Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment

Downregulation of miR-181c-5p in Alzheimer's disease weakens the response of microglia to Aβ phagocytosis

Alzheimer's disease (AD) is an age-associated neurodegenerative disease. Recently, studies have demonstrated the potential involvement of microRNA-181c-5p (miR-181c-5p) in AD. However, the mechanism through which miR-181c-5p is responsible for the onset and progression of this disease remains unclear, and our study aimed to explore this problem. Differential expression analysis of the AD dataset was performed to identify dysregulated genes. Based on hypergeometric analysis, AD differential the upstream regulation genes miR-181c-5p was found. We constructed a model where SH-SY5Y and BV2 cells were exposed to Aβ1-42 to simulate AD. Levels of tumor necrosis factor-alpha, interleukin-6, and IL-1β were determined using enzyme-linked immunosorbent assay or reverse transcription quantitative polymerase chain reaction. Phosphorylation levels of p-P38 and P38 were detected by Western blot. The level of apoptosis in BV2 cells under Aβ1-42 stress was exacerbated by miR-181c-5p mimic. Downregulated miR-181c-5p impaired the phagocytosis and degradation of Aβ by BV2 cells. The release of proinflammatory cytokines in BV2 cells with Aβ1-42 stress was alleviated by miR-181c-5p upregulation. Additionally, miR-181c-5p downregulation alleviated the phosphorylation of P38 in Aβ1-42-induced SH-SY5Y cells. In conclusion, miR-181c-5p improves the phagocytosis of Aβ by microglial cells in AD patients, thereby reducing neuroinflammation.

HNF4A-Bridging the Gap Between Intestinal Metaplasia and Gastric Cancer

Background

Intestinal metaplasia (IM) of gastric epithelium has traditionally been regarded as an irreversible stage in the process of the Correa cascade. Exploring the potential molecular mechanism of IM is significant for effective gastric cancer prevention.

Methods

The GSE78523 dataset, obtained from the Gene Expression Omnibus (GEO) database, was analyzed using RStudio software to identify the differently expressed genes (DEGs) between IM tissues and normal gastric epithelial tissues. Subsequently, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GESA), and protein-protein interaction (PPI) analysis were used to find potential genes. Additionally, the screened genes were analyzed for clinical, immunological, and genetic correlation aspects using single gene clinical correlation analysis (UALCAN), Tumor-Immune System Interactions Database (TISIDB), and validated through western blot experiments.

Results

Enrichment analysis showed that the lipid metabolic pathway was significantly associated with IM tissues and the apolipoprotein B (APOB) gene was identified in the subsequent analysis. Experiment results and correlation analysis showed that the expression of APOB was higher in IM tissues than in normal tissues. This elevated expression of APOB was also found to be associated with the expression levels of hepatocyte nuclear factor 4A (HNF4A) gene. HNF4A was also found to be associated with immune cell infiltration to gastric cancer and was linked to the prognosis of gastric cancer patients. Moreover, HNF4A was also highly expressed in both IM tissues and gastric cancer cells.

Conclusion

Our findings indicate that HNF4A regulates the microenvironment of lipid metabolism in IM tissues by targeting APOB. Higher expression of HNF4A tends to lead to a worse prognosis in gastric cancer patients implying it may serve as a predictive indicator for the progression from IM to gastric cancer.

Molecular network mechanism in cerebral ischemia-reperfusion rats treated with human urine stem cells

Objective

To explore the effect of human urine-derived stem cells (husc) in improving the neurological function of rats with cerebral ischemia-reperfusion (CIR), and report new molecular network by bioinformatics, combined with experiment validation.

Methods

After CIR model was established, and husc were transplanted into the lateral ventricle of rats，neurological severe score (NSS) andgene network analysis were performed. Firstly, we input the keywords "Cerebral reperfusion" and "human urine stem cells" into Genecard database and merged data with findings from PubMed so as to get their targets genes, and downloaded them to make Venny intersection plot. Then, Gene ontology (GO) analysis, kyoto encyclopedia of genes and genomes (KEGG) pathway analysis and protein-protein interaction (PPI) were performed to construct molecular network of core genes. Lastly, the expressional level of core genes was validated via quantitative real-time polymerase chain reaction (qRT-PCR), and localized by immunofluorescence.

