Parasite-derived non-coding RNAs (ncRNAs) not only contribute to life activities of parasites, and microRNA (miRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA) may generate a competitive endogenous RNA (ceRNA) regulatory network with host miRNAs and mRNAs via extracellular vesicles, thereby participating in infection and pathogenic processes. This article presents an overview of characterizing ncRNAs derived from parasites and the cross-species regulatory role of parasite-derived ncRNAs in host gene expression and its underlying mechanisms.
Animals ; Parasites ; Gene Regulatory Networks ; MicroRNAs/metabolism* ; RNA, Messenger/genetics* ; RNA, Circular/genetics* ; RNA, Competitive Endogenous

OBJECTIVES: Laryngeal cancer (LC) is a globally prevalent and highly lethal tumor. Despite extensive efforts, the underlying mechanisms of LC remain inadequately understood. This study aims to conduct an innovative bioinformatic analysis to identify hub genes that could potentially serve as biomarkers or therapeutic targets in LC. METHODS: We acquired a dataset consisting of 117 LC patient samples, 16 746 LC gene RNA sequencing data points, and 9 clinical features from the Cancer Genome Atlas (TCGA) database in the United States. We employed weighted gene co-expression network analysis (WGCNA) to construct multiple co-expression gene modules. Subsequently, we assessed the correlations between these co-expression modules and clinical features to validate their associations. We also explored the interplay between modules to identify pivotal genes within disease pathways. Finally, we used the Kaplan-Meier plotter to validate the correlation between enriched genes and LC prognosis. RESULTS: WGCNA analysis led to the creation of a total of 16 co-expression gene modules related to LC. Four of these modules (designated as the yellow, magenta, black, and brown modules) exhibited significant correlations with 3 clinical features: The age of initial pathological diagnosis, cancer status, and pathological N stage. Specifically, the yellow and magenta gene modules displayed negative correlations with the age of pathological diagnosis (r=-0.23, P<0.05; r=-0.33, P<0.05), while the black and brown gene modules demonstrated negative associations with cancer status (r=-0.39, P<0.05; r=-0.50, P<0.05). The brown gene module displayed a positive correlation with pathological N stage. Gene Ontology (GO) enrichment analysis identified 77 items, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified 30 related signaling pathways, including the calcium signaling pathway, cytokine-cytokine receptor interaction, neuro active ligand-receptor interaction, and regulation of lipolysis in adipocytes, etc. Consequently, central genes within these modules that were significantly linked to the overall survival rate of LC patients were identified. Central genes included CHRNB4, FOXL2, KCNG1, LOC440173, ADAMTS15, BMP2, FAP, and KIAA1644. CONCLUSIONS This study, utilizing WGCNA and subsequent validation, pinpointed 8 genes with potential as gene biomarkers for LC. These findings offer valuable references for the clinical diagnosis, prognosis, and treatment of LC.
Humans ; Laryngeal Neoplasms/genetics* ; Rosaniline Dyes ; Biomarkers ; Adipocytes ; Gene Regulatory Networks ; Gene Expression Profiling

