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 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.

scholarly journals Alterations in protein expression and site-specific N-glycosylation of prostate cancer tissues

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Sugár ◽  
Gábor Tóth ◽  
Fanni Bugyi ◽  
Károly Vékey ◽  
Katalin Karászi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Progression ◽  
Protein Interaction Networks ◽  
Biological Processes ◽  
Altered Expression ◽  
Protein Protein Interaction ◽  
Molecular Alterations ◽  
Prostate Biopsies ◽  
Protein Protein Interaction Networks ◽  
Cancer Tissues

AbstractIdentifying molecular alterations occurring during cancer progression is essential for a deeper understanding of the underlying biological processes. Here we have analyzed cancerous and healthy prostate biopsies using nanoLC-MS(MS) to detect proteins with altered expression and N-glycosylation. We have identified 75 proteins with significantly changing expression during disease progression. The biological processes involved were assigned based on protein–protein interaction networks. These include cellular component organization, metabolic and localization processes. Multiple glycoproteins were identified with aberrant glycosylation in prostate cancer, where differences in glycosite-specific sialylation, fucosylation, and galactosylation were the most substantial. Many of the glycoproteins with altered N-glycosylation were extracellular matrix constituents, and are heavily involved in the establishment of the tumor microenvironment.

scholarly journals Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yanghe Feng ◽  
Qi Wang ◽  
Tengjiao Wang
Keyword(s):  
Protein Interactions ◽  
Drug Target ◽  
Drug Targets ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Ppi Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Protein Protein Interaction Networks

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target’s homologue set containing 102 potential target proteins is predicted in the paper.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

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