scholarly journals Spica Prunellae Extract Enhances Fluorouracil Sensitivity of 5-Fluorouracil-Resistant Human Colon Carcinoma HCT-8/5-FU Cells via TOP2α and miR-494

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yi Fang ◽  
Chi Yang ◽  
Ling Zhang ◽  
Lihui Wei ◽  
Jiumao Lin ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Ethanol Extract ◽  
Human Colon ◽  
Protein Interaction Networks ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Beneficial Effects ◽  
Human Colon Carcinoma ◽  
Public Dataset ◽  
Protein Protein Interaction Networks

The use of 5-fluorouracil (5-FU) has been proven benefits, but it also has adverse events in colorectal cancer (CRC) chemotherapy. In this study, we explored the mechanism of 5-FU resistance by bioinformatics analysis of the NCBI public dataset series GSE81005. Fifteen hub genes were screened out of 582 different expressed genes. Modules of the hub genes in protein-protein interaction networks gathered to TOP2α showed a decrease in HCT-8 cells but an increase in 5-FU-resistant HCT-8/5-FU cells with 5-FU exposure. Downregulation of TOP2α with siRNA or miR-494 transfection resulted in an increase of cytotoxicity and decrease of cell colonies to 5-FU for HCT-8/5-FU cells. Moreover, we found that an ethanol extract of Spica Prunellae (EESP), which is a traditional Chinese medicine with clinically beneficial effects in various cancers, was able to enhance the sensitivity of 5-FU in HCT-8/5-FU cells and partly reverse the 5-FU resistance effect. It significantly helped suppress cell growth and induced cell apoptosis in HCT-8/5-FU cells with the expression of TOP2α being significantly suppressed, which increased by 5-FU. Consistently, miR-494, which reportedly regulates TOP2α, exhibited reverse trends in EESP/5-FU combination treatment. These results suggested that Spica Prunellae may be beneficial in the treatment of 5-FU-resistant CRC patients.

Comprehensive analysis of key proteins involved in radioresistance of prostate cancer by integrating protein-protein interaction networks.

2020 ◽  
Vol 15 ◽  
Author(s):  
Duocheng Qian ◽  
Quan Li ◽  
Yansong Zhu ◽  
Dujian Li
Keyword(s):  
Prostate Cancer ◽  
Kinase Activity ◽  
Bioinformatics Analysis ◽  
Prognostic Markers ◽  
Nuclear Division ◽  
Protein Interaction Networks ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Proteinprotein Interaction ◽  
Protein Protein Interaction Networks

Background: Radioresistance remains a significant obstacle in the treatment of prostate cancer (PCa). The mechanisms underlying the radioresistance in PCa remained to be further investigated. Methods: GSE53902 dataset was used in this study to identify radioresistance-related mRNAs. Proteinprotein interaction (PPI) network was constructed based on STRING analysis. DAVID system was used to predict the potential roles of radioresistance-related mRNAs. Results: We screened and re-annotated GSE53902 dataset to identify radioresistance-related mRNAs. A total of 445 up-regulated and 1036 downregulated mRNAs were identified in radioresistance PCa cells. Three key PPI network consisting of 81 proteins were further constructed in PCa. Bioinformatics analysis revealed these genes were involved in regulating MAP kinase activity, response to hypoxia, regulation of apoptotic process, mitotic nuclear division, and regulation of mRNA stability. Moreover, we observed radioresistance-related mRNAs, such as PRC1, RAD54L, PIK3R3, ASB2, FBXO32, LPAR1, RNF14, and UBA7, were dysregulated and correlated to the survival time in PCa. Conclusions: We thought this study will be useful to understand the mechanisms underlying radioresistance of PCa and identify novel prognostic markers for PCa.

scholarly journals Prenatal testosterone triggers long-term behavioral changes in male zebra finches: unravelling the neurogenomic mechanisms

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexandra B. Bentz ◽  
Chad E. Niederhuth ◽  
Laura L. Carruth ◽  
Kristen J. Navara
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Differentially Expressed ◽  
Zebra Finches ◽  
Hub Genes ◽  
Prenatal Testosterone ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

