Analysis of the association between urinary glyphosate exposure and fatty liver index: a study for US adults.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a prevalent condition that often goes unrecognized in the population, and many risk factors for this disease are not well understood. Glyphosate (GLY) is one of the most commonly used herbicides worldwide, and exposure to this chemical in the environment is significant. However, studies exploring the association between GLY exposure and NAFLD remain limited. Therefore, the aim of this study was to assess the association between urinary glyphosate (uGLY) level and fatty liver index (FLI) using data from the National Health and Nutrition Examination Survey (NHANES), which includes uGLY measurements. METHODS: The log function of uGLY was converted and expressed as Loge(uGLY) with the constant "e" as the base and used for subsequent analysis. The association between Loge(uGLY) (the independent variable) level and FLI (the dependent variable) was assessed by multiple linear regression analysis. Smoothing curve fitting and a generalized additive model were used to assess if there was a nonlinear association between the independent and the dependent variables. A subgroup analysis was used to find susceptible individuals of the association between the independent variable and the dependent variable. RESULTS: A final total of 2238 participants were included in this study. Participants were categorized into two groups (< -1.011 and ≥ -1.011 ng/ml) based on the median value of Loge(uGLY). A total of 1125 participants had Loge(uGLY) levels ≥ -1.011 ng/ml and higher FLI. The result of multiple linear regression analysis showed a positive association between Loge(uGLY) and FLI (Beta coefficient = 2.16, 95% CI: 0.71, 3.61). Smoothing curve fitting and threshold effect analysis indicated a linear association between Loge(uGLY) and FLI [likelihood ratio(LLR) = 0.364]. Subgroup analyses showed that the positive association between Loge(uGLY) and FLI was more pronounced in participants who were female, aged between 40 and 60 years, had borderline diabetes history, and without hypertension history. In addition, participants of races/ethnicities other than (Mexican American, White and Black) were particularly sensitive to the positive association between Loge(uGLY) and FLI. CONCLUSIONS: A positive linear association was found between Loge(uGLY) level and FLI. Participants who were female, 40 to 60 years old, and of ethnic backgrounds other than Mexican American, White, and Black, deserve more attention.

FBXO11 amplifies type I interferon signaling to exert antiviral effects by facilitating the assemble of TRAF3-TBK1-IRF3 complex.

As the key component of host innate antiviral immunity, type I interferons (IFN-Is) exert multiple antiviral effects by inducing hundreds of IFN-stimulated genes. However, the precise mechanism involved in host sensing of IFN-I signaling priming is particularly complex and remains incompletely resolved. This research identified F-box protein 11 (FBXO11), a component of the E3-ubiquitin ligase SKP/Cullin/F-box complex, acted as an important regulator of IFN-I signaling priming and antiviral process against several RNA/DNA viruses. FBXO11 functioned as an essential enhancer of IFN-I signaling by promoting the phosphorylation of TBK1 and IRF3. Mechanistically, FBXO11 facilitated the assembly of TRAF3-TBK1-IRF3 complex by mediating the K63 ubiquitination of TRAF3 in a NEDD8-dependent manner to amplify the activation of IFN-I signaling. Consistently, the NEDD8-activating enzyme inhibitor MLN4921 could act as a blocker for FBXO11-TRAF3-IFN-I axis of signaling. More significantly, examination of clinical samples of chronic hepatitis B virus (HBV) infection and public transcriptome database of severe acute respiratory syndrome coronavirus-2-, HBV-, and hepatitis C virus-infected human samples revealed that FBXO11 expression was positively correlated with the stage of disease course. Taken together, these findings suggest that FBXO11 is an amplifier of antiviral immune responses and might serve as a potential therapeutic target for a number of different viral diseases.

T cell-related ubiquitination genes as prognostic indicators in hepatocellular carcinoma.

