Variation in IL6ST cytokine family function and the potential of IL6 trans-signalling in ERα positive breast cancer cells

Mosly, Duniya; MacLeod, Kenneth G.; Moir, Nicholas; Turnbull, Arran; Sims, Andrew H.; Langdon, Simon P.

doi:10.1016/j.cellsig.2022.110563

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Conversely, in the neoplasms group, FBmiR is predominantly associated with cell stemness, cell growth and differentiation, and cell cycle functions. These findings suggest that the disparities in physiological processes related to infection immunity and tumor immunity may be more pronounced in women compared to men, and some of the available studies [ 29 ] side step our predictions.…”

Section: Resultssupporting

confidence: 66%

Identification and Analysis of Sex-Biased MicroRNAs in Human Diseases

Zhong,

Cui,

Cui

2023

Genes

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It is well known that significant differences exist between males and females in both physiology and disease. Thus, it is important to identify and analyze sex-biased miRNAs. However, previous studies investigating sex differences in miRNA expression have predominantly focused on healthy individuals or restricted their analysis to a single disease. Therefore, it is necessary to comprehensively identify and analyze the sex-biased miRNAs in diseases. For this purpose, in this study, we first identified the miRNAs showing sex-biased expression between males and females in diseases based on a number of miRNA expression datasets. Then, we performed a bioinformatics analysis for these sex-biased miRNAs. Notably, our findings revealed that women exhibit a greater number of conserved miRNAs that are highly expressed compared to men, and these miRNAs are implicated in a broader spectrum of diseases. Additionally, we explored the enriched transcription factors, functions, and diseases associated with these sex-biased miRNAs using the miRNA set enrichment analysis tool TAM 2.0. The insights gained from this study could carry implications for endeavors such as precision medicine and possibly pave the way for more targeted and tailored approaches to disease management.

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Section: Resultssupporting

confidence: 66%

Identification and Analysis of Sex-Biased MicroRNAs in Human Diseases

Zhong,

Cui,

Cui

2023

Genes

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“…We found that there was a critical error in the prior knowledge downstream of OSM, as IL6ST was annotated as an inhibitor of STAT3 and STAT1. IL6ST and OSM are in fact activators of JAK kinases 58 , explaining why OSM's score was the opposite of what would be expected. This mistake was corrected in the version of Omnipath of 2024.03.19, and the average score of OSM in this new version is 4.4.…”

Section: Moon Cytokine Prediction With the Cytosig Datasetmentioning

confidence: 88%

Modeling causal signal propagation in multi-omic factor space with COSMOS

Dugourd,

Lafrenz,

Mañanes

et al. 2024

Preprint

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Understanding complex diseases requires approaches that jointly analyze omic data across multiple biological layers, including signaling, gene regulation, and metabolism. Existing data-driven multi-omic analysis methods, such as multi-omic factor analysis (MOFA), can identify associations between molecular features and phenotypes, but they are not designed to integrate existing mechanistic molecular knowledge, which can provide further actionable insights. We introduce an approach that connects data-driven analysis of multi-omic data with systematic integration of mechanistic prior knowledge using COSMOS+ (Causal Oriented Search of Multi-Omics Space). We show how factor analysis’ output can be used to estimate activities of transcription factors and kinases as well as ligand-receptor interactions, which in turn are integrated with network-level prior-knowledge to generate mechanistic hypotheses about paths connecting deregulated molecular features. Our approach offers an interpretable framework to generate actionable insights from multi-omic data particularly suited for high dimensional datasets such as patient cohorts.Abstract Figure

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Association of Inflammation and Immune Cell Infiltration with Estrogen Receptor Alpha in an Estrogen and Ionizing Radiation-Induced Breast Cancer Model

Koning,

Calaf

2024

IJMS

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Breast cancer is the most diagnosed cancer in the world, and it is the primary cause of cancer death for women. The risk of breast cancer is increased by endogenous factors like hormones and exogenous factors like radiation exposure that causes damage to the mammary epithelial cells leading to an inflammatory response. Chronic inflammation creates a microenvironment composed of, among other factors, chemokines, and interleukins, which promote cancer. The gene expression of the interleukin 1 receptor type 1, the interleukin 1 receptor antagonist, the Interleukin 1 Receptor Accessory Protein, the interleukin 6 cytokine family signal transducer, the C-X-C motif chemokine ligand 3, the C-X-C motif chemokine ligand 5, and the C-X-C motif chemokine ligand 6 were analyzed in an estrogen and radiation experimental breast cancer model. Furthermore, the expression of these genes was correlated with immune cell infiltration, estrogen receptor expression, and their clinical relevance in breast cancer patients based on data provided by The Cancer Genome Atlas database online. Results given by the experimental breast cancer model showed that all genes related to inflammation respond to ionizing radiation alone or in combination with estrogen. On the other hand, the immune response depended on the breast cancer type and on the expression of the gene that encoded the estrogen receptor. Finally, the importance of the expression of these genes in breast cancer is such that high IL1R1 or IL1RAP is strongly related to patient survival. These findings may help to improve the understanding of the role of immune molecules in carcinogenesis and enhance therapeutic approaches.

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Variation in IL6ST cytokine family function and the potential of IL6 trans-signalling in ERα positive breast cancer cells

Cited by 3 publications

References 37 publications

Identification and Analysis of Sex-Biased MicroRNAs in Human Diseases

Identification and Analysis of Sex-Biased MicroRNAs in Human Diseases

Modeling causal signal propagation in multi-omic factor space with COSMOS

Association of Inflammation and Immune Cell Infiltration with Estrogen Receptor Alpha in an Estrogen and Ionizing Radiation-Induced Breast Cancer Model

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