Results 171 to 180 of about 320,395 (368)

Elucidating prognostic significance of purine metabolism in colorectal cancer through integrating data from transcriptomic, immunohistochemical, and single‐cell RNA sequencing analysis

Molecular Oncology, EarlyView.
Low expression of five purine metabolism‐related genes (ADSL, APRT, ADCY3, NME3, NME6) was correlated with poor survival in colorectal cancer. Immunohistochemistry analysis showed that low NME3 (early stage) and low ADSL/NME6 (late stage) levels were associated with high risk.
Sungyeon Kim, Myunghee Kang, Soyeon Jeong, Jisup Kim, Kyoung Oh Kim, Won‐Suk Lee, Jeong‐Heum Baek, Jung Ho Kim, Seungyoon Nam   +8 more
wiley   +1 more source

hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data

Cell Reports Methods, 2023
Samuel Morabito, Fairlie Reese, N. Rahimzadeh, Emily Miyoshi, V. Swarup   +4 more
semanticscholar   +1 more source

Exploration of heterogeneity and recurrence signatures in hepatocellular carcinoma

Molecular Oncology, EarlyView.
This study leveraged public datasets and integrative bioinformatic analysis to dissect malignant cell heterogeneity between relapsed and primary HCC, focusing on intercellular communication, differentiation status, metabolic activity, and transcriptomic profiles.
Wen‐Jing Wu, Jianchao Wang, Fuqing Chen, Xuefeng Wang, Bin Lan, Ruyi Fu, Hong Wen, Fangfang Chen, Wei Hong, Tian‐Yu Tang, Ying He, Gang Chen, Jianyin Zhou, Hai‐Long Piao, Di Chen, Shu‐Yong Lin   +15 more
wiley   +1 more source

The Eleanor ncRNAs activate the topological domain of the ESR1 locus to balance against apoptosis

Nature Communications, 2019
Long term estrogen deprivation can result in apoptosis in breast cancer cells. Here, the authors show that this apoptosis is induced by the long-range chromatin interaction of loci containing the ESR1 and FOXO 3 genes, resulting in FOXO 3-mediated ...
Mohamed Osama Ali Abdalla, Tatsuro Yamamoto, Kazumitsu Maehara, Jumpei Nogami, Yasuyuki Ohkawa, Hisashi Miura, Rawin Poonperm, Ichiro Hiratani, Hideki Nakayama, Mitsuyoshi Nakao, Noriko Saitoh   +10 more
doaj   +1 more source

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

Multidimensional OMICs reveal ARID1A orchestrated control of DNA damage, splicing, and cell cycle in normal‐like and malignant urothelial cells

Molecular Oncology, EarlyView.
Loss of the frequently mutated chromatin remodeler ARID1A, a subunit of the SWI/SNF cBAF complex, results in less open chromatin, alternative splicing, and the failure to stop cells from progressing through the cell cycle after DNA damage in bladder (cancer) cells. Created in BioRender. Epigenetic regulators, such as the SWI/SNF complex, with important
Rebecca M. Schlösser, Florian Krumbach, Eyleen Corrales, Geoffroy Andrieux, Christian Preisinger, Franziska Liss, Alexandra Golzmann, Melanie Boerries, Kerstin Becker, Ruth Knüchel, Stefan Garczyk, Bernhard Lüscher   +11 more
wiley   +1 more source

A comparative analysis of single-cell transcriptomic technologies in plants and animals

Current Plant Biology, 2023
The development of sequencing methods has resulted in the investigation of many unexplored research areas. Among the different sequencing methods, single-cell transcriptomics is versatile and has completely changed the researchers' perception of ...
Vamsidhar Reddy Netla, Harshraj Shinde, Gulshan Kumar, Ambika Dudhate, Jong Chan Hong, Ulhas Sopanrao Kadam   +5 more
doaj  

Escape from TGF‐β‐induced senescence promotes aggressive hallmarks in epithelial hepatocellular carcinoma cells

Molecular Oncology, EarlyView.
Chronic TGF‐β exposure drives epithelial HCC cells from a senescent state to a TGF‐β resistant mesenchymal phenotype. This transition is characterized by the loss of Smad3‐mediated signaling, escape from senescence, enhanced invasiveness and metastatic potential, and upregulation of key resistance modulators such as MARK1 and GRM8, ultimately promoting
Minenur Kalyoncu, Dilara Demirci, Sude Eris, Bengisu Dayanc, Ece Cakiroglu, Merve Basol, Merve Uysal, Gulcin Cakan‐Akdogan, Fang Liu, Mehmet Ozturk, Gökhan Karakülah, Serif Senturk   +11 more
wiley   +1 more source

Analysis of the Human Tissue-specific Expression by Genome-wide Integration of Transcriptomics and Antibody-based Proteomics*

Molecular & Cellular Proteomics, 2013
Global classification of the human proteins with regards to spatial expression patterns across organs and tissues is important for studies of human biology and disease.
Linn Fagerberg, B. Hallström, P. Oksvold, C. Kampf, Dijana Djureinovic, J. Odeberg, Masato Habuka, Simin Tahmasebpoor, A. Danielsson, K. Edlund, A. Asplund, E. Sjöstedt, E. Lundberg, C. Szigyarto, Marie Skogs, Jenny Ottosson Takanen, H. Berling, Hanna Tegel, J. Mulder, P. Nilsson, J. Schwenk, C. Lindskog, Frida Danielsson, A. Mardinoğlu, Åsa Sivertsson, K. von Feilitzen, Mattias Forsberg, M. Zwahlen, Ingmarie Olsson, S. Navani, M. Huss, J. Nielsen, F. Pontén, M. Uhlén   +33 more
semanticscholar   +1 more source

Association of normalization, non-differentially expressed genes and data source with machine learning performance in intra-dataset or cross-dataset modelling of transcriptomic and clinical data [PDF]

arXiv
Cross-dataset testing is critical for examining machine learning (ML) model's performance. However, most studies on modelling transcriptomic and clinical data only conducted intra-dataset testing. It is also unclear whether normalization and non-differentially expressed genes (NDEG) can improve cross-dataset modeling performance of ML.
arxiv