The role of the mitochondrial ribosomal protein family in detecting hepatocellular carcinoma and predicting prognosis, immune features, and drug sensitivity

Background Hepatocellular carcinoma (HCC) is one of the most common types of malignant tumors, with a slow onset, rapid progression, and frequent recurrence. Previous research has implicated mitochondrial ribosomal genes in the development, metastasis, and prognosis of various cancers. However, further research is necessary to establish a link between mitochondrial ribosomal protein (MRP) family expression and HCC diagnosis, prognosis, ferroptosis-related gene (FRG) expression, m6A modification-related gene expression, tumor immunity, and drug sensitivity. Methods Bioinformatics resources were used to analyze data from patients with HCC retrieved from the TCGA, ICGC, and GTEx databases (GEPIA, UALCAN, Xiantao tool, cBioPortal, STRING, Cytoscape, TISIDB, and GSCALite). Results Among the 82 MRP family members, 14 MRP genes (MRPS21, MRPS23, MRPL9, DAP3, MRPL13, MRPL17, MRPL24, MRPL55, MRPL16, MRPL14, MRPS17, MRPL47, MRPL21, and MRPL15) were significantly upregulated differentially expressed genes (DEGs) in HCC tumor samples in comparison to normal samples. Receiver-operating characteristic curve analysis indicated that all 14 DEGs show good diagnostic performance. Furthermore, TCGA analysis revealed that the mRNA expression of 39 MRPs was associated with overall survival (OS) in HCC. HCC was divided into two molecular subtypes (C1 and C2) with distinct prognoses using clustering analysis. The clusters showed different FRG expression and m6A methylation profiles and immune features, and prognostic models showed that the model integrating 5 MRP genes (MRPS15, MRPL3, MRPL9, MRPL36, and MRPL37) and 2 FRGs (SLC1A5 and SLC5A11) attained a greater clinical net benefit than three other prognostic models. Finally, analysis of the CTRP and GDSC databases revealed several potential drugs that could target prognostic MRP genes (AU)

The role of the mitochondrial ribosomal protein family in detecting hepatocellular carcinoma and predicting prognosis, immune features, and drug sensitivity.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common types of malignant tumors, with a slow onset, rapid progression, and frequent recurrence. Previous research has implicated mitochondrial ribosomal genes in the development, metastasis, and prognosis of various cancers. However, further research is necessary to establish a link between mitochondrial ribosomal protein (MRP) family expression and HCC diagnosis, prognosis, ferroptosis-related gene (FRG) expression, m6A modification-related gene expression, tumor immunity, and drug sensitivity. METHODS: Bioinformatics resources were used to analyze data from patients with HCC retrieved from the TCGA, ICGC, and GTEx databases (GEPIA, UALCAN, Xiantao tool, cBioPortal, STRING, Cytoscape, TISIDB, and GSCALite). RESULTS: Among the 82 MRP family members, 14 MRP genes (MRPS21, MRPS23, MRPL9, DAP3, MRPL13, MRPL17, MRPL24, MRPL55, MRPL16, MRPL14, MRPS17, MRPL47, MRPL21, and MRPL15) were significantly upregulated differentially expressed genes (DEGs) in HCC tumor samples in comparison to normal samples. Receiver-operating characteristic curve analysis indicated that all 14 DEGs show good diagnostic performance. Furthermore, TCGA analysis revealed that the mRNA expression of 39 MRPs was associated with overall survival (OS) in HCC. HCC was divided into two molecular subtypes (C1 and C2) with distinct prognoses using clustering analysis. The clusters showed different FRG expression and m6A methylation profiles and immune features, and prognostic models showed that the model integrating 5 MRP genes (MRPS15, MRPL3, MRPL9, MRPL36, and MRPL37) and 2 FRGs (SLC1A5 and SLC5A11) attained a greater clinical net benefit than three other prognostic models. Finally, analysis of the CTRP and GDSC databases revealed several potential drugs that could target prognostic MRP genes. CONCLUSION: We identified 14 MRP genes as HCC diagnostic markers. We investigated FRG and m6A modification-related gene expression profiles and immune features in patients with HCC, and developed and validated a model incorporating MRP and FRG expression that accurately and reliably predicts HCC prognosis and may predict disease progression and treatment response.

A simple prediction model of hyperuricemia for use in a rural setting.

