Network Pharmacology Prediction: The Possible Mechanisms of Cinobufotalin against Osteosarcoma

Objective. To explore the active compounds and targets of cinobufotalin (huachansu) compared with the osteosarcoma genes to obtain the potential therapeutic targets and pharmacological mechanisms of action of cinobufotalin on osteosarcoma through network pharmacology. Methods. The composition of cinobufotalin was searched by literature retrieval, and the target was selected from the CTD and TCMSP databases. The osteosarcoma genes, found from the GeneCards, OMIM, and other databases, were compared with the cinobufotalin targets to obtain potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets, constructed through the STRING database, was inputted into Cytoscape software to calculate the hub genes, using the NetworkAnalyzer. The hub genes were inputted into the Kaplan-Meier Plotter online database for exploring the survival curve. Functional enrichment analysis was identified using the DAVID database. Results. 28 main active compounds of cinobufotalin were explored, including bufalin, adenosine, oleic acid, and cinobufagin. 128 potential therapeutic targets on osteosarcoma are confirmed among 184 therapeutic targets form cinobufotalin. The hub genes included TP53, ACTB, AKT1, MYC, CASP3, JUN, TNF, VEGFA, HSP90AA1, and STAT3. Among the hub genes, TP53, ACTB, MYC, TNF, VEGFA, and STAT3 affect the patient survival prognosis of sarcoma. Through function enrichment analysis, it is found that the main mechanisms of cinobufotalin on osteosarcoma include promoting sarcoma apoptosis, regulating the cell cycle, and inhibiting proliferation and differentiation. Conclusion. The possible mechanisms of cinobufotalin against osteosarcoma are preliminarily predicted through network pharmacology, and further experiments are needed to prove these predictions.

A Novel Metastasis-related Genes Based Signature for Predicting the Progression-free Interval of Patients With Papillary Thyroid Carcinoma

Background: We aimed to build a novel model with metastasis-related genes (MTGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (PTC).Methods: We performed a bioinformatic analysis of integrated PTC datasets with the MTGs to identify differentially expressed MTGs (DE-MTGs). Then we generated PFI-related DE-MTGs and established a novel MTGs based signature. After that, we validated the signature on multiple datasets and PTC cell lines. Further, we carried out uni- and multivariate analysis to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC. Results: We identified 155 DE-MTGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic process. Consequently, we found a novel 10-gene signature and could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.76 and the relevant nomogram had a C-index of 0.80. Also, it was closely related to pivotal clinical characters of datasets and invasiveness of cell lines. And the signature was confirmed a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram's efficacy was satisfying in predicting PTC’s PFI. Conclusions: The MTG signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.

N6-Methyladenosine Modification Changes in the Genome of Paulownia Fortunei Seedlings Infected With Phytoplasma

Background: Phytoplasmas induce diseases in more than 1,000 plant species and cause substantial ecological damage and economic losses, but the specific pathogenesis of phytoplasma has not yet been clarified. N6-methyladenosine sequencing (m6A-seq) has been applied mainly to model plants and not to woody plants. Results: In this study, we applied m6A-seq to study changes in m6A modification in the Paulownia fortunei genome after phytoplasma infection. We found that the m6A modification level in seedlings infected with the phytoplasma that causes Paulownia witches' broom (PaWB) was slightly higher than the m6A modification level in PaWB-infected seedlings treated with 60 mg·L−1 methyl methanesulfonate (MMS). MMS has been shown to restore PaWB-infected seedlings to their normal form and no phytoplasma can be detected in MMS-treated PaWB-infected seedlings. RNA sequencing (RNA-seq) and m6A-seq were used to analyze the expression of genes with m6A peaks and m6A motifs in genes, respectively. The correlation analysis between the RNA-seq and m6A-seq data detected that a total of 315 differentially methylated genes were predicted to be significantly differentially expressed at the transcriptome level. The functions of genes related to PaWB were predicted by functional enrichment analysis, and two genes related to maintenance of the basic mechanism of stem cells in shoot apical meristem were discovered. One of the genes encodes the receptor protein kinase CLV2 (Paulownia_LG2G000076), and the other gene encodes the homeobox transcription factor STM (Paulownia_LG15G000976). The m6A modification levels were higher in PaWB-infected seedlings than they were in MMS-treated seedlings. In addition, genes F-box (Paulownia_LG17G000760) and MSH5 (Paulownia_LG8G001160) had exon skipping and mutually exclusive exon types of alternative splicing in PaWB-infected seedling treated with MMS. RT-PCR verified that the alternative splicing of these two genes was related to m6A modification. Conclusions: In this study, we applied m6A-seq to determine methylation levels in phytoplasma-infected Paulownia, and combined m6A-seq with transcriptome analysis to screen differentially expressed genes associated with PaWB. Also analyzed the effect of m6A methylation on alternative splicing. In future studies, we plan to verify genes directly related to PaWB and methylation-related enzymes in Paulownia to elucidate the pathogenicity mechanism of PaWB caused by phytoplasma invasion.

