plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2 style

Biomarkers of Arginine Methylation in Diabetic Nephropathy: Novel Insights from Bioinformatics Analysis

Background

Diabetic nephropathy (DN) is a severe complication of diabetes influenced by arginine methylation. This study aimed to elucidate the role of protein arginine methylation-related genes (PRMT-RGs) in DN and identify potential biomarkers.

Methods

Differentially expressed genes in two GEO datasets (GSE30122 and GSE104954) were integrated with 9 PRMT-RGs. Candidate genes were identified using WGCNA and differential expression analysis, then screened using support vector machine-recursive feature elimination and least absolute shrinkage and selection operator. Biomarkers were defined as genes with consistent differential expression across both datasets. Regulatory networks were constructed using the miRNet and Network Analyst databases. Gene set enrichment analysis was performed to identify the signaling pathways in which the biomarkers were enriched in DN. Different immune cells in DN were identified using immune infiltration analysis. Meanwhile, drug prediction and molecular docking identified potential DN therapies. Finally, qRT-PCR and immunohistochemistry validated two biomarkers in STZ-induced DN mice and DN patients.

Results

Two biomarkers (FAM98A and FAM13B) of DN were identified in this study. The molecular regulatory network revealed that FAM98A and FAM13B were co-regulated by 6 microRNAs and 1 transcription factor and were enriched in signaling pathways. Immune infiltration and correlation analyses revealed that FAM98A and FAM13B were involved in developing DN along with PRMT-RGs and immune cells. The expression levels of Fam98a and Fam13b were significantly upregulated in the kidneys of DN mice revealed by qRT-PCR analysis. The expression levels of FAM98A were significantly upregulated in the kidneys of DN patients revealed by immunohistochemistry staining. Molecular docking showed that estradiol and rotenone exerted potential therapeutic effects on DN by targeting FAM98A.

Conclusion

Comprehensive bioinformatics analysis revealed that FAM98A and FAM13B were potential DN biomarkers correlated with PRMT-RGs and immune cells. This study provided useful insights for elucidating the molecular mechanisms and developing targeted therapy for DN.

Effects of High-Grain Diet on Performance, Ruminal Fermentation, and Rumen Microbial Flora of Lactating Holstein Dairy Cows

The objectives of the current study were to evaluate the fluctuations in production performance, rumen fermentation, and microbial community in lactating dairy cows fed a high-grain diet (HG). In this study, 16 healthy Holstein lactating dairy cattle with similar milk yields of 16.80 ± 4.30 kg/d, days in milk 171.44 ± 23.25 days, and parity 2.2 ± 1.5 times were selected and randomly allocated into two groups. One group was fed a low-grain diet (LG; 40% concentrate, DM basis; n = 8), and the other group was fed a high-grain diet (HG; 60% concentrate, DM basis; n = 8). The experiment lasted 6 weeks, including 1 week for adaptation. The experimental results showed that the milk fat content in the milk of lactating cows in the HG group was significantly reduced (p < 0.05), and the milk urea nitrogen (MUN) content showed an increasing trend (0.05 < p < 0.10) compared with the LG group. Compared with the LG group, rumen fluid pH was significantly decreased after feeding a high-grain diet, and contents of total volatile fatty acids (TVFA), acetate, propionate, and butyrate were significantly increased (p < 0.05). The acetate/propionate significantly decreased (p < 0.05). HG group significantly increased the abundance of Prevotella and Bacteroides in rumen fluid while significantly reducing the abundance of Methanobrevibacter and Lachnospiraceae ND3007_group (p < 0.05). Microorganisms with LDA scores > 2 were defined as unique, with the bacterial genus Anaerorhabdus_furcosa_group identified as a biomarker for the LG group, and the unique bacterial genus in the HG group were Prevotella, Stenotrophomonas, and Xanthomonadaceae. The prediction results of microbial function showed that a total of 18 KEGG differential pathways were generated between the two treatment groups, mainly manifested in metabolic pathways, signal transduction, and the immune system. In conclusion, the HG group promoted rumen fermentation by altering the microbial composition of lactating cows. Our findings provide a theoretical basis for the rational use of high-grain diets to achieve high yields in intensive dairy farming.

Potential therapeutic targets for pelvic organ prolapse: insights from key genes related to blood vessel development

Objective

Pelvic organ prolapse (POP) is a disease in which pelvic floor support structures are dysfunctional due to disruption of the extracellular matrix (ECM). The vascular system is essential for maintaining ECM homeostasis. Therefore, this study explored the potential mechanism of blood vessel development-related genes (BVDRGs) in POP.

Methods

POP-related datasets and BVDRGs were included in this study. Differentially expressed genes (DEGs) between the POP and control groups were first identified in the GSE12852 and GSE208271 datasets, and DE-BVDRGs were identified by determining the intersection of these DEGs and BVDRGs. Subsequently, the feature genes were evaluated by machine learning. Feature genes with consistent expression trends in the GSE12852 and GSE208271 datasets were considered key genes. Afterward, the overall diagnostic efficacy of key genes in POP was evaluated through receiver operating characteristic (ROC) curve analysis. Based on the key genes, enrichment analysis, immune infiltration analysis and regulatory network construction were performed to elucidate the molecular mechanisms underlying the functions of the key genes in POP.

