Identification and Validation of Glomerulotubular Crosstalk Genes Mediating IgA Nephropathy by Integrated Bioinformatics

BackgroundIgA nephropathy (IgAN), which has been reported as the most prevalent glomerulonephritis globally, is the major contributor to end-stage renal illness. This bioinformatics study aimed to explore glomeruli-tubulointerstitial crosstalk genes and dysregulated pathways relating to the pathogenesis of IgAN. MethodsThe microarray datasets from the Gene Expression Omnibus (GEO) database were searched. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) of both glomeruli and tubulointerstitial were conducted individually. The co-expression gene modules of tubulointerstitial and glomeruli were compared via gene function enrichment analysis. Subsequently, the crosstalk co-expression network was constructed via the STRING database and key genes were mined from the crosstalk network. Results583 DEGs and eight modules were identified in glomeruli samples, while 272 DEGs and four modules were in tubulointerstitial samples. There were 119 overlapping DEGs of the two groups. Among the distinctive modules, four modules in glomeruli and one module in tubulointerstitial were positively associated with IgAN. While four modules in glomeruli and two modules in tubulointerstitial were negatively associated with IgAN. The top ten key genes screened by CytoHubba were ITGAM, ALB, TYROBP, ITGB2, CYBB, HCK, CSF1R, LAPTM5, FN1and CTSS. The above genes were all validated using another two datasets, and all of the key genes demonstrated possible diagnostic significance. Conclusionshe crosstalk genes confirmed in this study may provide novel insight into the pathogenesis of IgAN. Immune-related pathways are associated with both glomerular and tubulointerstitial injuries in IgAN. The glomerulotubular crosstalk might perform a role in the pathogenesis of IgAN.

Study on the mechanism of Jiawei Linggui Zhugan Decoction in the treatment of myelodysplastic syndrome based on network pharmacological analysis

the research method of network pharmacology is used to explore the material basis and mechanism of modified Linggui Zhugan Decoction in the treatment of myelodysplastic syndrome. Methods: the main active components of 8 traditional Chinese medicines of Jiawei Linggui Zhugan Decoction were searched through tcmsp database, and the target was predicted. The relevant targets of myelodysplastic syndrome were searched through geo database, and the common action targets were obtained by intersection of traditional Chinese medicine targets and disease targets. The core targets were selected by topological analysis with Cytoscape software. Finally, go-bp biological function enrichment and KEGG pathway analysis were carried out based on R software. Results: according to the database analysis, there were 248 active compounds and 3695 targets in the modified Linggui Zhugan decoction, of which 34 were common targets with metabolic syndrome; Through the topological analysis of common targets, 9 core targets were selected. Go-bp biological function enrichment and KEGG pathway analysis found that it can play its therapeutic role through p53, AGE-RAGE, cellular sensitivity, NF KB and other signal pathways. Conclusion: modified Linggui Zhugan decoction may play a therapeutic role through p53 signaling pathway, AGE-RAGE signaling pathway, cellular sensitivity, NF kappa B signaling pathway and cell cycle, so as to provide a new scientific basis for its clinical and basic research.

Integrative Identification of Crucial Genes Associated With Plant Hormone-Mediated Bud Dormancy in Prunus mume

Prunus mume is an important ornamental woody plant with winter-flowering property, which is closely related to bud dormancy. Despite recent scientific headway in deciphering the mechanism of bud dormancy in P. mume, the overall picture of gene co-expression regulating P. mume bud dormancy is still unclear. Here a total of 23 modules were screened by weighted gene co-expression network analysis (WGCNA), of which 12 modules were significantly associated with heteroauxin, abscisic acid (ABA), and gibberellin (GA), including GA1, GA3, and GA4. The yellow module, which was positively correlated with the content of ABA and negatively correlated with the content of GA, was composed of 1,426 genes, among which 156 transcription factors (TFs) were annotated with transcriptional regulation function. An enrichment analysis revealed that these genes are related to the dormancy process and plant hormone signal transduction. Interestingly, the expression trends of PmABF2 and PmABF4 genes, the core members of ABA signal transduction, were positively correlated with P. mume bud dormancy. Additionally, the PmSVP gene had attracted lots of attention because of its co-expression, function enrichment, and expression level. PmABF2, PmABF4, and PmSVP were the genes with a high degree of expression in the co-expression network, which was upregulated by ABA treatment. Our results provide insights into the underlying molecular mechanism of plant hormone-regulated dormancy and screen the hub genes involved in bud dormancy in P. mume.

