VEGFA Involves in the Use of Fluvastatin and Zoledronate Against Breast Cancer

High STAT4 expression correlates with poor prognosis in acute myeloid leukemia and facilitates disease progression by upregulating VEGFA expression

The aim of our study is to explore the mechanism of transcription-4 (STAT4) in acute myeloid leukemia (AML). STAT4 level in AML bone marrow samples/cells was analyzed using bioinformatics and quantitative real-time PCR. The correlation between high STAT4 expression and the prognosis of AML patients was analyzed. The viability, apoptosis, and angiogenesis of AML cells were detected. The levels of STAT4, vascular endothelial growth factor A (VEGFA), and apoptosis-related proteins (Bcl-2 and Bax) in transfected AML cells were examined. STAT4 level was upregulated in AML. STAT4 silencing decreased the viability and angiogenesis, yet increased the apoptosis of AML cells, while overexpressed STAT4 did conversely. VEGFA silencing counteracted the impacts of overexpressed STAT4 upon promoting viability and angiogenesis as well as repressing the apoptosis of AML cells. High STAT4 expression was correlated with poor prognosis of AML patients and facilitated disease progression via upregulating VEGFA expression.

Predicting the molecular mechanism-driven progression of breast cancer through comprehensive network pharmacology and molecular docking approach

Identification of key regulators is a critical step toward discovering biomarker that participate in BC. A gene expression dataset of breast cancer patients was used to construct a network identifying key regulators in breast cancer. Overexpressed genes were identified with BioXpress, and then curated genes were used to construct the BC interactome network. As a result of selecting the genes with the highest degree from the BC network and tracing them, three of them were identified as novel key regulators, since they were involved at all network levels, thus serving as the backbone. There is some evidence in the literature that these genes are associated with BC. In order to treat BC, drugs that can simultaneously interact with multiple targets are promising. When compared with single-target drugs, multi-target drugs have higher efficacy, improved safety profile, and are easier to administer. The haplotype and LD studies of the FN1 gene revealed that the identified variations rs6707530 and rs1250248 may both cause TB, and endometriosis respectively. Interethnic differences in SNP and haplotype frequencies might explain the unpredictability in association studies and may contribute to predicting the pharmacokinetics and pharmacodynamics of drugs using FN1.

Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer

Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.

Long Non-coding RNA IRAIN Inhibits VEGFA Expression via Enhancing Its DNA Methylation Leading to Tumor Suppression in Renal Carcinoma

Aims: Long non-coding RNA IRAIN (lncRNA IRAIN) plays a critical role in numerous malignancies. However, the function of lncRNA IRAIN in renal carcinoma (RC) remains enigmatic. The purpose of this study is to characterize the effects of lncRNA IRAIN on RC progression.

Methods: The expression pattern of lncRNA IRAIN and the vascular endothelial growth factor A (VEGFA) in RC tissues and cells was characterized by RT-qPCR and Western blot analysis. The roles of lncRNA IRAIN and VEGFA in the progression of RC were studied by gain- or loss-of-function experiments. Bioinformatics data analysis was used to predict CpG islands in the VEGFA promoter region. MSP was applied to detect the level of DNA methylation in RC cells. The interaction between lncRNA IRAIN and VEGFA was identified by RNA immunoprecipitation and RNA-protein pull down assays. Recruitment of DNA methyltransferases (Dnmt) to the VEGFA promoter region was achieved by chromatin immunoprecipitation. The subcellular localization of lncRNA IRAIN was detected by fractionation of nuclear and cytoplasmic RNA. Cell viability was investigated by CCK-8 assay, cell migration was tested by transwell migration assay, and apoptosis was analyzed by flow cytometry. The expression of epithelial–mesenchymal transition-related and apoptotic factors was evaluated by Western blot analysis. Finally, the effect of the lncRNA IRAIN/VEGFA axis was confirmed in an in vivo tumor xenograft model.

Results: LncRNA IRAIN was poorly expressed in RC tissues and cells with a primary localization in the nucleus, while VEGFA was highly expressed. Overexpression of lncRNA IRAIN or knockdown of VEGFA inhibited cell proliferation and migration and induced the apoptosis of RC cells. Bioinformatics analysis indicated the presence of CpG islands in the VEGFA promoter region. Lack of methylation at specific sites in the VEGFA promoter region was detected through MSP assay. We found that lncRNA IRAIN was able to inhibit VEGFA expression through recruitment of Dnmt1, Dnmt3a, and Dnmt3b to the VEGFA promoter region. LncRNA IRAIN was also able to suppress RC tumor growth via repression of VEGFA in an in vivo mouse xenograft model.

Conclusion: Our data shows that by downregulating VEGFA expression in RC, the lncRNA IRAIN has tumor-suppressive potential.