Network Module Detection to Decipher Heterogeneity of Cancer Mutations

2020 ◽  
pp. 151-166
Author(s):  
Yoo-Ah Kim
Keyword(s):  
Network Module ◽  
Module Detection ◽  
Cancer Mutations

scholarly journals Network Module Detection using Recursive Local Graph Sparsification and Clustering

2018 ◽  
Author(s):  
Michael Banf
Keyword(s):  
Biological Networks ◽  
Network Module ◽  
Identification Methods ◽  
Structure Preserving ◽  
Module Identification ◽  
Wide Range ◽  
Module Detection ◽  
Disease Module ◽  
Graph Sparsification ◽  
Local Graph

Here we present a fast and highly scalable community structure preserving network module detection that recursively integrates graph sparsification and clustering. Our algorithm, called SparseClust, participated in the most recent DREAM community challenge on disease module identification, an open competition to comprehensively assess module identification methods across a wide range of biological networks.

scholarly journals Rely on Each Other: DNA Binding Cooperativity Shapes p53 Functions in Tumor Suppression and Cancer Therapy

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2422
Author(s):  
Oleg Timofeev ◽  
Thorsten Stiewe
Keyword(s):  
Cancer Therapy ◽  
Dna Binding ◽  
Cell Fate ◽  
Tumor Suppression ◽  
Quaternary Structure ◽  
Three Dimensional ◽  
Structural Basis ◽  
Sequence Specificity ◽  
Cell Fate Decisions ◽  
Cancer Mutations

p53 is a tumor suppressor that is mutated in half of all cancers. The high clinical relevance has made p53 a model transcription factor for delineating general mechanisms of transcriptional regulation. p53 forms tetramers that bind DNA in a highly cooperative manner. The DNA binding cooperativity of p53 has been studied by structural and molecular biologists as well as clinical oncologists. These experiments have revealed the structural basis for cooperative DNA binding and its impact on sequence specificity and target gene spectrum. Cooperativity was found to be critical for the control of p53-mediated cell fate decisions and tumor suppression. Importantly, an estimated number of 34,000 cancer patients per year world-wide have mutations of the amino acids mediating cooperativity, and knock-in mouse models have confirmed such mutations to be tumorigenic. While p53 cancer mutations are classically subdivided into “contact” and “structural” mutations, “cooperativity” mutations form a mechanistically distinct third class that affect the quaternary structure but leave DNA contacting residues and the three-dimensional folding of the DNA-binding domain intact. In this review we discuss the concept of DNA binding cooperativity and highlight the unique nature of cooperativity mutations and their clinical implications for cancer therapy.

scholarly journals Ca2+ Signaling and Its Potential Targeting in Pancreatic Ductal Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3085
Author(s):  
Louay Bettaieb ◽  
Maxime Brulé ◽  
Axel Chomy ◽  
Mel Diedro ◽  
Malory Fruit ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Exocrine Pancreas ◽  
Ductal Carcinoma ◽  
Ductal Adenocarcinoma ◽  
Molecular Devices ◽  
Pancreatic Ductal Carcinoma ◽  
Aberrant Expression ◽  
Common Cause ◽  
Cancer Hallmarks ◽  
Cancer Mutations

Pancreatic cancer (PC) is a major cause of cancer-associated mortality in Western countries (and estimated to be the second cause of cancer deaths by 2030). The main form of PC is pancreatic adenocarcinoma, which is the fourth most common cause of cancer-related death, and this situation has remained virtually unchanged for several decades. Pancreatic ductal adenocarcinoma (PDAC) is inherently linked to the unique physiology and microenvironment of the exocrine pancreas, such as pH, mechanical stress, and hypoxia. Of them, calcium (Ca2+) signals, being pivotal molecular devices in sensing and integrating signals from the microenvironment, are emerging to be particularly relevant in cancer. Mutations or aberrant expression of key proteins that control Ca2+ levels can cause deregulation of Ca2+-dependent effectors that control signaling pathways determining the cells’ behavior in a way that promotes pathophysiological cancer hallmarks, such as enhanced proliferation, survival and invasion. So far, it is essentially unknown how the cancer-associated Ca2+ signaling is regulated within the characteristic landscape of PDAC. This work provides a complete overview of the Ca2+ signaling and its main players in PDAC. Special consideration is given to the Ca2+ signaling as a potential target in PDAC treatment and its role in drug resistance.

scholarly journals ModularBoost: an efficient network inference algorithm based on module decomposition

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinyu Li ◽  
Wei Zhang ◽  
Jianming Zhang ◽  
Guang Li
Keyword(s):  
Network Inference ◽  
Detection Methods ◽  
Inference Problem ◽  
Topological Constraints ◽  
Inference Algorithms ◽  
Module Detection ◽  
Series Expression ◽  
Gene Modules ◽  
Inference Methods ◽  
Complicated Task

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

scholarly journals Profiling PRMT methylome reveals roles of hnRNPA1 arginine methylation in RNA splicing and cell growth

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Wen-juan Li ◽  
Yao-hui He ◽  
Jing-jing Yang ◽  
Guo-sheng Hu ◽  
Yi-an Lin ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Cell Growth ◽  
Rna Splicing ◽  
Synergistic Effects ◽  
Arginine Methylation ◽  
Type I ◽  
Prostate Cancer Cells ◽  
Cancer Cell Growth ◽  
Cancer Mutations ◽  
Substrate Spectrum

AbstractNumerous substrates have been identified for Type I and II arginine methyltransferases (PRMTs). However, the full substrate spectrum of the only type III PRMT, PRMT7, and its connection to type I and II PRMT substrates remains unknown. Here, we use mass spectrometry to reveal features of PRMT7-regulated methylation. We find that PRMT7 predominantly methylates a glycine and arginine motif; multiple PRMT7-regulated arginine methylation sites are close to phosphorylations sites; methylation sites and proximal sequences are vulnerable to cancer mutations; and methylation is enriched in proteins associated with spliceosome and RNA-related pathways. We show that PRMT4/5/7-mediated arginine methylation regulates hnRNPA1 binding to RNA and several alternative splicing events. In breast, colorectal and prostate cancer cells, PRMT4/5/7 are upregulated and associated with high levels of hnRNPA1 arginine methylation and aberrant alternative splicing. Pharmacological inhibition of PRMT4/5/7 suppresses cancer cell growth and their co-inhibition shows synergistic effects, suggesting them as targets for cancer therapy.

scholarly journals Skin riddled with cancer mutations

Nature ◽  
2015 ◽  
Vol 521 (7553) ◽  
pp. 397-397
Keyword(s):  
Cancer Mutations
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