ObjectiveLeveraging the advanced capabilities of nanopore long-read sequencing technology， our study undertook a comprehensive analysis of the distinct transcriptomic alterations occurring in normal liver parenchymal cells and liver cancer cells subjected to hypoxic conditions. The primary goal was to elucidate the underlying mechanisms governing tumor cell survival and metastasis in low-oxygen environments， thereby paving the way for innovative targeted cancer therapies. MethodsThe normal liver parenchymal cell line THLE-3 and the hepatocellular carcinoma cell line Hep3B were chosen as the focal points of this investigation. Following a 48-hour incubation period in both normoxic and hypoxic conditions， total RNA was extracted from these cells. Subsequently， we employed nanopore sequencing technology to conduct a high-throughput， high-fidelity analysis of the transcriptomes of these two cell lines across different oxygen levels. ResultsThis study established a hypoxic transcriptome dataset using third-generation nanopore sequencing technology， achieving an unprecedented level of sequencing accuracy. By conducting a Gene Ontology （GO） enrichment analysis， we systematically identified and explored the key biological pathways associated with the hypoxic response （P＜0.05）. Furthermore， we integrated molecular dynamics simulation techniques to gain deeper insights into the dynamic structural changes of Solute Carrier Family 1 Member 5 （SLC1A5） during the translation of hypoxic-specific subtypes， providing direct evidence to elucidate its functional regulation. ConclusionThe application of nanopore long-read sequencing technology has proven to be a powerful tool， not only successfully capturing the distinctive expression patterns and specific subtypes of mRNA under hypoxic conditions， but also offering robust technical support for delving into the intricate transcriptomic landscape of hypoxic microenvironments. By further integrating protein structure simulations and molecular dynamics， we have proposed novel avenues for exploring protein structures in hypoxic microenvironments. The findings of this study have significantly enriched the field of hypoxic-specific transcriptomics， providing a more reliable data foundation for investigating hypoxic-specific protein structures. Moreover， these discoveries have unveiled potential hypoxic-specific targets that could be harnessed for the development of future targeted cancer treatment strategies.

OBJECTIVES: To explore the association between Chinese visceral adiposity index (CVAI) and the risk of nephrolithiasis. METHODS: This cross-sectional study analyzed data from 78 438 Chinese adults who underwent ultrasound examinations during health screening at the Health Examination Center of Affiliated Hospital of Yangzhou University. Participants were divided into quartiles (Q1-Q4 groups) based on CVAI. Multivariate logistic regression models were utilized to evaluate the association between CVAI and nephrolithiasis risk, followed by subgroup analyses to further explore potential relationships. The performance of CVAI in predicting the risk of nephrolithiasis was evaluated using receiver operating characteristic (ROC) curves. RESULTS: Increased CVAI was significantly associated with a higher risk of nephrolithiasis, with prevalence rising from 3.36% in the Q1 group to 10.67% in the Q4 group (P<0.01). In adjusted models, CVAI was positively correlated with the prevalence rate of nephrolithiasis (OR=1.002, 95%CI: 1.001-1.004, P<0.01). The risks of nephrolithiasis in the Q2, Q3, and Q4 groups were 1.196-fold (95%CI: 1.069-1.338, P<0.01), 1.260-fold (95%CI: 1.109-1.433, P<0.01), and 1.316-fold (95%CI: 1.125-1.539, P<0.01) higher than in the Q1 group, respectively. Subgroup analysis revealed that CVAI was positively associated with the risk of nephrolithiasis in male participants, individuals aged <60 years, the hypertension group, populations with or without diabetes mellitus, and the normal body mass index subgroup. Genders and age had an interaction effect on the correlation between CVAI and the risk of nephrolithiasis development (both P<0.05). The ROC curve analysis demonstrated that CVAI exhibited superior predictive efficacy compared to waist circumference, body mass index, visceral adiposity index, weight-adjusted waist index, cardiometabolic index and body shape index, with an area under the curve of 0.622. CONCLUSIONS In Chinese adults, CVAI is positively associated with the risk of nephrolithiasis development, which may serve as a potential predictive marker for nephrolithiasis.
Humans ; Nephrolithiasis/etiology* ; Male ; Female ; Middle Aged ; Cross-Sectional Studies ; Adult ; Intra-Abdominal Fat ; Risk Factors ; China/epidemiology* ; Adiposity ; Aged ; Logistic Models ; Obesity, Abdominal/epidemiology* ; East Asian People

Myocardial fibrosis is a serious cause of heart failure and even sudden cardiac death. However, the mechanisms underlying myocardial ischemia-induced cardiac fibrosis remain unclear. Here, we identified that the expression of sterile alpha and TIR motif containing 1 (SARM1), was increased significantly in the ischemic cardiomyopathy patients, dilated cardiomyopathy patients (GSE116250) and fibrotic heart tissues of mice. Additionally, inhibition or knockdown of SARM1 can improve myocardial fibrosis and cardiac function of myocardial infarction (MI) mice. Moreover, SARM1 fibroblasts-specific knock-in mice had increased deposition of extracellular matrix and impaired cardiac function. Mechanically, elevated expression of SARM1 promotes the deposition of extracellular matrix by directly modulating P4HA1. Notably, by using the Click-iT reaction, we identified that the increased expression of ZDHHC17 promotes the palmitoylation levels of SARM1, thereby accelerating the fibrosis process. Based on the fibrosis-promoting effect of SARM1, we screened several drugs with anti-myocardial fibrosis activity. In conclusion, we have unveiled that palmitoylated SARM1 targeting P4HA1 promotes collagen deposition and myocardial fibrosis. Inhibition of SARM1 is a potential strategy for the treatment of myocardial fibrosis. The sites where SARM1 interacts with P4HA1 and the palmitoylation modification sites of SARM1 may be the active targets for anti-fibrosis drugs.