Results

Compared with the Sham group, the neurological function of CIR rats was significantly improved after the injection of husc into the lateral ventricle; at 14 days, P = 0.028, which was statistically significant. There were 258 overlapping genes between CIR and husc, and integrated with 252 genes screened from PubMed and CNKI. GO enrichment analysis were mainly involved neutrophil degranulation, neutrophil activation in immune response and platelet positive regulation of degranulation, Hemostasis, blood coagulation, coagulation, etc. KEGG pathway analysis was mainly involved in complement and coagulation cascades, ECM-receptor. Hub genes screened by Cytoscape consist ofCD44, ACTB, FN1, ITGB1, PLG, CASP3, ALB, HSP90AA1, EGF, GAPDH. Lastly, qRT-PCR results showed statistic significance (P < 0.05) in ALB, CD44 and EGF before and after treatment, and EGF immunostaining was localized in neuron of cortex.

Conclusion

husc transplantation showed a positive effect in improving neural function of CIR rats, and underlying mechanism is involved in CD44, ALB, and EGF network.

Use of Machine Learning for the Identification and Validation of Immunogenic Cell Death Biomarkers and Immunophenotypes in Coronary Artery Disease

Objective

Immunogenic cell death (ICD) is part of the immune system's response to coronary artery disease (CAD). In this study, we bioinformatically evaluated the diagnostic and therapeutic utility of immunogenic cell death-related genes (IRGs) and their relationship with immune infiltration features in CAD.

Methods

We acquired the CAD-related datasets GSE12288, GSE71226, and GSE120521 from the Gene Expression Omnibus (GEO) database and the IRGs from the GeneCards database. After identifying the immune cell death-related differentially expressed genes (IRDEGs), we developed a risk model and detected immune subtypes in CAD. IRDEGs were identified using least absolute shrinkage and selection operator (LASSO) analysis. Using a nomogram, we confirmed that both the LASSO model and ICD signature genes had good diagnostic performance.

Results

There was a high degree of coincidence and immune representativeness between two CAD groups based on characteristic genes and hub genes. Hub genes were associated with the interaction of neuroactive ligands with receptors and cell adhesion receptors. The two groups differed in terms of adipogenesis, allograft rejection, and apoptosis, as well as the ICD signature and hub gene expression levels. The two CAD-ICD subtypes differed in terms of immune infiltration.

Conclusion

Quantitative real-time PCR (qRT-PCR) correlated CAD with the expression of OAS3, ITGAV, and PIBF1. The ICD signature genes are candidate biomarkers and reference standards for immune grouping in CAD and can be beneficial in precise immune-targeted therapy.

Identification and validation of genes associated with aging-related cardiovascular disease

Aging is acknowledged as the most significant risk factor for cardiovascular disease (CVD). This study sought to identify and validate potential aging-related genes associated with CVD by using bioinformatics. The confluence of the limma test, weighted correlation network analysis (WGCNA), and 2129 aging and senescence-associated genes led to the identification of aging-related differential expression genes (ARDEGs). By using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), potential biological roles and pathways of ARDEGs were identified. To find the significantly different functions between CVD and non-cardiovascular disease (nCVD) and to reckon the processes score, enrichment analysis of all genes was carried out using gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). By using GO and KEGG, potential biological roles and pathways of ARDEGs were identified. To evaluate the immune cell composition of the immune microenvironment, we performed an immune infiltration analysis on the dataset from the training group. We were able to acquire four ARDEGs (PTGS2, MMP9, HBEGF, and FN1). Aging, cellular senescence, and nitric oxide signal transduction were selected for biological function analysis. The diagnostic value of the four ARDEGs in distinguishing CVD from nCVD samples was deemed to be favorable. This research identified four ARDEGs that are associated with CVD. This study provides insight into prospective novel biomarkers for aging-related CVD diagnosis and progression monitoring.

Underlying Mechanism of Traditional Herbal Formula Chuang-Ling-Ye in the Treatment of Diabetic Foot Ulcer through Network Pharmacology and Molecular Docking

BACKGROUND

Chuang-Ling-Ye (CLY) has been clinically proven to be an effective Chinese medicine for the treatment of diabetic foot ulcers (DFU).

OBJECTIVES

This study aimed to investigate the possible mechanism of CLY in relation to DFU using network pharmacology and molecular docking.