OBJECTIVES: Graves' ophthalmopathy (GO) is a multifactorial disease, and the mechanism of non coding RNA interactions and inflammatory cell infiltration patterns are not fully understood. This study aims to construct a competing endogenous RNA (ceRNA) network for this disease and clarify the infiltration patterns of inflammatory cells in orbital tissue to further explore the pathogenesis of GO. METHODS: The differentially expressed genes were identified using the GEO2R analysis tool. The Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology analysis were used to analyze differential genes. RNA interaction relationships were extracted from the RNA interactome database. Protein-protein interactions were identified using the STRING database and were visualized using Cytoscape. StarBase, miRcode, and DIANA-LncBase Experimental v.2 were used to construct ceRNA networks together with their interacted non-coding RNA. The CIBERSORT algorithm was used to detect the patterns of infiltrating immune cells in GO using R software. RESULTS: A total of 114 differentially expressed genes for GO and 121 pathways were detected using both the KEGG and gene ontology enrichment analysis. Four hub genes (SRSF6, DDX5, HNRNPC,and HNRNPM) were extracted from protein-protein interaction using cytoHubba in Cytoscape, 104 nodes and 142 edges were extracted, and a ceRNA network was identified (MALAT1-MIR21-DDX5). The results of immune cell analysis showed that in GO, the proportions of CD8⁺ T cells and CD4⁺ memory resting T cells were upregulated and downregulated, respectively. The proportion of CD4 memory resting T cells was positively correlated with the expression of MALAT1, MIR21, and DDX5. CONCLUSIONS This study has constructed a ceRNA regulatory network (MALAT1-MIR21-DDX5) in GO orbital tissue, clarifying the downregulation of the proportion of CD4⁺ stationary memory T cells and their positive regulatory relationship with ceRNA components, further revealing the pathogenesis of GO.
Humans ; CD8-Positive T-Lymphocytes ; RNA, Long Noncoding/genetics* ; Algorithms ; CD4-Positive T-Lymphocytes ; Down-Regulation ; Graves Ophthalmopathy/genetics* ; Gene Regulatory Networks ; MicroRNAs/genetics* ; Serine-Arginine Splicing Factors ; Phosphoproteins

Humans ; Female ; Breast Neoplasms/genetics* ; Prognosis ; Gene Expression Profiling ; Biomarkers, Tumor/genetics* ; Gene Expression Regulation, Neoplastic/genetics* ; Receptor, ErbB-2/metabolism* ; Gene Regulatory Networks/genetics*

This study aimed to demonstrate the effect of Banxia Baizhu Tianma Decoction(BBTD) on realizing withdrawal of anti-epileptic drugs and explore the relationship between BBTD and the amino acid metabolism by transcriptomic analysis in the rat model of epilepsy induced by lithium chloride-pilocarpine. The rats with epilepsy were divided into a control group(Ctrl), an epilepsy group(Ep), a BBTD & antiepileptic drug integrative group(BADIG), and an antiepileptic drug withdrawal group(ADWG). The Ctrl and Ep were given ultrapure water by gavage for 12 weeks. The BADIG was given BBTD extract and carbamazepine solution by gavage for 12 weeks. The ADWG was given carbamazepine solution and BBTD extract by gavage for the former 6 weeks, and then only given BBTD extract for the latter 6 weeks. The therapeutic effect was evaluated by behavioral observation, electroencephalogram(EEG), and hippocampal neuronal morphological changes. High-throughput sequencing was used to obtain amino acid metabolism-related differen-tial genes in the hippocampus, and the mRNA expression in the hippocampus of each group was verified by real-time quantitative polymerase chain reaction(RT-qPCR). The hub genes were screened out through protein-protein interaction(PPI) network, and Gene Ontology(GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed. Two ceRNA networks, namely circRNA-miRNA-mRNA and lncRNA-miRNA-mRNA, were constructed for ADWG vs BADIG. The experimental results showed that compared with those in Ep, rats in ADWG were significantly improved in the behavioral observation, EEG, and hippocampal neuronal impairment. Thirty-four amino acid metabolism-related differential genes were obtained by transcriptomic analysis, and the sequencing results were confirmed by RT-qPCR. Eight hub genes were obtained through PPI network, involving several biological processes, molecular functions, and signal pathways related to amino acid metabolism. Finally, the circRNA-miRNA-mRNA ternary transcription network of 17 circRNA, 5 miRNA, and 2 mRNA, and a lncRNA-miRNA-mRNA ternary network of 10 lncRNA, 5 miRNA, and 2 mRNA were constructed in ADWG vs BADIG. In conclusion, BBTD can effectively achieve the withdrawal of antiepileptic drugs, which may be related to the transcriptomic regulation of amino acid metabolism.
Rats ; Animals ; RNA, Circular/genetics* ; Transcriptome ; RNA, Long Noncoding/genetics* ; Anticonvulsants ; MicroRNAs/genetics* ; RNA, Messenger ; Carbamazepine ; Amino Acids ; Gene Regulatory Networks

Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes, and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis, protein-protein interaction (PPI) network, and survival analysis based on the Gene Expression Omnibus (GEO) database.Methods By screening with highly expressed genes, we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites. Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis, PPI network, and survival analysis. Several software and platforms including Prism 8, R language, Cytoscape, DAVID, STRING, and GEPIA platform were used in the search and/or figure creation.Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma (ESCC) tissue. Enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer. Four genes including ALDH3A1, C2, SLC6A1,and ZBTB7C were screened with significant P value of survival curve.Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer. Keratinization may greatly impact the pathogenesis of esophageal cancer. Genes ALDH3A1, C2, SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.
Humans ; Esophageal Neoplasms/metabolism* ; Esophageal Squamous Cell Carcinoma/metabolism* ; Epithelial Cell Adhesion Molecule/metabolism* ; Gene Expression Profiling/methods* ; Gene Regulatory Networks ; Gene Expression ; Gene Expression Regulation, Neoplastic ; Intracellular Signaling Peptides and Proteins

OBJECTIVES: To investigate possible cross-talk genes, associated pathways, and transcription factors between chronic periodontitis (CP) and chronic obstructive pulmonary disease (COPD). METHODS: The gene expression profiles of CP (GSE10334 and GSE16134) and COPD (GSE76925) were downloaded from the GEO database. Differential expression and functional clustering analyses were performed. The protein‑protein interaction (PPI) network was constructed. The core cross-talk genes were filtered using four topological analysis algorithms and modular segmentation. Then, functional clustering analysis was performed again. RESULTS: GSE10334 detected 164 differentially expressed genes (DEGs) (119 upregulated and 45 downregulated). GSE16134 identified 208 DEGs (154 upregulated and 54 downregulated). GSE76925 identified 1 408 DEGs (557 upregulated and 851 downregulated). The PPI network included 21 nodes and 20 edges. The final screening included seven cross-talk genes: CD79A, FCRLA, CD19, IRF4, CD27, SELL, and CXCL13. Relevant pathways included primary immunodeficiency, the B-cell receptor signaling pathway, and cytokine-cytokine receptor interaction. CONCLUSIONS This study indicates the probability of shared pathophysiology between CP and COPD, and their cross-talk genes, associated pathways, and transcription factors may offer novel concepts for future mechanistic investigations.
Humans ; Chronic Periodontitis/genetics* ; Gene Regulatory Networks ; Gene Expression Profiling ; Protein Interaction Maps/genetics* ; Pulmonary Disease, Chronic Obstructive/genetics*

OBJECTIVES: The common differentially expressed mRNAs in brain, heart and liver tissues of deceased sudden infant death syndrome (SIDS) and infectious sudden death in infancy (ISDI) confirmed by autopsy was screened by bioinformatics to explore the common molecular markers and pathogenesis of SIDS and ISDI. METHODS: The datasets of GSE70422 and GSE136992 were downloaded, the limma of R software was used to screen differentially expressed mRNA in different tissue samples of SIDS and ISDI decedents for overlapping analysis. The clusterProfiler of R software was used to conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The protein-protein interaction (PPI) network was constructed by STRING database, while the hub gene was screened by cytoHubba plug-in. RESULTS: Compared with the control group, there were 19 significant differentially expressed genes in the tissue samples of SIDS and ISDI decedents, among which 16 in the heart tissue and 3 in the liver tissue, and the astrotactin 1 (ASTN1) gene expression difference in the heart tissue was most significant. The PPI network identified Ras homolog family member A (RHOA), integrin subunit alpha 1 (ITGA1), and H2B clustered histone 5 (H2BC5) were hub genes. The analysis of GO and KEGG showed that differentially expressed genes were enriched in the molecular pathways of actin cytoskeleton regulation, focal adhesion and response to mycophenolic acid. CONCLUSIONS ASTN1, RHOA and ITGA1 may participate in the development of SIDS and ISDI. The enrichment of differentially expressed genes in immune and inflammatory pathways suggests a common molecular regulatory mechanism between SIDS and ISDI. These findings are expected to provide new biomarkers for molecular anatomy and forensic identification of SIDS and ISDI.
Humans ; Infant ; Gene Expression Profiling ; Sudden Infant Death/genetics* ; Gene Regulatory Networks ; Protein Interaction Maps/genetics* ; Computational Biology