Deactivation of the mitochondrial pyruvate dehydrogenase complex (PDC) is important for the metabolic switching of cancer cell from oxidative phosphorylation to aerobic glycolysis. Studies examining PDC activity regulation have mainly focused on the phosphorylation of pyruvate dehydrogenase (E1), leaving other post-translational modifications largely unexplored. Here, we demonstrate that the acetylation of Lys 488 of pyruvate dehydrogenase complex component X (PDHX) commonly occurs in hepatocellular carcinoma, disrupting PDC assembly and contributing to lactate-driven epigenetic control of gene expression. PDHX, an E3-binding protein in the PDC, is acetylated by the p300 at Lys 488, impeding the interaction between PDHX and dihydrolipoyl transacetylase (E2), thereby disrupting PDC assembly to inhibit its activation. PDC disruption results in the conversion of most glucose to lactate, contributing to the aerobic glycolysis and H3K56 lactylation-mediated gene expression, facilitating tumor progression. These findings highlight a previously unrecognized role of PDHX acetylation in regulating PDC assembly and activity, linking PDHX Lys 488 acetylation and histone lactylation during hepatocellular carcinoma progression and providing a potential biomarker and therapeutic target for further development.
Humans ; Acetylation ; Carcinoma, Hepatocellular/genetics* ; Liver Neoplasms/genetics* ; Pyruvate Dehydrogenase Complex/genetics* ; Gene Expression Regulation, Neoplastic ; Animals ; Mice ; Cell Line, Tumor ; Protein Processing, Post-Translational ; Histones/metabolism* ; Disease Progression

Objective:To analyze the association between the triglyceride-glucose(TyG)index and the risk of nephrolithiasis across various demographic and clinical subgroups,aiming to enhance early di-agnosis and treatment of nephrolithiasis and promote personalized care in diverse populations.Methods:This cross-sectional study analyzed the medical records of 84 968 adults,stratified into three categories(low,middle,high)according to their TyG index scores.To evaluate the association between the TyG index and nephrolithiasis risk,multivariable Logistic regression models were employed,adjusting for po-tential confounders.Additionally,piecewise linear regression models were used to investigate the non-linear dynamics of the TyG index's relationship with nephrolithiasis risk.Subgroup analyses were per-formed to explore variations in the effects of the TyG index across different demographic and clinical populations.Results:Increasing TyG index was associated with a higher risk of nephrolithiasis,rising from 4.36％in the low group to 8.96％in the high group(P＜0.001).In adjusted models,males in the middle and high TyG index categories demonstrated significantly elevated risks of nephrolithiasis,with odds ratios of 1.18(95％CI:1.07-1.31,P=0.002)and 1.29(95％CI:1.15-1.45,P＜0.001),respectively.Conversely,in females,the association was not statistically significant post-adjustment(OR=0.98,95％CI:0.82-1.16,P=0.778).Among males,for each unit increment in the TyG index be-low the critical threshold of 8.98,there was a notable 40％escalation in the risk of developing nephroli-thiasis(OR=1.40,95％CI:1.24-1.58,P＜0.001).Surpassing this threshold,the TyG index no longer conferred a significant increase in risk(OR=0.91,95％CI:0.78-1.06,P=0.24).Subgroup analyses indicated that this association remained stable regardless of age,BMI,or hypertension status.Conclusion:The TyG index is positively associated with the risk of nephrolithiasis in males,demonstra-ting a nonlinear dose-response relationship that becomes especially pronounced at certain index levels.This biomarker could potentially serve as a valuable clinical tool for identifying males who are at a high risk of developing nephrolithiasis,thereby enabling targeted preventive strategies.Further research is urgently needed to explore the underlying mechanisms and to verify the applicability of these results across different populations.

OBJECTIVES: To classify bladder cancer based on immune cell infiltration score and to construct a risk assessment model for prognosis of patients. METHODS: The transcriptome data and data of breast cancer patients were obtained from the TCGA database. The single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was realized by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were extracted. A risk scoring model and a nomogram for risk assessment of prognosis for bladder cancer patients were constructed and verified. RESULTS: The immune cell infiltration scores of normal tissues and tumor tissues were calculated, and B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. Breast cancer patients were clustered into two groups (Cluster 1 and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). WGCNA screened out 35 genes related to key immune cells, and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients. CONCLUSIONS According to the immune cell infiltration score, bladder cancer patients can be classified. And the bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.