Dataset Information

When left does not seem right: epigenetic and bioelectric differences between left- and right-sided breast cancer.

ABSTRACT:

Background

During embryogenesis lateral symmetry is broken, giving rise to Left/Right (L/R) breast tissues with distinct identity. L/R-sided breast tumors exhibit consistently-biased incidence, gene expression, and DNA methylation. We postulate that a differential L/R tumor-microenvironment crosstalk generates different tumorigenesis mechanisms.

Methods

We performed in-silico analyses on breast tumors of public datasets, developed xenografted tumors, and conditioned MDA-MB-231 cells with L/R mammary extracts.

Results

We found L/R differential DNA methylation involved in embryogenic and neuron-like functions. Focusing on ion-channels, we discovered significant L/R epigenetic and bioelectric differences. Specifically, L-sided cells presented increased methylation of hyperpolarizing ion channel genes and increased Ca²⁺ concentration and depolarized membrane potential, compared to R-ones. Functional consequences were associated with increased proliferation in left tumors, assessed by KI67 expression and mitotic count.

Conclusions

Our findings reveal considerable L/R asymmetry in cancer processes, and suggest specific L/R epigenetic and bioelectric differences as future targets for cancer therapeutic approaches in the breast and many other paired organs.

SUBMITTER: Masuelli S

PROVIDER: S-EPMC8817536 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

When left does not seem right: epigenetic and bioelectric differences between left- and right-sided breast cancer.

Masuelli Sofía S Real Sebastián S Campoy Emanuel E Branham María Teresita MT Marzese Diego Matías DM Salomon Matthew M De Blas Gerardo G Arias Rodolfo R Levin Michael M Roqué María M

Molecular medicine (Cambridge, Mass.) 20220205 1

<h4>Background</h4>During embryogenesis lateral symmetry is broken, giving rise to Left/Right (L/R) breast tissues with distinct identity. L/R-sided breast tumors exhibit consistently-biased incidence, gene expression, and DNA methylation. We postulate that a differential L/R tumor-microenvironment crosstalk generates different tumorigenesis mechanisms.<h4>Methods</h4>We performed in-silico analyses on breast tumors of public datasets, developed xenografted tumors, and conditioned MDA-MB-231 cel ...[more]

PMID: 35123413

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Project description:BACKGROUND:In recent years, the differences between left-sided colon cancer (LCC) and right-sided colon cancer (RCC) have received increasing attention due to the clinicopathological variation between them. However, some of these differences have remained unclear and conflicting results have been reported. METHODS:From The Cancer Genome Atlas (TCGA), we obtained RNA sequencing data and gene mutation data on 323 and 283 colon cancer patients, respectively. Differential analysis was firstly done on gene expression data and mutation data between LCC and RCC, separately. Machine learning (ML) methods were then used to select key genes or mutations as features to construct models to classify LCC and RCC patients. Finally, we conducted correlation analysis to identify the correlations between differentially expressed genes (DEGs) and mutations using logistic regression (LR) models. RESULTS:We found distinct gene mutation and expression patterns between LCC and RCC patients and further selected the 30 most important mutations and 17 most important gene expression features using ML methods. The classification models created using these features classified LCC and RCC patients with high accuracy (areas under the curve (AUC) of 0.8 and 0.96 for mutation and gene expression data, respectively). The expression of PRAC1 and BRAF V600E mutation (rs113488022) were the most important feature for each model. Correlations of mutations and gene expression data were also identified using LR models. Among them, rs113488022 was found to have significance relevance to the expression of four genes, and thus should be focused on in further study. CONCLUSIONS:On the basis of ML methods, we found some key molecular differences between LCC and RCC, which could differentiate these two groups of patients with high accuracy. These differences might be key factors behind the variation in clinical features between LCC and RCC and thus help to improve treatment, such as determining the appropriate therapy for patients.

Dataset Information

When left does not seem right: epigenetic and bioelectric differences between left- and right-sided breast cancer.

Background

Methods

Results

Conclusions

Publications

When left does not seem right: epigenetic and bioelectric differences between left- and right-sided breast cancer.

Similar Datasets

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