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

BackgroundOsteosarcoma is a common malignant primary bone tumor in adolescents and children. Numerous studies have shown that circRNAs were involved in the proliferation and invasion of various tumors. However, the role of circRNAs in osteosarcoma remains unclear. Here, we aimed to explore the regulatory network among circRNA-miRNA-mRNA in osteosarcoma.MethodsThe circRNA (GSE140256), microRNA (GSE28423), and mRNA (GSE99671) expression profiles of osteosarcoma were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs, miRNAs and mRNAs were identified. CircRNA-miRNA interactions and miRNA-mRNA interactions were determined by Circular RNA Interactome (CircInteractome) database and microRNA Data Integration Portal (mirDIP) database, respectively. Then, we constructed a regulatory network. Function enrichment analysis of miRNA and mRNA was performed by DIANA-miRPath v3.0 and Metascape database, respectively. mRNAs with significant prognostic value were identified based on expression profiles from The Cancer Genome Atlas (TCGA) database, and we constructed a subnetwork for them. To make the most of the network, we used the CLUE database to predict potential drugs for the treatment of osteosarcoma based on mRNA expression in the network. And we used the STITCH database to analyze and validate the interactions among these drugs and mRNAs, and to further screen for potential drugs.ResultsA total of 9 circRNAs, 19 miRNAs, 67 mRNAs, 54 pairs of circRNA-miRNA interactions and 110 pairs of miRNA-mRNA interactions were identified. A circRNA-miRNA-mRNA network was constructed. Function enrichment analysis indicated that these miRNAs and mRNAs in the network were involved in the process of tumorigenesis and immune response. Among these mRNAs, STC2 and RASGRP2 with significantly prognostic value were identified, and we constructed a subnetwork for them. Based on mRNA expression in the network, three potential drugs, quinacridine, thalidomide and zonisamide, were screened for the treatment of osteosarcoma. Among them, quinacridine and thalidomide have been proved to have anti-tumor effects in previous studies, while zonisamide has not been reported. And a corresponding drug-protein interaction network was constructed.ConclusionOverall, we constructed a circRNA-miRNA-mRNA regulatory network to investigate the possible mechanism in osteosarcoma, and predicted that quinacridine, thalidomide and zonisamide could be potential drugs for the treatment of osteosarcoma.

Expression level of long non-coding RNA colon adenocarcinoma hypermethylated serve as a novel prognostic biomarker in patients with thyroid carcinoma

This study attempts to identify the prognostic value and potential mechanism of action of colorectal adenocarcinoma hypermethylated(CAHM) in thyroid carcinoma(THCA) by using the RNA sequencing dataset from The Cancer Genome Atlas(TCGA). The functional mechanism of CAHM was explored by using RNA sequencing dataset and multiple functional enrichment analysis approaches. Connectivity map online analysis tool was also used to predict CAHM targeted drugs. Survival analysis suggests that THCA patients with high CAHM expression have lower risk of death than these low CAHM expression(Log-rank P=0.022, adjusted P=0.011, HR=0.187, 95%CI=0.051-0.685). Function enrichment of CAHM co-expression genes suggests that CAHM may play a role in the following biological processes: DNA repair, cell adhesion, DNA replication, vascular endothelial growth factor receptor, Erb-B2 receptor tyrosine kinase 2, ErbB and thyroid hormone signaling pathways. Function enrichment of DEGs between low- and high-CAHM phenotype suggests that different CAHM expression levels may have the following differences in biological processes in THCA: cell adhesion, cell proliferation, extracellular signal regulated kinase 1(ERK1) and ERK2 cascade, G-protein coupled receptor, chemokine, and phosphatidylinositol-3-kinase-Akt signaling pathways. Connectivity map have identified five drugs (levobunolol, NU-1025, quipazine, anisomycin and sulfathiazole) for CAHM targeted therapy in THCA. Gene set enrichment analysis suggest that low CAHM phenotype were notably enriched in p53, nuclear factor kappa B, Janus kinase-signal transducer and activators of transcription, tumor necrosis factor, epidermal growth factor receptor and other signaling pathways. In the present study, we have identified CAHM may be serve as a novel prognostic biomarkers for predicting overall survival in patients with THCA.

Discovery and function exploration of microRNA-155 as a molecular biomarker for early detection of breast cancer

Background MicroRNA-155 (miR-155) may function as a diagnostic biomarker of breast cancer (BC). Nevertheless, the available evidence is controversial. Therefore, we performed this study to summarize the global predicting role of miR-155 for early detection of BC and preliminarily explore the functional roles of miR-155 in BC. Methods We first collected published studies and applied the bivariate meta-analysis model to generate the pooled diagnostic parameters of miR-155 in diagnosing BC such as sensitivity, specificity and area under curve (AUC). Then, we applied function enrichment and protein–protein interactions (PPI) analyses to explore the potential mechanisms of miR-155. Results A total of 21 studies were finally included. The results indicated that miR-155 allowed for the discrimination between BC patients and healthy controls with a sensitivity of 0.87 (95% CI 0.78–0.93), specificity of 0.82 (0.72–0.89), and AUC of 0.91 (0.88–0.93). In addition, the overall sensitivity, specificity and AUC for circulating miR-155 were 0.88 (0.76–0.95), 0.83 (0.72–0.90), and 0.92 (0.89–0.94), respectively. Function enrichment analysis revealed several vital ontologies terms and pathways associated with BC occurrence and development. Furthermore, in the PPI network, ten hub genes and two significant modules were identified to be involved in some important pathways associated with the pathogenesis of BC. Conclusions We demonstrated that miR-155 has great potential to facilitate accurate BC detection and may serve as a promising diagnostic biomarker for BC. However, well-designed cohort studies and biological experiments should be implemented to confirm the diagnostic value of miR-155 before it can be applied to routine clinical procedures.

Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

Objective. Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need. Methods. The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape. Results. Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions. Conclusion. This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

Comprehensive Analysis of Prognostic lncRNAs, miRNAs, and mRNAs Forming a Competing Endogenous RNA Network in LGG

Background: Messenger RNA(mRNA) and Long non coding RNA (lncRNA) targets can interact through the ability to compete for microRNA binding. However, the roles of cancer specific lncRNAs in lncRNA-related ceRNA network of low grade glioma (LGG) are still unclear.Methods: This study obtained two types of RNAs sequencing data in Solid Tissue Normal and LGG Primary Tumor from TCGA database. We used a computational method to analyse the relation between mRNAs, lncRNAs and miRNAs. The function enrichment of Go item and KEGG pathway were analyzed to predict the biological process and pathway of the screened gene. Kaplan‐Meier survival analysis was used to evaluate the association with the expression levels of mRNAs, lncRNAs, and micRNAs and the overall survival of the patients. the ceRNA network of mRNA-lncRNA-miRNA was constructed with the version of cycloscape 3.5.1.Results: 2555 DEmRNA, 218 DElncRNA. 192 DEmiRNAs were screened by using the R package. We analyzed the function enrichment of Go item and KEGG pathway of mRNAs and lncRNAs in ceRNA network. The main 10 BP items, 10 CC items, 10 MF items and 48 KEGG pathways were selected.55 survival related lncRNAs, 50 survival related miRNAs and top 10 survival most related mRNAs in LGG. Finally, 59 miRNAs, 235 mRNAs and 17 lncRNAs, a total of 313 nodes and 1046 edges, constructed the ceRNA network of mRNA-lncRNA-miRNA.Conclusions: This study is advantageous to deeply understand the biological mechanism of ceRNA and to clarify the pathogenesis of LGG.

Integrative Analysis of Three Novel Competing Endogenous RNA Biomarkers with a Prognostic Value in Lung Adenocarcinoma

Increasing evidence has shown competitive endogenous RNAs (ceRNAs) play key roles in numerous cancers. Nevertheless, the ceRNA network that can predict the prognosis of lung adenocarcinoma (LUAD) is still lacking. The aim of the present study was to identify the prognostic value of key ceRNAs in lung tumorigenesis. Differentially expressed (DE) RNAs were identified between LUAD and adjacent normal samples by limma package in R using The Cancer Genome Atlas database (TCGA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway function enrichment analysis was performed using the clusterProfiler package in R. Subsequently, the LUAD ceRNA network was established in three steps based on ceRNA hypothesis. Hub RNAs were identified using degree analysis methods based on Cytoscape plugin cytoHubba. Multivariate Cox regression analysis was implemented to calculate the risk score using the candidate ceRNAs and overall survival information. The survival differences between the high-risk and low-risk ceRNA groups were determined by the Kaplan-Meier and log-rank test using survival and survminer package in R. A total of 2,989 mRNAs, 185 lncRNAs, and 153 miRNAs were identified. GO and KEGG pathway function enrichment analysis showed that DE mRNAs were mainly associated with “sister chromatid segregation,” “regulation of angiogenesis,” “cell adhesion molecules (CAMs),” “cell cycle,” and “ECM-receptor interaction.” LUAD-related ceRNA network was constructed, which comprised of 54 nodes and 78 edges. Top ten hub RNAs (hsa-miR-374a-5p, hsa-miR-374b-5p, hsa-miR-340-5p, hsa-miR-377-3p, hsa-miR-21-5p, hsa-miR-326, SNHG1, RALGPS2, and PITX2) were identified according to their degree. Kaplan-Meier survival analyses demonstrated that hsa-miR-21-5p and RALGPS2 had a significant prognostic value. Finally, we found that a high risk of three novel ceRNA interactions (SNHG1-hsa-miR-21-5p-RALGPS2, SNHG1-hsa-miR-326-RALGPS2, and SNHG1-hsa-miR-377-3p-RALGPS2) was positively associated with worse prognosis. Three novel ceRNAs (SNHG1-hsa-miR-21-5p-RALGPS2, SNHG1-hsa-miR-326-RALGPS2, and SNHG1-hsa-miR-377-3p-RALGPS2) might be potential biomarkers for the prognosis and treatment of LUAD.