Polygenic and Network-based studies in risk identification and demystification of cancer

ABSTRACT

Introduction

Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer.

Areas Covered

The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.

Expert Opinion

In the future, comprehensive profiling of all kinds of patient data (e.g. serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.

KEYWORDS:

• Cancer risk
• complex disease
• gene networks
• mutation
• polymorphism
• GWAS
• module

Article highlights

• Diseases were originally thought to be the result of a single gene mutation, but advances in DNA sequencing have proven otherwise.

• Methods for classifying genetic variants are evolving, and they all show that genetic variants need to be studied in a more robust manner.

• Complex diseases occur when the right environmental factors and SNPs exist. The latter are accumulating without understanding their significance, requiring integration into multiomic studies.

• Gene networks are dominated by universal laws, proving they are credible to consider as the new ‘units of study’ instead of single genes.

• Network and polygenic studies allow for more accurate prediction and prognosis of cancer risk, treatment-useful cancer subtyping, and the discovery of interesting cancer therapies.

• Comprehensive network studies integrating all types of data (e.g. transcriptomics, blood serum omics, and environmental agents) are the future of medical care.

Funding

This paper was not funded.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants, or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.