# UNITe: exploiting conserved links between oncogenesis and homeostasis to identify novel cancer drivers

## TL;DR

This paper explores how genes involved in wound healing and regeneration are also linked to cancer, and introduces a new tool to identify cancer driver genes.

## Contribution

The paper introduces UNITe, a novel network-based tool for predicting cancer drivers using conserved homeostatic process genes.

## Key findings

- HP genes show elevated mutations and expression differences in tumors compared to healthy tissue.
- HP genes cluster with cancer driver genes in the human protein interaction network.
- UNITe outperforms existing methods with an AUROC of 0.81 in predicting cancer drivers.

## Abstract

Similarities between oncogenesis and several homeostatic processes (HPs)—wound healing, regeneration, and cellular stress response—have long been recognized. However, the molecular underpinning of these similarities is not fully understood. While several molecular aspects of HPs are evolutionarily conserved, different species exhibit substantial variation in the genes involved in these HPs, as well as in their predisposition to cancer.

We leveraged 75 published (with 321 experiments across 14 species) experimental datasets of genes implicated in HPs across multiple species from Gene Expression Omnibus (GEO), pan-cancer (32 cancer types) multi-omics datasets from The Cancer Genome Atlas (TCGA), and several benchmarking datasets from public repositories [such as Genotype Tissue Expression (GTEx), MSigDB, COSMIC, and the Cancer Dependency Map (DepMap)], as well as literature to comprehensively investigate links between conserved aspects of HPs and human cancers. We performed several analyses to understand broad mechanistic links between cancer and the homeostatic processes.

We compiled high-confidence conserved consensus gene sets for stress response (SR), wound healing (WH), and regeneration (RG)—jointly as HPs for “homeostatic processes”. We found that broadly across cancers, the HP genes exhibit elevated mutations, including copy number aberrations and differential gene expression in the tumor compared to healthy tissue, and are associated with patient survival. In the human protein interaction network, HP genes cluster by the process type as well as with the known cancer driver genes. Leveraging this observation, here, we presented a tool, UNITe (Uncovering Network-based Interactions between Homeostatic processes and Tumorigenesis), which predicts cancer drivers based solely on network proximity to HP genes, with an area under the receiving operating characteristic (AUROC) of 0.81, far better than several current approaches, and across multiple benchmark datasets. Applying UNITe genome-wide, we reported several novel potential cancer drivers and validated them using multiple lines of evidence.

Overall, we presented a first comparative analysis of cancer drivers with conserved homeostatic processes, suggesting a complementary approach to prioritizing cancer drivers. The model and the codes are freely available for public usage at https://github.com/hannenhalli-lab/conserved_links_homeostasis_oncogenesis.