MATERIALS AND METHODS

Firstly, relevant targets of CLY against DFU were obtained from TCMSP, Swiss Target Prediction database and GEO database. Then, topological analysis was employed by Cytoscape to screen the top 6 core active ingredients and the top 8 hub targets. Furthermore, the OmicShare Tools were applied for gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis. Finally, the results of network pharmacology were verified by molecular docking method.

RESULTS

CLY has 61 active compounds and 361 targets after de-duplication, and the top 8 hub targets were EGFR, TP53, CCND1, IL-1B, CREBBP, AR, PTGS2 and PGR. GO enrichment analysis is mainly related to signal transducer activity, receptor activity, and molecular transducer activity. KEGG pathway analysis indicated that these shared targets were primarily focused on AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, IL-17 signaling pathway, and JAK-STAT signaling pathway. Molecular docking results showed that physciondiglucoside, 2-cinnamoyl-glucose and kinobeon A were well bound with EGFR, IL-1B, AR and PTGS2.

CONCLUSION

This study demonstrated that CLY has anti-oxidative stress and anti-inflammatory effects in the treatment of DFU through various constituents, multiple targets, and multiple pathways, which provides a valuable point of reference for future investigations on CLY.

Identification of the function of FOSB in cholangiocarcinoma using bioinformatics analysis

Background

Exploring the potential mechanism of cholangiocarcinoma (CCA) metabolic reprogramming is significant for guiding clinical treatment. However, related research and exploration are still lacking. Therefore, we aimed to identify a reliable metabolism-related gene or biomarker of CCA using bioinformatics analysis.

Methods

The GSE26566, GSE45001, and GSE132305 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) between CCA tissues and adjacent tissues were screened out. The key gene was identified through enrichment and functional analysis, and its immune and clinical correlation was investigated utilizing the Tumor Immune Evaluation Resource (TIMER2.0), the Tumor-Immune System Interactions Database (TISIDB), the Gene Expression Profiling Interactive Analysis (GEPIA2), and the Kaplan-Meier Plotter. Finally, immunohistochemistry (IHC) was performed to validate the results.

Results

By analysis, the expression of FBJ murine osteosarcoma viral oncogene homolog B (FOSB) was significantly downregulated in CCA tissues when compared with adjacent tissues. Moreover, the expression levels of FOSB positively correlated with tumor-infiltrating immune cells in most tumors, and patients with high FOSB expression tended to have a better prognosis. The FOSB and SIRT3/HIF1A axes have similar expression trends and metabolic functions in CCA cells, and the correlation between of them was preliminarily explored by IHC experiments.

Conclusions

The expression levels of FOSB are closely related to the prognosis of CCA patients, which may be a predictive indicator for prognosis and immunotherapy.

Integrative analyses and validation of ferroptosis-related genes and mechanisms associated with cerebrovascular and cardiovascular ischemic diseases

BACKGROUND

There has been a gradual increase in the occurrence of cardiovascular and cerebrovascular ischemic diseases, particularly as comorbidities. Yet, the mechanisms underlying these diseases remain unclear. Ferroptosis has emerged as a potential contributor to cardio-cerebral ischemic processes. Therefore, this study investigated the shared biological mechanisms between the two processes, as well as the role of ferroptosis genes in cardio-cerebral ischemic damage, by constructing co-expression modules for myocardial ischemia (MI) and ischemic stroke (IS) and a network of protein-protein interactions, mRNA-miRNA, mRNA-transcription factors (TFs), mRNA-RNA-binding proteins (RBPs), and mRNA-drug interactions.

RESULTS

The study identified seven key genes, specifically ACSL1, TLR4, ADIPOR1, G0S2, PDK4, HP, PTGS2, and subjected them to functional enrichment analysis during ischemia. The predicted miRNAs were found to interact with 35 hub genes, and interactions were observed between 11 hub genes and 30 TF transcription factors. Additionally, 10 RBPs corresponding to 16 hub genes and 163 molecular compounds corresponding to 30 hub genes were identified. This study also clarified the levels of immune infiltration between MI and IS and different subtypes. Finally, we identified four hub genes, including TLR4, by using a diagnostic model constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis; ADIPOR1, G0S2, and HP were shown to have diagnostic value for the co-pathogenesis of MI and cerebral ischemia by both validation test data and RT-qPCR assay.

