Cancer systems biology: From molecular profiles to pathways, signalling networks, and therapeutic vulnerabilities

2019 ◽  
pp. 375-393
Author(s):  
Lieven Verbeke ◽  
Steven Van Laere
Keyword(s):  
Cancer Research ◽  
Systems Biology ◽  
Biological Networks ◽  
Cancer Biology ◽  
Biological Response ◽  
Holistic Approach ◽  
Gene Set ◽  
Cancer Systems Biology ◽  
Gene Network Analysis ◽  
Different Levels

Cancer systems biology encompasses the application of systems biology approaches to cancer research. Historically, systems biology was first applied in cancer research to enable a pathway-oriented interpretation of gene expression data and this strategy has undoubtedly delivered relevant insights with respect to many aspects of cancer biology. Nowadays, cancer is regarded as a complex system that integrates signals from different levels (i.e. (epi)genomics, transcriptomics, micro-environment) through a network of interconnected proteins to generate a biological response. This holistic approach not only allows the identification of new and relevant signal transduction pathways, but also provides a better understanding of several key properties of cancer cells that can be best understood from a network-level perspective: robustness, evolvability, and plasticity. This chapter provides an overview of several key concepts of systems biology, including reference gene set libraries, network topology, and available strategies to establish biological networks. Next, these concepts are utilized to explain gene set and gene network analysis with particular focus on cancer biology. Finally, the caveats and challenges that are facing cancer systems biology are summarized.

scholarly journals Cancer systems biology: signal processing for cancer research

2011 ◽  
Vol 30 (4) ◽  
pp. 221-225 ◽  
Author(s):  
Olli Yli-Harja ◽  
Antti Ylipaa ◽  
Matti Nykter ◽  
Wei Zhang
Keyword(s):  
Cancer Research ◽  
Signal Processing ◽  
Systems Biology ◽  
Cancer Systems Biology

Social networks to biological networks: systems biology of Mycobacterium tuberculosis

2013 ◽  
Vol 9 (7) ◽  
pp. 1584 ◽  
Author(s):  
Rohit Vashisht ◽  
Anshu Bhardwaj ◽  
OSDD Consortium ◽  
Samir K. Brahmachari
Keyword(s):  
Social Networks ◽  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Biological Networks

scholarly journals Bridging Cancer Biology with the Clinic: Relative Expression of a GRHL2-Mediated Gene-Set Pair Predicts Breast Cancer Metastasis

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e56195 ◽  
Author(s):  
Xinan Yang ◽  
Prabhakaran Vasudevan ◽  
Vishwas Parekh ◽  
Aleks Penev ◽  
John M. Cunningham
Keyword(s):  
Breast Cancer ◽  
Cancer Biology ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Relative Expression ◽  
Gene Set

scholarly journals Comparison of Metabolic Network between Muscle and Intramuscular Adipose Tissues in Hanwoo Beef Cattle Using a Systems Biology Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Hyun-Jeong Lee ◽  
Hye-Sun Park ◽  
Woonsu Kim ◽  
Duhak Yoon ◽  
Seongwon Seo
Keyword(s):  
Systems Biology ◽  
Beef Cattle ◽  
Metabolic Regulation ◽  
Bos Taurus ◽  
Adipose Tissues ◽  
Chemokine Signaling ◽  
Gene Network Analysis ◽  
Chemokine Signaling Pathway ◽  
Subcutaneous Adipose

The interrelationship between muscle and adipose tissues plays a major role in determining the quality of carcass traits. The objective of this study was to compare metabolic differences between muscle and intramuscular adipose (IMA) tissues in thelongissimus dorsi(LD) of Hanwoo (Bos taurus coreanae) using the RNA-seq technology and a systems biology approach. The LD sections between the 6th and 7th ribs were removed from nine (each of three cows, steers, and bulls) Hanwoo beef cattle (carcass weight of430.2±40.66 kg) immediately after slaughter. The total mRNA from muscle, IMA, and subcutaneous adipose and omental adipose tissues were isolated and sequenced. The reads that passed quality control were mapped onto the bovine reference genome (build bosTau6), and differentially expressed genes across tissues were identified. The KEGG pathway enrichment tests revealed the opposite direction of metabolic regulation between muscle and IMA. Metabolic gene network analysis clearly indicated that oxidative metabolism was upregulated in muscle and downregulated in IMA. Interestingly, pathways for regulating cell adhesion, structure, and integrity and chemokine signaling pathway were upregulated in IMA and downregulated in muscle. It is thus inferred that IMA may play an important role in the regulation of development and structure of the LD tissues and muscle/adipose communication.

Modern Technologies and Approaches for Decoding Non-Coding Rna-Mediated Biological Networks in Systems Biology and Their Applications

2016 ◽  
pp. 106-132
Author(s):  
Devyani Samantarrai ◽  
Mousumi Sahu ◽  
Garima Singh ◽  
Jyoti Roy ◽  
Chandra Bhushan ◽  
...  
Keyword(s):  
Systems Biology ◽  
Biological Networks ◽  
Non Coding Rna ◽  
Modern Technologies

scholarly journals Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

2021 ◽  
Author(s):  
Yong Jin Heo ◽  
Chanwoong Hwa ◽  
Gang-Hee Lee ◽  
Jae-Min Park ◽  
Joon-Yong An
Keyword(s):  
Cancer Research ◽  
Biological Networks

scholarly journals Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Sajib Chakraborty ◽  
Md. Ismail Hosen ◽  
Musaddeque Ahmed ◽  
Hossain Uddin Shekhar
Keyword(s):  
Cancer Research ◽  
Systems Biology ◽  
Cancer Cells ◽  
Cellular Response ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Resolving Power ◽  
Cancer Hallmarks ◽  
Molecular Alterations ◽  
Integrated Omics

The acquisition of cancer hallmarks requires molecular alterations at multiple levels including genome, epigenome, transcriptome, proteome, and metabolome. In the past decade, numerous attempts have been made to untangle the molecular mechanisms of carcinogenesis involving single OMICS approaches such as scanning the genome for cancer-specific mutations and identifying altered epigenetic-landscapes within cancer cells or by exploring the differential expression of mRNA and protein through transcriptomics and proteomics techniques, respectively. While these single-level OMICS approaches have contributed towards the identification of cancer-specific mutations, epigenetic alterations, and molecular subtyping of tumors based on gene/protein-expression, they lack the resolving-power to establish the casual relationship between molecular signatures and the phenotypic manifestation of cancer hallmarks. In contrast, the multi-OMICS approaches involving the interrogation of the cancer cells/tissues in multiple dimensions have the potential to uncover the intricate molecular mechanism underlying different phenotypic manifestations of cancer hallmarks such as metastasis and angiogenesis. Moreover, multi-OMICS approaches can be used to dissect the cellular response to chemo- or immunotherapy as well as discover molecular candidates with diagnostic/prognostic value. In this review, we focused on the applications of different multi-OMICS approaches in the field of cancer research and discussed how these approaches are shaping the field of personalized oncomedicine. We have highlighted pioneering studies from “The Cancer Genome Atlas (TCGA)” consortium encompassing integrated OMICS analysis of over 11,000 tumors from 33 most prevalent forms of cancer. Accumulation of huge cancer-specific multi-OMICS data in repositories like TCGA provides a unique opportunity for the systems biology approach to tackle the complexity of cancer cells through the unification of experimental data and computational/mathematical models. In future, systems biology based approach is likely to predict the phenotypic changes of cancer cells upon chemo-/immunotherapy treatment. This review is sought to encourage investigators to bring these different approaches together for interrogating cancer at molecular, cellular, and systems levels.

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