Essentiality, protein–protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning

TL;DR

This paper uses machine learning to identify cancer-related genes by analyzing their essentiality and protein interactions, helping to speed up cancer research.

Contribution

The study introduces a machine learning model that effectively predicts cancer-associated genes using essentiality and network properties.

Findings

Cancer-associated genes have higher essentiality scores than non-cancer genes.

A machine learning model achieved 89% accuracy in predicting cancer genes.

Essentiality and protein interaction features are key predictors in the model.

Abstract

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein–protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

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