CONCLUSIONS

To the best our knowledge, this study is the first to utilize multiple algorithms to comprehensively analyze the biological processes of MI and IS from various perspectives. The four hub genes, TLR4, ADIPOR1, G0S2, and HP, have proven valuable in offering insights for the investigation of shared injury pathways in cardio-cerebral injuries. Therefore, these genes may serve as diagnostic markers for cardio-cerebral ischemic diseases.

LIRcentral: a manually curated online database of experimentally validated functional LIR motifs

Several selective macroautophagy receptor and adaptor proteins bind members of the Atg8 (autophagy related 8) family using short linear motifs (SLiMs), most often referred to as Atg8-family interacting motifs (AIMs) or LC3-interacting regions (LIRs). AIM/LIR motifs have been extensively studied during the last fifteen years, since they can uncover the underlying biological mechanisms and possible substrates for this key catabolic process of eukaryotic cells. Prompted by the fact that experimental information regarding LIR motifs can be found scattered across heterogeneous literature resources, we have developed LIRcentral (https://lircentral.eu), a freely available online repository for user-friendly access to comprehensive, high-quality information regarding LIR motifs from manually curated publications. Herein, we describe the development of LIRcentral and showcase currently available data and features, along with our plans for the expansion of this resource. Information incorporated in LIRcentral is useful for accomplishing a variety of research tasks, including: (i) guiding wet biology researchers for the characterization of novel instances of LIR motifs, (ii) giving bioinformaticians/computational biologists access to high-quality LIR motifs for building novel prediction methods for LIR motifs and LIR containing proteins (LIRCPs) and (iii) performing analyses to better understand the biological importance/features of functional LIR motifs. We welcome feedback on the LIRcentral content and functionality by all interested researchers and anticipate this work to spearhead a community effort for sustaining this resource which will further promote progress in studying LIR motifs/LIRCPs.Abbreviations: AIM, Atg8-family interacting motif; Atg8, autophagy related 8; GABARAP, GABA type A receptor-associated protein; LIR, LC3-interacting region; LIRCP, LIR-containing protein; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; PMID, PubMed identifier; PPI, protein-protein interaction; SLiM, short linear motif.

Exploration of potential targets and mechanisms of naringenin in the treatment of nonalcoholic fatty liver disease through network pharmacology

OBJECTIVE

This study aimed to use network pharmacology to investigate the molecular mechanisms and potential targets of naringenin (NR) for nonalcoholic fatty liver disease (NAFLD) treatment to offer new drug development ideas.

METHODS

The structure and compound information of NR were obtained from PubChem and the traditional Chinese medicine system pharmacology database and analysis platform. The traditional Chinese medicine system pharmacology database and analysis platform Database, Comparative Toxicogenomics Database and Encyclopedia of Traditional Chinese Medicine Database were then used to predict the related targets of NR. Online mendelian inheritance in man, Disgenet, Gene cards, The therapeutic target database and Drug bank were used to screen NAFLD targets, and the intersection analysis was performed with the targets of NR active components to obtain the targets of NR in the treatment of NAFLD. The protein-protein interaction network of therapeutic targets was constructed by protein-protein interaction networks functional enrichment analysis 11.0, and gene ontology (GO) functional enrichment analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis of therapeutic targets was performed by Metascape platform.

RESULTS

In this study, 171 NR targets and 1748 potential targets of NAFLD were screened, and 89 crossover targets and 16 core targets were screened and finally obtained. A total of 176 GO items were obtained by GO enrichment analysis (P < .05), including 389 biological process, 6 cell composition and 30 molecular function. A total of 137 signaling pathways were obtained by Kyoto encyclopedia of genes and genomes pathway enrichment and screening (P < .05). The core targets of NR in the treatment of NAFLD are TP53, CASP3, PRKCA, AKT1, RELA, PPARG, NCOA2, CYP1A1, ESR1, MAPK3, STAT3, JAK1, MAPK1, TNF, PPARA and PRKCB. Enrichment analysis showed that NR mainly involved in biological processes such as cellular response to nitrogen compound, regulation of miRNA transcription and negative regulation of miRNA-mediated gene silencing. It regulates Hepatitis B, Lipid and atherosclerosis, cytomegalovirus infection, Hepatitis C, AGE-RAGE signaling pathway in diabetic patients complications and other ways play a role in the treatment of NAFLD.

