YY1 is a cis-regulator in the organoid models of high mammographic density

Abstract

Motivation Our previous study has shown that ERBB2 is overexpressed in the organoid model of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). We now aim to identify key transcription factors (TFs) and functional enhancers that regulate processes associated with increased stiffness of the microenvironment in the organoid models of premalignant human mammary cell lines. Results 3D colony organizations and the cis-regulatory networks of two human mammary epithelial cell lines (184A1 and MCF10A) are investigated as a function of the increased stiffness of the microenvironment within the range of MD. The 3D colonies are imaged using confocal microscopy, and the morphometries of colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment using BioSig3D. In a surrogate assay, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices. Next, a subset of enriched data are processed against publicly available ChIP-Seq data using Model-based Analysis of Regulation of Gene Expression to predict regulatory transcription factors. This integrative analysis of morphometric and transcriptomic data predicted YY1 as one of the cis-regulators in both cell lines as a result of the increased stiffness of the microenvironment. Subsequent experiments validated that YY1 is expressed at protein and mRNA levels for MCF10A and 184A1, respectively. Also, there is a causal relationship between activation of YY1 and ERBB2 when YY1 is overexpressed at the protein level in MCF10A. Supplementary information Supplementary data are available at Bioinformatics online.

Protein Ligands in the Secretome of CD36+ Fibroblasts Induce Growth Suppression in a Subset of Breast Cancer Cell Lines

“…After six days of treatment, the colonies were fixed, and no significant growth suppression was observed. Previously, we showed that the YY1 transcription factor plays a role in reversing aberrant polarity [ 19 ] and has a causal effect on the regulation of ERBB2 [ 16 ]. YY1 is involved in differentiation and proliferation, is overexpressed in cancer [ 32 ], and its genetic manipulation has been associated with phenotypic reversion between MCF10A and MCF7 [ 33 ].…”

“…Our hypothesis is formulated based on two key observations: CD36 is downregulated in FBs of non-cancerous breast tissues with high mammographic density (MD) [ 10 ], where MD is associated with an increased breast cancer risk [ 11 , 12 , 13 ]; and normal FBs have been shown to have an anti-tumorigenic function through paracrine signaling [ 2 , 14 , 15 ]. Moreover, tissues with high MD, as well as tumor epithelial cells, secrete activin A, which is known to downregulate CD36 in FBs in ex vivo samples [ 10 ] as well as 3D models of MD [ 16 ]. Activin A is one of the secreted macromolecules that is overexpressed by cancer cells [ 17 , 18 ]; it is a member of the TGFβ superfamily of cytokines and, like TGFβ, signals through pSmad 2/3 effectors to activate cell cycle checkpoints in normal cells.…”

The Role of Radiotherapy in Soft Tissue Sarcoma on Extremities With Lymph Nodes Metastasis: An IPTW Propensity Score Analysis of the SEER Database

“…During different times, the ratio of radiation therapy has shown a relatively escalating trend, which may indicate doctors’ increasing tendency to use radiation therapy as an adjuvant method for surgery. Based on the IPTW calculated according to all the covariables that mislead treatment allocation, allocation bias was attenuated mainly ( 20 ). The significant treatment effect of adjuvant radiotherapy in the original cohort was also noted in the adjusted groups based on IPTW.…”

“…Inverse probability of treatment weighting (IPTW) was used to balance the bias of confounding factors that may affect radiotherapy allocation ( 20 ). We calculated the standardized mean difference (SMD) to assess the balance of baseline characteristics after IPTW.…”

BARTweb: a web server for transcription factor association analysis

“…To predict TFs regulating a query gene set, BART first apply Model-based Analysis of Regulation of Gene Expression (MARGE) (8) to derive a genomic cis-regulatory (enhancer) profile from the input gene set using a semi-supervised learning approach leveraging compendium ChIP-seq data for active enhancer histone mark H3K27ac, then generate a ranked list of TFs that have a highly correlated binding profile with the cis-regulatory profile. While proven to work for identifying functional TFs from many case studies (7,(9)(10)(11)(12), BART requires users to download large ChIP-seq data libraries that can be storage and memory-consuming, and sometimes runs slow primarily due to step-wise regression computation in MARGE.…”