Results

A total of 888 DEGs1 and 643 DEGs2 were identified in the GSE12852 and GSE208271 datasets, and 26 candidate genes and 4 DE-BVDRGs were identified. Furthermore, Hyaluronan synthase 2 (HAS2), Matrix metalloproteinase 19 (MMP19) and Plexin Domain Containing 1 (PLXDC1) were identified as key genes in POP and had promising value for diagnosing POP (AUC > 0.8). Additional research revealed that the key genes were predominantly implicated in immune cell activation, chemotaxis, and cytokine release via the chemokine signaling pathway, the Nod-like receptor signaling pathway, and the Toll-like receptor signaling pathway. Analysis of immune cell infiltration confirmed a decrease in the proportion of plasma cells in POP, and MMP19 expression showed a significant negative correlation with plasma cell numbers. In addition, regulatory network analysis revealed that MALAT1 (a lncRNA) targeted hsa-miR-503-5p, hsa-miR-23a-3p and hsa-miR-129-5p to simultaneously regulate three key genes.

Conclusion

We identified three key BVDRGs (HAS2, MMP19 and PLXDC1) related to the ECM in POP, providing markers for diagnostic studies and investigations of the molecular mechanism of POP.

Pan-cancer analysis of ARFs family and ARF5 promoted the progression of hepatocellular carcinoma

Background

ARF family proteins are a kind of small GTPases, which are involved in regulating a variety of basic functions of cells. In recent years, the role and molecular regulatory mechanisms of ARFs in tumor progression have received increasing attention, and research reports on most of their family members are increasing. However, research on the clinical and pathological relevance of ARF5 in cancer, especially in hepatocellular carcinoma, still needs to be improved.

Methods

RNA-seq data in the Cancer Genome Atlas (TCGA) and genome tissue expression (GTEx) databases were used to analyze the expression and pathological data of ARFs family in Pan-cancer. Kaplan-Meier and Cox regression were used for prognostic analysis of ARF5 and Pan-cancer. Combined with ImmuCellAI database and TIMER2 database, the relationship between ARF5 expression and immune cell tumor infiltration in hepatocellular carcinoma (HCC) was analyzed. WGCNA is used to construct the co-expression gene network related to ARF5 expression in HCC and screen important modules and central genes. GO and KEGG path enrichment analysis were carried out for the genes in the modules with clinical significance. GSEA analysis was performed to take into account the role of genes with small differences. Finally, ceRNA network analysis was used to explore the molecular mechanism of miRNAs and lncRNAs regulating ARF5 expression.

Results

ARFs family (ARF1, ARF3, ARF4, ARF5, ARF6) are generally highly expressed in Pan-cancer. ARF5 is significantly highly expressed in 29 cancers, and the high expression of ARF5 in HCC patients is significantly negatively correlated with OS, DFI, PFI and DSS, which may lead to cancer deterioration by participating in tumor immune infiltration of HCC. Through WGCNA analysis, the expression of ARF5 in HCC may be involved in many cellular processes that consume a lot of energy, such as ribosome formation, RNA and protein synthesis and lipids, as well as COVID-19, nonalcoholic fatty liver, neurodegenerative diseases and other disease pathways.

Conclusion

ARFs, especially ARF5, are overexpressed in many human tumors. This study shows for the first time that ARF5 is significantly correlated with the poor prognosis of HCC patients, which may play a role as an oncogene, suggesting that ARF5 has the potential as a biomarker for the diagnosis and treatment of HCC.

Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning

This study developed a deep vein thrombosis (DVT) risk prediction model based on multiple machine learning methods for patients with digestive system tumors undergoing surgical treatment. Data of 1048 patients with digestive system tumors admitted to Shanxi Provincial People's Hospital (College of Shanxi Medical University) from January 2020 to January 2023 were retrospectively analyzed, and 845 cases were screened according to the inclusion and exclusion criteria. The patients were divided into a training group (586 patients), and a validation group (259 patients), then feature selection was performed using six models, including Lasso regression, XGBoost, Random Forest, Decision Tree, Support Vector Machine, and Logistics. Predictive models were subsequently constructed from column-line plots, and the predictive validity of the models was assessed using receiver operating characteristic curves, precision-recall curves, and decision-curve analysis. In the model comparison, the XGBoost model showed the largest area under the curve (AUC) on the validation set (P < 0.05), demonstrating excellent predictive performance and generalization ability. We selected the common characteristic factors in the six models to further develop the column line plots to assess the DVT risk. The model performed well in clinical validation and effectively differentiated high-risk and low-risk patients. The differences in BMI, procedure time, and D-dimer were statistically significant between patients in the thrombus group and those in the non-thrombus group (P < 0.05). However, the AUC of the Xgboost model was found to be greater than that of the column chart model by the Delong test (P < 0.05). BMI, procedure time, and D-dimer are critical predictors of DVT risk in patients with digestive system tumors. Our model is an adequate assessment tool for DVT risk, which can help improve the prevention and treatment of DVT.