CONCLUSIONS

The therapeutic effect of NR on NAFLD has the characteristics of multi-targets and multi-pathways, which provides a preliminary theoretical basis for clinical trials and the development of new drugs.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

Screening for diagnostic targets in tuberculosis and study on its pathogenic mechanism based on mRNA sequencing technology and miRNA-mRNA-pathway regulatory network

Purpose

Tuberculosis is common infectious diseases, characterized by infectivity, concealment and chronicity, and the early diagnosis is helpful to block the spread of tuberculosis and reduce the resistance of Mycobacterium tuberculosis to anti-tuberculosis drugs. At present, there are obvious limitations in the application of clinical detection methods used for the early diagnosis of tuberculosis. RNA sequencing (RNA-Seq) has become an economical and accurate gene sequencing method for quantifying transcripts and detecting unknown RNA species.

Methods

A peripheral blood mRNA sequencing was used to screen the differentially expressed genes between healthy people and tuberculosis patients. A protein-protein interaction (PPI) network of differentially expressed genes was constructed through Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The potential diagnostic targets of tuberculosis were screened by the calculation of degree, betweenness and closeness in Cytoscape 3.9.1 software. Finally, the functional pathways and the molecular mechanism of tuberculosis were clarified in combination of the prediction results of key gene miRNAs, and by Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia Genes and Genomes (KEGG) pathway annotation analysis.

Results

556 Differential genes of tuberculosis were screened out by mRNA sequencing. Six key genes (AKT1, TP53, EGF, ARF1, CD274 and PRKCZ) were screened as the potential diagnostic targets for tuberculosis by analyzing the PPI regulatory network and using three algorithms. Three pathways related to the pathogenesis of tuberculosis were identified by KEGG pathway analysis, and two key miRNAs (has-miR-150-5p and has-miR-25-3p) that might participate in the pathogenesis of tuberculosis were screened out by constructing a miRNA-mRNA pathway regulatory network.

Conclusion

Six key genes and two important miRNAs that could regulate them were screened out by mRNA sequencing. The 6 key genes and 2 important miRNAs may participate in the pathogenesis of infection and invasion of Mycobacterium tuberculosis through herpes simplex virus 1 infection, endocytosis and B cell receptor signaling pathways.

Identification and validation of key immune-related genes with promising diagnostic and predictive value in systemic sclerosis

AIMS

To screen and confirm key immune-related genes (IRGs) with diagnostic and predictive value in systemic sclerosis (SSc) and provide potential therapeutic targets for patients with SSc.

MATERIALS AND METHODS

In Gene Expression Omnibus database (GEO), four datasets of gene expression profiling related to SSc were downloaded and used for the analysis in this study. After differential analysis of SSc cases and controls in GSE130955, the differentially expressed genes (DEGs) were overlapped with IRGs to obtain the immune-related differentially expressed genes (IR-DEGs) in SSc. In addition, functional annotation and pathway enrichment of IR-DEGs were conducted. The protein-protein interaction network (PPI) was constructed to identify key IR-DEGs. Using GSE58095, GSE181549 and GSE130953, the diagnostic and predictive abilities for the key IR-DEGs in SSc were validated. Finally, the screened key genes were confirmed in skin derived bleomycin (BLM)-induced SSc mice by Real-time PCR.

KEY FINDINGS

NGFR, TNFSF13B, FCER1G, GIMAP5, TYROBP and CSF1R may have important or very high diagnostic value for SSc. TYROBP and TNFSF13B had moderate and mild predictive value respectively in SSc patients after treatment. Real-time PCR assay further confirmed that the expressions of Ngfr, Tyrobp, Csf1r, Fcer1g and Gimap5 were significantly higher in skin of BLM-induced SSc mice than that in controls.

SIGNIFICANCE

The key IR-DEGs, including NGFR, TNFSF13B, TYROBP, CSF1R, FCER1G and GIMAP5, may become auxiliary diagnostic indicators and potential biomarkers for SSc. Moreover, TNFSF13B and TYROBP could have good prospects as predictive indicators in SSc patients that accepted cyclophosphamide or transplantation therapy.

