BackgroundMost ovarian cancer patients with poor prognosis and immune microenvironment play a vital role in the progression of ovarian cancer. We aim to develop a tumor-associated macrophage related gene (TAMRGs) prognostic signature that can stratify and predict overall survival for ovarian cancer. MethodsWe acquired single cell and bulk transcriptome raw data of ovarian cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The immune landscape was identi ed in primary and ascites of ovarian cancer. CIBERSORT deconvolution algorithm, Weighted gene co-expression network analysis (WGCNA), univariate cox analysis, LASSO algorithm, and multivariate cox analysis were performed for the identi cation of TAMRG and the development of prognostic signature. ResultsWe identi ed inter-and intra-patient heterogeneity for immune in ltration cells at a single-cell resolution. Tumor in ltration macrophages showed immunosuppressive characteristics with an M2 phenotype. T cell CD4 memory activated, mast cell activated, neutrophils and macrophages M2 were negatively correlated with overall survival, while macrophages M1 was positively correlated. A total of 219 TAMRGs were identi ed and a novel 6-gene signature (TAP1, CD163, VSIG4, IGKV4-1, CD3E, and MS4A7) with superior prognostic independence was established. ConclusionsThe TAMRG-based signature is expected to be a promising target for prognosis and treatment response of ovarian cancer. BackgroundOvarian cancer (OC), accounts for the highest mortality rate among gynecological malignancies, with 13770 estimated new deaths in the United States, 2021(1). More than 70% of cases of OC are diagnosed at advanced stages with ve-year survival rates approximating 48%(2). Despite appropriate surgery and platinum-based chemotherapy, most patients with ovarian cancer relapse and disseminates(3). It is urgent to identify novel clinical biomarkers and develop new therapeutic strategies for ovarian cancer.Recent evidence has unraveled the tumor microenvironment (TME) plays a vital role in the metastasis of ovarian cancer and is considered a possible therapeutic target for ovarian cancer(4). Among solid tumors, TME of epithelial ovarian cancer is unique because the cancer cells are frequently shed from the primary tumor into the peritoneal cavity, build up a sole microenvironment of malignant ascites. The treatment of ovarian cancer targeting the tumor microenvironment is developing rapidly. Targets mainly focusing on
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.
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.
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/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.
BackgroundMost ovarian cancer patients with poor prognosis and immune microenvironment play a vital role in the progression of ovarian cancer. We aim to develop a tumor-associated macrophage related gene (TAMRGs) prognostic signature that can stratify and predict overall survival for ovarian cancer.MethodsWe acquired single cell and bulk transcriptome raw data of ovarian cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The immune landscape was identified in primary and ascites of ovarian cancer. CIBERSORT deconvolution algorithm, Weighted gene co-expression network analysis (WGCNA), univariate cox analysis, LASSO algorithm, and multivariate cox analysis were performed for the identification of TAMRG and the development of prognostic signature.Results We identified inter-and intra-patient heterogeneity for immune infiltration cells at a single-cell resolution. Tumor infiltration macrophages showed immunosuppressive characteristics with an M2 phenotype. T cell CD4 memory activated, mast cell activated, neutrophils and macrophages M2 were negatively correlated with overall survival, while macrophages M1 was positively correlated. A total of 219 TAMRGs were identified and a novel 6-gene signature (TAP1, CD163, VSIG4, IGKV4-1, CD3E, and MS4A7) with superior prognostic independence was established.ConclusionsThe TAMRG-based signature is expected to be a promising target for prognosis and treatment response of ovarian cancer.
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