Background: T lymphocytes, integral to the adaptive immune system, wield pivotal influence in bolstering anti-tumor responses, and are strictly regulated by ubiquitination modification. The objective of this investigation was to devise a novel prognostic and immunotherapeutic efficacy predictor for hepatocellular carcinoma patients utilizing T cell-related ubiquitination genes (TCRUG). Method: The single-cell RNA sequencing (scRNA-seq) data and bulk RNA data of HCC patients are derived from the GEO database and TCGA database. Based on the processing of scRNA-seq, T cell marker genes are obtained and TCRUG is obtained. Further combined with WGCNA, differential analysis, univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis to filter and screen TCRUG. Finally construct a riskscore for predicting the prognosis of HCC patients, the predictive effect of which is validated in the GEO dataset. In addition, we also studied the correlation between riskscore and immunotherapy efficacy. Finally, the oncogenic role of UBE2E1 in HCC was explored through various in vitro experiments. Result: Based on patients' scRNA-seq data, we finally obtained 3050 T cell marker genes. Combined with bulk RNA data and clinical data from the TCGA database, we constructed a riskscore that accurately predicts the prognosis of HCC patients. This riskscore is an independent prognostic factor for HCC and is used to construct a convenient column chart. In addition, we found that the high-risk group is more suitable for immunotherapy. Finally, the proliferation, migration, and invasion abilities of HCC cells significantly decreased after UBE2E1 expression reduction. Conclusion: This study developed a riskscore based on TCRUG that can accurately and stably predict the prognosis of HCC patients. This riskscore is also effective in predicting the immune therapy response of HCC patients.

Association of weight-adjusted-waist index with non-alcoholic fatty liver disease and liver fibrosis: a cross-sectional study based on NHANES.

AIM: The purpose of this study was to explore the association of weight-adjusted-waist index (WWI) with non-alcoholic fatty liver disease (NAFLD) and liver fibrosis. METHODS: A cross-sectional study including 6587 participants was conducted in the National Health and Nutrition Examination Survey (NHANES). Multiple linear regression was used to validate the association of WWI with NAFLD and liver fibrosis, and smoothed curve fitting and threshold effect models were used to validate non-linear relationships. Subgroup analyses were used to verify the stability of the relationship between the independent and dependent variables in different populations. RESULTS: There was a positive association of WWI with NAFLD and liver fibrosis. In the model adjusted for all covariates, the effect values of WWI with NAFLD and liver fibrosis were (OR = 3.44, 95% CI: 3.09-3.82) and (OR = 2.40, 95% CI: 2.05-2.79), respectively. This positive correlation became more significant as WWI increased when WWI was presented in quartiles (P for trend < 0.01). Smoothed curve fitting and threshold effects analysis suggested a non-linear correlation between WWI and NAFLD (LLR < 0.01), with the positive correlation between WWI and NAFLD becoming more significant when WWI was less than 11.44 [5.93 (95% CI: 5.04-6.98)]. However, there was a linear correlation between WWI and liver fibrosis (LLR = 0.291). When subgroup analyses were performed by indicators such as age, race and gender, we found that the positive association between WWI and the dependent variables (NAFLD and liver fibrosis) was more pronounced in white male participants aged < 40 years. CONCLUSIONS: Among adults in the United States, WWI was positively associated with the prevalence of NAFLD and liver fibrosis. Participants with a WWI less than 11.44 should be cautious about the possibility of an increased risk of NAFLD development due to a higher WWI. Meanwhile, white males younger than 40 years of age should be more cautious about the higher risk of NAFLD and liver fibrosis that might be associated with an increased WWI.

The association between blood manganese and liver stiffness in participants with chronic obstructive pulmonary disease: a cross-sectional study from NHANES 2017-2018.

BACKGROUND: To explore the relationship between blood manganese and liver stiffness in the United States among participants with chronic obstructive pulmonary disease (COPD). METHODS: All data were obtained from the 2017-2018 National Health and Nutrition Examination Survey database (NHANES). A total of 4690 participants were included in the study. All participants included complete information on COPD, liver stiffness, and blood manganese. Liver stiffness (kPa) was measured from "Examination Date" and blood manganese (ug/L) was obtained from "Laboratory Data". A multiple linear regression model was used to assess the correlation between blood manganese and liver stiffness. RESULTS: Among the 4690 participants, blood manganese was lower in the COPD group but liver stiffness was higher (p < 0.05). There was a positive correlation between blood manganese and liver stiffness (ß = 0.08, 95% CI 0.03, 0.12). This positive association was more pronounced in COPD participants (ß = 0.25, 95% CI 0.08, 0.42) and there was a non-linear relationship, which was more significant when blood manganese exceeded 14.43 ug/L (ß = 1.76, 95% CI 1.10, 2.41). CONCLUSIONS: The association between blood manganese and liver stiffness was positive, which was more apparent in COPD patients.