Exploration of potential targets and mechanisms of Naringenin in treating autism spectrum disorder via network pharmacology and molecular docking

Naringenin (NR) is a kind of flavonoid which plays a great role in the treatment of autism spectrum disorder (ASD). However, the underlying mechanism of NR in treating ASD still remains unclear. This study used network pharmacology and molecular docking to examine the potential targets and pharmacological mechanism of NR on ASD. Targets related to NR were screened from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), Encyclopedia of Traditional Chinese Medicine Database (ETCM), Traditional Chinese Medicine Integrated Database (TCMID), PharmaMapper database, and targets related to ASD were screened from Online Mendelian Inheritance In Man (OMIM), Disgenet, GeneCards, Therapeutic Target Database (TTD), Drugbank, and ETCM. Screened of the intersected gene targets. Then, we used the protein-protein interaction (PPI) networks to construct a PPI network and used Network Analyzer plug-in to perform topological analysis to screen out the core target. We used Metascape platform to perform gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and used Chem draw, Pymol, AutoDock 1.5.6 software for molecular docking verification with core targets. A total of 149 targets of NR and 1594 potential targets of ASD were screened, and 43 intersected targets and 8 key targets were obtained and screened. A total of 176 GO items were obtained by GO enrichment analysis (P < .05), 153 entries on biological process (BP), 12 entries on BP and 11entries on cell composition (CC) were included. A total of 100 signaling pathways were obtained by KEGG pathway enrichment screening (P < .05).The pathways that are closely related to the pathogenesis of ASD are estrogen signaling, thyroid hormone signaling pathway, prolactin signaling pathway, and endocrine resistance pathway. Molecular docking results showed that NR had the best docking activity with the core target CASP3, and had good binding ability with AKT1, ESR1, ACTB and MAPK3. Taken together, our findings support that NR exerts therapeutic effects on ASD with multi-target, and multi-pathway characteristics, which provides a preliminary theoretical basis for clinical trials. The mechanism of anti-oxidative stress response, anti-apoptosis, regulation of cell growth and metabolism, anti-inflammatory, balance hormone levels may be important for the therapeutic effect.

A Novel M7G-Related MicroRNAs Risk Signature Predicts the Prognosis and Tumor Microenvironment of Kidney Renal Clear Cell Carcinoma

Background: M7G modification is extremely vital for the development of many cancers, especially tumor immunity. M7G modification is a novel functional regulator of miRNA, and the researches on m7G-related miRNAs in kidney renal clear cell carcinoma (KIRC) are still insufficient. This research aims to establish a risk signature on the foundation of m7G-associated miRNAs, which can precisely forecast the prognosis of KIRC patients.

Methods: Transcriptome data and clinical data used in this study come from The Cancer Genome Atlas database. Our team utilized univariable Cox, Lasso and multivariable Cox analyses to construct a m7G-associated miRNAs risk signature that can forecast the prognosis of KIRC patients. Kaplan-Meier method, time-dependent receiver operating characteristic (ROC) curve, and the independent analysis of risk signatures were employed to verify the predictability and accuracy of the risk signature. Subsequently, based on CIBERSORT, ESTIMATE and ssGSEA algorithms, we speculated the potential impact of the proposed risk signature on tumor immune microenvironment. Ultimately, by virtue of the risk signature and tumor immunity, the hub genes affecting the prognosis of KIRC patients were screened out.

Results: Our team established and verified a prognostic signature comprising 7 m7G-associated miRNAs (miR-342-3p, miR-221-3p, miR-222-3p, miR-1277-3p, miR-6718-5p, miR-1251-5p, and miR-486-5p). The results of the Kaplan-Meier survival analysis revealed that the prognosis of KIRC sufferers in the high-risk group was often unsatisfactory. The accuracy of the prediction ability of the risk signature was verified by calculating the area under the ROC curve. Univariate-multivariate Cox analyses further showed that this risk signature could be utilized as an independent prognosis-related biomarker for KIRC sufferers. The results of the immune analysis revealed that remarkable diversities existed in immune status and tumor microenvironment between high-risk and low-risk groups. On the foundation of the proposed risk signature and other clinical factors, a nomogram was established to quantitatively forecast the survival of KIRC sufferers at 1, 3 and 5 years.

Conclusion: Based on m7G-related miRNAs, a risk signature was successfully constructed, which could precisely forecast the prognosis of sufferers and guide personalized immunotherapy for KIRC patients.