GOLPH2), a resident type II Golgi transmembrane protein, is a well-recognized clinical biomarker for liver diseases, including hepatocellular carcinoma (HCC). Despite its clinical significance, the comprehensive physiological and pathological functions of GP73 remain partially understood. This study aims to establish an APEX2-mediated proximity labeling system specifically targeting GP73 to map its interactome in a living cellular environment, thereby providing new insights into its molecular roles and regulatory mechanisms. MethodsTo achieve spatial specificity, we first constructed a stable cell line expressing a fusion protein consisting of GP73 and the engineered soybean peroxidase APEX2. The localization of the GP73-APEX2 fusion protein was validated to ensure it correctly targeted the Golgi apparatus. The proximity labeling reaction was initiated by incubating the cells with biotin-phenol (BP) for 30 min, followed by a brief (1 min) treatment with1 mmol/L hydrogen peroxide (H₂O₂). This catalytic reaction converts BP into highly reactive, short-lived biotin-phenoxyl radicals that covalently attach to endogenous proteins within a small labeling radius of the GP73-APEX2 enzyme. Subsequently, the cells were quenched, and biotinylated proteins were enriched using high-affinity streptavidin-coated magnetic beads. The captured “neighbor” proteins were subjected to on-bead digestion and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS) for high-throughput identification. Rigorous bioinformatics analysis, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction network mapping, was performed to interpret the biological significance of the identified candidates. ResultsOur results demonstrate the successful establishment of a robust and sensitive APEX2-based proximity labeling system for GP73. We identified a total of 95 high-confidence interacting proteins that were significantly enriched in the GP73 proximity proteome compared to control groups. Bioinformatics analysis revealed that these interactors were predominantly associated with biological processes such as vesicular transport, protein localization, and, most notably, molecular functions related to “ribosome binding” and “translation regulation”. This suggested an unexpected role for the Golgi-resident GP73 in the cellular translation machinery. To validate these findings, we performed targeted biochemical assays which confirmed a direct interaction between GP73 and the subunits of the eukaryotic translation initiation factor 3 (eIF3) complex, specifically EIF3G and EIF3I. Furthermore, functional validation using the surface sensing of translation (SUnSET) assay—a non-radioactive method to monitor protein synthesis—revealed that the overexpression of GP73 significantly promoted global protein translation levels in the cell, whereas its depletion or inhibition resulted in reduced translation efficiency. ConclusionThis study successfully utilized APEX2-mediated proximity labeling to provide the first systematic map of GP73 interactome in living cells. Our findings uncover a novel, unconventional function of GP73 as a regulator of cellular protein translation, likely mediated through its interaction with the eIF3 complex. This discovery significantly broadens our understanding of the biological roles of GP73 beyond its traditional function in the Golgi apparatus and suggests that it may act as a bridge between Golgi-related trafficking and the protein synthesis machinery. Furthermore, the technical framework established in this study provides a valuable template for investigating other complex organelle-associated protein networks and resolving transient macromolecular interactions in various physiological and pathological contexts.

