Multi-omics inference of differential breast cancer-related transcriptional regulatory network gene hubs between young Black and White patients

Pharmacological Effects of Antioxidant Mycosporine-Glycine in Alleviating Ultraviolet B-Induced Skin Photodamage: Insights from Metabolomic and Transcriptomic Analyses

Mycosporine-glycine (M-Gly), a member of the mycosporine-like amino acid (MAA) family, is known for its potent antioxidant and anti-inflammatory properties. However, its in vivo efficacy in alleviating acute skin photodamage, primarily caused by oxidative stress, has not been well explored. In this investigation, 30 female ICR mice were divided into four groups: a control group and three Ultraviolet B (UVB)-exposed groups treated with saline or M-Gly via intraperitoneal injection for 30 days. At the end of the experiment, UVB exposure caused erythema, wrinkling, collagen degradation, and mast cell infiltration in mouse dorsal skin. M-Gly treatment improved skin appearance and reduced mast cell numbers, while also elevating antioxidant levels, including superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH). Furthermore, M-Gly reduced inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and IL-1β, typically upregulated after UVB exposure. M-Gly also protected skin collagen by upregulating type I procollagen and decreasing MMP-1 levels. Skin metabolomic profiling identified 34 differentially abundant metabolites, and transcriptomic analysis revealed 752 differentially expressed genes. The combined metabolomic and transcriptomic data indicate that M-Gly's protective effects may involve the regulation of ion transport, cellular repair, metabolic stability, collagen preservation, and the Nrf2/HO-1 pathway. These findings highlight M-Gly's potential as an endogenous antioxidant for protecting skin from UVB-induced damage.

Clinical and Multiomic Features Differentiate Young Black and White Breast Cancer Cohorts Derived by Machine Learning Approaches

BACKGROUND

There are documented differences in Breast cancer (BrCA) presentations and outcomes between Black and White patients. In addition to molecular factors, socioeconomic, racial, and clinical factors result in disparities in outcomes for women in the United States. Using machine learning and unsupervised biclustering methods within a multiomics framework, here we sought to shed light on the biological and clinical underpinnings of observed differences between Black and White BrCA patients.

MATERIALS AND METHODS

We examined The Cancer Genome Atlas BrCA samples from stage II patients aged 50 or younger that are Black (BAA50) or White (W50) (n = 139 patients; 36 BAA50 and 103 W50) These patients were chosen because marked differences in survival were observed in an earlier study. A variety of multiomic data sets were analyzed to further characterize the clinical and molecular disparities for insights.

RESULTS

We coupled RNAseq data with protein-protein interaction as well as BrCA-specific protein co-expression network data to identify 2 novel biclusters. These biclusters are significantly associated with clinical features including race, number of lymph nodes involved with disease, estrogen receptor status, progesterone receptor status and menopausal status. There were also differentially mutated genes. Using DNA methylation data, we identified differentially methylated genes. Machine learning algorithms were trained on differential methylation values of driver genes. The trained algorithms were successful in predicting the bicluster assignment of each sample.

CONCLUSION

These results demonstrate that there was a significant association between the cluster membership and BAA50 and W50 cohorts, indicating that these biclusters accurately stratify these cohorts.

Onco-Breastomics: An Eco-Evo-Devo Holistic Approach

Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized as a dynamic pseudo-organ, a living organism able to build a complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, a population, a species, a local community, a biocenosis, or an evolving dynamical ecosystem (i.e., immune or metabolic ecosystem) that emphasizes both developmental continuity and spatio-temporal change. Moreover, a cancer cell community, also known as an oncobiota, has been described as non-sexually reproducing species, as well as a migratory or invasive species that expresses intelligent behavior, or an endangered or parasite species that fights to survive, to optimize its features inside the host's ecosystem, or that is able to exploit or to disrupt its host circadian cycle for improving the own proliferation and spreading. BC tumorigenesis has also been compared with the early embryo and placenta development that may suggest new strategies for research and therapy. Furthermore, BC has also been characterized as an environmental disease or as an ecological disorder. Many mechanisms of cancer progression have been explained by principles of ecology, developmental biology, and evolutionary paradigms. Many authors have discussed ecological, developmental, and evolutionary strategies for more successful anti-cancer therapies, or for understanding the ecological, developmental, and evolutionary bases of BC exploitable vulnerabilities. Herein, we used the integrated framework of three well known ecological theories: the Bronfenbrenner's theory of human development, the Vannote's River Continuum Concept (RCC), and the Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, to explain and understand several eco-evo-devo-based principles that govern BC progression. Multi-omics fields, taken together as onco-breastomics, offer better opportunities to integrate, analyze, and interpret large amounts of complex heterogeneous data, such as various and big-omics data obtained by multiple investigative modalities, for understanding the eco-evo-devo-based principles that drive BC progression and treatment. These integrative eco-evo-devo theories can help clinicians better diagnose and treat BC, for example, by using non-invasive biomarkers in liquid-biopsies that have emerged from integrated omics-based data that accurately reflect the biomolecular landscape of the primary tumor in order to avoid mutilating preventive surgery, like bilateral mastectomy. From the perspective of preventive, personalized, and participatory medicine, these hypotheses may help patients to think about this disease as a process governed by natural rules, to understand the possible causes of the disease, and to gain control on their own health.

Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

RNA atlas and competing endogenous RNA regulation in tissue-derived exosomes from luminal B and triple-negative breast cancer patients

Background

Luminal B and triple-negative breast cancer (TNBC) are malignant subtypes of breast cancer (BC), which can be attributed to the multifaceted roles of tissue-derived exosomes (T-exos). Competing endogenous RNA (ceRNA) networks can regulate gene expression post-transcriptionally.

Methods

RNAs in T-exos from luminal B BC (n=8) and TNBC (n=8) patients were compared with those from persons with benign breast disease (n=8). The differentially expressed (DE) mRNA, microRNA (miRNA), and long noncoding RNA (lncRNA) target genes were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the relevant biological processes.The ceRNA networks were constructed to show distinct regulation, and the mRNAs involved were annotated. The miRNAs involved in the ceRNA networks were screened with the Kaplan-Meier Plotter database to identify dysregulated ceRNAs with prognostic power.

Results

In total, 802 DE mRNAs, 441 DE lncRNAs, and 104 DE miRNAs were identified in luminal B BC T-exos, while 1699 DE mRNAs, 590 DE lncRNAs, and 277 DE miRNAs were identified in TNBC T-exos. Gene annotation revealed that the RAS-MAPK pathway was the primary biological process in luminal B BC T-exos, while endocrine system development and growth were the main processes in TNBC T-exos. Survival analysis established seven survival-related lncRNA/miRNA/mRNA regulations in luminal B BC T-exos, and nineteen survival-related lncRNA/miRNA/mRNA regulations in TNBC T-exos.

Conclusion

In addition to survival-related ceRNA regulations, ceRNA regulation of RAS-MAPK in luminal B and endocrine system development and growth regulation in TNBC might contribute to the tumorigenesis of BC.