Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Wang, Xuezhu; Dong, Yucheng; Zheng, Yongchang; Chen, Yang

doi:10.1016/j.jgg.2021.05.008

Cited by 8 publications

(2 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One common theme in the epigenetics field over the past 20 years was research focused on genome sequencing methods, techniques, and data science (Figure 3). We identified substantial interest in analysis techniques associated with interpreting epigenetic data, such as the use of enrichment networks in conjunction with epigenetic data (Wang et al, 2021). The development of these methodologies and associated analysis techniques is critically important for how we assess the epigenomes of wild-living populations and individuals.…”

Section: What Is the Future Of Eco-epi?mentioning

confidence: 99%

Epigenetics in Ecology, Evolution, and Conservation

et al. 2022

View full text Add to dashboard Cite

Epigenetic variation is often characterized by modifications to DNA that do not alter the underlying nucleotide sequence, but can influence behavior, morphology, and physiological phenotypes by affecting gene expression and protein synthesis. In this review, we consider how the emerging field of ecological epigenetics (eco-epi) aims to use epigenetic variation to explain ecologically relevant phenotypic variation and predict evolutionary trajectories that are important in conservation. Here, we focus on how epigenetic data have contributed to our understanding of wild populations, including plants, animals, and fungi. First, we identified published eco-epi literature and found that there was limited taxonomic and ecosystem coverage and that, by necessity of available technology, these studies have most often focused on the summarized epigenome rather than locus- or nucleotide-level epigenome characteristics. We also found that while many studies focused on adaptation and heritability of the epigenome, the field has thematically expanded into topics such as disease ecology and epigenome-based ageing of individuals. In the second part of our synthesis, we discuss key insights that have emerged from the epigenetic field broadly and use these to preview the path toward integration of epigenetics into ecology. Specifically, we suggest moving focus to nucleotide-level differences in the epigenome rather than whole-epigenome data and that we incorporate several facets of epigenome characterization (e.g., methylation, chromatin structure). Finally, we also suggest that incorporation of behavior and stress data will be critical to the process of fully integrating eco-epi data into ecology, conservation, and evolutionary biology.

show abstract

Section: What Is the Future Of Eco-epi?mentioning

confidence: 99%

Epigenetics in Ecology, Evolution, and Conservation

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Most studies on circRNAs in cancer have not addressed these issues but rather focused on a specific cancer type and para-cancer tissues. They failed to study circRNAs from a pan-cancer view, although the circRNAs related to the common dysregulations in oncogenesis may reveal the principles of circRNA dysregulations in cancer and have higher robustness as therapeutic targets and cancer biomarkers (17). It was ignored that circRNAs are expressed by diverse normal tissues in vivo and secreted into plasma exosomes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers

Wang

Dong

et al. 2021

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

A growing body of evidence has shown that circular RNA (circRNA) is a promising exosomal cancer biomarker candidate. However, global circRNA alterations in cancer and the underlying mechanism, essential for identification of ideal circRNA cancer biomarkers, remain under investigation. We comparatively analyzed the circRNA landscape in pan-cancer and pan-normal tissues. Using co-expression and LASSO regularization analyses, as well as a support vector machine, we analyzed 265 pan-cancer and 319 pan-normal tissues in order to identify the circRNAs with the highest ability to distinguish between pan-cancer and pan-normal tissues. We further studied their expression in plasma exosomes from patients with cancer and their relation with cancer mutations and tumor microenvironment landscape. We discovered that circRNA expression was globally reduced in pan-cancer tissues and plasma exosomes from cancer patients than in pan-normal tissues and plasma exosomes from healthy controls. We identified dynein axonemal heavy chain 14 (DNAH14), the top back-spliced gene exclusive to pan-cancer tissues, as the host gene of three pan-cancer tissue-enriched circRNAs. Among these three circRNAs, chr1_224952669_224968874_+ was significantly elevated in plasma exosomes from hepatocellular carcinoma and colorectal cancer patients. It was also related to the cancer mutation chr1:224952669: G>A, a splice acceptor variant, and was increasingly transcription-driven in cancer tissues. Moreover, pan-cancer tissue-enriched and pan-normal tissue-enriched circRNAs were associated with distinct tumor microenvironment patterns. Our machine learning-based analysis provides insights into the aberrant landscape and biogenesis of circRNAs in cancer and highlights cancer mutation-related and DNAH14-derived circRNA, chr1_224952669_224968874_+, as a potential cancer biomarker.

show abstract

Integrated metabolic and epigenetic mechanisms in cardiomyocyte proliferation

Huang

Wang

et al. 2023

Journal of Molecular and Cellular Cardiology

View full text Add to dashboard Cite

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Cited by 8 publications

References 97 publications

Epigenetics in Ecology, Evolution, and Conservation

Epigenetics in Ecology, Evolution, and Conservation

Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers

Integrated metabolic and epigenetic mechanisms in cardiomyocyte proliferation

Contact Info

Product

Resources

About