Metabolic Signatures of Type 2 Diabetes Mellitus and Hypertension in COVID-19 Patients With Different Disease Severity

Introduction: Increased COVID-19 disease severity is higher among patients with type 2 diabetes mellitus and hypertension. However, the metabolic pathways underlying this association are not fully characterized. This study aims to identify the metabolic signature associated with increased COVID-19 severity in patients with diabetes mellitus and hypertension.Methods: One hundred and fifteen COVID-19 patients were divided based on disease severity, diabetes status, and hypertension status. Targeted metabolomics of serum samples from all patients was performed using tandem mass spectrometry followed by multivariate and univariate models.Results: Reduced levels of various triacylglycerols were observed with increased disease severity in the diabetic patients, including those containing palmitic (C16:0), docosapentaenoic (C22:5, DPA), and docosahexaenoic (C22:6, DHA) acids (FDR < 0.01). Functional enrichment analysis revealed triacylglycerols as the pathway exhibiting the most significant changes in severe COVID-19 in diabetic patients (FDR = 7.1 × 10−27). Similarly, reduced levels of various triacylglycerols were also observed in hypertensive patients corresponding with increased disease severity, including those containing palmitic, oleic (C18:1), and docosahexaenoic acids. Functional enrichment analysis revealed long-chain polyunsaturated fatty acids (n-3 and n-6) as the pathway exhibiting the most significant changes with increased disease severity in hypertensive patients (FDR = 0.07).Conclusions: Reduced levels of triacylglycerols containing specific long-chain unsaturated, monounsaturated, and polyunsaturated fatty acids are associated with increased COVID-19 severity in diabetic and hypertensive patients, offering potential novel diagnostic and therapeutic targets.

Identification of a Prognostic Six-Immune-Gene Signature and a Nomogram Model for Uveal Melanoma

Background: To identify an immune-related prognostic signature and find potential therapeutic targets for uveal melanoma. Methods: The RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic six-immune-gene signature was constructed through least absolute shrinkage and selection operator and multi-variate Cox regression analyses. Functional enrichment analysis and single sample GSEA were carried out. In addition, a nomogram model established by integrating clinical variables and this signature risk score was also constructed and evaluated.Results: We obtained 130 prognostic immune genes, and six of them were selected to construct a prognostic signature in the TCGA uveal melanoma dataset. Patients were classified into high-risk and low-risk groups according to a median risk score of this signature. High-risk group patients had poorer overall survival in comparison to the patients in the low-risk group (p < 0.001). These findings were further validated in two external GEO datasets. A nomogram model proved to be a good classifier for uveal melanoma by combining this signature. Both functional enrichment analysis and single sample GSEA analysis verified that this signature was truly correlated with immune system. In addition, in vitro cell experiments results demonstrated the consistent trend of our computational findings.Conclusion: Our newly identified six-immune-gene signature and a nomogram model could be used as meaningful prognostic biomarkers, which might provide uveal melanoma patients with individualized clinical prognosis prediction and potential novel treatment targets.

Construction of an Immune-related Gene Model for Predicting Prognosis and Immune Infiltration in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is a common pathological type of kidney cancer with high immune infiltration that has been proven to be treatable with immune checkpoint inhibitor (ICI) therapy. However, the role of immunity in ccRCC remains poorly understood. Therefore, this paper aimed to develop and validate a novel immune-related prognostic marker to predict both the overall survival rate (OS) of ccRCC patients and the response to ICI therapy.Methods: Based on the transcriptome and clinicopathological data of ccRCC from The Cancer Genome Atlas (TCGA) dataset and immune-related genes (IRGs) from immune datasets, IRGs related to prognosis were screened to construct an IRG prognostic index (IRGPI) via coexpression analysis and Cox regression. After verifying that IRGPI was a prognostic indicator independent of clinical parameters, a nomogram was established. In addition, functional enrichment analysis, the CIBERSORT algorithm and single-sample gene set enrichment analysis (ssGSEA) were performed to compare the molecular and immune characteristics of IRGPI-defined subgroups. Finally, the expression of immunosuppressive genes, tumor mutational burden (TMB) and the TIDE algorithm were used to predict the response of ICI therapy in different IRGPI subgroups. Results: A total of 11 IRGs (IFNG, XCL1, APOBEC3G, CD86, CXCR3, IL10RA, IL2RG, CD244, SH2D1A, CD3D and FCER1G) were included in the IRGPI module. IRGPIhigh patients had a worse OS and had poorer clinical pathological status than IRGPIlow patients. A nomogram containing clinical features and IRGPI scores may guide the clinical practice of ccRCC. Chemokine signaling pathways were mainly involved in functional enrichment analysis. Furthermore, the IRGPI could effectively reflect the immune characteristics and immune checkpoint gene expression of ccRCC and the response to ICI therapy.Conclusions: The IRGPI is a promising biomarker for determining prognosis and has the potential to be used to predict immunotherapy response in ccRCC.

