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Biomolecules
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Cancer Systems Biology: Investigating Cancer Dynamics from a Complex Systems...
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Cancer Systems Biology: Investigating Cancer Dynamics from a Complex Systems Perspective

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 10930

Special Issue Editors

Prof. Dr. Prakash Kulkarni

E-Mail Website
Guest Editor
Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
Interests: cancer systems biology; evolution; non-genetic mechanisms; intrinsically disordered proteins
Special Issues, Collections and Topics in MDPI journals
Dr. Mohit Kumar Jolly

E-Mail Website
Guest Editor
Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
Interests: metastasis; mathematical oncology; systems biology; computational biology; phenotypic plasticity; cellular decision-making; cancer stem cells; epithelial-mesenchymal transition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer has been traditionally thought of as a genetic disease. However, emerging evidence suggests that non-genetic mechanisms can also contribute to complex and adaptive dynamics of cancer. Thus, it is being increasingly appreciated that cancer progression is an emergent outcome of complex interactions among genetic and non-genetic mechanisms across multiple spatiotemporal scales, from the molecular level to the cell and tissue level, thus creating cell population level heterogeneity.

Technological advances to interrogate cellular and molecular heterogeneity at high-throughput levels, availability of powerful computing tools, and the application of game theory to discern group behavior have bolstered multiscale modeling approaches to enable quantitative predictive view of different steps in tumorigenesis and cancer progression. However, performing pre-clinical studies and clinical trials for various targets and combinatorial drug treatments is often infeasible due to resource and time consumed. Thus, calibrated and validated mathematical models offer an attractive approach to evaluate untested protocols in silico to identify promising treatment schemas, to decode new therapeutic targets, and to reduce the risk of adverse clinical outcomes due to complex multi-scale feedback loops.

Through this Special Issue, we invite submissions in the interdisciplinary field of cancer systems biology to contribute their latest research articles and/or perspectives/review articles that showcase the application of different computational and/or statistical tools, algorithms and techniques to (a) unravel the dynamics of disease progression; (b) pinpoint new therapeutic targets; and (c) design more effective therapy regimens.

Prof. Dr. Prakash Kulkarni
Dr. Mohit Kumar Jolly
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical modeling
  • single-cell analysis
  • tumor microenvironment
  • feedback loops
  • cellular plasticity
  • game theory
  • non-genetic heterogeneity

Published Papers (3 papers)

