Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers.

BACKGROUND: Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment. AIM: To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis. METHODS: Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms. RESULTS: This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively. CONCLUSION: A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.

Identifying the predictive role and the related drugs of oxidative stress genes in the hepatocellular carcinoma.

BACKGROUND AND AIMS: Oncogenesis and tumor development have been related to oxidative stress (OS). The potential diagnostic utility of OS genes in hepatocellular carcinoma (HCC), however, remains uncertain. As a result, this work aimed to create a novel OS related-genes signature that could be used to predict the survival of HCC patients and to screen OS related-genes drugs that might be used for HCC treatment. METHODS: We used The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database to acquire mRNA expression profiles and clinical data for this research and the GeneCards database to obtain OS related-genes. Following that, biological functions from Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on differentially expressed OS-related genes (DEOSGs). Subsequently, the prognostic risk signature was constructed based on DEOSGs from the TCGA data that were screened by using univariate cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression, and multivariate cox analysis. At the same time, we developed a prognostic nomogram of HCC patients based on risk signature and clinical-pathological characteristics. The GEO data was used for validation. We used the receiver operating characteristic (ROC) curve, calibration curves, and Kaplan-Meier (KM) survival curves to examine the prediction value of the risk signature and nomogram. Finally, we screened the differentially expressed OS genes related drugs. RESULTS: We were able to recognize 9 OS genes linked to HCC prognosis. In addition, the KM curve revealed a statistically significant difference in overall survival (OS) between the high-risk and low-risk groups. The area under the curve (AUC) shows the independent prognostic value of the risk signature model. Meanwhile, the ROC curves and calibration curves show the strong prognostic power of the nomogram. The top three drugs with negative ratings were ZM-336372, lestaurtinib, and flunisolide, all of which inversely regulate different OS gene expressions. CONCLUSION: Our findings indicate that OS related-genes have a favorable prognostic value for HCC, which sheds new light on the relationship between oxidative stress and HCC, and suggests potential therapeutic strategies for HCC patients.

Association and mediation analyses among multiple metal exposure, mineralocorticoid levels, and serum ion balance in residents of northwest China.

Toxic metals are vital risk factors affecting serum ion balance; however, the effect of their co-exposure on serum ions and the underlying mechanism remain unclear. We assessed the correlations of single metal and mixed metals with serum ion levels, and the mediating effects of mineralocorticoids by investigating toxic metal concentrations in the blood, as well as the levels of representative mineralocorticoids, such as deoxycorticosterone (DOC), and serum ions in 471 participants from the Dongdagou-Xinglong cohort. In the single-exposure model, sodium and chloride levels were positively correlated with arsenic, selenium, cadmium, and lead levels and negatively correlated with zinc levels, whereas potassium and iron levels and the anion gap were positively correlated with zinc levels and negatively correlated with selenium, cadmium and lead levels (all P < 0.05). Similar results were obtained in the mixed exposure models considering all metals, and the major contributions of cadmium, lead, arsenic, and selenium were highlighted. Significant dose-response relationships were detected between levels of serum DOC and toxic metals and serum ions. Mediation analysis showed that serum DOC partially mediated the relationship of metals (especially mixed metals) with serum iron and anion gap by 8.3% and 8.6%, respectively. These findings suggest that single and mixed metal exposure interferes with the homeostasis of serum mineralocorticoids, which is also related to altered serum ion levels. Furthermore, serum DOC may remarkably affect toxic metal-related serum ion disturbances, providing clues for further study of health risks associated with these toxic metals.

Cadmium exacerbates liver injury by remodeling ceramide metabolism: Multiomics and laboratory evidence.

Cadmium (Cd) is a toxic heavy metal that primarily targets the liver. Cd exposure disrupts specific lipid metabolic pathways; however, the underlying mechanisms remain unclear. This study aimed to investigate the lipidomic characteristics of rat livers after Cd exposure as well as the potential mechanisms of Cd-induced liver injury. Our analysis of established Cd-exposed rat and cell models showed that Cd exposure resulted in liver lipid deposition and hepatocyte damage. Lipidomic detection, transcriptome sequencing, and experimental analyses revealed that Cd mainly affects the sphingolipid metabolic pathway and that the changes in ceramide metabolism are the most significant. In vitro experiments revealed that the inhibition of ceramide synthetase activity or activation of ceramide decomposing enzymes ameliorated the proapoptotic and pro-oxidative stress effects of Cd, thereby alleviating liver injury. In contrast, the exogenous addition of ceramide aggravated liver injury. In summary, Cd increased ceramide levels by remodeling ceramide synthesis and catabolism, thereby promoting hepatocyte apoptosis and oxidative stress and ultimately aggravating liver injury. Reducing ceramide levels can serve as a potential protective strategy to mitigate the liver toxicity of Cd. This study provides new evidence for understanding Cd-induced liver injury at the lipidomic level and insights into the health risks and toxicological mechanisms associated with Cd.

