Integrated Bioinformatic Analysis of Competing Endogenous RNA Network of Choriocarcinoma.

BACKGROUND Numerous studies have demonstrated that noncoding RNAs are involved in choriocarcinoma (CC). The competing endogenous RNA (ceRNA) network plays an important role in the occurrence and development of carcinoma. However, the involvement of the ceRNA network in CC remains unclear. The current study aimed to investigate the regulatory mechanism of ceRNA in CC. MATERIAL AND METHODS We downloaded the messenger RNAs (mRNAs) expression profiles (GSE20510 and GSE65654) and microRNAs (miRNAs) expression profiles (GSE32346 and GSE130489) from GEO datasets. The limma package of R software was used to identify differentially expressed RNAs (DERNAs). Then, we performed functional annotation of the differentially expressed mRNAs (DEmRNAs). TargetScan, miRDB, miRWalk, and Starbase were used to construct a CC-specific ceRNA network and select key molecules. RESULTS The results identified a total of 177 DEmRNAs and 189 differentially expressed miRNAs (DEmiRNAs) between the trophoblast and CC cell line samples. Ten differentially expressed lncRNAs (DElncRNAs) were obtained based on experimental studies. The DEmRNAs were mainly enriched in cell proliferation, positive regulation of the apoptotic process, and cell death. A total of 10 genes were ascertained as hub genes. Based on DEmRNAs, DEmiRNAs, and DElncRNAs, a CC-specific ceRNA network was established. Five DElncRNAs, 15 DEmiRNAs, and 45 DEmRNAs were identified. In addition, LINC00261, MEG3, MALAT1, H19, and OGFRP1 were identified as 5 key lncRNAs in choriocarcinoma. CONCLUSIONS This study provides novel insights into CC mechanisms and identified potential therapeutic targets for CC.

Identification and Validation of miRNA-TF-mRNA Regulatory Networks in Uterine Fibroids.

Uterine fibroids (UF) are the most common benign gynecologic tumors and lead to heavy menstrual bleeding, severe anemia, abdominal pain, and infertility, which seriously harm a women's health. Unfortunately, the regulatory mechanisms of UF have not been elucidated. Recent studies have demonstrated that miRNAs play a vital role in the development of uterine fibroids. As a high-throughput technology, microarray is utilized to identify differentially expressed genes (DEGs) and miRNAs (DEMs) between UF and myometrium. We identified 373 candidate DEGs and the top 100 DEMs. Function enrichment analysis showed that candidate DEGs were mainly enriched in biological adhesion, locomotion and cell migration, and collagen-containing extracellular matrix. Subsequently, protein-protein interaction (PPI) networks are constructed to analyze the functional interaction between DEGs and screen hub DEGs. Subsequently, the expression levels of hub DEGs were validated by real-time PCR of clinical UF samples. The DGIdb database was used to select candidate drugs for hub DEGs. Molecular docking was applied to test the affinity between proteins and drugs. Furthermore, target genes for 100 candidate DEMs were predicted by miRwalk3.0. After overlapping with 373 candidate DEGs, 28 differentially expressed target genes (DEGTs) were obtained. A miRNA-mRNA network was constructed to investigate the interactions between miRNA and mRNA. Additionally, two miRNAs (hsa-miR-381-3p and hsa-miR-181b-5p) were identified as hub DEMs and validated through RT-PCR. In order to better elucidate the pathogenesis of UF and the synergistic effect between miRNA and transcription factor (TF), we constructed a miRNA-TF-mRNA regulatory network. Meanwhile, in vitro results suggested that dysregulated hub DEMs were associated with the proliferation, migration, and apoptosis of UF cells. Our findings provided a novel horizon to reveal the internal mechanism and novel targets for the diagnosis and treatment of UF.

Integrated analysis of tumor-associated macrophage infiltration and prognosis in ovarian cancer.

Ovarian cancer (OC) is a frequently lethal gynecologic malignancy, characterized by a poor prognosis and high recurrence rate. The immune microenvironment has been implicated in the progression of OC. We characterized the immune landscape in primary and malignant OC ascites using single-cell and bulk transcriptome raw OC data acquired from the Gene Expression Omnibus and The Cancer Genome Atlas databases. We then used the CIBERSORT deconvolution algorithm, weighted gene co-expression network analysis, univariate and multivariate Cox analyses, and the LASSO algorithm to develop a tumor-associated macrophage-related gene (TAMRG) prognostic signature, which enabled us to stratify and predict overall survival (OS) of OC patients. In addition, inter- and intra-patient heterogeneity of infiltrating immune cells was characterized at single-cell resolution. Tumor-infiltrating macrophages with an M2 phenotype exhibited immunosuppressive activity. M1 macrophages positively correlated with OS, whereas activated mast cells, neutrophils, M2 macrophages, and activated memory CD4+ T cells were all negatively correlated with OS. A total of 219 TAMRGs were identified, and a novel 6-gene signature (TAP1, CD163, VSIG4, IGKV4-1, CD3E, and MS4A7) with independent prognostic value was established. These results show that a TAMRG-based signature may be a promising prognostic and therapeutic target in OC.

Bioinformatics Analysis of Choriocarcinoma-Related MicroRNA-Transcription Factor-Target Gene Regulatory Networks and Validation of Key miRNAs.

OBJECTIVE: The aim of the current research was to construct a miRNA-transcription factor (TF)-target gene regulatory network in order to investigate the mechanism underlying choriocarcinoma and to verify the network through the overexpression or silencing of hub miRNAs in vitro. MATERIALS AND METHODS: A mRNA expression dataset and two miRNA expression datasets were analysed to identify differentially expressed genes (DEGs) and miRNAs (DEMs) between normal cells and choriocarcinoma cells. The top 400 upregulated and downregulated DEGs were identified as candidate DEGs, which were then mapped to construct protein-protein interaction (PPI) networks and select hub genes. Moreover, the DGIdb database was utilized to select candidate drugs for hub genes. Moreover, DEM target genes were predicted through the miRWalk2.0 database and overlaid with candidate DEGs to identify the differentially expressed target genes (DETGs). Furthermore, we established miRNA-TF-target gene regulatory networks and performed functional enrichment analysis of hub DEMs. Finally, we transfected mimics or inhibitors of hub DEMs into choriocarcinoma cells and assessed cell proliferation and migration to verify the vital role of hub DEMs in choriocarcinoma. RESULTS: A total of 140 DEMs and 400 candidate DEGs were screened from choriocarcinoma cells and normal cells. A PPI network of 400 candidate DEGs was established. Twenty-nine hub genes and 99 associated small molecules were identified to provide potential target drugs for choriocarcinoma treatment. We obtained 70 DETGs of DEMs derived from the intersection between predicted miRNA target genes and candidate DEGs. Subsequently, 3 hub DEMs were selected, and miRNA-TF-target gene regulatory networks containing 4 TFs, 3 TFs and 3 TFs for each network were constructed. The RT-PCR results confirmed that miR-29b-3p was highly expressed and that miR-519c-3p and miR-520a-5p were expressed at low levels in choriocarcinoma cells. The overexpression or silencing results suggested that 3 dysregulated hub DEMs jointly accelerated the proliferation and migration of choriocarcinoma. CONCLUSION: Association of miRNA-TF-target gene regulatory networks may help us explore the underlying mechanism and provide potential targets for the diagnosis and treatment of choriocarcinoma.