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Research

17 pages, 8277 KiB  
Article
TGF-β Pathways Stratify Colorectal Cancer into Two Subtypes with Distinct Cartilage Oligomeric Matrix Protein (COMP) Expression-Related Characteristics
by Jia-Tong Ding, Hao-Nan Zhou, Ying-Feng Huang, Jie Peng, Hao-Yu Huang, Hao Yi, Zhen Zong and Zhi-Kun Ning
Biomolecules 2022, 12(12), 1877; https://doi.org/10.3390/biom12121877 - 14 Dec 2022
Cited by 3 | Viewed by 1659
Abstract
Background: Colorectal cancers (CRCs) continue to be the leading cause of cancer-related deaths worldwide. The exact landscape of the molecular features of TGF-β pathway-inducing CRCs remains uncharacterized. Methods: Unsupervised hierarchical clustering was performed to stratify samples into two clusters based on the differences [...] Read more.
Background: Colorectal cancers (CRCs) continue to be the leading cause of cancer-related deaths worldwide. The exact landscape of the molecular features of TGF-β pathway-inducing CRCs remains uncharacterized. Methods: Unsupervised hierarchical clustering was performed to stratify samples into two clusters based on the differences in TGF-β pathways. Weighted gene co-expression network analysis was applied to identify the key gene modules mediating the different characteristics between two subtypes. An algorithm integrating the least absolute shrinkage and selection operator (LASSO), XGBoost, and random forest regression was performed to narrow down the candidate genes. Further bioinformatic analyses were performed focusing on COMP-related immune infiltration and functions. Results: The integrated machine learning algorithm identified COMP as the hub gene, which exhibited a significant predictive value for two subtypes with an area under the curve (AUC) value equaling 0.91. Further bioinformatic analysis revealed that COMP was significantly upregulated in various cancers, especially in advanced CRCs, and regulated the immune infiltration, especially M2 macrophages and cancer-associated fibroblasts in CRCs. Conclusions: Comprehensive immune analysis and experimental validation demonstrate that COMP is a reliable signature for subtype prediction. Our results could provide a new point for TGFβ-targeted anticancer drugs and contribute to guiding clinical decision making for CRC patients. Full article
(This article belongs to the Special Issue Cancer Systems Biology: Investigating Cancer Dynamics from a Complex Systems Perspective)
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19 pages, 26942 KiB  
Article
Dynamical Analysis of a Boolean Network Model of the Oncogene Role of lncRNA ANRIL and lncRNA UFC1 in Non-Small Cell Lung Cancer
by Shantanu Gupta and Ronaldo F. Hashimoto
Biomolecules 2022, 12(3), 420; https://doi.org/10.3390/biom12030420 - 09 Mar 2022
Cited by 16 | Viewed by 4292
Abstract
Long non-coding RNA (lncRNA) such as ANRIL and UFC1 have been verified as oncogenic genes in non-small cell lung cancer (NSCLC). It is well known that the tumor suppressor microRNA-34a (miR-34a) is downregulated in NSCLC. Furthermore, miR-34a induces senescence and apoptosis in breast, [...] Read more.
Long non-coding RNA (lncRNA) such as ANRIL and UFC1 have been verified as oncogenic genes in non-small cell lung cancer (NSCLC). It is well known that the tumor suppressor microRNA-34a (miR-34a) is downregulated in NSCLC. Furthermore, miR-34a induces senescence and apoptosis in breast, glioma, cervical cancer including NSCLC by targeting Myc. Recent evidence suggests that these two lncRNAs act as a miR-34a sponge in corresponding cancers. However, the biological functions between these two non-coding RNAs (ncRNAs) have not yet been studied in NSCLC. Therefore, we present a Boolean model to analyze the gene regulation between these two ncRNAs in NSCLC. We compared our model to several experimental studies involving gain- or loss-of-function genes in NSCLC cells and achieved an excellent agreement. Additionally, we predict three positive circuits involving miR-34a/E2F1/ANRIL, miR-34a/E2F1/UFC1, and miR-34a/Myc/ANRIL. Our circuit- perturbation analysis shows that these circuits are important for regulating cell-fate decisions such as senescence and apoptosis. Thus, our Boolean network permits an explicit cell-fate mechanism associated with NSCLC. Therefore, our results support that ANRIL and/or UFC1 is an attractive target for drug development in tumor growth and aggressive proliferation of NSCLC, and that a valuable outcome can be achieved through the miRNA-34a/Myc pathway. Full article
(This article belongs to the Special Issue Cancer Systems Biology: Investigating Cancer Dynamics from a Complex Systems Perspective)
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19 pages, 21151 KiB  
Article
Population Dynamics of Epithelial-Mesenchymal Heterogeneity in Cancer Cells
by Paras Jain, Sugandha Bhatia, Erik W. Thompson and Mohit Kumar Jolly
Biomolecules 2022, 12(3), 348; https://doi.org/10.3390/biom12030348 - 23 Feb 2022
Cited by 9 | Viewed by 3022
Abstract
Phenotypic heterogeneity is a hallmark of aggressive cancer behaviour and a clinical challenge. Despite much characterisation of this heterogeneity at a multi-omics level in many cancers, we have a limited understanding of how this heterogeneity emerges spontaneously in an isogenic cell population. Some [...] Read more.
Phenotypic heterogeneity is a hallmark of aggressive cancer behaviour and a clinical challenge. Despite much characterisation of this heterogeneity at a multi-omics level in many cancers, we have a limited understanding of how this heterogeneity emerges spontaneously in an isogenic cell population. Some longitudinal observations of dynamics in epithelial-mesenchymal heterogeneity, a canonical example of phenotypic heterogeneity, have offered us opportunities to quantify the rates of phenotypic switching that may drive such heterogeneity. Here, we offer a mathematical modeling framework that explains the salient features of population dynamics noted in PMC42-LA cells: (a) predominance of EpCAMhigh subpopulation, (b) re-establishment of parental distributions from the EpCAMhigh and EpCAMlow subpopulations, and (c) enhanced heterogeneity in clonal populations established from individual cells. Our framework proposes that fluctuations or noise in content duplication and partitioning of SNAIL—an EMT-inducing transcription factor—during cell division can explain spontaneous phenotypic switching and consequent dynamic heterogeneity in PMC42-LA cells observed experimentally at both single-cell and bulk level analysis. Together, we propose that asymmetric cell division can be a potential mechanism for phenotypic heterogeneity. Full article
(This article belongs to the Special Issue Cancer Systems Biology: Investigating Cancer Dynamics from a Complex Systems Perspective)
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