A Non-Genetic Basis for Cancer Progression and Metastasis: Self-Organizing Attractors in Cell Regulatory Networks

Cancer stem cells (CSCs) possess properties such as self-renewal, resistance to apoptotic cues, quiescence, and DNA-damage repair capacity. Moreover, CSCs strongly influence the tumour microenvironment (TME) and may account for cancer progression, recurrence, and relapse. CSCs represent a distinct subpopulation in tumours and the detection, characterisation, and understanding of the regulatory landscape and cellular processes that govern their maintenance may pave the way to improving prognosis, selective targeted therapy, and therapy outcomes. In this review, we have discussed the characteristics of CSCs identified in various cancer types and the role of autophagy and long noncoding RNAs (lncRNAs) in maintaining the homeostasis of CSCs. Further, we have discussed methods to detect CSCs and strategies for treatment and relapse, taking into account the requirement to inhibit CSC growth and survival within the complex backdrop of cellular processes, microenvironmental interactions, and regulatory networks associated with cancer. Finally, we critique the computationally reinforced triangle of factors inclusive of CSC properties, the process of autophagy, and lncRNA and their associated networks with respect to hypoxia, epithelial-to-mesenchymal transition (EMT), and signalling pathways.

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Genetic Variants of DICE1/INTS6 in German Prostate Cancer Families with Linkage to 13q14

Urologia Internationalis ◽

10.1159/000366229 ◽

2015 ◽

Vol 95 (4) ◽

pp. 386-389 ◽

Cited By ~ 2

Author(s):

Malte Böhm ◽

Christiane Maier ◽

Rainer Küfer ◽

Albrecht Röpke ◽

Walther Vogel ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Patients ◽

Cancer Progression ◽

Genetic Basis ◽

Cancer Susceptibility ◽

Suppressor Gene ◽

Tumour Suppressor Gene ◽

Caucasian Men ◽

Chromosome 13Q14 ◽

Exon 10

Introduction: Prostate cancer is the most frequent malignancy found to occur in Caucasian men, but its genetic basis remains elusive. A prostate cancer-susceptibility locus has been identified on chromosome 13q14. The tumour suppressor gene deleted in cancer cells 1 (DICE1/INTS6) is located within this interval on 13q14.3. Materials and Methods: We performed mutation analysis of the DICE1/INTS6 gene in thirteen German prostate cancer families. Results and Conclusion: None of the patients harboured DICE1 mutations, and similar frequencies of the previously identified 13 bp deletion polymorphism in the DICE1 promoter were observed in the familial prostate cancer patients as compared with sporadic prostate cancer patients and controls. However, in one family with three affected brothers, the variations c.1215A>C (p.T405T) in exon 10 and c.2568A>G (p.S856S) in exon 17 were detected in a heterozygous pattern. In sporadic prostate cancer patients, variant c.2568A>G (p.S856S) was detected in 10/325 (3.08%) compared with 5/207 (2.42%) control samples (p > 0.05). We conclude that DICE1 appears to be involved in prostate cancer progression rather than in the initiation of prostate cancer.

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Bacterial regulatory networks—from self‐organizing molecules to cell shape and patterns in bacterial communities

EMBO Reports ◽

10.1038/embor.2013.93 ◽

2013 ◽

Vol 14 (8) ◽

pp. 667-669

Author(s):

Regine Hengge ◽

Victor Sourjik

Keyword(s):

Bacterial Communities ◽

Cell Shape ◽

Regulatory Networks ◽

Self Organizing

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CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720009004291 ◽

2009 ◽

Vol 07 (04) ◽

pp. 645-661 ◽

Cited By ~ 11

Author(s):

XIN CHEN

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Regulatory Networks ◽

Time Course ◽

Clustering Algorithm ◽

Expression Patterns ◽

Self Organizing Map ◽

Expression Data ◽

Wide Range ◽

Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

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The role of gene regulatory networks in promoting cancer progression and metastasis

Future Oncology ◽

10.2217/fon.13.264 ◽

2014 ◽

Vol 10 (5) ◽

pp. 735-748 ◽

Cited By ~ 9

Author(s):

Caroline Coghlin ◽

Graeme I Murray

Keyword(s):

Cancer Progression ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory

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El lenguaje y los lugares construidos / The Language and the Built Places

Revista Internacional de Ciencias Humanas ◽

10.37467/gka-revhuman.v2.703 ◽

2013 ◽

Vol 2 (1) ◽

Author(s):

Francisco Javier Fuentes Farias

Keyword(s):