OBJECTIVE: To assess the efficacy of Qingda Granule (QDG) in ameliorating hypertension-induced cardiac damage and investigate the underlying mechanisms involved. METHODS: Twenty spontaneously hypertensive rats (SHRs) were used to develope a hypertension-induced cardiac damage model. Another 10 Wistar Kyoto (WKY) rats were used as normotension group. Rats were administrated intragastrically QDG [0.9 g/(kg•d)] or an equivalent volume of pure water for 8 weeks. Blood pressure, histopathological changes, cardiac function, levels of oxidative stress and inflammatory response markers were measured. Furthermore, to gain insights into the potential mechanisms underlying the protective effects of QDG against hypertension-induced cardiac injury, a network pharmacology study was conducted. Predicted results were validated by Western blot, radioimmunoassay immunohistochemistry and quantitative polymerase chain reaction, respectively. RESULTS: The administration of QDG resulted in a significant decrease in blood pressure levels in SHRs (P<0.01). Histological examinations, including hematoxylin-eosin staining and Masson trichrome staining revealed that QDG effectively attenuated hypertension-induced cardiac damage. Furthermore, echocardiography demonstrated that QDG improved hypertension-associated cardiac dysfunction. Enzyme-linked immunosorbent assay and colorimetric method indicated that QDG significantly reduced oxidative stress and inflammatory response levels in both myocardial tissue and serum (P<0.01). CONCLUSIONS Both network pharmacology and experimental investigations confirmed that QDG exerted its beneficial effects in decreasing hypertension-induced cardiac damage by regulating the angiotensin converting enzyme (ACE)/angiotensin II (Ang II)/Ang II receptor type 1 axis and ACE/Ang II/Ang II receptor type 2 axis.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Hypertension/pathology* ; Renin-Angiotensin System/drug effects* ; Rats, Inbred SHR ; Oxidative Stress/drug effects* ; Male ; Rats, Inbred WKY ; Blood Pressure/drug effects* ; Myocardium/pathology* ; Rats ; Inflammation/pathology*

In the process of maintaining the steady state of bone tissue, the transcription network and signal pathway of the body play a vital role. These complex regulatory mechanisms need precise coordination to ensure the balance between bone formation and bone absorption. Once this balance is broken, it may lead to pathological changes of bone and cartilage, and then lead to various bone diseases. Therefore, it is of great significance to understand these regulatory mechanisms for the prevention and treatment of bone diseases. In recent years, with the deepening of research, more and more lncRNA has been found to be closely related to bone health. Among them, nuclear paraspeckle assembly transcript 1 (NEAT1), as an extremely abundant RNA molecule in mammalian nuclei, has attracted extensive attention. NEAT1 is mainly transcribed from a specific site in human chromosome 11 by RNA polymerase II (RNaseP), which can form two different subtypes NEAT1_1 and NEAT1_2. These two subtypes are different in intracellular distribution and function, but they participate in many biological processes together. Studies have shown that NEAT1 plays a specific role in the process of cell growth and stress response. For example, it can regulate the development of osteoblasts (OB), osteoclasts (OC) and chondrocytes by balancing the differentiation of bone marrow mesenchymal stem cells (BMSCs), thus maintaining the steady state of bone metabolism. This discovery reveals the important role of NEAT1 in bone development and remodeling. In addition, NEAT1 is closely related to a variety of bone diseases. In patients with bone diseases such as osteoporosis (OP), osteoarthritis (OA) and osteosarcoma (OS), the expression level of NEAT1 is different. These differential expressions may be closely related to the pathogenesis and progression of bone diseases. By regulating the level of NEAT1, it can affect a variety of signal transduction pathways, and then affect the development of bone diseases. For example, some studies show that by regulating the expression level of NEAT1, the activity of osteoclasts can be inhibited, and the proliferation and differentiation of osteoblasts can be promoted, thus improving the symptoms of osteoporosis. It is worth noting that NEAT1 can also be used as a key sensor for the prevention and treatment of bone diseases. When exercising or receiving some natural products, the expression level of NEAT1 will change, thus reflecting the response of bones to external stimuli. This feature makes NEAT1 an important target for studying the prevention and treatment strategies of bone diseases. However, although the role of NEAT1 in bone biology and bone diseases has been initially recognized, its specific mechanism and regulatory relationship are still controversial. For example, the expression level, mode of action and interaction with other molecules of NEAT1 in different bone diseases still need further in-depth study. This paper reviews the role of NEAT1 in maintaining bone and cartilage metabolism, and discusses its expression and function in various bone diseases. By combing the existing research results and controversial points, this paper aims to provide new perspectives and ideas for the prevention and treatment of bone diseases, and provide useful reference and enlightenment for future research.