Abstract Background Maternal hormones, like testosterone, can strongly influence developing offspring, even generating long-term organizational effects on adult behavior; yet, the mechanisms facilitating these effects are still unclear. Here, we experimentally elevated prenatal testosterone in the eggs of zebra finches (Taeniopygia guttata) and measured male aggression in adulthood along with patterns of neural gene expression (RNA-seq) and DNA methylation (MethylC-Seq) in two socially relevant brain regions (hypothalamus and nucleus taenia of the amygdala). We used enrichment analyses and protein-protein interaction networks to find candidate processes and hub genes potentially affected by the treatment. We additionally identified differentially expressed genes that contained differentially methylated regions. Results We found that males from testosterone-injected eggs displayed more aggressive behaviors compared to males from control eggs. Hundreds of genes were differentially expressed, particularly in the hypothalamus, including potential aggression-related hub genes (e.g., brain derived neurotrophic factor). There were also enriched processes with well-established links to aggressive phenotypes (e.g., somatostatin and glutamate signaling). Furthermore, several highly connected genes identified in protein-protein interaction networks also showed differential methylation, including adenylate cyclase 2 and proprotein convertase 2. Conclusions These results highlight genes and processes that may play an important role in mediating the effects of prenatal testosterone on long-term phenotypic outcomes, thereby providing insights into the molecular mechanisms that facilitate hormone-mediated maternal effects.

scholarly journals Identification of key genes as predictive biomarkers for osteosarcoma metastasis using translational bioinformatics

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fu-peng Ding ◽  
Jia-yi Tian ◽  
Jing Wu ◽  
Dong-feng Han ◽  
Ding Zhao
Keyword(s):  
Clinical Significance ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Support Vector ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
Key Genes

Abstract Background Osteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis. Methods We performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways. Results Eighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein–protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein–protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial–mesenchymal transition. Conclusion We identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

scholarly journals Prognostic and Predictive Values of MAD2L1 in Cholangiocarcinoma

2020 ◽  
Author(s):  
Yuan Gao ◽  
Xiwu Ouyang ◽  
Tianping Luo ◽  
Chunfu Zhu ◽  
Xihu Qin
Keyword(s):  
Protein Interaction ◽  
Microarray Data ◽  
Mitotic Arrest ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Networks Analysis ◽  
Protein Protein Interaction Networks

Abstract Background: This study aimed to investigate the expression of Mitotic arrest deficient 2-like protein 1 (MAD2L1) in cholangiocarcinoma (CCA) and its biological function. Methods: Our study performed bioinformatics to analyze the microarray data from the Gene Expression Omnibus (GEO) database and obtained the differentially expressed genes (DEGs). Enrichment assay and protein-protein interaction networks analysis were conducted to extract hub genes. Mitotic arrest deficient 2-like protein 1 (MAD2L1) was investigated in tumor and adjacent nonneoplastic biliary ducts in 42 samples by immunohistochemistry. Subsequently, MAD2L1 was manipulated, and its function was examined in CCA cell lines and in vivo model. Results: 297 DEGs were extracted from the microarray data. And seven hub genes were defined through the enrichment assay and protein-protein interaction networks analysis. MAD2L1 was picked up as a novel biomarker, according to hierarchical cluster analysis and Kaplan-Meier survival analysis. MAD2L1 was specifically expressed in cancer tissues but not in the surrounding normal tissue, and 31 (73.81%) of 42 CCAs were MAD2L1 positive by immunohistochemistry. MAD2L1 expression was significantly correlated with tumor size, pathological grade, and clinical stage. Kaplan-Meier analysis demonstrated an inverse correlation between MAD2L1 expression and overall survival. Real-time polymerase chain reaction (RT-PCR) and Immunoblotting results further confirmed the results of immunohistochemistry and bioinformatic analysis from the database. In vitro and in vivo models, decreasing MAD2L1 could significantly suppress tumor growth.Conclusion: High MAD2L1 expression predicts the advanced stage of CCA. Targeting MAD2L1 may be a potential tumor suppressor and may provide the biological basis for a new therapeutic strategy.Trial registration: [2020]KY157-01. Registered 1st April 2020 - Retrospectively registered, http://114.255.48.20/login#.

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