# Translating ceRNA susceptibilities into correlation functions

## TL;DR

This paper demonstrates that ceRNA susceptibilities to perturbations can be inferred from correlation functions, providing a practical method to analyze ceRNA crosstalk in biological datasets.

## Contribution

It establishes a direct link between ceRNA susceptibilities and correlation functions, enabling easier estimation of ceRNA interactions from data.

## Key findings

- Correlation functions encode ceRNA susceptibilities
- Validated approach with TCGA breast cancer data
- Numerical simulations support the theoretical framework

## Abstract

Competition to bind microRNAs induces an effective positive crosstalk between their targets, therefore known as `competing endogenous RNAs' or ceRNAs. While such an effect is known to play a significant role in specific conditions, estimating its strength from data and, experimentally, in physiological conditions appears to be far from simple. Here we show that the susceptibility of ceRNAs to different types of perturbations affecting their competitors (and hence their tendency to crosstalk) can be encoded in quantities as intuitive and as simple to measure as correlation functions. We confirm this scenario by extensive numerical simulations and validate it by re-analyzing PTEN's crosstalk pattern from TCGA breast cancer dataset. These results clarify the links between different quantities used to estimate the intensity of ceRNA crosstalk and provide new keys to analyze transcriptional datasets and effectively probe ceRNA networks in silico.