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.

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 Gibbs Free Energy of Protein-Protein Interactions Reflects Tumor Stage

2015 ◽  
Author(s):  
Edward Rietman ◽  
Alex Bloemendal ◽  
John Platig ◽  
Jack Tuszynski ◽  
Giannoula Lakka Klement
Keyword(s):  
Free Energy ◽  
Gibbs Free Energy ◽  
Cancer Progression ◽  
Protein Interaction ◽  
Survival Data ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Chemical Thermodynamics ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

The sequential changes occurring with cancer progression are now being harnessed with therapeutic intent. Yet, there is no understanding of the chemical thermodynamics of proteomic changes associated with cancer progression/ cancer stage. This manuscript reveals a strong correlation of a chemical thermodynamic measure (known as Gibbs free energy) of protein-protein interaction networks for several cancer types and 5-year overall survival and stage in patients with cancer. Earlier studies have linked degree entropy of signaling networks to patient survival data, but not with stage. It appears that Gibbs free energy is a more general metric and accounts better for the underlying energetic landscape of protein expression in cells, thus correlating with stage as well as survival. This is an especially timely finding because of improved ability to obtain and analyze genomic/ proteomic information from individual patients. Yet, at least at present, only candidate gene imaging (FISH or immunohistochemistry) can be used for entropy computations. With continually expanding use of genomic information in clinical medicine, there is an ever-increasing need to understand the thermodynamics of protein-protein interaction networks.

scholarly journals In vivo interactome profiling by enzyme‐catalyzed proximity labeling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yangfan Xu ◽  
Xianqun Fan ◽  
Yang Hu
Keyword(s):  
State Of The Art ◽  
Catalytic Efficiency ◽  
Biological Processes ◽  
Protein Protein Interaction ◽  
Current State ◽  
Protein Interactome ◽  
Potential Applications ◽  
Protein Protein Interaction Networks ◽  
Temporal And Spatial

AbstractEnzyme-catalyzed proximity labeling (PL) combined with mass spectrometry (MS) has emerged as a revolutionary approach to reveal the protein-protein interaction networks, dissect complex biological processes, and characterize the subcellular proteome in a more physiological setting than before. The enzymatic tags are being upgraded to improve temporal and spatial resolution and obtain faster catalytic dynamics and higher catalytic efficiency. In vivo application of PL integrated with other state of the art techniques has recently been adapted in live animals and plants, allowing questions to be addressed that were previously inaccessible. It is timely to summarize the current state of PL-dependent interactome studies and their potential applications. We will focus on in vivo uses of newer versions of PL and highlight critical considerations for successful in vivo PL experiments that will provide novel insights into the protein interactome in the context of human diseases.

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