Results 221 to 230 of about 569,117 (264)

CEACAM1 participation in breast cancer progression

open access: yesMolecular Oncology, EarlyView.
In invasive breast cancer (BC), CEACAM1 shifts from an apical to a uniform membranous/cytoplasmic pattern, or is lost, as tumors dedifferentiate, inversely tracking the Ki‐67 proliferative index. In MCF‐7 cells, only CEACAM1‐4L suppresses proliferation, repressing cell cycle and growth factor genes.
Mykola Lyndin   +3 more
wiley   +1 more source

Pharmacological chromatin remodeling enhances response to estrogen therapy in ER+ breast cancer

open access: yesMolecular Oncology, EarlyView.
Estrogen therapy elicits clinical benefit in ~ 30% of patients with endocrine‐resistant estrogen receptor (ER)‐positive breast cancer. Based on findings that ER transcriptional activation underlies response to estrogen therapy, we tested the effects of epigenetic dysregulation via pharmacological inhibition of histone deacetylases (HDACi).
Anneka L. Johnson Thomas   +16 more
wiley   +1 more source

PANoptosis in the pathogenesis of myelodysplastic syndromes

open access: yesMolecular Oncology, EarlyView.
PANoptosis, a combination of three types of programmed cell death, is mediated by a large protein complex called a PANoptosome. In healthy bone marrow hematopoietic cells, PANoptosis is restricted by inhibitory signaling. In MDS, bone marrow cells become sensitive to the PANoptotic stimuli due to the aberrant inactivation of inhibitory signaling or ...
Rohit Thalla   +4 more
wiley   +1 more source

Cancer cell‐intrinsic type 1 interferon: production, signaling, and outcomes within sex‐biased, female malignancies

open access: yesMolecular Oncology, EarlyView.
The cell‐autonomous roles of interferon type 1 vary based on duration and intensity. While acute, robust signaling results in cancer cell cytotoxic effects, sustained, low‐level signaling is associated with tumor‐promoting effects. This review summarizes current research of IFN‐1 in cancer and within the clinical setting, with an emphasis on female ...
Ashlyn Conant   +7 more
wiley   +1 more source

Intrapatient tumour heterogeneity and clonal evolution in an autopsy study of metastatic salivary gland cancer

open access: yesMolecular Oncology, EarlyView.
Tumour heterogeneity and clonal evolution of metastatic salivary gland cancer were evaluated in two patients with adenoid carcinoma and one patient with myoepithelial carcinoma. Radiology‐guided autopsy enabled multi‐region sampling (total samples n = 149), followed by whole‐genome sequencing and phylogenetic reconstruction (17 tumour samples, 4–7 per ...
Gerben Lassche   +10 more
wiley   +1 more source

Somatostatin receptor 4 (SSTR4) is a tumor suppressor in cutaneous and head & neck squamous cell carcinomas

open access: yesMolecular Oncology, EarlyView.
This study identifies somatostatin receptor 4 (Sstr4) as a critical tumor suppressor against skin and head/neck cancers (HNSCC, cSCC, and BCC). The loss of Sstr4 removes a check on cell growth, causing hyperactivation of the MAPK‐ERK signaling pathway (↑).
Ali Taqvi   +6 more
wiley   +1 more source

Coupled Principal Component Analysis

IEEE Transactions on Neural Networks, 2004
A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the
Möller, Ralf, Könies, Axel
openaire   +5 more sources

Directed Principal Component Analysis

Operations Research, 2014
We consider a problem involving estimation of a high-dimensional covariance matrix that is the sum of a diagonal matrix and a low-rank matrix, and making a decision based on the resulting estimate. Such problems arise, for example, in portfolio management, where a common approach employs principal component analysis (PCA) to estimate factors used in ...
Yi-Hao Kao, Benjamin Van Roy
openaire   +2 more sources

Principal Components Analysis

2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D.   +3 more
openaire   +3 more sources

Kernel Principal Component Analysis

1997
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images.
Bernhard Schölkopf   +2 more
openaire   +3 more sources

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