Currently, the most widely used screening methods for hyperuricemia (HUA) involves invasive laboratory tests, which are lacking in many rural hospitals in China. This study explored the use of non-invasive physical examinations to construct a simple prediction model for HUA, in order to reduce the economic burden and invasive operations such as blood sampling, and provide some help for the health management of people in poor areas with backward medical resources. Data of 9252 adults from April to June 2017 in the Affiliated Hospital of Guilin Medical College were collected and divided randomly into a training set (n = 6364) and a validation set (n = 2888) at a ratio of 7:3. In the training set, non-invasive physical examination indicators of age, gender, body mass index (BMI) and prevalence of hypertension were included for logistic regression analysis, and a nomogram model was established. The classification and regression tree (CART) algorithm of the decision tree model was used to build a classification tree model. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analyses (DCA) were used to test the distinction, accuracy and clinical applicability of the two models. The results showed age, gender, BMI and prevalence of hypertension were all related to the occurrence of HUA. The area under the ROC curve (AUC) of the nomogram model was 0.806 and 0.791 in training set and validation set, respectively. The AUC of the classification tree model was 0.802 and 0.794 in the two sets, respectively, but were not statistically different. The calibration curves and DCAs of the two models performed well on accuracy and clinical practicality, which suggested these models may be suitable to predict HUA for rural setting.

Increased Risk of Retinal Vasculitis in Patients With Systemic Lupus Erythematosus: A Nationwide Population-Based Cohort Study.

Objectives: To evaluate the relationship between systemic lupus erythematosus (SLE) and the risk of retinal vasculitis (RV) using a population-based database. Methods: Using the 1997-2013 Taiwanese National Health Insurance Database, we identified newly diagnosed SLE patients between 2001 and 2012 as the SLE group. We matched the SLE group with non-SLE individuals selected from a representative one million sample of the population in a 1:20 ratio for age, sex, and the year of the index date. After adjusting for potential confounders, including urbanization of the patient's residence, the level of the payroll-related insured amount, and selected comorbidities, we examined the association between SLE and the risk of RV using the Cox proportional hazard model shown as hazard ratios (HRs) with 95% confidence intervals (CIs). Sensitivity analyses were conducted using various definitions of RV. Results: We included 11,586 patients with SLE and 231,720 matched non-SLE individuals. The mean age of the study participants was 36.7 ± 16.9 years, and the female-to-male ratio was 6.8:1. The incidence rates of RV were 56.39 cases per 100,000 person-years and 2.45 cases per 100,000 person-years, respectively. After adjusting for potential confounders, the incidence rate of RV in the SLE cohort was 22.99 times higher than that in the non-SLE cohort (56.39 vs. 2.45 per 100,000 person-years). The adjusted HR for RV in the SLE group was 23.61 (95% CI, 14.94-37.32). The results remained robust in the sensitivity analysis. Conclusion: This nationwide population-based study revealed that SLE patients had a significantly higher risk of RV than non-SLE individuals.

[Trends in Vehicle Emissions in Sichuan Province, 2010-2017].

Vehicle pollution in Sichuan Province is becoming increasingly serious. Here, based on specific inventory calculation methods and multi-caliber activity level data, this study calculated vehicle exhaust emissions from 2010 to 2017 in Sichuan Province. The results show that the average growth rate of vehicle ownership in Sichuan is higher than the national trend. In 2017, vehicle emissions of CO, NOx, SO2, NH3, HC, PM2.5, PM10, BC, and OC were 706.9, 275.3, 0.3, 5.7, 164.8, 8.1, 8.9, 4.1, and 1.4 kt, respectively. Except for NH3, showed a downward trend, peaking in 2014-2016. Diesel vehicle ownership showed a strong correlation with the emission of NOx. Based on these observations, stricter vehicle emission standards offer the greatest potential for emissions reductions, with early implementation offering the greatest reduction potential. The improvement of fuel quality will also have more than a 6% emission reduction effect on pollutant emission each year. HC and NOx emissions reductions will be an important control on vehicle pollution in Sichuan Province in the future.

[Method for High-resolution Emission Inventory for Road Vehicles in Chengdu Based on Traffic Flow Monitoring Data].

A method for developing a high-resolution emission inventory for road vehicles based on traffic flow monitoring data is proposed in this study. The characteristics of road traffic flow were analyzed and a high-resolution emission inventory of vehicle in Chengdu was established. The results showed that the traffic flow and emissions in Chengdu exhibited an obvious "double peak" distribution, and that the traffic volume of vehicles during peak hours accounted for 39.85% of the total. China IV vehicles, small vehicles, and gasoline vehicles were the main types of road vehicles classified. The daily emissions of SO2, NOx, CO, PM10, PM2.5, BC, OC, and VOCs from road vehicles were 3.89, 162.08, 324.11, 4.79, 4.36, 1.89, 0.78, and 44.37 t, respectively. The overall spatial distribution showed a decreasing trend from the city center to the periphery, and the time distribution essentially presented a "double peak" distribution. The related indicators of particulate matter were greatly affected by the number of trucks. The main source of NOx, PM10, PM2.5, BC, and OC was large diesel vehicles, and the main source of CO was small gasoline vehicles. NOx emissions from large vehicles accounted for up to 80% of the total. The method based on registered vehicles led to an overestimation of the emissions from road vehicles in Chengdu, with a proportion between 1% and 30%.