Results 91 to 100 of about 509,029 (282)

Transcriptional network analysis of PTEN‐protein‐deficient prostate tumors reveals robust stromal reprogramming and signs of senescent paracrine communication

Molecular Oncology, EarlyView.
Combining PTEN protein assessment and transcriptomic profiling of prostate tumors, we uncovered a network enriched in senescence and extracellular matrix (ECM) programs associated with PTEN loss and conserved in a mouse model. We show that PTEN‐deficient cells trigger paracrine remodeling of the surrounding stroma and this information could help ...
Ivana Rondon‐Lorefice, Jose I. Lopez, Aitziber Ugalde‐Olano, Maite Zufiaurre, Ianire Astobiza, Natalia Martin‐Martin, Laura Bozal‐Basterra, Saioa Garcia‐Longarte, Amaia Zabala‐Letona, Sofia Rey, Aida Santos‐Martin, Miguel Unda, Ana Loizaga‐Iriarte, Mariona Graupera, Paolo Nuciforo, Arkaitz Carracedo, Isabel Mendizabal   +16 more
wiley   +1 more source

Inference of Gene Regulatory Networks for Overcoming Low Performance in Real-World Data

IEEE Access
The identification of gene regulatory networks is important for understanding the mechanisms of various biological phenomena. Many methods have been proposed to infer networks from time-series gene expression data obtained by high-throughput next ...
Yusuke Hiki, Yuta Tokuoka, Takahiro G. Yamada, Akira Funahashi   +3 more
doaj   +1 more source

SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes

Communications Biology, 2022
SimiC, a single-cell gene regulatory inference framework is presented that can infer multiple gene regulatory networks across related cell phenotypes, unraveling complex regulatory dynamics.
Jianhao Peng, Guillermo Serrano, Ian M. Traniello, Maria E. Calleja-Cervantes, Ullas V. Chembazhi, Sushant Bangru, Teresa Ezponda, Juan Roberto Rodriguez-Madoz, Auinash Kalsotra, Felipe Prosper, Idoia Ochoa, Mikel Hernaez   +11 more
doaj   +1 more source

Estimating gene regulatory networks with pandaR [PDF]

Bioinformatics, 2017
Abstract PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that begins with a model of transcription factor–target gene interactions and uses message passing to update the network model given available transcriptomic and protein–protein interaction data.
Daniel Schlauch, Joseph N. Paulson, Albert Young, Kimberly Glass, John Quackenbush   +4 more
openaire   +3 more sources

Potential therapeutic targeting of BKCa channels in glioblastoma treatment

Molecular Oncology, EarlyView.
This review summarizes current insights into the role of BKCa and mitoBKCa channels in glioblastoma biology, their potential classification as oncochannels, and the emerging pharmacological strategies targeting these channels, emphasizing the translational challenges in developing BKCa‐directed therapies for glioblastoma treatment.
Kamila Maliszewska‐Olejniczak, Karolina Pytlak, Sandra Jaworowska, Bogusz Kulawiak, Piotr Bednarczyk   +4 more
wiley   +1 more source

On the basic computational structure of gene regulatory networks

, 2009
Gene regulatory networks constitute the first layer of the cellular computation for cell adaptation and surveillance. In these webs, a set of causal relations is built up from thousands of interactions between transcription factors and their target genes.
Albert, Aldana, Apweiler, Babu, Balaji, Balaji, Balázsi, Bauer, Bernat Corominas-Murtra, Bornholdt, Bray, Broder, Carlos Rodríguez-Caso, Clauset, Correale, Cosentino-Lagomarsino, Cosentino-Lagomarsino, de la Fuente, Dobrin, Espinosa-Soto, Freyre-González, Gama-Castro, Hartwell, Harwood, Hogeweg, Hong, Isalan, Istrail, Jeong, Kauffman, Keseler, Lee, Lee, Lewis, Li, Luscombe, Ma, Ma, Macía, Maere, Monk, Moszer, Newman, Palla, Ravasz, Resendis-Antonio, Ricard V. Solé, Rodríguez-Caso, Sales-Pardo, Salgado, Sellerio, Serrano, Shen-Orr, Shmulevich, Sierro, Swinburne, Swinburne, Thieffry, Thieffry, Thomas, Trusina, Vázquez, Wagner, Wolf, Yu, Zhu   +65 more
core   +1 more source

Dammarenediol II enhances etoposide‐induced apoptosis by targeting O‐GlcNAc transferase and Akt/GSK3β/mTOR signaling in liver cancer

Molecular Oncology, EarlyView.
Etoposide induces DNA damage, activating p53‐dependent apoptosis via caspase‐3/7, which cleaves PARP1. Dammarenediol II enhances this apoptotic pathway by suppressing O‐GlcNAc transferase activity, further decreasing O‐GlcNAcylation. The reduction in O‐GlcNAc levels boosts p53‐driven apoptosis and influences the Akt/GSK3β/mTOR signaling pathway ...
Jaehoon Lee, Byung‐Cheol Han, Gi‐Bang Koo, Jihye Park, Mijin Kwon, Young Bin Park, Jae‐Mun Choi, Seung‐Ho Lee, Sangho Roh   +8 more
wiley   +1 more source

Failed "nonaccelerating" models of prokaryote gene regulatory networks

, 2004
Much current network analysis is predicated on the assumption that important biological networks will either possess scale free or exponential statistics which are independent of network size allowing unconstrained network growth over time. In this paper,
Gagen, M. J., Mattick, J. S.
core  

Finding regulatory modules through large-scale gene-expression data analysis

, 2004
The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of ...
Kloster, Morten, Tang, Chao, Wingreen, Ned   +2 more
core   +1 more source

Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment

Molecular Oncology, EarlyView.
This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley   +1 more source