A novel ferroptosis-related gene signature associated with cell cycle for prognosis prediction in patients with clear cell renal cell carcinoma

Background It is of great urgency to explore useful prognostic markers for patients with clear cell renal cell carcinoma (ccRCC). Prognostic models based on ferroptosis-related gene (FRG) in ccRCC is poorly reported for now. Methods Comprehensive analysis of 22 FRGs were performed in 629 ccRCC samples from two independent patient cohorts. We carried out least absolute shrinkage and selection operator analysis to screen out prognosis-related FRGs and constructed prognosis model for patients with ccRCC. Weighted gene co-expression network analysis was also carried out for potential functional enrichment analysis. Results Based on the TCGA cohort, a total of 11 prognosis-associated FRGs were selected for the construction of the prognosis model. Significantly differential overall survival (hazard ratio = 3.61, 95% CI: 2.68–4.87, p < 0.0001) was observed between patients with high and low FRG score in the TCGA cohort, which was further verified in the CPTAC cohort with hazard ratio value of 5.13 (95% CI: 1.65–15.90, p = 0.019). Subgroup survival analysis revealed that our FRG score could significantly distinguish patients with high survival risk among different tumor stages and different tumor grades. Functional enrichment analysis illustrated that the process of cell cycle, including cell cycle-mitotic pathway, cytokinesis pathway and nuclear division pathway, might be involved in the regulation of ccRCC through ferroptosis. Conclusions We developed and verified a FRG signature for the prognosis prediction of patients with ccRCC, which could act as a risk factor and help to update the tumor staging system when integrated with clinicopathological characteristics. Cell cycle-related pathways might be involved in the regulation of ccRCC through ferroptosis.

Identification and Validation of Immune-Related Prognostic Genes in the Tumor Microenvironment of Colon Adenocarcinoma

The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan–Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein–protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients’ overall survival (log rank p &lt; 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan–Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.

Identification of Latent Diagnostic Biomarkers and Biological Pathways in Dermatomyositis Based on WGCNA

Introduction. Dermatomyositis (DM) is a chronic autoimmune disease of predominantly lymphocytic infiltration mainly involving the transverse muscle. Its pathogenesis is remaining unknown. This research is designed to probe the latent pathogenesis of dermatomyositis, identify potential biomarkers, and reveal the pathogenesis of dermatomyositis through information biology analysis of gene chips. Methods. In this study, we utilised the GSE14287 and GSE11971 datasets rooted in the Gene Expression Omnibus (GEO) databank, which included a total of 62 DM samples and 9 normal samples. The datasets were combined, and the differentially expressed gene sets were subjected to weighted gene coexpression network analysis, and the hub gene was screened using a protein interaction network from genes in modules highly correlated with dermatomyositis progression. Results. A total of 3 key genes—myxovirus resistance-2 (MX2), oligoadenylate synthetase 1 (OAS1), and oligoadenylate synthetase 2 (OAS2)—were identified in combination with cell line samples, and the expressions of the 3 genes were verified separately. The results showed that MX2, OAS1, and OAS2 were highly expressed in LPS-treated cell lines compared to normal cell lines. The results of pathway enrichment analysis of the genes indicated that all 3 genes were enriched in the cytosolic DNA signalling and cytokine and cytokine receptor interaction signalling pathways; the results of functional enrichment analysis showed that all 3 were enriched in interferon-α response and interferon-γ response functions. Conclusions. This is important for the study of the pathogenesis and objective treatment of dermatomyositis and provides important reference information for the targeted therapy of dermatomyositis.

Effects of Erythrodiol on the Antioxidant Response and Proteome of HepG2 Cells

Erythrodiol (EO) is a pentacyclic triterpenic alcohol found in olive tree leaves and olive oil, and it has important effects on the health properties and quality of olive oil. In this study, we characterized the cytotoxic effects of EO on human hepatocarcinoma (HepG2) cells by studying changes in cell viability, reactive oxygen species (ROS) production, antioxidant defense systems, and the proteome. The results reveal that EO markedly decreased HepG2 cell viability without changing ROS levels. The concentrations of glutathione and NADPH were significantly reduced, with selective changes in the activity of several antioxidant enzymes: glutathione peroxidase, glutathione reductase, glucose 6-phosphate dehydrogenase, and 6-phosphogluconate dehydrogenase. Proteomic data reveal that EO led to the complete elimination or decreased abundance of 41 and 3 proteins, respectively, and the abundance of 29 proteins increased. The results of functional enrichment analysis show that important metabolic processes and the nuclear transport of mature mRNA were impaired, whereas AMP biosynthesis and cell cycle G2/M phase transition were induced. The transcription factors and miRNAs involved in this response were also identified. These potent antiproliferative effects make EO a good candidate for the further analysis of its hepatic antitumor effects in in vivo studies.