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Zinc finger protein 639 (ZNF639) is often found within the overlapping amplicon of PIK3CA, and previous studies suggest its involvement in the pathogenesis of esophageal and oral squamous cell carcinomas. However, its expression and significance in breast cancer remain uncharacterized. Methods: Immunohistochemical analysis of ZNF639 was performed using tissue microarrays.Functional studies, including colony formation, Transwell cell migration, and in vivo metastasis, were conducted on breast tumor cells with ZNF639 knockdown via small interfering RNA transfection. Results: Reduced ZNF639 immunoreactivity was observed in 82% of the breast cancer samples, independent of hormone receptor and human epidermal growth factor receptor 2 status. In multivariate Cox regression analyses, ZNF639 expression was associated with favorable survival outcomes, including recurrence-free survival (hazard ratio, 0.35; 95% confidence interval [CI], 0.14–0.89) and overall survival (hazard ratio, 0.41; 95% CI, 0.16– 1.05). ZNF639 knockdown increased clonogenicity, cell motility, and lung metastasis in NOD/ SCID mice. Following the ZNF639 knockdown, the expression of Snail1, vimentin, and C-C chemokine ligand 20 (CCL20) was upregulated, and the changes in cell phenotype mediated by ZNF639 were reversed by the subsequent knockdown of CCL20. Conclusion Low ZNF639 expression is a novel prognostic factor for recurrence-free survival in patients with breast cancer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Suhuang Zhike Capsules are widely used in clinical practice for the treatment of respiratory diseases and have been included in Medicine Catalogue for National Basic Medical Insurance, Work Injury Insurance, and Maternity Insurance and National Essential Medicines List. However, problems remain, such as unclear definitions of treatment courses and unidentified contraindications for certain populations. Therefore, this consensus was developed collaboratively by clinical experts in traditional Chinese medicine(TCM) related to pulmonary diseases, respiratory, and critical care medicine, as well as methodology and pharmacy experts, adhering strictly to the consensus development procedures established by the China Association of Chinese Medicine for clinical application of Chinese patent medicines, with the aim to guide the correct clinical use of Suhuang Zhike Capsules for the treatment of cough variant asthma, post-infectious cough, and other respiratory diseases. This consensus employed questionnaire surveys and expert interviews to identify clinical concerns based on the PICOS principle and conduct evidence evaluation and GRADE grading. Utilizing nominal group techniques and GRADE networking methods, it resulted in 17 recommendations and consensus suggestions. The consensus further clarifies the indications, TCM syndromes, usage, and clinical safety of Suhuang Zhike Capsules in the treatment of cough variant asthma and post-infectious cough, aiming to promote standardized medication use and facilitate the rational clinical application of Suhuang Zhike Capsules.
Humans ; Drugs, Chinese Herbal/administration & dosage* ; Consensus ; Capsules ; Respiratory Tract Diseases/drug therapy* ; Medicine, Chinese Traditional

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

Objective To investigate the value of ultrasonographic features combined with immunohistochemistry in predicting axillary lymph node metastasis in middle-aged women with breast cancer.Methods A retrospective analysis was conducted on 827 middle-aged female breast cancer patients who underwent surgical treatment at the Affiliated Hospital of Chengde Medical University from June 2017 to June 2023.Ultrasonographic and immunohistochemical information was collected,and the patients were randomly allocated into a training set(579 patients)and a validation set(248 patients).Univariate and multivariate Logistic regression analyses were performed to identify ultrasonographic and immunohistochemical risk factors associated with axillary lymph node metastasis in these patients,and a nomogram model was developed.Receiver operating characteristic curves and calibration curves were established to evaluate the performance of the nomogram model,and clinical decision curves were built to assess the clinical value of the model.Results The maximum diameter,morphology,boundary,calcification,and expression of human epidermal growth facor receptor 2 and Ki-67 in breast cancer lesions were identified as risk factors for predicting axillary lymph node metastasis in middle-aged women.The areas under the curve of the nomogram model on the training and validation sets were 0.747(0.707-0.787)and 0.714(0.647-0.780),respectively.Calibration curves and clinical decision curves indicated good consistency and performance of the model.Conclusion The nomogram model constructed based on ultrasonographic features and immunohistochemistry of the primary breast cancer lesion demonstrates high value in predicting axillary lymph node metastasis in middle-aged women with breast cancer.
Humans ; Female ; Breast Neoplasms/diagnostic imaging* ; Middle Aged ; Lymphatic Metastasis/diagnostic imaging* ; Axilla ; Retrospective Studies ; Nomograms ; Ultrasonography ; Immunohistochemistry ; Lymph Nodes/diagnostic imaging* ; Risk Factors ; Ki-67 Antigen