Cognitive Processes ◽

Genetic Basis ◽

Point Of View ◽

Human Being ◽

Language And Cognition ◽

Human Capacity ◽

The Mind ◽

The Relationship ◽

Self Organizing

ABSTRACTIf we don't explain the role of language in the construction of places to live, their study will be incomplete; therefore the built space poses the challenge of defining a method of analysis that takes into account the emergence of cognitive processes in human being, of which perception and categorization of objects in space seems to be the most difficult to explain. And here is where the focus on language, from the point of view of the studies of complexity, admits to interpret and explain the evolution of the human capacity of build. In this sense, it is necessary to review the problem of in witch sense it can be said that language is innate or learned, and if the mind is a blank paper at birth, or has a genetic basis and how would be like. We observed the acquisition of language and cognition, and the construction of places to live, as the product of a cultural-genetic legacy. It is necessary to offer a point of view about the relationship between culture-nature, taking built places as a superior order and self-organizing subsystem: the built spaceRESUMENMientras no se exponga el papel del lenguaje en la construcción de lugares para vivir, su estudio estará incompleto; por ello, el espacio construido plantea el reto de definir un método de análisis que tome en cuenta el surgimiento de procesos cognitivos en la especie humana, de los cuales la percepción y categorización de los objetos en el espacio parece ser el más difícil de explicar. Y es aquí donde el enfoque en el lenguaje, desde el punto de vista de los estudios de la complejidad, permite interpretar y explicar la evolución de la capacidad constructiva del ser humano. En tal sentido, es necesario revisar el problema de en qué medida puede afirmarse que el lenguaje es innato o aprendido, y si la mente es un papel en blanco al nacer, o tiene una base genética y cómo sería ello. Se examina la adquisición del lenguaje y la cognición, y la construcción de lugares para vivir, como producto de una herencia genético-cultual. Se ofrece un punto de vista necesario acerca de la relación cultura-naturaleza, considerando los lugares construidos como subsistemas de un orden superior y auto-organizado: el espacio construido.

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Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification

Frontiers in Immunology ◽

10.3389/fimmu.2021.765923 ◽

2021 ◽

Vol 12 ◽

Author(s):

Edwin Yuan ◽

Magdalena Matusiak ◽

Korsuk Sirinukunwattana ◽

Sushama Varma ◽

Łukasz Kidziński ◽

...

Keyword(s):

Cancer Progression ◽

In Silico ◽

Therapy Response ◽

Cell Labeling ◽

Cell Segmentation ◽

Self Organizing Map ◽

Cellular Composition ◽

Systematic Classification ◽

Human Pathology ◽

Self Organizing

Cellular composition and structural organization of cells in the tissue determine effective antitumor response and can predict patient outcome and therapy response. Here we present Seg-SOM, a method for dimensionality reduction of cell morphology in H&E-stained tissue images. Seg-SOM resolves cellular tissue heterogeneity and reveals complex tissue architecture. We leverage a self-organizing map (SOM) artificial neural network to group cells based on morphological features like shape and size. Seg-SOM allows for cell segmentation, systematic classification, and in silico cell labeling. We apply the Seg-SOM to a dataset of breast cancer progression images and find that clustering of SOM classes reveals groups of cells corresponding to fibroblasts, epithelial cells, and lymphocytes. We show that labeling the Lymphocyte SOM class on the breast tissue images accurately estimates lymphocytic infiltration. We further demonstrate how to use Seq-SOM in combination with non-negative matrix factorization to statistically describe the interaction of cell subtypes and use the interaction information as highly interpretable features for a histological classifier. Our work provides a framework for use of SOM in human pathology to resolve cellular composition of complex human tissues. We provide a python implementation and an easy-to-use docker deployment, enabling researchers to effortlessly featurize digitalized H&E-stained tissue.

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Identification of EMT signaling cross-talk and gene regulatory networks by single-cell RNA sequencing

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2102050118 ◽

2021 ◽

Vol 118 (19) ◽

pp. e2102050118

Author(s):

Abhijeet P. Deshmukh ◽

Suhas V. Vasaikar ◽

Katarzyna Tomczak ◽

Shubham Tripathi ◽

Petra den Hollander ◽

...

Keyword(s):

Single Cell ◽

Signaling Pathways ◽

Rna Sequencing ◽

Cancer Progression ◽

Tyrosine Kinases ◽

Regulatory Networks ◽

Expression Profiles ◽

Critical Role ◽

Mesenchymal Transition ◽

Single Cell Rna Sequencing

The epithelial-to-mesenchymal transition (EMT) plays a critical role during normal development and in cancer progression. EMT is induced by various signaling pathways, including TGF-β, BMP, Wnt–β-catenin, NOTCH, Shh, and receptor tyrosine kinases. In this study, we performed single-cell RNA sequencing on MCF10A cells undergoing EMT by TGF-β1 stimulation. Our comprehensive analysis revealed that cells progress through EMT at different paces. Using pseudotime clustering reconstruction of gene-expression profiles during EMT, we found sequential and parallel activation of EMT signaling pathways. We also observed various transitional cellular states during EMT. We identified regulatory signaling nodes that drive EMT with the expression of important microRNAs and transcription factors. Using a random circuit perturbation methodology, we demonstrate that the NOTCH signaling pathway acts as a key driver of TGF-β–induced EMT. Furthermore, we demonstrate that the gene signatures of pseudotime clusters corresponding to the intermediate hybrid EMT state are associated with poor patient outcome. Overall, this study provides insight into context-specific drivers of cancer progression and highlights the complexities of the EMT process.

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