Metabolism-related signatures is correlated with poor prognosis and immune infiltration in hepatocellular carcinoma via multi-omics analysis and basic experiments.

Background: Metabolism is an ordered series of biological processes that occur in an organism. Altered cellular metabolism is often closely associated with the development of cancer. The aim of this research was to construct a model by multiple metabolism-related molecules to diagnose and assess the prognosis of patients. Method: WGCNA analysis was used to screen out differential genes. GO, KEGG are used to explore potential pathways and mechanisms. The lasso regression model was used to filter out the best indicators to construct the model. Single-sample GSEA (ssGSEA) assess immune cells abundance, immune terms in different Metabolism Index (MBI) groups. Human tissues and cells were used to verify the expression of key genes. Result: WGCNA clustering grouped genes into 5 modules, of which 90 genes from the MEbrown module were selected for subsequent analysis. GO analysis was found that BP mainly has mitotic nuclear division, while KEGG pathway is enriched to Cell cycle, Cellular senescence. Mutation analysis revealed that the frequency of TP53 mutations was much higher in samples from the high MBI group than in the low MBI group. Immunoassay revealed that patients with higher MBI have higher macrophage and Regulatory T cells (Treg) abundance, while NK cells were lowly expressed in the high MBI group. RT-qPCR and immunohistochemistry (IHC) revealed that the hub genes expression is higher in cancer tissues. The expression in hepatocellular carcinoma cells was also much higher than that in normal hepatocytes. Conclusion: In conclusion, a metabolism-related model was constructed that can be used to estimate the prognosis of hepatocellular carcinoma, and the clinical treatment of different hepatocellular carcinoma patients with medications was guided.

Gene Signature and Prognostic Value of Ubiquitin-Specific Proteases Members in Hepatocellular Carcinoma and Explored the Immunological Role of USP36.

BACKGROUND: Ubiquitination is one of the most common post-translational modifications in cells and dysregulation is closely associated with the development of cancer. However, a comprehensive analysis of the role of ubiquitination in hepatocellular carcinoma (HCC) is still lacking. In this study we analyzed expression and prognostic value of Ubiquitin-Specific Proteases (USPs) in HCC, and the immunological role of USP36 in HCC. METHODS: Expression data, prognostic data, and DNA methylation data in cases of HCC were obtained from the cancer genome atlas (TCGA). Overexpression of USP36 in HCC was confirmed in the gene expression omnibus (GEO) database and verified by quantitative PCR in 10 pairs of HCC samples. ULCAN was used to analyze the correlation between USP36 and clinicopathological features. TIMER2.0 and DriverDBv3 were used to analyze the USP36 mutational profile. GSEA analysis explored the potential signaling pathways of USP36 affecting HCC. The immune and stromal scores of HCC samples were calculated using the ESTIMATE algorithm. TIMER1.0 was used to explore the correlation between USP36 and immune cell infiltration. Finally, we analyzed the correlation of USP36 expression with immune checkpoint molecules and determined the IC50 values of 6 chemotherapeutic drugs using the pRRophetic software package. RESULTS: Most USPs are abnormally expressed in HCC, among which USP36 and USP39 are most closely associated with HCC prognosis. We also found that USP36 is associated with TP53 mutational status. GSEA analysis indicated that USP36 may affect HCC progression through the dysregulation of various pathways such as ubiquitin-mediated proteolysis. USP36 expression positively correlated with both macrophage infiltration levels and multiple immune checkpoint molecules. Finally, chemosensitivity analysis indicated that chemosensitivity was lower in cells within the USP36 high expression group. CONCLUSIONS: Most USPs are abnormally expressed in HCC. Overexpression of USP36 in HCC is closely related to poor prognosis. In particular, the unique immunological role of USP36 may have potential clinical application value.

Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES.

Background: Prevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential. Objective: An XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM. Methods: All data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort. Results: A total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa]. Conclusions: XGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.

Prognostic value of LECT2 and relevance to immune infiltration in hepatocellular carcinoma.

Background: Previous studies have shown that Leukocyte cell-derived chemotaxin2 (LECT2) is associated with the development of HCC. However, there are still no studies with a comprehensive analysis of the role of LECT2 in hepatocellular carcinoma (HCC). Methods: TCGA data sets were used to analyze the expression of LECT2 in HCC. In addition, the prognostic value of LECT2 in HCC was also investigated. DriverDBv3 was used to analyze the Mutation, CNV, and methylation profiles of LECT2. And, validated by immunohistochemistry in 72 HCC samples. The prognostic value of LECT2 and the correlation with clinicopathological features were analyzed. The GO/KEGG enrichment analysis of LECT2 co-expression and gene set enrichment analysis (GSEA) was performed using the R software package. The PPI interaction network was constructed by Search Tool for the Retrieval of Interacting Genes (STRING) database. Immune infiltration was estimated by the XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT abs and CIBERSORT algorithms, and Spearman was used to analyzing their correlation with LECT2. Moreover, we analyzed the correlation of LECT2 expression with immune checkpoint molecules and HLA genes. Finally, we analyzed the IC50 values of six chemotherapeutic drugs by the pRRophetic package. Results: Reduced LECT2 expression levels found in HCC patients. Moreover, decreased levels of LECT2 were associated with poor overall survival, disease-free survival, disease-specific survival, and progression-free survival. Besides, methylation was significantly associated with LECT2 expression. The functional enrichment analysis revealed that LECT2 may affect HCC progression through various pathways such as JAK/STAT signaling pathway, cell cycle, and pathways in cancer. Additionally, the results showed that LECT2 expression was negatively correlated with immune infiltration of B cells, Neutrophil, Monocyte, Cancer-associated fibroblast, and Myeloid dendritic cell, and positively correlated with T cell CD8+ naive, Endothelial cell, and Hematopoietic stem cell. LECT2 expression was negatively correlated with multiple immune checkpoint molecules and HLA genes. Chemosensitivity analysis showed that chemosensitivity was lower in the LECT2 high expression group. We validated the prognostic value of LECT2 and analysis of clinicopathological features showed a lower TNM stage in the group with high expression of LECT2. Conclusion: Low expression of LECT2 in HCC is closely associated with poor prognosis, LECT2 may have potential clinical applications due to its unique immunological effects.

LECT2, A Novel and Direct Biomarker of Liver Fibrosis in Patients With CHB.

Chronic hepatitis B (CHB) patients with severe liver fibrosis would be more likely to progress to a poorer prognosis. Treatment is considered once the liver fibrosis reaches significant liver fibrosis (≥S2). Leukocyte cell-derived chemotaxin-2 (LECT2) has been shown to contribute to liver fibrosis progression. No research has focused on the role of LECT2 in liver fibrosis in CHB patients. This study enrolled 227 CHB patients and divided them into the training group (n = 147) and validation group (n = 80), respectively. The expression of LECT2 in serum, protein and mRNA of the human liver tissues was detected to analyze the possible associations between LECT2 and liver fibrosis. A receiver operating characteristic curve (ROC) was used to estimate the efficacy of LECT2 for predicting liver fibrosis. The data showed that there was a positive relationship between LECT2 and the progression of liver fibrosis. In the training group, LECT2 was demonstrated to have better effectiveness than APRI and FIB-4. The AUC was 0.861, 0.698, and 0.734 for significant liver fibrosis, and 0.855, 0.769, and 0.752 for advanced liver fibrosis. Besides, the efficacy of LECT2 in different statuses of patients with CHB was examined and the effectiveness of LECT2 had also been confirmed in the validation group. All the results confirmed that LECT2 could act as a perfect predictor and thus offers a novel and direct biomarker to estimate liver fibrosis more accurately.