The association between trace metals in both cancerous and non-cancerous tissues with the risk of liver and gastric cancer progression in northwest China.

Liver cancer and gastric cancer have extremely high morbidity and mortality rates worldwide. It is well known that an increase or decrease in trace metals may be associated with the formation and development of a variety of diseases, including cancer. Therefore, this study aimed to evaluate the contents of aluminium (Al), arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), selenium (Se), and zinc (Zn) in cancerous liver and gastric tissues, compared to adjacent healthy tissues, and to investigate the relationship between trace metals and cancer progression. During surgery, multiple samples were taken from the cancerous and adjacent healthy tissues of patients with liver and gastric cancer, and trace metal levels within these samples were analysed using inductively coupled plasma mass spectrometry (ICP-MS). We found that concentrations of As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, and Zn in tissues from patients with liver cancer were significantly lower than those in healthy controls (P < 0.05). Similarly, patients with gastric cancer also showed lower levels of Cd, Co, Cr, Mn, Ni, and Zn-but higher levels of Cu and Se-compared to the controls (P < 0.05). In addition, patients with liver and gastric cancers who had poorly differentiated tumours and positive lymph node metastases showed lower levels of trace metals (P < 0.05), although no significant changes in their concentrations were observed to correlate with sex, age, or body mass index (BMI). Logistic regression, principal component analysis (PCA), Bayesian kernel regression (BKMR), weighted quantile sum (WQS) regression, and quantile-based g computing (qgcomp) models were used to analyse the relationships between trace metal concentrations in liver and gastric cancer tissues and the progression of these cancers. We found that single or mixed trace metal levels were negatively associated with poor differentiation and lymph node metastasis in both liver and gastric cancer, and the posterior inclusion probability (PIP) of each metal showed that Cd contributed the most to poor differentiation and lymph node metastasis in both liver and gastric cancer (all PIP = 1.000). These data help to clarify the relationship between changes in trace metal levels in cancerous liver and gastric tissues and the progression of these cancers. Further research is warranted, however, to fully elucidate the mechanisms and causations underlying these findings.

Association between Heavy Metals and Trace Elements in Cancerous and Non-cancerous Tissues with the Risk of Colorectal Cancer Progression in Northwest China.

Alterations in heavy metals and trace element levels may be associated with various cancers. However, the role of this interaction in colorectal cancer (CRC) progression is unclear. In recent years, Principal Component Analysis (PCA) and Bayesian Kernel Machine Regression (BKMR) models have provided new ideas for analyzing the effects of metal mixtures on CRC progression. Herein, we assessed the differences in the levels of arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), selenium (Se), and zinc (Zn) in tumors and adjacent healthy tissues, to investigate the relationship between heavy metals/trace elements and CRC progression. Surgical samples of CRC and noncancerous tissues were collected, and trace metal levels were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Logistic regression, PCA, and BKMR models were used to investigate the relationship between heavy metals and trace elements and the degree of tumor differentiation and lymph node metastasis in CRC. Cancer tissues showed lower As, Cd, Co, and Cr concentrations, and higher Se concentrations than healthy tissues (P < 0.05). In addition, CRC patients with poorly differentiated tumors and/or positive lymph node metastases had lower levels of Cd, Zn, Cu, and Se (P < 0.05). Logistic regression showed that single metal concentration was negatively correlated with CRC progression. PCA and BKMR models also showed that the metal mixture concentration was negatively correlated with CRC progression, with Cd contributing the most. Overall, changes in heavy metal and trace element levels may be related to the development of CRC; however, further mechanistic studies are required.

Construction and validation of a novel nomogram to predict cancer-specific survival in patients with gastric adenocarcinoma.

Background and aims: Adenocarcinoma is one of the most common pathological types of gastric cancer. The aims of this study were to develop and validate prognostic nomograms that could predict the probability of cancer-specific survival (CSS) for gastric adenocarcinoma (GAC) patients at 1, 3, and 5 years. Methods: In total, 7747 patients with GAC diagnosed between 2010 and 2015, and 4591 patients diagnosed between 2004 and 2009 from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. The 7747 patients were used as a prognostic cohort to explore GAC-related prognostic risk factors. Moreover, the 4591 patients were used for external validation. The prognostic cohort was also divided into a training and internal validation sets for construction and internal validation of the nomogram. CSS predictors were screened using least absolute shrinkage and selection operator regression analysis. A prognostic model was built using Cox hazard regression analysis and provided as static and dynamic network-based nomograms. Results: The primary site, tumor grade, surgery of the primary site, T stage, N stage, and M stage were determined to be independent prognostic factors for CSS and were subsequently included in construction of the nomogram. CSS was accurately estimated using the nomogram at 1, 3, and 5 years. The areas under the curve (AUCs) for the training group at 1, 3, and 5 years were 0.816, 0.853, and 0.863, respectively. Following internal validation, these values were 0.817, 0.851, and 0.861. Further, the AUC of the nomogram was much greater than that of American Joint Committee on Cancer (AJCC) or SEER staging. Moreover, the anticipated and actual CSS values were in good agreement based on decision curves and time-calibrated plots. Then, patients from the two subgroups were divided into high- and low-risk groups based on this nomogram. The survival rate of high-risk patients was considerably lower than that of low-risk patients, according to Kaplan-Meier (K-M) curves (p<0.0001). Conclusions: A reliable and convenient nomogram in the form of a static nomogram or an online calculator was constructed and validated to assist physicians in quantifying the probability of CSS in GAC patients.