This study aims to identify the novel biomarkers of cold-dampness syndrome(RA-Cold) of rheumatoid arthritis(RA) by gene set enrichment analysis(GSEA), weighted gene correlation network analysis(WGCNA), and clinical validation. Firstly, transcriptome sequencing was carried out for the whole blood samples from RA-Cold patients, RA patients with other traditional Chinese medicine(TCM) syndromes, and healthy volunteers. The differentially expressed gene(DEG) sets of RA-Cold were screened by comparison with the RA patients with other TCM syndromes and healthy volunteers. Then, GSEA and WGCNA were carried out to screen the key DEGs as candidate biomarkers for RA-Cold. Experimentally, the expression levels of the candidate biomarkers were determined by RT-qPCR for an independent clinical cohort(not less than 10 cases/group), and the clinical efficacy of the candidates was assessed using the receiver operating characteristic(ROC) curve. The results showed that 3 601 DEGs associated with RA-Cold were obtained, including 106 up-regulated genes and 3 495 down-regulated genes. The DEGs of RA-Cold were mainly enriched in the pathways associated with inflammation-immunity regulation, hormone regulation, substance and energy metabolism, cell function regulation, and synovial pannus formation. GSEA and WGCNA showed that recombinant proteasome 26S subunit, ATPase 2(PSMC2), which ranked in the top 50% in terms of coefficient of variation, representativeness of pathway, and biological modules, was a candidate biomarker of RA-Cold. Furthermore, the validation results based on the clinical independent sample set showed that the F1 value, specificity, accuracy, and precision of PSMC2 for RA-Cold were 70.3%, 61.9%, 64.5%, and 81.3%, respectively, and the area under the curve(AUC) value was 0.96. In summary, this study employed the "GSEA-WGCNA-validation" integrated strategy to identify novel biomarkers of RA-Cold, which helped to improve the TCM clinical diagnosis and treatment of core syndromes in RA and provided an experimental basis for TCM syndrome differentiation.
Humans ; Arthritis, Rheumatoid/drug therapy* ; Biomarkers/metabolism* ; Medicine, Chinese Traditional ; Gene Expression Profiling/methods* ; Computational Biology ; Gene Regulatory Networks ; ATPases Associated with Diverse Cellular Activities/therapeutic use* ; Proteasome Endopeptidase Complex/therapeutic use*

Objective: To screen and analyze the key differentially expressed genes characteristics in nonalcoholic fatty liver disease (NAFLD) with bioinformatics method. Methods: NAFLD-related expression matrix GSE89632 was downloaded from the GEO database. Limma package was used to screen differentially expressed genes (DEGs) in healthy, steatosis (SS), and nonalcoholic steatohepatitis (NASH) samples. WGCNA was used to analyze the output gene module. The intersection of module genes and differential genes was used to determine the differential genes characteristic, and then GO function and KEGG signaling pathway enrichment analysis were performed. The protein-protein interaction network (PPI) was constructed using the online website STRING and Cytoscape software, and the key (Hub) genes were screened. Finally, R software was used to analyze the receiver operating characteristic curve (ROC) of the Hub gene. Results: 92 differentially expressed genes characteristic were obtained through screening, which were mainly enriched in inflammatory response-related functions of "lipopolysaccharide response and molecular response of bacterial origin", as well as cancer signaling pathways of "proteoglycan in cancer" and "T-cell leukemia virus infection-related". 10 hub genes (FOS, CXCL8, SERPINE1, CYR61, THBS1, FOSL1, CCL2, MYC, SOCS3 and ATF3) had good diagnostic value. Conclusion: The differentially expressed hub genes among the 10 NAFLD disease-related characteristics obtained with bioinformatics analysis may become a diagnostic and prognostic marker and potential therapeutic target for NAFLD. However, further basic and clinical studies are needed to validate.
Computational Biology/methods* ; Gene Expression Profiling/methods* ; Gene Regulatory Networks ; Humans ; Non-alcoholic Fatty Liver Disease/genetics* ; Protein Interaction Maps/genetics*