Effects of cadmium and lead co-exposure on glucocorticoid levels in rural residents of northwest China.

Cadmium (Cd) and lead (Pb) are important environmental endocrine disruptors that are associated with adverse health problems. However, the effects of co-exposure to Cd and Pb on glucocorticoids (GCs) in the body at environmental levels are limited. A total of 468 subjects from the Dongdagou-Xinglong cohort (DDG-XL) were included in this study. We measured the serum levels of two representative endogenous GCs [cortisol (CRL) and cortisone (CRN)], and whole blood levels of Cd and Pb. Multiple linear regression models were constructed to explore the associations of single or combined Cd and Pb exposure with serum CRL and CRN levels. The interactive effects of Cd and Pb on GCs were further assessed using mediation analysis and moderation analysis. Single-heavy metal exposure analysis with adjustment for potential confounders showed that the serum CRL level decreased when the blood Cd or Pb concentration gradually increased (P trend <0.01). Additionally, subjects with high Cd or Pb exposure (Q4) had significantly reduced serum CRN levels compared to those with low Cd or Pb exposure (Q1) (P < 0.05). In Cd and Pb co-exposure analysis, significant negative dose-response relationships were observed between co-exposure to Cd and Pb and serum CRL and CRN levels. Furthermore, mediation analysis showed that Cd completely mediated the relationship between Pb and GCs, and moderation analysis suggested that Pb might weaken the negative relationship between Cd and GCs. These findings suggest that single or combined exposure to Cd and Pb interferes with the homeostasis of serum CRL and CRN levels. Furthermore, we innovatively propose that Cd and Pb may have interactive effects on GCs levels, and Pb can antagonize the negative relationship between Cd and GCs, which may provide clues for further studies on endocrine and metabolic disorders related to these heavy metals.

Liver cirrhosis and complications from the perspective of dysbiosis.

The gut-liver axis refers to the intimate relationship and rigorous interaction between the gut and the liver. The intestinal barrier's integrity is critical for maintaining liver homeostasis. The liver operates as a second firewall in this interaction, limiting the movement of potentially dangerous compounds from the gut and, as a result, contributing in barrier management. An increasing amount of evidence shows that increased intestinal permeability and subsequent bacterial translocation play a role in liver damage development. The major pathogenic causes in cirrhotic individuals include poor intestinal permeability, nutrition, and intestinal flora dysbiosis. Portal hypertension promotes intestinal permeability and bacterial translocation in advanced liver disease, increasing liver damage. Bacterial dysbiosis is closely related to the development of cirrhosis and its related complications. This article describes the potential mechanisms of dysbiosis in liver cirrhosis and related complications, such as spontaneous bacterial peritonitis, hepatorenal syndrome, portal vein thrombosis, hepatic encephalopathy, and hepatocellular carcinoma, using dysbiosis of the intestinal flora as an entry point.

Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model.

BACKGROUND: Portal vein thrombosis (PVT) is a significant issue in cirrhotic patients, necessitating early detection. This study aims to develop a data-driven predictive model for PVT diagnosis in chronic hepatitis liver cirrhosis patients. METHODS: We employed data from a total of 816 chronic cirrhosis patients with PVT, divided into the Lanzhou cohort (n = 468) for training and the Jilin cohort (n = 348) for validation. This dataset encompassed a wide range of variables, including general characteristics, blood parameters, ultrasonography findings and cirrhosis grading. To build our predictive model, we employed a sophisticated stacking approach, which included Support Vector Machine (SVM), Naïve Bayes and Quadratic Discriminant Analysis (QDA). RESULTS: In the Lanzhou cohort, SVM and Naïve Bayes classifiers effectively classified PVT cases from non-PVT cases, among the top features of which seven were shared: Portal Velocity (PV), Prothrombin Time (PT), Portal Vein Diameter (PVD), Prothrombin Time Activity (PTA), Activated Partial Thromboplastin Time (APTT), age and Child-Pugh score (CPS). The QDA model, trained based on the seven shared features on the Lanzhou cohort and validated on the Jilin cohort, demonstrated significant differentiation between PVT and non-PVT cases (AUROC = 0.73 and AUROC = 0.86, respectively). Subsequently, comparative analysis showed that our QDA model outperformed several other machine learning methods. CONCLUSION: Our study presents a comprehensive data-driven model for PVT diagnosis in cirrhotic patients, enhancing clinical decision-making. The SVM-Naïve Bayes-QDA model offers a precise approach to managing PVT in this population.